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Information systems From Wikipedia, the free encyclopedia Jump to: navigation, search Not to be confused with Information system. This article has multiple issues. Please help improve it or discuss these issues on the talk page. It needs additional citations for verification. Tagged since June 2010. It is written like a personal reflection or essay rather than an encyclopedic description of the subject. Tagged since April 2010. This article needs attention from an expert on the subject. Please add a reason or a talk parameter to this template to explain the issue with the article. WikiProject Systems may be able to help recruit an expert. (February 2009) CS, SE, IS, IT, & Customer Venn Diagram where functionality spans left and design spans right stemming from discovery.[1][2][3] Information Systems (IS) is an academic/professional discipline bridging the business field and the well-defined computer science field that is evolving toward a new scientific area of study.[4][5][6][7] An information systems discipline therefore is supported by the theoretical foundations of information and computations such that learned scholars have unique opportunities to explore the academics of various business models as well as related algorithmic processes within a computer science discipline.[8][9][10] Typically, information systems or the more common legacy information systems include people, procedures, data, software, and hardware (by degree) that are used to gather and analyze digital information.[11][12] Specifically computer-based information systems are complementary networks of hardware/software that people and organizations use to collect, filter, process, create, & distribute data (computing).[13] Computer Information System(s) (CIS) is often a track within the computer science field studying computers and algorithmic processes, including their principles, their software & hardware designs, their applications, and their impact on society.[14][15][16] Overall, an IS discipline emphasizes functionality over design.[17] As illustrated by the Venn Diagram on the right, the history of information systems coincides with the history of computer science that began long before the modern discipline of computer science emerged in the twentieth century.[18] Regarding the circulation of information and ideas, numerous legacy information systems still exist today that are continuously updated to promote ethnographic approaches, to ensure data integrity, and to improve the social effectiveness & efficiency of the whole process.[19] In general, information systems are focused upon processing information within organizations, especially within business enterprises, and sharing the benefits with modern society.[20] Contents [hide] 1 Overview 2 Definition 3 The Discipline of Information Systems 4 The Impact on Economic Models 5 Differentiating IS from Related Disciplines 6 Types of information systems 7 Information systems career pathways 8 Information systems development 9 Information systems research 10 See also 11 References 12 Further reading 13 External links [edit] Overview Silver et al. (1995) provided two views on (IS) and IS-centered view that includes software, hardware, data, people, and procedures. A second managerial view includes people, business processes and Information Systems. There are various types of information systems, for example: transaction processing systems, office systems, decision support systems, knowledge management systems, database management systems, and office information systems. Critical to most information systems are information technologies, which are typically designed to enable humans to perform tasks for which the human brain is not well suited, such as: handling large amounts of information, performing complex calculations, and controlling many simultaneous processes. Information technologies are a very important and malleable resource available to executives.[21] Many companies have created a position of Chief Information Officer (CIO) that sits on the executive board with the Chief Executive Officer (CEO), Chief Financial Officer (CFO), Chief Operating Officer (COO) and Chief Technical Officer (CTO). The CTO may also serve as CIO, and vice versa. The Chief Information Security Officer (CISO) focuses on information security management. [edit] Definition Silver et al.[22] defined Information Systems as follows: Information systems are implemented within an organization for the purpose of improving the effectiveness and efficiency of that organization. Capabilities of the information system and characteristics of the organization, its work systems, its people, and its development and implementation methodologies together determine the extent to which that purpose is achieved. [edit] The Discipline of Information Systems Several IS scholars have debated the nature and foundations of Information Systems which has its roots in other reference disciplines such as Computer Science, Engineering, Mathematics, Management Science, Cybernetics, and others.[23][24][25][26]. Information systems also can be defined as a collection of hardware, software, data, people and procedures that work together to produce quality information. [edit] The Impact on Economic Models Microeconomic theory model[clarification needed] Transaction cost theory[clarification needed] Agency Theory[clarification needed] [edit] Differentiating IS from Related Disciplines Information Systems relationship to Information Technology, Computer Science, Information Science, and Business. Similar to computer science, other disciplines can be seen as both related disciplines and foundation disciplines of IS. The domain of study of IS involves the study of theories and practices related to the social and technological phenomena, which determine the development, use and effects of information systems in organizations and society. [27] But, while there may be considerable overlap of the disciplines at the boundaries, the disciplines are still differentiated by the focus, purpose and orientation of their activities.[28] In a broad scope, the term Information Systems (IS) is a scientific field of study that addresses the range of strategic, managerial and operational activities involved in the gathering, processing, storing, distributing and use of information, and its associated technologies, in society and organizations.[29] The term information systems is also used to describe an organizational function that applies IS knowledge in industry, government agencies and not-for-profit organizations.[30] Information Systems often refers to the interaction between algorithmic processes and technology. This interaction can occur within or across organizational boundaries. An information system is not only the technology an organization uses, but also the way in which the organizations interact with the technology and the way in which the technology works with the organization’s business processes. Information systems are distinct from information technology (IT) in that an information system has an information technology component that interacts with the processes components. [edit] Types of information systems A four level pyramid model of different types of Information Systems based on the different levels of hierarchy in an organization The 'classic' view of Information systems found in the textbooks[31] of the 1980s was of a pyramid of systems that reflected the hierarchy of the organization, usually transaction processing systems at the bottom of the pyramid, followed by management information systems, decision support systems and ending with executive information systems at the top. Although the pyramid model remains useful, since it was first formulated a number of new technologies have been developed and new categories of information systems have emerged, some of which no longer fit easily into the original pyramid model. Some examples of such systems are: data warehouses enterprise resource planning enterprise systems expert systems geographic information system global information system office automation [edit] Information systems career pathways Information Systems have a number of different areas of work: Information systems strat Information systems management. Information systems development. Information systems security. Information systems iteration. Information system organization. There are a wide variety of career paths in the information systems discipline. "Workers with specialized technical knowledge and strong communications skills will have the best prospects. Workers with management skills and an understanding of business practices and principles will have excellent opportunities, as companies are increasingly looking to technology to drive their revenue."[32] [edit] Information systems development Information technology departments in larger organizations tend to strongly influence information technology development, use, and application in the organizations, which may be a business or corporation. A series of methodologies and processes can be used in order to develop and use an information system. Many developers have turned and used a more engineering approach such as the System Development Life Cycle (SDLC) which is a systematic procedure of developing an information system through stages that occur in sequence. An Information system can be developed in house (within the organization) or outsourced. This can be accomplished by outsourcing certain components or the entire system.[33] A specific case is the geographical distribution of the development team (Offshoring, Global Information System). A computer based information system, following a definition of Langefors,[34] is: a technologically implemented medium for recording, storing, and disseminating linguistic expressions, as well as for drawing conclusions from such expressions. which can be formulated as a generalized information systems design mathematical program. Geographic Information Systems, Land Information systems and Disaster Information Systems are also some of the emerging information systems but they can be broadly considered as Spatial Information Systems. System development is done in stages which include: Problem recognition and specification Information gathering Requirements specification for the new system System design System construction System implementation Review and maintenance[35] [edit] Information systems research Information systems research is generally interdisciplinary concerned with the study of the effects of information systems on the behavior of individuals, groups, and organizations.[36][37] Hevner et al. (2004) [38] categorized research in IS into two scientific paradigms including behavioral science which is to develop and verify theories that explain or predict human or organizational behavior and design science which extends the boundaries of human and organizational capabilities by creating new and innovative artifacts. Salvatore March and Gerald Smith [39] proposed a framework for researching different aspects of Information Technology including outputs of the research (research outputs) and activities to carry out this research (research activities). They identified research outputs as follows: 1. Constructs which are concepts that form the vocabulary of a domain. They constitute a conceptualization used to describe problems within the domain and to specify their solutions. 2. A model which is a set of propositions or statements expressing relationships among constructs. 3. A method which is a set of steps (an algorithm or guideline) used to perform a task. Methods are based on a set of underlying constructs and a representation (model) of the solution space. 4. An instantiation is the realization of an artifact in its environment. Also research activities including: 1. Build an artifact to perform a specific task. 2. Evaluate the artifact to determine if any progress has been achieved. 3. Given an artifact whose performance has been evaluated, it is important to determine why and how the artifact worked or did not work within its environment. Therefore theorize and justify theories about IT artifacts. Although Information Systems as a discipline has been evolving for over 30 years now,[40] the core focus or identity of IS research is still subject to debate among scholars such as.[41][42][43] There are two main views around this debate: a narrow view focusing on the IT artifact as the core subject matter of IS research, and a broad view that focuses on the interplay between social and technical aspects of IT that is embedded into a dynamic evolving context.[44] A third view provided by [45] calling IS scholars to take a balanced attention for both the IT artifact and its context. Since information systems is an applied field, industry practitioners expect information systems research to generate findings that are immediately applicable in practice. However, that is not always the case. Often information systems researchers explore behavioral issues in much more depth than practitioners would expect them to do. This may render information systems research results difficult to understand, and has led to criticism.[46] To study an information system itself, rather than its effects, information systems models are used, such as EATPUT. The international body of Information Systems researchers, the Association for Information Systems (AIS), and its Senior Scholars Forum Subcommittee on Journals (23 April 2007), proposed a 'basket' of journals that the AIS deems as 'excellent', and nominated: Management Information Systems Quarterly (MISQ), Information Systems Research (ISR), Journal of the Association for Information Systems (JAIS), Journal of Management Information Systems (JMIS), European Journal of Information Systems (EJIS), and Information Systems Journal (ISJ).[47] A number of annual information systems conferences are run in various parts of the world, the majority of which are peer reviewed. The AIS directly runs the International Conference on Information Systems (ICIS) and the Americas Conference on Information Systems (AMCIS), while AIS affiliated conferences include the Pacific Asia Conference on Information Systems (PACIS), European Conference on Information Systems (ECIS), the Mediterranean Conference on Information Systems (MCIS), the International Conference on Information Resources Management (Conf-IRM) and the Wuhan International Conference on E-Business (WHICEB). AIS chapter conferences include Australasian Conference on Information Systems (ACIS), Information Systems Research Conference in Scandinavia (IRIS), Conference of the Italian Chapter of AIS (itAIS), Annual Mid-Western AIS Conference (MWAIS) and Annual Conference of the Southern AIS (SAIS). [edit] See also Related studies Computer Science Human–computer interaction Components Data architect Data modeling Data Processing Implementation Enterprise Information System Environmental Modeling Bioinformatics Health informatics Business informatics Cheminformatics Disaster informatics Geoinformatics Information system MIS Formative Context System Data Reference Model Database EATPUT Metadata Predictive Model Markup Language Semantic translation Three schema approach Center European Research Center for Information Systems Information Processing System INFORMS [edit] References 1. ^ Archibald, J.A. (May 1975). "Computer Science education for majors of other disciplines". AFIPS Joint Computer Conferences: 903–906. "Computer science spreads out over several related disciplines, and shares with these disciplines certain subdisciplines that traditionally have been located exclusively in the more conventional disciplines" 2. ^ Denning, Peter (July 1999). "COMPUTER SCIENCE: THE DISCIPLINE". Encyclopedia of Computer Science (2000 Edition). "The Domain of Computer Science: Even though computer science addresses both human-made and natural information processes, the main effort in the discipline has been directed toward human-made processes, especially information processing systems and machines" 3. ^ Coy, Wolfgang (June 2004). "Between the disciplines". ACM SIGCSE Bulletin 36 (2): 7–10. ISSN 0097-8418. "Computer science may be in the core of these processes. The actual question is not to ignore disciplinary boundaries with its methodological differences but to open the disciplines for collaborative work. We must learn to build bridges, not to start in the gap between disciplines" 4. ^ Hoganson, Ken (December 2001). "Alternative curriculum models for integrating computer science and information systems analysis, recommendations, pitfalls, opportunities, accreditations, and trends". Journal of Computing Sciences in Colleges 17 (2): 313–325. ISSN 1937-4771. "... Information Systems grew out of the need to bridge the gap between business management and computer science ..." 5. ^ Davis, Timothy; Geist, Robert; Matzko, Sarah; Westall, James (March 2004). "τ´εχνη: A First Step". Technical Symposium on Computer Science Education: 125–129. ISBN 158113-798-2. "In 1999, Clemson University established a (graduate) degree program that bridges the arts and the sciences... All students in the program are required to complete graduate level work in both the arts and computer science" 6. ^ Hoganson, Ken (December 2001). "Alternative curriculum models for integrating computer science and information systems analysis, recommendations, pitfalls, opportunities, accreditations, and trends". Journal of Computing Sciences in Colleges 17 (2): 313–325. ISSN 1937-4771. "The field of information systems as a separate discipline is relatively new and is undergoing continuous change as technology evolves and the field matures" 7. ^ Khazanchi, Deepak; Bjorn Erik Munkvold (Summer 2000). "Is information system a science? an inquiry into the nature of the information systems discipline". ACM SIGMIS Database 31 (3): 24–42. doi:10.1145/381823.381834. ISSN 0095-0033. "From this we have concluded that IS is a science, i.e., a scientific discipline in contrast to purportedly non-scientific fields" 8. ^ Denning, Peter (June 2007). Ubiquity a new interview with Peter Denning on the great principles of computing. 2007. pp. 1–1. "People from other fields are saying they have discovered information processes in their deepest structures and that collaboration with computing is essential to them." 9. ^ "Computer science is the study of computation." Computer Science Department, College of Saint Benedict, Saint John's University 10. ^ "Computer Science is the study of all aspects of computer systems, from the theoretical foundations to the very practical aspects of managing large software projects." Massey University 11. ^ Kelly, Sue; Gibson, Nicola; Holland, Christopher; Light, Ben (July 1999). "Focus Issue on Legacy Information Systems and Business Process Engineering: a Business Perspective of Legacy Information Systems". Communications of the AIS 2 (7): 1–27. 12. ^ Pearson Custom Publishing & West Chester University, Custom Program for Computer Information Systems (CSC 110), (Pearson Custom Publishing, 2009) Glossary p. 694 13. ^ Jessup, Leonard M.; Joseph S. Valacich (2008). Information Systems Today (3rd ed.). Pearson Publishing. Pages ??? & Glossary p. 416 14. ^ Polack, Jennifer (December 2009). "Planning a CIS Education Within a CS Framework". Journal of Computing Sciences in Colleges 25 (2): 100–106. ISSN 19374771. 15. ^ Hayes, Helen; Onkar Sharma (February 2003). "A decade of experience with a common first year program for computer science, information systems and information technology majors". Journal of Computing Sciences in Colleges 18 (3): 217–227. ISSN 1937-4771. "In 1988, a degree program in Computer Information Systems (CIS) was launched with the objective of providing an option for students who were less inclined to become programmers and were more interested in learning to design, develop, and implement Information Systems, and solve business problems using the systems approach" 16. ^ CSTA Committee, Allen Tucker, et alia, A Model Curriculum for K-12 Computer Science (Final Report), (Association for Computing Machinery, Inc., 2006) Abstraction & p. 2 17. ^ Freeman, Peter; Hart, David (August 2004). "A Science of Design for SoftwareIntensive Systems Computer science and engineering needs an intellectually rigorous, analytical, teachable design process to ensure development of systems we all can live with.". Communications of the ACM 47 (8): 19–21. ISSN 0001-0782. "Though the other components' connections to the software and their role in the overall design of the system are critical, the core consideration for a software-intensive system is the software itself, and other approaches to systematizing design have yet to solve the "software problem"— which won't be solved until software design is understood scientifically" 18. ^ History of Computer Science 19. ^ Kelly, Sue; Gibson, Nicola; Holland, Christopher; Light, Ben (July 1999). "Focus Issue on Legacy Information Systems and Business Process Engineering: a Business Perspective of Legacy Information Systems". Communications of the AIS 2 (7): 1–27. 20. ^ "Scoping the Discipline of Information Systems" 21. ^ Rockart et al. (1996) Eight imperatives for the new IT organization Sloan Management review. 22. ^ Mark S. Silver, M. Lynne Markus, Cynthia Mathis Beath (1995) The Information Technology Interaction Model: A Foundation for the MBA Core Course, MIS Quarterly, Vol. 19, No. 3, Special Issue on IS Curricula and Pedagogy (Sep., 1995), pp. 361-390 23. ^ Culnan, M. J. Mapping the Intellectual Structure of MIS, 1980-1985: A Co-Citation Analysis, MIS Quarterly, 1987, pp. 341-353. 24. ^ Keen, P. G. W. MIS Research: Reference Disciplines and A Cumulative Tradition, in Proceedings of the First International Conference on Information Systems, E. McLean (ed.), Philadelphia, PA, 1980, pp. 9-18. 25. ^ Lee, A. S. Architecture as A Reference Discipline for MIS, in Information Systems Research: Contemporary Approaches and Emergent Traditions, H.-E. Nisen, H. K. Klein, and R. A. Hirschheim (eds.), North-Holland, Amsterdam, 1991, pp. 573-592. 26. ^ Mingers, J., and Stowell, F. (eds.). Information Systems: An Emerging Discipline?, McGraw- Hill, London, 1997. 27. ^ John, W., and Joe, P. (2002) "Strategic Planning for Information System." 3rd Ed. West Sussex. John wiley & Sons Ltd 28. ^ "Scoping the Discipline of Information Systems" 29. ^ "Scoping the Discipline of Information Systems" 30. ^ "Scoping the Discipline of Information Systems" 31. ^ Laudon, K.C. and Laudon, J.P. Management Information Systems, (2nd edition), Macmillan, 1988. 32. ^ Sloan Career Cornerstone Center (2008). Information Systems. Alfred P. Sloan Foundation. Access date June 2, 2008. 33. ^ Using MIS. Kroenke. 2009. ISBN 0-13-713029-5. 34. ^ Börje Langefors (1973). Theoretical Analysis of Information Systems. Auerbach. ISBN 0-87769-151-7. 35. ^ Computer Studies. Frederick Nyawaya. 2008. ISBN 9966-781-24-2. 36. ^ Galliers, R.D., Markus, M.L., & Newell, S. (Eds) (2006). Exploring Information Systems Research Approaches. New York, NY: Routledge. 37. ^ Ciborra, C. (2002). The Labyrinths of Information: Challenging the Wisdom of Systems. Oxford, UK: Oxford University Press 38. ^ Hevner, March, Park & Ram (2004): Design Science in Information Systems Research. MIS Quarterly, 28(1), 75-105. 39. ^ March S., Smith G. (1995) Design and natural science in Information Technology (IT), Decision Support Systems, Vol. 15, pp. 251- 266. 40. ^ Avgerou, C. (2000): Information systems: what sort of science is it? Omega, 28, 567579. 41. ^ Benbasat, I., Zmud, R. (2003): The identity crisis within the IS discipline: defining and communicating the discipline’s core properties, MIS Quarterly, 27(2), 183-194. 42. ^ Agarwal, R., Lucas, H. (2005): The information systems identity crisis: focusing on high- visibility and high-impact research, MIS Quarterly, 29(3), 381-398. 43. ^ El Sawy, O. (2003): The IS core –IX: The 3 faces of IS identity: connection, immersion, and fusion. Communications of AIS, 12, 588-598. 44. ^ Mansour, O., Ghazawneh, A. (2009) Research in Information Systems: Implications of the constant changing nature of IT capabilities in the social computing era, in MolkaDanielsen, J. (Ed.): Proceedings of the 32nd Information Systems Research Seminar in Scandinavia, IRIS 32, Inclusive Design, Molde University College, Molde, Norway, August 9–12, 2009. ISBN 978-82-7962-120-1. 45. ^ Orlikowski, W., Iacono, C. (2001): Research commentary: desperately seeking the “IT” in IT research—a call to theorizing about the IT artifact. Information Systems Research, 12(2), 121-134. 46. ^ Kock, N., Gray, P., Hoving, R., Klein, H., Myers, M., & Rockart, J. (2002). Information Systems Research Relevance Revisited: Subtle Accomplishment, Unfulfilled Promise, or Serial Hypocrisy? Communications of the Association for Information Systems, 8(23), 330-346. 47. ^ Senior Scholars (2007) AIS Senior Scholars Forum Subcommittee on Journals: A baseket of six (or eight) A* journals in Information Systems Archived at http://home.aisnet.org/associations/7499/files/Senior%20Scholars%20Letter.pdf. [edit] Further reading Rainer, R. Kelly and Cegielski, Casey G. (2009). "Introduction to Information Systems: Enabling and Transforming Business, 3rd Edition" Kroenke, David (2008). Using MIS - 2nd Edition. Lindsay, John (2000). Information Systems – Fundamentals and Issues. Kingston University, School of Information Systems Dostal, J. School information systems (Skolni informacni systemy). In Infotech 2007 modern information and communication technology in education. Olomouc, EU: Votobia, 2007. s. 540 – 546. ISBN 978-80-7220-301-7. O'Leary, Timothy and Linda. (2008). Computing Essentials Introductory 2008. McGrawHill on Computing2008.com [edit] External links Association for Information Systems (AIS) Center for Information Systems Research - Massachusetts Institute of Technology European Research Center for Information Systems Index of Information Systems Journals Information Systems Department, The George Washington University Information Systems Department, UMBC Information Systems and Innovation Group, Department of Management , London School of Economics Information Systems Network a research network from the Social Science Research Network School of Information Systems, Deakin University Department of Information Systems, Universiti Teknologi Malaysia [hide] v t e Systems and systems science Systems theory Systems science Systems scientists o Conceptual o Physical o Social Biological Complex Complex adaptive Conceptual Database management Dynamical Economical Ecosystem Systems categories Systems Theoretical fields Systems scientists Formal Global Positioning System Human anatomy Information systems Legal systems of the world Systems of measurement Metric system Multi-agent system Nervous system Nonlinearity Operating system Physical system Political system Sensory system Social system Solar System Systems art Chaos theory Complex systems Control theory Cybernetics Living systems Sociotechnical systems theory Systems biology System dynamics Systems ecology Systems engineering Systems neuroscience Systems psychology Systems science Systems theory Russell L. Ackoff William Ross Ashby Béla H. Bánáthy Gregory Bateson Richard E. Bellman Stafford Beer Ludwig von Bertalanffy Murray Bowen Kenneth E. Boulding C. West Churchman George Dantzig Heinz von Foerster Jay Wright Forrester George Klir Edward Lorenz Niklas Luhmann Humberto Maturana Margaret Mead Donella Meadows Mihajlo D. Mesarovic James Grier Miller Howard T. Odum Talcott Parsons Ilya Prigogine Anatol Rapoport Claude Shannon Qian Xuesen Francisco Varela Kevin Warwick Norbert Wiener Anthony Wilden Charles A S Hall View page ratings Rate this page What's this? Trustworthy Objective Complete Well-written I am highly knowledgeable about this topic (optional) Categories: Information Information systems Enterprise information system From Wikipedia, the free encyclopedia (Redirected from Enterprise Information System) Jump to: navigation, search This article does not cite any references or sources. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. (October 2009) An enterprise information system is generally any kind of computing system that is of "enterprise class". This means typically offering high quality of service, dealing with large volumes of data and capable of supporting some large organization ("an enterprise"). Enterprise information systems provide a technology platform that enables organizations to integrate and coordinate their business processes. An enterprise information system provides a single system that is central to the organization and that ensures information can be shared across all functional levels and management hierarchies. Enterprise systems create a standard data structure and are invaluable in eliminating the problem of information fragmentation caused by multiple information systems within an organization. A typical enterprise information system would be housed in one or more data centers, would run enterprise software, and could include applications that typically cross organizational borders such as content management systems. The word enterprise can have various connotations. Frequently the term is used only to refer to very large organizations. However, the term may be used to mean virtually anything, by virtue of it having become the latest corporate-speak buzzword. (See Criticisms of enterprise software) [edit] See also Management information system Enterprise planning systems Enterprise software Management information system From Wikipedia, the free encyclopedia Jump to: navigation, search This article is written like a personal reflection or essay rather than an encyclopedic description of the subject. Please help improve it by rewriting it in an encyclopedic style. (January 2011) This article may require copy editing for grammar, style, cohesion, tone, or spelling. You can assist by editing it. (August 2011) A management information system (MIS) provides information that is needed to manage organizations efficiently and effectively.[1] Management information systems involve three primary resources: people, technology, and information or decision making. Management information systems are distinct from other information systems in that they are used to analyze operational activities in the organization.[2] Academically, the term is commonly used to refer to the group of information management methods tied to the automation or support of human decision making, e.g. decision support systems, expert systems, and executive information systems.[2] Contents [hide] 1 Overview 2 History 3 Terminology 4 Types 5 Advantages 6 Enterprise applications 7 Developing Information Systems 8 See also 9 References 10 External links [edit] Overview Initially in businesses and other organizations, internal reporting was produced manually and only periodically, as a by-product of the accounting system and with some additional statistic(s), and gave limited and delayed information on management performance. Data was organized manually according to the requirements and necessity of the organization. As computational technology developed, information began to be distinguished from data and systems were developed to produce and organize abstractions, summaries, relationships and generalizations based on the data. Early business computers were used for simple operations such as tracking sales or payroll data, with little detail or structure. Over time, these computer applications became more complex, hardware storage capacities grew, and technologies improved for connecting previously isolated applications. As more and more data was stored and linked, managers sought greater detail as well as greater abstraction with the aim of creating entire management reports from the raw, stored data. The term "MIS" arose to describe such applications providing managers with information about sales, inventories, and other data that would help in managing the enterprise. Today, the term is used broadly in a number of contexts and includes (but is not limited to): decision support systems, resource and people management applications, enterprise resource planning (ERP), enterprise performance management (EPM), supply chain management (SCM), customer relationship management (CRM), project management and database retrieval applications. The successful MIS supports a business' long range plans, providing reports based upon performance analysis in areas critical to those plans, with feedback loops that allow for titivation of every aspect of the enterprise, including recruitment and training regimens. MIS not only indicate how things are going, but also why and where performance is failing to meet the plan. These reports include near-real-time performance of cost centers and projects with detail sufficient for individual accountability. [edit] History Kenneth and Jane Laudon identify five eras of MIS evolution corresponding to five phases in the development of computing technology: 1) mainframe and minicomputer computing, 2) personal computers, 3) client/server networks, 4) enterprise computing, and 5) cloud computing.[3] The first (mainframe and minicomputer) era was ruled by IBM and their mainframe computers, these computers would often take up whole rooms and require teams to run them, IBM supplied the hardware and the software. As technology advanced these computers were able to handle greater capacities and therefore reduce their cost. Smaller, more affordable minicomputers allowed larger businesses to run their own computing centers in-house. The second (personal computer) era began in 1965 as microprocessors started to compete with mainframes and minicomputers and accelerated the process of decentralizing computing power from large data centers to smaller offices. In the late 1970s minicomputer technology gave way to personal computers and relatively low cost computers were becoming mass market commodities, allowing businesses to provide their employees access to computing power that ten years before would have cost tens of thousands of dollars. This proliferation of computers created a ready market for interconnecting networks and the popularization of the Internet. As the complexity of the technology increased and the costs decreased, the need to share information within an enterprise also grew, giving rise to the third (client/server) era in which computers on a common network were able to access shared information on a server. This allowed for large amounts of data to be accessed by thousands and even millions of people simultaneously. The fourth (enterprise) era enabled by high speed networks, tied all aspects of the business enterprise together offering rich information access encompassing the complete management structure. The fifth and latest (cloud computing) era of information systems employs networking technology to deliver applications as well as data storage independent of the configuration, location or nature of the hardware. This, along with high speed cellphone and wifi networks, led to new levels of mobility in which managers access the MIS remotely with laptops, tablet pcs, and smartphones. [edit] Terminology The terms MIS, information system, ERP and, information technology management are often confused. Information systems and MIS are broader categories that include ERP. Information technology management concerns the operation and organization of information technology resources independent of their purpose. [edit] Types Most management information systems specialize in particular commercial and industrial sectors, aspects of the enterprise, or management substructure. Management information systems (MIS), per se, produce fixed, regularly scheduled reports based on data extracted and summarized from the firm’s underlying transaction processing systems[4] to middle and operational level managers to identify and inform structured and semi-structured decision problems. Decision support systems (DSS) are computer program applications used by middle management to compile information from a wide range of sources to support problem solving and decision making. Executive information systems (EIS) is a reporting tool that provides quick access to summarized reports coming from all company levels and departments such as accounting, human resources and operations. Marketing information systems are MIS designed specifically for managing the marketing aspects of the business. Office automation systems (OAS) support communication and productivity in the enterprise by automating work flow and eliminating bottlenecks. OAS may be implemented at any and all levels of management. School management information systems (MIS) cover school administration, often including teaching and learning materials. [edit] Advantages The following are some of the benefits that can be attained for different types of management information systems.[5] Companies are able to highlight their strengths and weaknesses due to the presence of revenue reports, employees' performance record etc. The identification of these aspects can help the company improve their business processes and operations. Giving an overall picture of the company and acting as a communication and planning tool. The availability of the customer data and feedback can help the company to align their business processes according to the needs of the customers. The effective management of customer data can help the company to perform direct marketing and promotion activities. Information is considered to be an important asset for any company in the modern competitive world. The consumer buying trends and behaviours can be predicted by the analysis of sales and revenue reports from each operating region of the company. [edit] Enterprise applications Enterprise systems, also known as enterprise resource planning (ERP) systems provide an organization with integrated software modules and a unified database which enable efficient planning, managing, and controlling of all core business processes across multiple locations. Modules of ERP systems may include finance, accounting, marketing, human resources, production, inventory management and distribution. Supply chain management (SCM) systems enable more efficient management of the supply chain by integrating the links in a supply chain. This may include suppliers, manufacturer, wholesalers, retailers and final customers. Customer relationship management (CRM) systems help businesses manage relationships with potential and current customers and business partners across marketing, sales, and service. Knowledge management system (KMS) helps organizations facilitate the collection, recording, organization, retrieval, and dissemination of knowledge. This may include documents, accounting records, and unrecorded procedures, practices and skills. [edit] Developing Information Systems "The actions that are taken to create an information system that solves an organizational problem are called system development"[6]. These include system analysis, system design, programming/implementation, testing, conversion, production and finally maintenance. These actions usually take place in that specified order but some may need to repeat or be accomplished concurrently. Conversion is the process of changing or converting the old system into the new. This can be done in four ways: Direct cutover – The new system replaces the old at an appointed time. Pilot study – Introducing the new system to a small portion of the operation to see how it fares. If good then the new system expands to the rest of the company. Phased approach – New system is introduced in stages. [edit] See also Enterprise Information System Bachelor of Computer Information Systems Computing Management Business intelligence Business performance management Business rules Data mining o Predictive analytics o Purchase order request Enterprise Architecture Enterprise planning systems Information technology governance Knowledge management Management by objectives Online analytical processing Online office suite Information Technology Real-time Marketing [edit] References 1. ^ http://www.occ.treas.gov/handbook/mis.pdf 2. ^ a b O’Brien, J (1999). Management Information Systems – Managing Information Technology in the Internetworked Enterprise. Boston: Irwin McGraw-Hill. ISBN 0-07112373-3. 3. ^ Laudon, Kenneth C.; Laudon, Jane P. (2009). Management Information Systems: Managing the Digital Firm (11 ed.). Prentice Hall/CourseSmart. p. 164. 4. ^ Transaction processing systems (TPS) collect and record the routine transactions of an organization. Examples of such systems are sales order entry, hotel reservations, payroll, employee record keeping, and shipping. 5. ^ Pant, S., Hsu, C., (1995), Strategic Information Systems Planning: A Review, Information Resources Management Association International Conference, May 21–24, Atlanta. 6. ^ Laudon, K.,&Laudon, J. (2010). Management information systems: Managing the digital firm. (11th ed.). Upper Saddle River, NJ: Pearson Prentice Hall. [edit] External links Computer and Information Systems Managers (U.S. Department of Labor) Index of Information Systems Journals MIS Web sites (Bournemouth University) MIS Links (University of York) Executive Information Systems: Minimising the risk of development Management information system From Wikipedia, the free encyclopedia Jump to: navigation, search This article is written like a personal reflection or essay rather than an encyclopedic description of the subject. Please help improve it by rewriting it in an encyclopedic style. (January 2011) This article may require copy editing for grammar, style, cohesion, tone, or spelling. You can assist by editing it. (August 2011) A management information system (MIS) provides information that is needed to manage organizations efficiently and effectively.[1] Management information systems involve three primary resources: people, technology, and information or decision making. Management information systems are distinct from other information systems in that they are used to analyze operational activities in the organization.[2] Academically, the term is commonly used to refer to the group of information management methods tied to the automation or support of human decision making, e.g. decision support systems, expert systems, and executive information systems.[2] Contents [hide] 1 Overview 2 History 3 Terminology 4 Types 5 Advantages 6 Enterprise applications 7 Developing Information Systems 8 See also 9 References 10 External links [edit] Overview Initially in businesses and other organizations, internal reporting was produced manually and only periodically, as a by-product of the accounting system and with some additional statistic(s), and gave limited and delayed information on management performance. Data was organized manually according to the requirements and necessity of the organization. As computational technology developed, information began to be distinguished from data and systems were developed to produce and organize abstractions, summaries, relationships and generalizations based on the data. Early business computers were used for simple operations such as tracking sales or payroll data, with little detail or structure. Over time, these computer applications became more complex, hardware storage capacities grew, and technologies improved for connecting previously isolated applications. As more and more data was stored and linked, managers sought greater detail as well as greater abstraction with the aim of creating entire management reports from the raw, stored data. The term "MIS" arose to describe such applications providing managers with information about sales, inventories, and other data that would help in managing the enterprise. Today, the term is used broadly in a number of contexts and includes (but is not limited to): decision support systems, resource and people management applications, enterprise resource planning (ERP), enterprise performance management (EPM), supply chain management (SCM), customer relationship management (CRM), project management and database retrieval applications. The successful MIS supports a business' long range plans, providing reports based upon performance analysis in areas critical to those plans, with feedback loops that allow for titivation of every aspect of the enterprise, including recruitment and training regimens. MIS not only indicate how things are going, but also why and where performance is failing to meet the plan. These reports include near-real-time performance of cost centers and projects with detail sufficient for individual accountability. [edit] History Kenneth and Jane Laudon identify five eras of MIS evolution corresponding to five phases in the development of computing technology: 1) mainframe and minicomputer computing, 2) personal computers, 3) client/server networks, 4) enterprise computing, and 5) cloud computing.[3] The first (mainframe and minicomputer) era was ruled by IBM and their mainframe computers, these computers would often take up whole rooms and require teams to run them, IBM supplied the hardware and the software. As technology advanced these computers were able to handle greater capacities and therefore reduce their cost. Smaller, more affordable minicomputers allowed larger businesses to run their own computing centers in-house. The second (personal computer) era began in 1965 as microprocessors started to compete with mainframes and minicomputers and accelerated the process of decentralizing computing power from large data centers to smaller offices. In the late 1970s minicomputer technology gave way to personal computers and relatively low cost computers were becoming mass market commodities, allowing businesses to provide their employees access to computing power that ten years before would have cost tens of thousands of dollars. This proliferation of computers created a ready market for interconnecting networks and the popularization of the Internet. As the complexity of the technology increased and the costs decreased, the need to share information within an enterprise also grew, giving rise to the third (client/server) era in which computers on a common network were able to access shared information on a server. This allowed for large amounts of data to be accessed by thousands and even millions of people simultaneously. The fourth (enterprise) era enabled by high speed networks, tied all aspects of the business enterprise together offering rich information access encompassing the complete management structure. The fifth and latest (cloud computing) era of information systems employs networking technology to deliver applications as well as data storage independent of the configuration, location or nature of the hardware. This, along with high speed cellphone and wifi networks, led to new levels of mobility in which managers access the MIS remotely with laptops, tablet pcs, and smartphones. [edit] Terminology The terms MIS, information system, ERP and, information technology management are often confused. Information systems and MIS are broader categories that include ERP. Information technology management concerns the operation and organization of information technology resources independent of their purpose. [edit] Types Most management information systems specialize in particular commercial and industrial sectors, aspects of the enterprise, or management substructure. Management information systems (MIS), per se, produce fixed, regularly scheduled reports based on data extracted and summarized from the firm’s underlying transaction processing systems[4] to middle and operational level managers to identify and inform structured and semi-structured decision problems. Decision support systems (DSS) are computer program applications used by middle management to compile information from a wide range of sources to support problem solving and decision making. Executive information systems (EIS) is a reporting tool that provides quick access to summarized reports coming from all company levels and departments such as accounting, human resources and operations. Marketing information systems are MIS designed specifically for managing the marketing aspects of the business. Office automation systems (OAS) support communication and productivity in the enterprise by automating work flow and eliminating bottlenecks. OAS may be implemented at any and all levels of management. School management information systems (MIS) cover school administration, often including teaching and learning materials. [edit] Advantages The following are some of the benefits that can be attained for different types of management information systems.[5] Companies are able to highlight their strengths and weaknesses due to the presence of revenue reports, employees' performance record etc. The identification of these aspects can help the company improve their business processes and operations. Giving an overall picture of the company and acting as a communication and planning tool. The availability of the customer data and feedback can help the company to align their business processes according to the needs of the customers. The effective management of customer data can help the company to perform direct marketing and promotion activities. Information is considered to be an important asset for any company in the modern competitive world. The consumer buying trends and behaviours can be predicted by the analysis of sales and revenue reports from each operating region of the company. [edit] Enterprise applications Enterprise systems, also known as enterprise resource planning (ERP) systems provide an organization with integrated software modules and a unified database which enable efficient planning, managing, and controlling of all core business processes across multiple locations. Modules of ERP systems may include finance, accounting, marketing, human resources, production, inventory management and distribution. Supply chain management (SCM) systems enable more efficient management of the supply chain by integrating the links in a supply chain. This may include suppliers, manufacturer, wholesalers, retailers and final customers. Customer relationship management (CRM) systems help businesses manage relationships with potential and current customers and business partners across marketing, sales, and service. Knowledge management system (KMS) helps organizations facilitate the collection, recording, organization, retrieval, and dissemination of knowledge. This may include documents, accounting records, and unrecorded procedures, practices and skills. [edit] Developing Information Systems "The actions that are taken to create an information system that solves an organizational problem are called system development"[6]. These include system analysis, system design, programming/implementation, testing, conversion, production and finally maintenance. These actions usually take place in that specified order but some may need to repeat or be accomplished concurrently. Conversion is the process of changing or converting the old system into the new. This can be done in four ways: Direct cutover – The new system replaces the old at an appointed time. Pilot study – Introducing the new system to a small portion of the operation to see how it fares. If good then the new system expands to the rest of the company. Phased approach – New system is introduced in stages. [edit] See also Enterprise Information System Bachelor of Computer Information Systems Computing Management Business intelligence Business performance management Business rules Data mining o Predictive analytics o Purchase order request Enterprise Architecture Enterprise planning systems Information technology governance Knowledge management Management by objectives Online analytical processing Online office suite Information Technology Real-time Marketing [edit] References 1. ^ http://www.occ.treas.gov/handbook/mis.pdf 2. ^ a b O’Brien, J (1999). Management Information Systems – Managing Information Technology in the Internetworked Enterprise. Boston: Irwin McGraw-Hill. ISBN 0-07112373-3. 3. ^ Laudon, Kenneth C.; Laudon, Jane P. (2009). Management Information Systems: Managing the Digital Firm (11 ed.). Prentice Hall/CourseSmart. p. 164. 4. ^ Transaction processing systems (TPS) collect and record the routine transactions of an organization. Examples of such systems are sales order entry, hotel reservations, payroll, employee record keeping, and shipping. 5. ^ Pant, S., Hsu, C., (1995), Strategic Information Systems Planning: A Review, Information Resources Management Association International Conference, May 21–24, Atlanta. 6. ^ Laudon, K.,&Laudon, J. (2010). Management information systems: Managing the digital firm. (11th ed.). Upper Saddle River, NJ: Pearson Prentice Hall. [edit] External links Computer and Information Systems Managers (U.S. Department of Labor) Index of Information Systems Journals MIS Web sites (Bournemouth University) MIS Links (University of York) Executive Information Systems: Minimising the risk of development Bachelor of Computer Information Systems From Wikipedia, the free encyclopedia Jump to: navigation, search The Bachelor of Computer Information Systems (abbreviated BSc CIS)(UCAS: G500) is an undergraduate or bachelor's degree, similar to the Bachelor of Science in Information Technology and Bachelor of Computer Science, but focused more on practical applications of technology to support organizations while adding value to their offerings. In order to apply technology effectively in this manner, a broad range of subjects are covered, such as communications, business, networking, software design, and mathematics. This degree is seen as one of the most broad IT qualifications, and therefore it can be useful when applying to IT Companies of various sectors. Some BCIS programs offer minors or concentrations as options to the degree program. Some computer information systems programs have received accreditation from ABET, the recognized U.S. accreditor of college and university programs in applied science, computing, engineering, and technology. Contents [hide] 1 The Course 2 Institutions That Teach It 3 References 4 External links [edit] The Course Generally the course will last for 3 years, however most institutions will leave the candidate with the choice of taking a year in industry between the second and third years. This then means that the student would take 4 years to complete the course. [edit] Institutions That Teach It There are a relatively small number of universities that teach this course, albeit the number is growing. Some of these are[1]: Appalachian State University[2] Arizona State University[3] Arkansas Tech University Australian Catholic University Babcock University[4] Bakersfield College Bethune Cookman University Brigham Young University[5] Al-Balqa` Applied University California State Polytechnic University, Pomona California State University Chico[6] California State University Los Angeles California State University Stanislaus California University of Pennsylvania[8] Cedar Crest College[9] Chapman University[10] Clemson University[11] Coleman University[12] Colorado State University-Pueblo Davenport University[13] Delta State University[14] Devry University[15] Drury University Eastern Washington University[16] Ferris State University[17] Frostburg State University [7] Florida Institute of Technology [18] Florida Gulf Coast University Georgia State University Idaho State University[19] Inoorero University{kenya} MARA University of Technology[20] Mayville State University[21] McKendree University[22] Missouri State University[23] Missouri Western State University Mount Royal University[24] Murray State University[25] Near East University[26] Northern Arizona University[27] Northern Michigan University[28] Oxford Brookes University Payap University[29] Pfeiffer University Pokhara University[30] Quinnipiac University[31] Saint Leo University[32] St Francis Xavier University Stephen F. Austin State University[33] SUNY Fredonia[34] SUNY Cobleskill[35] Texas State University The Wescoe School of Muhlenberg College Muhlenberg College University of Akron University of Colombo[36] University of Florida[37] University of Georgia[38] University of Houston[39] University of Jordan University of Lincoln[40] University of Liverpool University of Louisville University of Maine at Augusta University of North Dakota[41] University of North Texas[42] University of Oklahoma University of Puerto Rico University of South Alabama University of South Carolina Upstate University of the Incarnate Word University of Wisconsin - Stevens Point[43] Valley City State University[44] Western Michigan University Yarmouk University Guru Gobind Singh Indraprastha University[45] Guru Gobind Singh Indraprastha University[46] [edit] References 1. ^ http://www.whatuni.com/degrees/courses/degree-courses/g500-degree-courses-unitedkingdom/g500/m/united+kingdom/united+kingdom/25/0/a1/0/r/0/1/0/uc/page.html 2. ^ http://www.business.appstate.edu/cis/major.php 3. ^ https://webapp4.asu.edu/programs/t5/majorinfo/ASU00/BACISBS/undergrad/false?init=f alse&nopassive=true 4. ^ http://www.babcockuni.edu.ng/ 5. ^ http://marriottschool.byu.edu/bsisys/ 6. ^ http://www.csuchico.edu/catalog/csci/CINSNONEBS.html 7. ^ http://http://www.calstatela.edu/.html 8. ^ http://www.calu.edu/academics/programs/computer-information-systems/index.htm 9. ^ http://www2.cedarcrest.edu/academic/mathinfo/cis_home.shtm 10. ^ http://www.chapman.edu/SCS/CS/BSCIS.asp 11. ^ http://www.clemson.edu/ces/computing/prospective/pro_under/index.html 12. ^ http://www.coleman.edu/programs/undergraduate/information-systems.php 13. ^ http://www.davenport.edu/programs/technology/bachelors-degree/computerinformation-systems-major-programming-bs 14. ^ http://www.deltastate.edu/pages/1550.asp Delta State University 15. ^ http://www.devry.edu/degree-programs/college-engineering-informationsciences/computer-information-systems-about.jsp 16. ^ http://www.ewu.edu/CSHE/Programs/Computer-Science/CS-Degrees/BSCIS.xml 17. ^ http://www.ferris.edu/htmls/statewide/programs/bachelors/computerinfosys.htm 18. ^ "Bachelor of Computer Information Systems". Florida Institute of Technology. 19. ^ http://isu.edu/cob/programs.shtml 20. ^ http://fim.uitm.edu.my/index.php?option=com_content&view=article&id=71:is221&cati d=63:undergraduate&Itemid=63 21. ^ http://www.mayvillestate.edu/Academics/MajorsMinors/Pages/ComputerInformationSyst ems.aspx 22. ^ http://www.mckendree.edu/Kentucky/CIS.aspx 23. ^ http://www.missouristate.edu/majors/apg/ComputerInformationSystems.htm 24. ^ http://www.mtroyal.ca/ProgramsCourses/FacultiesSchoolsCentres/ScienceTechnology/Pr ograms/BachelorofComputerInformationSystems/index.htm 25. ^ http://www.murraystate.edu/ces/computing/prospective/pro_under/index.html 26. ^ http://feas.neu.edu.tr/departments/CIS/ 27. ^ http://www.cba.nau.edu/degreeprograms/undergrad/cis/ 28. ^ http://webb.nmu.edu/Colleges/Business/SiteSections/Programs/BCISBac.shtml 29. ^ http://ic.payap.ac.th/undergraduate/cis/about.php 30. ^ http://www.pu.edu.np/PROGRAMM.HTM 31. ^ http://www.quinnipiac.edu/x497.xml 32. ^ http://www.saintleo.edu/Academics/School-of-Business/Undergraduate-DegreePrograms/Computer-Information-Systems 33. ^ http://cobweb.sfasu.edu/csc/computerscience_undergraduate_002.html 34. ^ http://www.cs.fredonia.edu 35. ^ http://www.cobleskill.edu/academics/schools/business/business-administrationaccounting-computer-tech/computer-information-systems-aas.asp 36. ^ http://www.ucsc.cmb.ac.lk// 37. ^ http://www.cise.ufl.edu/ 38. ^ http://www.terry.uga.edu/mis/ 39. ^ http://www.tech.uh.edu/programs/undergraduate/computer-information-systems/ 40. ^ http://www.lincoln.ac.uk/socs/_courses/undergraduate/computer_information_systems/de fault.asp 41. ^ http://business.und.edu/dept/programs/bbainfosystems.cfm 42. ^ http://www.cob.unt.edu/itds/bs_bcis.php 43. ^ http://www.uwsp.edu 44. ^ http://www.vcsu.edu/catalogsearch/program/?p=10 45. ^ http://www.ipu.ac.in/ 46. ^ http://www.ipu.ac.in/ Computing From Wikipedia, the free encyclopedia Jump to: navigation, search For the formal concept of computation, see computation. For the magazine, see Computing (magazine). For the scientific journal, see Computing (journal). A difference engine: computing the solution to a polynomial function Computer laboratory, Moody Hall, James Madison University, 2003 Wikimedia servers Computing is the activity of using computer hardware and software. Contents [hide] 1 Definitions 2 Computer o 2.1 Computer software 2.1.1 Application software 3 Computer user o 3.1 Enthusiast computing 4 Computer programming o 4.1 Computer programmer 5 Computer science 6 History of computing 7 Computer network o 7.1 Internet 8 Computer industry o 8.1 Software industry 9 See also 10 References 11 External links [edit] Definitions Computing Curricula 2005[1] defined "computing" as: "In a general way, we can define computing to mean any goal-oriented activity requiring, benefiting from, or creating computers. Thus, computing includes designing and building hardware and software systems for a wide range of purposes; processing, structuring, and managing various kinds of information; doing scientific studies using computers; making computer systems behave intelligently; creating and using communications and entertainment media; finding and gathering information relevant to any particular purpose, and so on. The list is virtually endless, and the possibilities are vast." The term "computing" has sometimes been narrowly defined, as in a 1989 ACM report on Computing as a Discipline[2]: The discipline of computing is the systematic study of algorithmic processes that describe and transform information: their theory, analysis, design, efficiency, implementation, and application. The fundamental question underlying all computing is "What can be (efficiently) automated?" Computing Curricula 2005[1] also recognizes that the meaning of "computing" depends on the context: Computing also has other meanings that are more specific, based on the context in which the term is used. For example, an information systems specialist will view computing somewhat differently from a software engineer. Regardless of the context, doing computing well can be complicated and difficult. Because society needs people to do computing well, we must think of computing not only as a profession but also as a discipline. The term "computing" is also synonymous with counting and calculating. In earlier times, it was used in reference to mechanical computing machines. A computer is a machine that reads, stores, manipulates and displays data. The most common example are the various personal computers. Other common examples include: mobile phones, mp3 players, or video game consoles. [edit] Computer Main articles: Computer, Outline of computers, and Glossary of computer terms A computer is a machine that manipulates data according to a set of instructions called a computer program. The program has an executable form that the computer can use directly to execute the instructions. The same program in its human-readable source code form, enables a programmer to study and develop the algorithm. Because the instructions can be carried out in different types of computers, a single set of source instructions converts to machine instructions according to the central processing unit type. The execution process carries out the instructions in a computer program. Instructions express the computations performed by the computer. They trigger sequences of simple actions on the executing machine. Those actions produce effects according to the semantics of the instructions. [edit] Computer software Main article: Software Computer software or just "software", is a collection of computer programs and related data that provides the instructions for telling a computer what to do and how to do it. Software refers to one or more computer programs and data held in the storage of the computer for some purposes. In other words, software is a set of programs, procedures, algorithms and its documentation concerned with the operation of a data processing system. Program software performs the function of the program it implements, either by directly providing instructions to the computer hardware or by serving as input to another piece of software. The term was coined to contrast to the old term hardware (meaning physical devices). In contrast to hardware, software "cannot be touched".[3] Software is also sometimes used in a more narrow sense, meaning application software only. Sometimes the term includes data that has not traditionally been associated with computers, such as film, tapes, and records.[4] [edit] Application software Main article: Application software Application software, also known as an "application" or an "app", is computer software designed to help the user to perform specific tasks. Examples include enterprise software, accounting software, office suites, graphics software and media players. Many application programs deal principally with documents. Apps may be bundled with the computer and its system software, or may be published separately. Some users are satisfied with the bundled apps and need never install one. Application software is contrasted with system software and middleware, which manage and integrate a computer's capabilities, but typically do not directly apply them in the performance of tasks that benefit the user. The system software serves the application, which in turn serves the user. Similar relationships apply in other fields. For example, a shopping mall does not provide the merchandise a shopper is seeking, but provides space and services for retailers that serve the shopper. A bridge may similarly support rail tracks which support trains, allowing the trains to transport passengers. Application software applies the power of a particular computing platform or system software to a particular purpose. Some apps such as Microsoft Office are available in versions for several different platforms; others have narrower requirements and are thus called, for example, a Geography application for Windows or an Android application for education or Linux gaming. Sometimes a new and popular application arises which only runs on one platform, increasing the desirability of that platform. This is called a killer application. [edit] Computer user Main articles: User (computing) and End-user A user is an agent, either a human agent (end-user) or software agent, who uses a computer or network service. A user often has a user account and is identified by a username (also user name), screen name (also screenname), nickname (also nick), or handle, which is derived from the identical Citizen's Band radio term. Users are also widely characterized as the class of people that use a system without complete technical expertise required to understand the system fully.[1] In hacker-related contexts, such users are also divided into lusers and power users. In projects in which the actor of the system is another system or a software agent, it is quite possible that there is no end-user for the system. In this case, the end-users for the system would be indirect end-users. [edit] Enthusiast computing Main article: Enthusiast computing Enthusiast computing refers to a sub-culture of personal computer users who focus on extremely high-performance computers. Manufacturers of performance-oriented parts typically include an enthusiast model in their offerings. Enthusiast computers (often referred to as a "box", "build", or "rig" by their owners) commonly feature extravagant cases and high-end components, and are sometimes liquid cooled. Although high-end computers may be bought retail in the same manner as the common computer, they are frequently built by their owners. Enthusiasts build their systems in order to produce a computer that will out-perform an opponent's computer, thereby "winning" in a contest; to simply enjoy the best images and effects a new PC game has to offer; or even simply to obtain the best possible performance at a variety of tasks. [edit] Computer programming Main articles: Computer programming and Outline of computer programming Computer programming in general is the process of writing, testing, debugging, and maintaining the source code and documentation of computer programs. This source code is written in a programming language, which is an artificial language often more restrictive or demanding than natural languages, but easily translated by the computer. The purpose of programming is to invoke the desired behaviour (customization) from the machine. The process of writing high quality source code requires knowledge of both the application's domain and the computer science domain. The highest-quality software is thus developed by a team of various domain experts, each person a specialist in some area of development. But the term programmer may apply to a range of program quality, from hacker to open source contributor to professional. And a single programmer could do most or all of the computer programming needed to generate the proof of concept to launch a new "killer" application. [edit] Computer programmer Main article: Programmer A programmer, computer programmer, or coder is a person who writes computer software. The term computer programmer can refer to a specialist in one area of computer programming or to a generalist who writes code for many kinds of software. One who practices or professes a formal approach to programming may also be known as a programmer analyst. A programmer's primary computer language (C, C++, Java, Lisp, Python etc.) is often prefixed to the above titles, and those who work in a web environment often prefix their titles with web. The term programmer can be used to refer to a software developer, software engineer, computer scientist, or software analyst. However, members of these professions typically[citation needed] possess other software engineering skills, beyond programming; for this reason, the term programmer is sometimes considered an insulting or derogatory oversimplification of these other professions[citation needed]. This has sparked much debate amongst developers, analysts, computer scientists, programmers, and outsiders who continue to be puzzled at the subtle differences in the definitions of these occupations.[5][6][7][8][9] [edit] Computer science Main article: Computer science Computer science or computing science (abbreviated CS or compsci) is the scientific and mathematical approach in information technology and computing.[10][11] A computer scientist is a person who does work at a professional level in computer science and/or has attained a degree in computer science or a related field. Its subfields can be divided into practical techniques for its implementation and application in computer systems and purely theoretical areas. Some, such as computational complexity theory, which studies fundamental properties of computational problems, are highly abstract, while others, such as computer graphics, emphasize real-world applications. Still others focus on the challenges in implementing computations. For example, programming language theory studies approaches to description of computations, while the study of computer programming itself investigates various aspects of the use of programming languages and complex systems, and human-computer interaction focuses on the challenges in making computers and computations useful, usable, and universally accessible to humans. [edit] History of computing Main articles: History of computing and Timeline of computing The history of computing is longer than the history of computing hardware and modern computing technology and includes the history of methods intended for pen and paper or for chalk and slate, with or without the aid of tables. Computing is intimately tied to the representation of numbers. But long before abstractions like the number arose, there were mathematical concepts to serve the purposes of civilization. These concepts include one-to-one correspondence (the basis of counting), comparison to a standard (used for measurement), and the 3-4-5 right triangle (a device for assuring a right angle). Eventually, the concept of numbers became concrete and familiar enough for counting to arise, at times with sing-song mnemonics to teach sequences to others. All the known languages have words for at least "one" and "two" (although this is disputed: see Piraha language), and even some animals like the blackbird can distinguish a surprising number of items.[12] The earliest known tool for use in computation was the abacus, and it was thought to have been invented in Babylon circa 2400 BC. Its original style of usage was by lines drawn in sand with pebbles. Abaci, of a more modern design, are still used as calculation tools today. This was the first known computer and most advanced system of calculation known to date - preceding Greek methods by 2,000 years. [edit] Computer network Main article: Computer network A computer network, often simply referred to as a network, is a collection of hardware components and computers interconnected by communication channels that allow sharing of resources and information.[13] Where at least one process in one device is able to send/receive data to/from at least one process residing in a remote device, then the two devices are said to be in a network. Networks may be classified according to a wide variety of characteristics such as the medium used to transport the data, communications protocol used, scale, topology, and organizational scope. Communications protocols define the rules and data formats for exchanging information in a computer network, and provide the basis for network programming. Well-known communications protocols are Ethernet, a hardware and Link Layer standard that is ubiquitous in local area networks, and the Internet Protocol Suite, which defines a set of protocols for internetworking, i.e. for data communication between multiple networks, as well as host-to-host data transfer, and application-specific data transmission formats. Computer networking is sometimes considered a sub-discipline of electrical engineering, telecommunications, computer science, information technology or computer engineering, since it relies upon the theoretical and practical application of these disciplines. [edit] Internet Main articles: Internet, Outline of the Internet, and Glossary of Internet-related terms The Internet is a global system of interconnected computer networks that use the standard Internet protocol suite (TCP/IP) to serve billions of users worldwide. It is a network of networks that consists of millions of private, public, academic, business, and government networks, of local to global scope, that are linked by a broad array of electronic, wireless and optical networking technologies. The Internet carries an extensive range of information resources and services, such as the inter-linked hypertext documents of the World Wide Web (WWW) and the infrastructure to support email. [edit] Computer industry Main article: Computer industry The computer industry is made up of all of the businesses involved in developing computer software, designing computer hardware and computer networking infrastructures, the manufacture of computer components and the provision of information technology services. [edit] Software industry Main article: Software industry The software industry includes businesses for development, maintenance and publication of software that are using any business model. The industry also includes software services, such as training, documentation, and consulting. Management From Wikipedia, the free encyclopedia Jump to: navigation, search This article is about organization and coordination. For the film, see Management (film). Management is the act of getting people together to accomplish desired goals and objectives using available resources efficiently and effectively. Management comprises planning, organizing, staffing, leading or directing, and controlling an organization (a group of one or more people or entities) or effort for the purpose of accomplishing a goal. Resourcing encompasses the deployment and manipulation of human resources, financial resources, technological resources and natural resources. Since organizations can be viewed as systems, management can also be defined as human action, including design, to facilitate the production of useful outcomes from a system. This view opens the opportunity to 'manage' oneself, a pre-requisite to attempting to manage others. Contents [hide] 1 History 2 Various Definitions o 2.1 Theoretical scope 3 Nature of managerial work 4 Historical development o 4.1 Early writing 4.1.1 Sun Tzu's The Art of War 4.1.2 Chanakya's Arthashastra 4.1.3 Niccolò Machiavelli's The Prince 4.1.4 Adam Smith's The Wealth of Nations o 4.2 19th century o 4.3 20th century o 4.4 21st century 5 Topics o 5.1 Basic roles o 5.2 Management skills o 5.3 Formation of the business policy 5.3.1 Implementation of policies and strategies 5.3.2 Policies and strategies in the planning process o 5.4 Levels of management 5.4.1 Top-level managers 5.4.2 Middle-level managers 5.4.3 low-level managers 6 Management-focused journals 7 See also 8 References [edit] History The verb manage comes from the Italian maneggiare (to handle, train, control horses), which in turn derives from the Latin manus (hand). The French word mesnagement (later ménagement) influenced the development in meaning of the English word management in the 15th and 16th centuries.[1] Some definitions of management are: Organization and coordination of the activities of an enterprise in accordance with certain policies and in achievement of clearly defined objectives. Management is often included as a factor of production along with machines, materials and money. According to the management guru Peter Drucker (1909–2005), the basic task of a management is twofold: marketing and innovation. Directors and managers have the power and responsibility to make decisions to manage an enterprise when given the authority by the shareholders. As a discipline, management comprises the interlocking functions of formulating corporate policy and organizing, planning, controlling, and directing the firm's resources to achieve the policy's objectives. The size of management can range from one person in a small firm to hundreds or thousands of managers in multinational companies. In large firms the board of directors formulates the policy which is implemented by the chief executive officer. [edit] Various Definitions There are various definitions of Management by different experts and the contributors of different schools of management: Donald J. Cough defines management as, "Management is the art and science of decision making and leadership." Louis Allen defines, "Management is what a manager does". [edit] Theoretical scope At first, one views management functionally, such as measuring quantity, adjusting plans, meeting goals,foresighting/forecasting. This applies even in situations when planning does not take place. From this perspective, Henri Fayol (1841–1925)[2] considers management to consist of six functions: forecasting, planning, organizing, commanding, coordinating and controlling. He was one of the most influential contributors to modern concepts of management. Another way of thinking, Mary Parker Follett (1868–1933), defined management as "the art of getting things done through people". She described management as philosophy.[3] Some people, however, find this definition useful but far too narrow. The phrase "management is what managers do" occurs widely, suggesting the difficulty of defining management, the shifting nature of definitions and the connection of managerial practices with the existence of a managerial cadre or class. One habit of thought regards management as equivalent to "business administration" and thus excludes management in places outside commerce, as for example in charities and in the public sector. More realistically, however, every organization must manage its work, people, processes, technology, etc. to maximize effectiveness. Nonetheless, many people refer to university departments which teach management as "business schools." Some institutions (such as the Harvard Business School) use that name while others (such as the Yale School of Management) employ the more inclusive term "management." English speakers may also use the term "management" or "the management" as a collective word describing the managers of an organization, for example of a corporation. Historically this use of the term was often contrasted with the term "Labor" referring to those being managed. [edit] Nature of managerial work In for-profit work, management has as its primary function the satisfaction of a range of stakeholders. This typically involves making a profit (for the shareholders), creating valued products at a reasonable cost (for customers) and providing rewarding employment opportunities (for employees). In nonprofit management, add the importance of keeping the faith of donors. In most models of management/governance, shareholders vote for the board of directors, and the board then hires senior management. Some organizations have experimented with other methods (such as employee-voting models) of selecting or reviewing managers; but this occurs only very rarely. In the public sector of countries constituted as representative democracies, voters elect politicians to public office. Such politicians hire many managers and administrators, and in some countries like the United States political appointees lose their jobs on the election of a new president/governor/mayor. [edit] Historical development Difficulties arise in tracing the history of management. Some see it (by definition) as a late modern (in the sense of late modernity) conceptualization. On those terms it cannot have a premodern history, only harbingers (such as stewards). Others, however, detect management-likethought back to Sumerian traders and to the builders of the pyramids of ancient Egypt. Slaveowners through the centuries faced the problems of exploiting/motivating a dependent but sometimes unenthusiastic or recalcitrant workforce, but many pre-industrial enterprises, given their small scale, did not feel compelled to face the issues of management systematically. However, innovations such as the spread of Arabic numerals (5th to 15th centuries) and the codification of double-entry book-keeping (1494) provided tools for management assessment, planning and control. Given the scale of most commercial operations and the lack of mechanized record-keeping and recording before the industrial revolution, it made sense for most owners of enterprises in those times to carry out management functions by and for themselves. But with growing size and complexity of organizations, the split between owners (individuals, industrial dynasties or groups of shareholders) and day-to-day managers (independent specialists in planning and control) gradually became more common. [edit] Early writing While management has been present for millennia, several writers have created a background of works that assisted in modern management theories.[4] [edit] Sun Tzu's The Art of War Written by Chinese general Sun Tzu in the 6th century BC, The Art of War is a military strategy book that, for managerial purposes, recommends being aware of and acting on strengths and weaknesses of both a manager's organization and a foe's.[4] [edit] Chanakya's Arthashastra Chanakya wrote the Arthashastra around 300BC in which various strategies, techniques and management theories were written which gives an account on the management of empires, economy and family. The work is often compared to the later works of Machiavelli. [edit] Niccolò Machiavelli's The Prince Believing that people were motivated by self-interest, Niccolò Machiavelli wrote The Prince in 1513 as advice for the city of Florence, Italy.[5] Machiavelli recommended that leaders use fear— but not hatred—to maintain control. [edit] Adam Smith's The Wealth of Nations Written in 1776 by Adam Smith, a Scottish moral philosopher, The Wealth of Nations aims for efficient organization of work through Specialization of labor.[5] Smith described how changes in processes could boost productivity in the manufacture of pins. While individuals could produce 200 pins per day, Smith analyzed the steps involved in manufacture and, with 10 specialists, enabled production of 48,000 pins per day.[5] [edit] 19th century Classical economists such as Adam Smith (1723–1790) and John Stuart Mill (1806–1873) provided a theoretical background to resource-allocation, production, and pricing issues. About the same time, innovators like Eli Whitney (1765–1825), James Watt (1736–1819), and Matthew Boulton (1728–1809) developed elements of technical production such as standardization, quality-control procedures, cost-accounting, interchangeability of parts, and work-planning. Many of these aspects of management existed in the pre-1861 slave-based sector of the US economy. That environment saw 4 million people, as the contemporary usages had it, "managed" in profitable quasi-mass production. [edit] 20th century By about 1900 one finds managers trying to place their theories on what they regarded as a thoroughly scientific basis (see scientism for perceived limitations of this belief). Examples include Henry R. Towne's Science of management in the 1890s, Frederick Winslow Taylor's The Principles of Scientific Management (1911), Frank and Lillian Gilbreth's Applied motion study (1917), and Henry L. Gantt's charts (1910s). J. Duncan wrote the first college management textbook in 1911. In 1912 Yoichi Ueno introduced Taylorism to Japan and became first management consultant of the "Japanese-management style". His son Ichiro Ueno pioneered Japanese quality assurance. The first comprehensive theories of management appeared around 1920. The Harvard Business School offered the first Master of Business Administration degree (MBA) in 1921. People like Henri Fayol (1841–1925) and Alexander Church described the various branches of management and their inter-relationships. In the early 20th century, people like Ordway Tead (1891–1973), Walter Scott and J. Mooney applied the principles of psychology to management, while other writers, such as Elton Mayo (1880–1949), Mary Parker Follett (1868–1933), Chester Barnard (1886–1961), Max Weber (1864–1920), Rensis Likert (1903–1981), and Chris Argyris (1923 - ) approached the phenomenon of management from a sociological perspective. Peter Drucker (1909–2005) wrote one of the earliest books on applied management: Concept of the Corporation (published in 1946). It resulted from Alfred Sloan (chairman of General Motors until 1956) commissioning a study of the organisation. Drucker went on to write 39 books, many in the same vein. H. Dodge, Ronald Fisher (1890–1962), and Thornton C. Fry introduced statistical techniques into management-studies. In the 1940s, Patrick Blackett combined these statistical theories with microeconomic theory and gave birth to the science of operations research. Operations research, sometimes known as "management science" (but distinct from Taylor's scientific management), attempts to take a scientific approach to solving management problems, particularly in the areas of logistics and operations. Some of the more recent developments include the Theory of Constraints, management by objectives, reengineering, Six Sigma and various information-technology-driven theories such as agile software development, as well as group management theories such as Cog's Ladder. As the general recognition of managers as a class solidified during the 20th century and gave perceived practitioners of the art/science of management a certain amount of prestige, so the way opened for popularised systems of management ideas to peddle their wares. In this context many management fads may have had more to do with pop psychology than with scientific theories of management. Towards the end of the 20th century, business management came to consist of six separate branches, namely: Human resource management Operations management or production management Strategic management Marketing management Financial management Information technology management responsible for management information systems [edit] 21st century In the 21st century observers find it increasingly difficult to subdivide management into functional categories in this way. More and more processes simultaneously involve several categories. Instead, one tends to think in terms of the various processes, tasks, and objects subject to management. Branches of management theory also exist relating to nonprofits and to government: such as public administration, public management, and educational management. Further, management programs related to civil-society organizations have also spawned programs in nonprofit management and social entrepreneurship. Note that many of the assumptions made by management have come under attack from business ethics viewpoints, critical management studies, and anti-corporate activism. As one consequence, workplace democracy has become both more common, and more advocated, in some places distributing all management functions among the workers, each of whom takes on a portion of the work. However, these models predate any current political issue, and may occur more naturally than does a command hierarchy. All management to some degree embraces democratic principles in that in the long term workers must give majority support to management; otherwise they leave to find other work, or go on strike. Despite the move toward workplace democracy, command-and-control organization structures remain commonplace and the de facto organization structure. Indeed, the entrenched nature of command-and-control can be seen in the way that recent layoffs have been conducted with management ranks affected far less than employees at the lower levels. In some cases, management has even rewarded itself with bonuses after laying off level workers.[6] According to leading leadership academic Manfred F.R. Kets de Vries, it's almost inevitable these days that there will be some personality disorders in a senior management team.[7] [edit] Topics [edit] Basic roles Interpersonal: roles that involve coordination and interaction with employees. Informational: roles that involve handling, sharing, and analyzing information. Decisional: roles that require decision-making. [edit] Management skills Political: used to build a power base and establish connections. Conceptual: used to analyze complex situations. Interpersonal: used to communicate, motivate, mentor and delegate. Diagnostic: the ability to visualize most appropriate response to a situation . [8] [edit] Formation of the business policy The mission of the business is the most obvious purpose—which may be, for example, to make soap. The vision of the business reflects its aspirations and specifies its intended direction or future destination. The objectives of the business refer to the ends or activity at which a certain task is aimed. The business's policy is a guide that stipulates rules, regulations and objectives, and may be used in the managers' decision-making. It must be flexible and easily interpreted and understood by all employees. The business's strategy refers to the coordinated plan of action that it is going to take, as well as the resources that it will use, to realize its vision and long-term objectives. It is a guideline to managers, stipulating how they ought to allocate and utilize the factors of production to the business's advantage. Initially, it could help the managers decide on what type of business they want to form. [edit] Implementation of policies and strategies All policies and strategies must be discussed with all managerial personnel and staff. Managers must understand where and how they can implement their policies and strategies. A plan of action must be devised for each department. Policies and strategies must be reviewed regularly. Contingency plans must be devised in case the environment changes. Assessments of progress ought to be carried out regularly by top-level managers. A good environment and team spirit is required within the business. The missions, objectives, strengths and weaknesses of each department must be analysed to determine their roles in achieving the business's mission. The forecasting method develops a reliable picture of the business's future environment. A planning unit must be created to ensure that all plans are consistent and that policies and strategies are aimed at achieving the same mission and objectives. All policies must be discussed with all managerial personnel and staff that is required in the execution of any departmental policy. Organizational change is strategically achieved through the implementation of the eight-step plan of action established by John P. Kotter: Increase urgency, form a coalition, get the vision right, communicate the buy-in, empower action, create short-term wins, don't let up, and make change stick.[9] [edit] Policies and strategies in the planning process They give mid- and lower-level managers a good idea of the future plans for each department in an organization. A framework is created whereby plans and decisions are made. Mid- and lower-level management may adapt their own plans to the business's strategic ones. [edit] Levels of management Most organizations have three management levels: low-level, middle-level, and top-level managers.[citation needed] These managers are classified in a hierarchy of authority, and perform different tasks. In many organizations, the number of managers in every level resembles a pyramid. Each level is explained below in specifications of their different responsibilities and likely job titles.[10] [edit] Top-level managers Consists of board of directors, president, vice-president, CEOs, etc. They are responsible for controlling and overseeing the entire organization. They develop goals, strategic plans, company policies, and make decisions on the direction of the business. In addition, top-level managers play a significant role in the mobilization of outside resources and are accountable to the shareholders and general public. According to Lawrence S. Kleiman, the following skills are needed at the top managerial level.[11] Broadened understanding of how: competition, world economies, politics, and social trends effect organizational effectiveness . [edit] Middle-level managers Consist of general managers, branch managers and department managers. They are accountable to the top management for their department's function. They devote more time to organizational and directional functions. Their roles can be emphasized as executing organizational plans in conformance with the company's policies and the objectives of the top management, they define and discuss information and policies from top management to lower management, and most importantly they inspire and provide guidance to lower level managers towards better performance. Some of their functions are as follows: Designing and implementing effective group and intergroup work and information systems. Defining and monitoring group-level performance indicators. Diagnosing and resolving problems within and among work groups. Designing and implementing reward systems supporting cooperative behavior. [edit] low-level managers Consist of supervisors, section leads, foremen, etc. They focus on controlling and directing. They usually have the responsibility of assigning employees tasks, guiding and supervising employees on day-to-day activities, ensuring quality and quantity production, making recommendations, suggestions, and upchanneling employee problems, etc. First-level managers are role models for employees that provide: Basic supervision. Motivation. Career planning. Performance feedback. supervising the staffs. [edit] Management-focused journals Administrative Science Quarterly Academy of Management Journal Academy of Management Review Journal of Management Management Science: A Journal of the Institute for Operations Research and the Management Sciences Organization Science: A Journal of the Institute for Operations Research and the Management Sciences [edit] See also Book: Management Wikipedia books are collections of articles that can be downloaded or ordered in print. Main article: Outline of business management Scientific management Human relations movement Strategic management Total quality management [edit] References Find more about Management on Wikipedia's sister projects: Definitions and translations from Wiktionary Images and media from Commons Learning resources from Wikiversity News stories from Wikinews Quotations from Wikiquote Source texts from Wikisource Textbooks from Wikibooks 1. ^ Oxford English Dictionary 2. ^ Administration industrielle et générale - prévoyance organization - commandment, coordination – contrôle, Paris : Dunod, 1966 3. ^ Vocational Business: Training, Developing and Motivating People by Richard Barrett - Business & Economics - 2003. - Page 51. 4. ^ a b Gomez-Mejia, Luis R.; David B. Balkin and Robert L. Cardy (2008). Management: People, Performance, Change, 3rd edition. New York, New York USA: McGraw-Hill. pp. 19. ISBN 978-007-302743-2. 5. ^ a b c Gomez-Mejia, Luis R.; David B. Balkin and Robert L. Cardy (2008). Management: People, Performance, Change, 3rd edition. New York, New York USA: McGraw-Hill. pp. 20. ISBN 978-007-302743-2. 6. ^ Craig, S. (2009, January 29). Merrill Bonus Case Widens as Deal Struggles. Wall Street Journal. [1] 7. ^ Manfred F. R. Kets de Vries The Dark Side of Leadership - Business Strategy Review 14(3), Autumn Page 26 (2003). 8. ^ Kleiman, Lawrence S. "Management and Executive Development." Reference for Business: Encyclopedia of Business (2010): n. pag. Web. 25 Mar 2011. [2]. 9. ^ Kotter, John P. & Dan S. Cohen. (2002). The Heart of Change. Boston: Harvard Business School Publishing. 10. ^ Juneja hu Juneja, FirstHimanshu, and Prachi Juneja. "Management." Management Study Guide. WebCraft Pvt Ltd, 2011. Web. 17 Mar 2011.[3]. 11. ^ Kleiman, Lawrence S. " MANAGEMENT AND EXECUTIVE DEVELOPMENT."Reference for Business:Encyclopedia of Business(2010): n. pag. Web. 25 Mar 2011. [4]. Business intelligence From Wikipedia, the free encyclopedia Jump to: navigation, search Business intelligence (BI) mainly refers to computer-based techniques used in identifying, extracting,[clarification needed] and analyzing business data, such as sales revenue by products and/or departments, or by associated costs and incomes.[1] BI technologies provide historical, current and predictive views of business operations. Common functions of business intelligence technologies are reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining and predictive analytics. Business intelligence aims to support better business decision-making. Thus a BI system can be called a decision support system (DSS).[2] Though the term business intelligence is sometimes used as a synonym for competitive intelligence, because they both support decision making, BI uses technologies, processes, and applications to analyze mostly internal, structured data and business processes while competitive intelligence gathers, analyzes and disseminates information with a topical focus on company competitors. Business intelligence understood broadly can include the subset of competitive intelligence.[3] Contents [hide] 1 History 2 Business intelligence and data warehousing 3 Business intelligence and business analytics 4 Applications in an enterprise 5 Prioritization of business intelligence projects 6 Success factors of implementation o 6.1 Business sponsorship o 6.2 Implementation should be driven by clear business needs o 6.3 The amount and quality of the available data 7 User aspect 8 Marketplace o 8.1 Industry-specific 9 Semi-structured or unstructured data o 9.1 Unstructured data vs. semi-structured data o 9.2 Problems with semi-structured or unstructured data o 9.3 The use of metadata 10 Future 11 See also 12 References 13 Bibliography 14 External links [edit] History In a 1958 article, IBM researcher Hans Peter Luhn used the term business intelligence. He defined intelligence as: "the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal."[4] Business intelligence as it is understood today is said to have evolved from the decision support systems which began in the 1960s and developed throughout the mid-1980s. DSS originated in the computer-aided models created to assist with decision making and planning. From DSS, data warehouses, Executive Information Systems, OLAP and business intelligence came into focus beginning in the late 80s. In 1989, Howard Dresner (later a Gartner Group analyst) proposed "business intelligence" as an umbrella term to describe "concepts and methods to improve business decision making by using fact-based support systems."[2] It was not until the late 1990s that this usage was widespread.[5] [edit] Business intelligence and data warehousing Often BI applications use data gathered from a data warehouse or a data mart. However, not all data warehouses are used for business intelligence, nor do all business intelligence applications require a data warehouse. In order to distinguish between concepts of business intelligence and data warehouses, Forrester Research often defines business intelligence in one of two ways: Using a broad definition: "Business Intelligence is a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making."[6] When using this definition, business intelligence also includes technologies such as data integration, data quality, data warehousing, master data management, text and content analytics, and many others that the market sometimes lumps into the Information Management segment. Therefore, Forrester refers to data preparation and data usage as two separate, but closely linked segments of the business intelligence architectural stack. Forrester defines the latter, narrower business intelligence market as "referring to just the top layers of the BI architectural stack such as reporting, analytics and dashboards."[7] [edit] Business intelligence and business analytics Thomas Davenport has argued that business intelligence should be divided into querying, reporting, OLAP, an "alerts" tool, and business analytics. In this definition, business analytics is the subset of BI based on statistics, prediction, and optimization.[8] [edit] Applications in an enterprise Business intelligence can be applied to the following business purposes, in order to drive business value.[citation needed] 1. Measurement – program that creates a hierarchy of performance metrics (see also Metrics Reference Model) and benchmarking that informs business leaders about progress towards business goals (business process management). 2. Analytics – program that builds quantitative processes for a business to arrive at optimal decisions and to perform business knowledge discovery. Frequently involves: data mining, process mining, statistical analysis, predictive analytics, predictive modeling, business process modeling, complex event processing. 3. Reporting/enterprise reporting – program that builds infrastructure for strategic reporting to serve the strategic management of a business, not operational reporting. Frequently involves data visualization, executive information system and OLAP. 4. Collaboration/collaboration platform – program that gets different areas (both inside and outside the business) to work together through data sharing and electronic data interchange. 5. Knowledge management – program to make the company data driven through strategies and practices to identify, create, represent, distribute, and enable adoption of insights and experiences that are true business knowledge. Knowledge management leads to learning management and regulatory compliance/compliance. In addition to above, business intelligence also can provide a pro-active approach, such as ALARM function to alert immediately to end-user. There are many types of alerts, for example if some business value exceeds the threshold value the color of that amount in the report will turn RED and the business analyst is alerted. Sometimes an alert mail will be sent to the user as well. This end to end process requires data governance, which should be handled by the expert.[citation needed] [edit] Prioritization of business intelligence projects It is often difficult to provide a positive business case for business intelligence initiatives and often the projects will need to be prioritized through strategic initiatives. Here are some hints to increase the benefits for a BI project. As described by Kimball[9] you must determine the tangible benefits such as eliminated cost of producing legacy reports. Enforce access to data for the entire organization. In this way even a small benefit, such as a few minutes saved, will make a difference when it is multiplied by the number of employees in the entire organization. As described by Ross, Weil & Roberson for Enterprise Architecture,[10] consider letting the BI project be driven by other business initiatives with excellent business cases. To support this approach, the organization must have Enterprise Architects, which will be able to detect suitable business projects. [edit] Success factors of implementation Before implementing a BI solution, it is worth taking different factors into consideration before proceeding. According to Kimball et al., these are the three critical areas that you need to assess within your organization before getting ready to do a BI project:[11] 1. The level of commitment and sponsorship of the project from senior management 2. The level of business need for creating a BI implementation 3. The amount and quality of business data available. [edit] Business sponsorship The commitment and sponsorship of senior management is according to Kimball et al., the most important criteria for assessment.[12] This is because having strong management backing will help overcome shortcomings elsewhere in the project. But as Kimball et al. state: “even the most elegantly designed DW/BI system cannot overcome a lack of business [management] sponsorship”.[13] It is very important that the management personnel who participate in the project have a vision and an idea of the benefits and drawbacks of implementing a BI system. The best business sponsor should have organizational clout and should be well connected within the organization. It is ideal that the business sponsor is demanding but also able to be realistic and supportive if the implementation runs into delays or drawbacks. The management sponsor also needs to be able to assume accountability and to take responsibility for failures and setbacks on the project. It is imperative that there is support from multiple members of the management so the project will not fail if one person leaves the steering group. However, having many managers that work together on the project can also mean that the there are several different interests that attempt to pull the project in different directions. For instance if different departments want to put more emphasis on their usage of the implementation. This issue can be countered by an early and specific analysis of the different business areas that will benefit the most from the implementation. All stakeholders in project should participate in this analysis in order for them to feel ownership of the project and to find common ground between them. Another management problem that should be encountered before start of implementation is if the Business sponsor is overly aggressive. If the management individual gets carried away by the possibilities of using BI and starts wanting the DW or BI implementation to include several different sets of data that were not included in the original planning phase. However, since extra implementations of extra data will most likely add many months to the original plan, it is probably a good idea to make sure that the person from management is aware of his actions. [edit] Implementation should be driven by clear business needs Because of the close relationship with senior management, another critical thing that needs to be assessed before the project is implemented is whether or not there actually is a business need and whether there is a clear business benefit by doing the implementation.[14] The needs and benefits of the implementation are sometimes driven by competition and the need to gain an advantage in the market. Another reason for a business-driven approach to implementation of BI is the acquisition of other organizations that enlarge the original organization it can sometimes be beneficial to implement DW or BI in order to create more oversight. [edit] The amount and quality of the available data This ought to be the most important factor, since without good data – it does not really matter how good your management sponsorship or your business-driven motivation is. If you do not have the data, or the data does not have sufficient quality, any BI implementation will fail. Before implementation it is a very good idea to do data profiling; this analysis will be able to describe the “content, consistency and structure [..]”[14] of the data. This should be done as early as possible in the process and if the analysis shows that your data is lacking, it is a good idea to put the project on the shelf temporarily while the IT department figures out how to do proper data collection. [edit] User aspect Some considerations must be made in order to successfully integrate the usage of business intelligence systems in a company. Ultimately the BI system must be accepted and utilized by the users in order for it to add value to the organization.[15][16] If the usability of the system is poor, the users may become frustrated and spend a considerable amount of time figuring out how to use the system or may not be able to really use the system. If the system does not add value to the users´ mission, they will simply not use it.[16] In order to increase the user acceptance of a BI system, it may be advisable to consult the business users at an early stage of the DW/BI lifecycle, for example at the requirements gathering phase.[15] This can provide an insight into the business process and what the users need from the BI system. There are several methods for gathering this information, such as questionnaires and interview sessions. When gathering the requirements from the business users, the local IT department should also be consulted in order to determine to which degree it is possible to fulfill the business's needs based on the available data.[15] Taking on a user-centered approach throughout the design and development stage may further increase the chance of rapid user adoption of the BI system.[16] Besides focusing on the user experience offered by the BI applications, it may also possibly motivate the users to utilize the system by adding an element of competition. Kimball[15] suggests implementing a function on the business intelligence portal website where reports on system usage can be found. By doing so, managers can see how well their departments are doing and compare themselves to others and this may spur them to encourage their staff to utilize the BI system even more. In a 2007 article, H. J. Watson gives an example of how the competitive element can act as an incentive.[17] Watson describes how a large call centre has implemented performance dashboards for all the call agents and that monthly incentive bonuses have been tied up to the performance metrics. Furthermore the agents can see how their own performance compares to the other team members. The implementation of this type of performance measurement and competition significantly improved the performance of the agents. Other elements which may increase the success of BI can be by involving senior management in order to make BI a part of the organizational culture and also by providing the users with the necessary tools, training and support.[17] By offering user training, more people may actually use the BI application.[15] Providing user support is necessary in order to maintain the BI system and assist users who run into problems.[16] User support can be incorporated in many ways, for example by creating a website. The website should contain great content and tools for finding the necessary information. Furthermore, helpdesk support can be used. The helpdesk can be manned by e.g. power users or the DW/BI project team.[15] [edit] Marketplace There are a number of business intelligence vendors, often categorized into the remaining independent "pure-play" vendors and the consolidated "megavendors" which have entered the market through a recent trend of acquisitions in the BI industry.[18] Some companies adopting BI software decide to pick and choose from different product offerings (best-of-breed) rather than purchase one comprehensive integrated solution (fullservice).[19] [edit] Industry-specific Specific considerations for business intelligence systems have to be taken in some sectors such as governmental banking regulations. The information collected by banking institutions and analyzed with BI software must be protected from some groups or individuals, while being fully available to other groups or individuals. Therefore BI solutions must be sensitive to those needs and be flexible enough to adapt to new regulations and changes to existing laws. [edit] Semi-structured or unstructured data Businesses create a huge amount of valuable information in the form of e-mails, memos, notes from call-centers, news, user groups, chats, reports, web-pages, presentations, image-files, videofiles, and marketing material and news. According to Merrill Lynch, more than 85% of all business information exists in these forms. These information types are called either semistructured or unstructured data. However, organizations often only use these documents once.[20] The management of semi-structured data is recognized as a major unsolved problem in the information technology industry.[21] According to projections from Gartner (2003), white collar workers will spend anywhere from 30 to 40 percent of their time searching, finding and assessing unstructured data. BI uses both structured and unstructured data, but the former is easy to search, and the latter contains a large quantity of the information needed for analysis and decision making.[21][22] Because of the difficulty of properly searching, finding and assessing unstructured or semi-structured data, organizations may not draw upon these vast reservoirs of information, which could influence a particular decision, task or project. This can ultimately lead to poorly-informed decision making.[20] Therefore, when designing a business intelligence/DW-solution, the specific problems associated with semi-structured and unstructured data must be accommodated for as well as those for the structured data.[22] [edit] Unstructured data vs. semi-structured data Unstructured and semi-structured data have different meanings depending on their context. In the context of relational database systems, it refers to data that cannot be stored in columns and rows. It must be stored in a BLOB (binary large object), a catch-all data type available in most relational database management systems. But many of these data types, like e-mails, word processing text files, PPTs, image-files, and video-files conform to a standard that offers the possibility of metadata. Metadata can include information such as author and time of creation, and this can be stored in a relational database. Therefore it may be more accurate to talk about this as semi-structured documents or data,[21] but no specific consensus seems to have been reached. [edit] Problems with semi-structured or unstructured data There are several challenges to developing BI with semi-structured data. According to Inmon & Nesavich,[23] some of those are: 1. Physically accessing unstructured textual data – unstructured data is stored in a huge variety of formats. 2. Terminology – Among researchers and analysts, there is a need to develop a standardized terminology. 3. Volume of data – As stated earlier, up to 85% of all data exists as semi-structured data. Couple that with the need for word-to-word and semantic analysis. 4. Searchability of unstructured textual data – A simple search on some data, e.g. apple, results in links where there is a reference to that precise search term. (Inmon & Nesavich, 2008)[23] gives an example: “a search is made on the term felony. In a simple search, the term felony is used, and everywhere there is a reference to felony, a hit to an unstructured document is made. But a simple search is crude. It does not find references to crime, arson, murder, embezzlement, vehicular homicide, and such, even though these crimes are types of felonies.” [edit] The use of metadata To solve problems with searchability and assessment of data, it is necessary to know something about the content. This can be done by adding context through the use of metadata.[20] Many systems already capture some metadata (e.g. filename, author, size, etc.), but more useful would be metadata about the actual content – e.g. summaries, topics, people or companies mentioned. Two technologies designed for generating metadata about content are automatic categorization and information extraction. [edit] Future A 2009 Gartner paper predicted[24] these developments in the business intelligence market: Because of lack of information, processes, and tools, through 2012, more than 35 percent of the top 5,000 global companies will regularly fail to make insightful decisions about significant changes in their business and markets. By 2012, business units will control at least 40 percent of the total budget for business intelligence. By 2012, one-third of analytic applications applied to business processes will be delivered through coarse-grained application mashups. A 2009 Information Management special report predicted the top BI trends: "green computing, social networking, data visualization, mobile BI, predictive analytics, composite applications, cloud computing and multitouch."[25] Other business intelligence trends include the following:[26] Third party SOA-BI products increasingly address ETL issues of volume and throughput. Cloud computing and Software-as-a-Service (SaaS) are ubiquitous. Companies embrace in-memory processing, 64-bit processing, and pre-packaged analytic BI applications. Operational applications have callable BI components, with improvements in response time, scaling, and concurrency. Near or real time BI analytics is a baseline expectation. Open source BI software replaces vendor offerings. Other lines of research include the combined study of business intelligence and uncertain data.[27][28] In this context, the data used is not assumed to be precise, accurate and complete. Instead, data is considered uncertain and therefore this uncertainty is propagated to the results produced by BI. According to a study by the Aberdeen Group, there has been increasing interest in Software-as-aService (SaaS) business intelligence over the past years, with twice as many organizations using this deployment approach as one year ago – 15% in 2009 compared to 7% in 2008.[citation needed] An article by InfoWorld’s Chris Kanaracus points out similar growth data from research firm IDC, which predicts the SaaS BI market will grow 22 percent each year through 2013 thanks to increased product sophistication, strained IT budgets, and other factors.[29] [edit] See also Accounting intelligence Analytic applications Artificial intelligence marketing Business Intelligence 2.0 Business Intelligence 3.0 Business process discovery Business process management Business activity monitoring Business service management Customer dynamics Data Presentation Architecture Data visualization Decision engineering Enterprise planning systems Document intelligence Integrated business planning Location intelligence Meteorological intelligence Mobile business intelligence Operational intelligence Process mining Runtime intelligence Sales intelligence Spend management Test and learn [edit] References 1. ^ "BusinessDictionary.com definition". Retrieved 17 March 2010. 2. ^ a b D. J. Power (10 March 2007). "A Brief History of Decision Support Systems, version 4.0". DSSResources.COM. Retrieved 10 July 2008. 3. ^ Kobielus, James (30 April 2010). "What’s Not BI? Oh, Don’t Get Me Started....Oops Too Late...Here Goes....". "“Business” intelligence is a non-domain-specific catchall for all the types of analytic data that can be delivered to users in reports, dashboards, and the like. When you specify the subject domain for this intelligence, then you can refer to “competitive intelligence,” “market intelligence,” “social intelligence,” “financial intelligence,” “HR intelligence,” “supply chain intelligence,” and the like." 4. ^ H P Luhn (1958). "A Business Intelligence System". IBM Journal 2 (4): 314. doi:10.1147/rd.24.0314. 5. ^ Power, D. J.. "A Brief History of Decision Support Systems". Retrieved 1 November 2010. 6. ^ Evelson, Boris (21 November 2008). "Topic Overview: Business Intelligence". 7. ^ Evelson, Boris (29 April 2010). "Want to know what Forrester's lead data analysts are thinking about BI and the data domain?". 8. ^ Henschen, Doug (4 January 2010). Analytics at Work: Q&A with Tom Davenport. (Interview). 9. ^ Kimball et al., 2008: 29 10. ^ Jeanne W. Ross, Peter Weil, David C. Robertson (2006) "Enterprise Architecture As Strategy", p. 117 ISBN 1-59139-839-8. 11. ^ Kimball et al. 2008: p. 298 12. ^ Kimball et al., 2008: 16 13. ^ Kimball et al., 2008: 18 14. ^ a b Kimball et al., 2008: 17 15. ^ a b c d e f Kimball 16. ^ a b c d Swain Scheps "Business Intelligence For Dummies", 2008, ISBN 978-0-470-12726-0 17. ^ a b Watson, Hugh J.; Wixom, Barbara H. (2007). "The Current State of Business Intelligence". Computer 40 (9): 96. doi:10.1109/MC.2007.331. 18. ^ Pendse, Nigel (7 March 2008). "Consolidations in the BI industry". The OLAP Report. 19. ^ Imhoff, Claudia (4 April 2006). "Three Trends in Business Intelligence Technology". 20. ^ a b c Rao, R. (2003). "From unstructured data to actionable intelligence". IT Professional 5 (6): 29. doi:10.1109/MITP.2003.1254966. 21. ^ a b c Blumberg, R. & S. Atre (2003). "The Problem with Unstructured Data". DM Review: 42–46. 22. ^ a b Negash, S (2004). "Business Intelligence". Communications of the Association of Information Systems 13: 177–195. 23. ^ a b Inmon, B. & A. Nesavich, "Unstructured Textual Data in the Organization" from "Managing Unstructured data in the organization", Prentice Hall 2008, pp. 1–13 24. ^ Gartner Reveals Five Business Intelligence Predictions for 2009 and Beyond. gartner.com. 15 January 2009 25. ^ Campbell, Don (23 June 2009). "10 Red Hot BI Trends". Information Management. 26. ^ Wik, Philip (11 August 2011). "10 Service-Oriented Architecture and Business Intelligence". Information Management. 27. ^ Rodriguez, Carlos; Daniel, Florian; Casati, Fabio; Cappiello, Cinzia (2010). "Toward Uncertain Business Intelligence: The Case of Key Indicators". IEEE Internet Computing 14 (4): 32. doi:10.1109/MIC.2010.59. 28. ^ Rodriguez, C., Daniel, F., Casati, F. & Cappiello, C. (2009), Computing Uncertain Key Indicators from Uncertain Data, pp. 106-120 | conference = ICIQ'09 | year = 2009 29. ^ SaaS BI growth will soar in 2010 | Cloud Computing. InfoWorld (2010-02-01). Retrieved on 17 January 2012. [edit] Bibliography Ralph Kimball et al. "The Data warehouse Lifecycle Toolkit" (2nd ed.) Wiley ISBN 0-470-47957-4 [edit] External links Chaudhuri, Surajit; Dayal, Umeshwar; Narasayya, Vivek (August 2011). "An Overview Of Business Intelligence Technology". Communications of the ACM 54 (8): 88–98. doi:10.1145/1978542.1978562. Retrieved 26 October 2011 Business performance management From Wikipedia, the free encyclopedia Jump to: navigation, search This article has multiple issues. Please help improve it or discuss these issues on the talk page. It is written like an advertisement and needs to be rewritten from a neutral point of view. Tagged since November 2008. It may contain wording that merely promotes the subject without imparting verifiable information. Tagged since November 2008. It contains weasel words: vague phrasing that often accompanies biased or unverifiable information. Tagged since March 2009. Business performance management is a set of management and analytic processes that enable the management of an organization's performance to achieve one or more pre-selected goals. Synonyms for "business performance management" include "corporate performance management" and "enterprise performance management".[1][2] Business performance management is contained within approaches to business process management.[3] Business performance management has three main activities: 1. selection of goals, 2. consolidation of measurement information relevant to an organization’s progress against these goals, and 3. interventions made by managers in light of this information with a view to improving future performance against these goals. Although presented here sequentially, typically all three activities will run concurrently, with interventions by managers affecting the choice of goals, the measurement information monitored, and the activities being undertaken by the organization. Because business performance management activities in large organizations often involve the collation and reporting of large volumes of data, many software vendors, particularly those offering business intelligence tools, market products intended to assist in this process. As a result of this marketing effort, business performance management is often incorrectly understood as an activity that necessarily relies on software systems to work, and many definitions of business performance management explicitly suggest software as being a definitive component of the approach.[4] This interest in business performance management from the software community is salesdriven[citation needed] - "The biggest growth area in operational BI analysis is in the area of business performance management."[5] Since 1992, business performance management has been strongly influenced by the rise of the balanced scorecard framework. It is common for managers to use the balanced scorecard framework to clarify the goals of an organization, to identify how to track them, and to structure the mechanisms by which interventions will be triggered. These steps are the same as those that are found in BPM, and as a result balanced scorecard is often used as the basis for business performance management activity with organizations.[citation needed] In the past, owners have sought to drive strategy down and across their organizations, transform these strategies into actionable metrics and use analytics to expose the cause-and-effect relationships that, if understood, could give insight into decision-making. Contents [hide] 1 History 2 Definition and scope 3 Methodologies 4 Metrics and key performance indicators 5 Application software types 6 Design and implementation 7 See also 8 References 9 Further reading 10 External links [edit] History Reference to non-business performance management occurs[citation needed] in Sun Tzu's The Art of War. Sun Tzu claims that to succeed in war, one should have full knowledge of one's own strengths and weaknesses as well as those of one's enemies. Lack of either set of knowledge might result in defeat. Parallels between the challenges in business and those of war include[citation needed]: collecting data - both internal and external discerning patterns and meaning in the data (analyzing) responding to the resultant information Prior to the start of the Information Age in the late 20th century, businesses sometimes took the trouble to laboriously collect data from non-automated sources. As they lacked computing resources to properly analyze the data, they often made commercial decisions primarily on the basis of intuition. As businesses started automating more and more systems, more and more data became available. However, collection often remained a challenge due to a lack of infrastructure for data exchange or due to incompatibilities between systems. Reports on the data gathered sometimes took months to generate. Such reports allowed informed long-term strategic decision-making. However, short-term tactical decision-making often continued to rely on intuition. In 1989 Howard Dresner, a research analyst at Gartner, popularized "business intelligence" (BI) as an umbrella term to describe a set of concepts and methods to improve business decisionmaking by using fact-based support systems. Performance management builds on a foundation of BI, but marries it to the planning-and-control cycle of the enterprise - with enterprise planning, consolidation and modeling capabilities. Increasing standards, automation, and technologies have led to vast amounts of data becoming available. Data warehouse technologies have allowed the building of repositories to store this data. Improved ETL and enterprise application integration tools have increased the timely collecting of data. OLAP reporting technologies have allowed faster generation of new reports which analyze the data. As of 2010, business intelligence has become the art of sieving through large amounts of data, extracting useful information and turning that information into actionable knowledge.[citation needed] [edit] Definition and scope Business performance management consists of a set of management and analytic processes, supported by technology, that enable businesses to define strategic goals and then measure and manage performance against those goals. Core business performance management processes include financial planning, operational planning, business modeling, consolidation and reporting, analysis, and monitoring of key performance indicators linked to strategy. Business performance management involves consolidation of data from various sources, querying, and analysis of the data, and putting the results into practice. [edit] Methodologies Various methodologies for implementing business performance management exist. The discipline gives companies a top-down framework by which to align planning and execution, strategy and tactics, and business-unit and enterprise objectives. Reactions may include the Six Sigma strategy, balanced scorecard, activity-based costing (ABC), Total Quality Management, economic value-add, integrated strategic measurement and Theory of Constraints. The balanced scorecard is the most widely adopted[citation needed] performance management methodology. Methodologies on their own cannot deliver a full solution to an enterprise's CPM needs. Many pure-methodology implementations fail to deliver the anticipated benefits due to lack of integration with fundamental CPM processes.[citation needed] [edit] Metrics and key performance indicators Some of the areas from which bank management may gain knowledge by using business performance management include: customer-related numbers: o new customers acquired o status of existing customers o attrition of customers (including breakup by reason for attrition) turnover generated by segments of the customers - possibly using demographic filters outstanding balances held by segments of customers and terms of payment - possibly using demographic filters collection of bad debts within customer relationships demographic analysis of individuals (potential customers) applying to become customers, and the levels of approval, rejections and pending numbers delinquency analysis of customers behind on payments profitability of customers by demographic segments and segmentation of customers by profitability campaign management real-time dashboard on key operational metrics o overall equipment effectiveness clickstream analysis on a website key product portfolio trackers marketing-channel analysis sales-data analysis by product segments callcenter metrics Though the above list describes what a bank might monitor, it could refer to a telephone company or to a similar service-sector company. Items of generic importance include: 1. consistent and correct KPI-related data providing insights into operational aspects of a company 2. timely availability of KPI-related data 3. KPIs designed to directly reflect the efficiency and effectiveness of a business 4. information presented in a format which aids decision-making for management and decision-makers 5. ability to discern patterns or trends from organized information Business performance management integrates the company's processes with CRM or[citation needed] ERP. Companies should become better able to gauge customer satisfaction, control customer trends and influence shareholder value.[citation needed] [edit] Application software types People working in business intelligence have developed tools that ease the work of business performance management, especially when the business-intelligence task involves gathering and analyzing large amounts of unstructured data. Tool categories commonly used for business performance management include: OLAP — online analytical processing, sometimes simply called "analytics" (based on dimensional analysis and the so-called "hypercube" or "cube") scorecarding, dashboarding and data visualization data warehouses document warehouses text mining DM — data mining BPO — business performance optimization EPM — enterprise performance management EIS — executive information systems DSS — decision support systems MIS — management information systems SEMS — strategic enterprise management software [edit] Design and implementation Questions asked when implementing a business performance management program include: Goal-alignment queries Determine the short- and medium-term purpose of the program. What strategic goal(s) of the organization will the program address? What organizational mission/vision does it relate to? A hypothesis needs to be crafted that details how this initiative will eventually improve results / performance (i.e. a strategy map). Baseline queries Assess current information-gathering competency. Does the organization have the capability to monitor important sources of information? What data is being collected and how is it being stored? What are the statistical parameters of this data, e.g., how much random variation does it contain? Is this being measured? Cost and risk queries Estimate the financial consequences of a new BI initiative. Assess the cost of the present operations and the increase in costs associated with the BPM initiative. What is the risk that the initiative will fail? This risk assessment should be converted into a financial metric and included in the planning. Customer and stakeholder queries Determine who will benefit from the initiative and who will pay. Who has a stake in the current procedure? What kinds of customers / stakeholders will benefit directly from this initiative? Who will benefit indirectly? What quantitative / qualitative benefits follow? Is the specified initiative the best or only way to increase satisfaction for all kinds of customers? How will customer benefits be monitored? What about employees, shareholders, and distribution channel members? Metrics-related queries Information requirements need operationalization into clearly defined metrics. Decide which metrics to use for each piece of information being gathered. Are these the best metrics and why? How many metrics need to be tracked? If this is a large number (it usually is), what kind of system can track them? Are the metrics standardized, so they can be benchmarked against performance in other organizations? What are the industry standard metrics available? Measurement methodology-related queries Establish a methodology or a procedure to determine the best (or acceptable) way of measuring the required metrics. How frequently will data be collected? Are there any industry standards for this? Is this the best way to do the measurements? How do we know that? Results-related queries Monitor the BPM program to ensure that it meets objectives. The program itself may require adjusting. The program should be tested for accuracy, reliability, and validity. How can it be demonstrated that the BI initiative, and not something else, contributed to a change in results? How much of the change was probably random? [edit] See also Business process management Integrated business planning Data Presentation Architecture List of management topics List of information technology management topics Electronic performance support systems [edit] References 1. ^ Frolick, Mark N.; Thilini R. Ariyachandra (Winter 2006). "Business performance management: one truth" (PDF). Information Systems Management (www.ismjournal.com): 41–48. Retrieved 2010-02-21. "Business Performance Management (BPM) [...] is also known and identified by other names, such as corporate performance management and enterprise performance management." 2. ^ "Technology-enabled Business Performance Management: Concept, Framework, and Technology". 3rd International Management Conference. 2005-12-20. p. 2. doi:10.1.1.116.9581. Retrieved 2010-02-21. "Confusion also arises because industry experts can not agree what to call BPM, let alone how to define it, META Group and IDC use the term 'Business Performance Management', Gartner Group prefers 'Corporate Performance Management', and others favor 'Enterprise Performance Management'." 3. ^ vom Brocke, J. & Rosemann, M. (2010), Handbook on Business Process Management: Strategic Alignment, Governance, People and Culture (International Handbooks on Information Systems). Berlin: Springer 4. ^ BPM Mag, What is BPM? 5. ^ The Next Generation of Business Intelligence: Operational BI White, Colin (May 2005). "The Next Generation of Business Intelligence: Operational BI". Information Management Magazine. Retrieved 2010-02-21. "The biggest growth area in operational BI analysis is in the area of business performance management (BPM)." [edit] Further reading This article's further reading may not follow Wikipedia's content policies or guidelines. Please improve this article by removing excessive, less relevant or many publications with the same point of view; or by incorporating the relevant publications into the body of the article through appropriate citations. (August 2010) Mosimann, Roland P., Patrick Mosimann and Meg Dussault, The Performance Manager. 2007 ISBN 978-0-9730124-1-5 Dresner, Howard, The Performance Management Revolution: Business Results Through Insight and Action. 2007 ISBN 978-0470124833 Dresner, Howard, Profiles in Performance: Business Intelligence Journeys and the Roadmap for Change. 2009 ISBN 978-0470408865 Cokins, Gary, Performance Management: Integrating Strategy Execution, Methodologies, Risk, and Analytics. 2009 ISBN 978-0-470-44998-1 Cokins, Gary, Performance Management: Finding the Missing Pieces (to Close the Intelligence Gap). 2004 ISBN 978-0-471-57690-7 Paladino, Bob, Five Key Principles of Corporate Performance Management. 2007 ISBN 978-0470009918 Wade, David and Ronald Recardo, Corporate Performance Management. ButterworthHeinemann, 2001 ISBN 0-87719-386-X Coveney, Michael, Strategy to the Max FSN Publishing Limited, 2010 ISBN 9780955590061 [edit] External links "Giving the Boss the Big Picture: A dashboard pulls up everything the CEO needs to run the show". BusinessWeek. Bloomberg L.P.. 2006-02-13. Retrieved 2010-02-22. Business Finance: Bred Tough: The Best-of-Breed, 2009 (July 2009) Business rule From Wikipedia, the free encyclopedia (Redirected from Business rules) Jump to: navigation, search A business rule is a statement that defines or constrains some aspect of the business and always resolves to either true or false. Business rules are intended to assert business structure or to control or influence the behavior of the business.[1] Business rules describe the operations, definitions and constraints that apply to an organization. Business rules can apply to people, processes, corporate behavior and computing systems in an organization, and are put in place to help the organization achieve its goals. For example a business rule might state that no credit check is to be performed on return customers. Other examples of business rules include requiring a rental agent to disallow a rental tenant if their credit rating is too low, or requiring company agents to use a list of preferred suppliers and supply schedules. While a business rule may be informal or even unwritten, writing the rules down clearly and making sure that they don't conflict is a valuable activity. When carefully managed, rules can be used to help the organization to better achieve goals, remove obstacles to market growth, reduce costly mistakes, improve communication, comply with legal requirements, and increase customer loyalty. Contents [hide] 1 Introduction 2 Categories of business rules 3 Real world applications and obstacles 4 Formal specification 5 See also 6 External links 7 References [edit] Introduction Business rules tell an organization what it can do in detail, while strategy tells it how to focus the business at a macro level to optimize results. Put differently, a strategy provides high-level direction about what an organization should do. Business rules provide detailed guidance about how a strategy can be translated to action. Business rules exist for an organization whether or not they are ever written down, talked about or even part of the organization’s consciousness. However it is a fairly common practice for organizations to gather business rules. This may happen in one of two ways. Organizations may choose to proactively describe their business practices, producing a database of rules. While this activity may be beneficial, it may be expensive and time consuming. For example, they might hire a consultant to come through the organization to document and consolidate the various standards and methods currently in practice. More commonly, business rules are discovered and documented informally during the initial stages of a project. In this case the collecting of the business rules is coincidental. In addition, business projects, such as the launching of a new product or the re-engineering of a complex process, might lead to the definition of new business rules. This practice of coincidental business rule gathering is vulnerable to the creation of inconsistent or even conflicting business rules within different organizational units, or within the same organizational unit over time. This inconsistency creates problems that can be difficult to find and fix. Allowing business rules to be documented during the course of business projects is less expensive and easier to accomplish than the first approach, but if the rules are not collected in a consistent manner, they are not valuable. In order to teach business people about the best ways to gather and document business rules, experts in business analysis have created the Business Rules Methodology. This methodology defines a process of capturing business rules in natural language, in a verifiable and understandable way. This process is not difficult to learn, can be performed in real-time, and empowers business stakeholders to manage their own business rules in a consistent manner. Gathering business rules is also called rules harvesting or business rule mining. The business analyst or consultant can extract the rules from IT documentation (like use cases, specifications or system code). They may also organize workshops and interviews with subject matter experts (commonly abbreviated as SMEs). Software technologies designed to capture business rules through analysis of legacy source code or of actual user behavior can accelerate the rule gathering processing. [edit] Categories of business rules According to the white paper by the Business Rules Group,[1] a statement of a business rule falls into one of four categories: Definitions of business terms The most basic element of a business rule is the language used to express it. The very definition of a term is itself a business rule that describes how people think and talk about things. Thus, defining a term is establishing a category of business rule. Terms have traditionally been documented in a Glossary or as entities in a conceptual model. Facts relating terms to each other The nature or operating structure of an organization can be described in terms of the facts that relate terms to each other. To say that a customer can place an order is a business rule. Facts can be documented as natural language sentences or as relationships, attributes, and generalization structures in a graphical model. Constraints (also called "action assertions") Every enterprise constrains behavior in some way, and this is closely related to constraints on what data may or may not be updated. To prevent a record from being made is, in many cases, to prevent an action from taking place. Derivations Business rules (including laws of nature) define how knowledge in one form may be transformed into other knowledge, possibly in a different form. [edit] Real world applications and obstacles Business rules are gathered in these situations: 1. When dictated by law 2. During the business analysis 3. As an ephemeral aid to engineers. This lack of consistent approach is mostly due to the cost and effort required to maintain the list of rules. The cost of maintaining the list increases in situations where the rules are rapidly changing, such as in a start-up company. Another common obstacle to the adoption of formal business rule management is resistance from employees who understand that their knowledge of business rules is key to their employment. Because technologies require and enforce consistency in their use, technology is often used to address these issues. As a result, there has been substantial investment in tools to perform business rules management and rules execution. Software tools such as Wolf Frameworks are an example of this trend[2]. Note that many tools make a distinction between Business Rules Engines and Business Rules Management, and require a translation between the two. Commercially available tools now also offer the possibility to combine both management and execution of rules. Combined with an easy to use interface and a proper notation which can be maintained by business users, customers of these tools hope to reduce or eliminate the obstacles mentioned above. While newer software tools are able to combine business rule management and execution, it is important to realize that these two ideas are distinct, and each provides value that is different from the other. Software packages automate business rules using business logic. The term business rule is sometimes used interchangeably with business logic; however the latter connotes an engineering practice and the former an intrinsic business practice. There is value in outlining an organization's business rules regardless of whether this information is used to automate its operations. One of the pitfalls in trying to fill the gap between rules management and execution is trying to give business rules the syntax of logic, and merely describing logical constructs in a natural language. Translation for engines is easier, but business users will no longer be able to write down the rules. [edit] Formal specification Business rules can be expressed in formal languages such as Unified Modeling Language, Z notation, Business Process Execution Language, Business Process Modeling Notation, or the Semantics of Business Vocabulary and Business Rules (SBVR). [edit] See also Business Process Business rules approach BRMS [edit] External links Workshop summary paper: Six Views on the Business Rule Management System [edit] References 1. ^ a b Business Rules Group, Defining Business Rules ~ What Are They Really?, [1] 2. ^ James Taylor, 'James Taylor on Everything Decision Management, [2] This article includes a list of references, related reading or external links, but its sources remain unclear because it lacks inline citations. Please improve this article by introducing more precise citations. (April 2009) WALKER, Adrian et al. (1990). Knowledge Systems and Prolog. Addison-Wesley. ISBN 0-201-52424-4. VON HALLE, Barbara & GOLDBERG, Larry (2006 October 9). The Business Rule Revolution. Happy About. ISBN 1-60005-013-1. VON HALLE, Barbara (2001). Business Rules Applied. Wiley. ISBN 0-471-41293-7. Principles Of Business Rule Approach, Ronald G. Ross (Aw Professional, 2003) ISBN 0201-78893-4 Business Process Management with a Business Rule Approach, Tom Debevoise (Business Knowledge Architects, 2005) ISBN 0-9769048-0-2 Data mining From Wikipedia, the free encyclopedia Jump to: navigation, search Not to be confused with analytics, information extraction, or data analysis. Data mining (the analysis step of the knowledge discovery in databases process,[1] or KDD), a relatively young and interdisciplinary field of computer science[2][3] is the process of discovering new patterns from large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics and database systems.[2] The overall goal of the data mining process is to extract knowledge from a data set in a human-understandable structure[2] and besides the raw analysis step involves database and data management aspects, data preprocessing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of found structure, visualization and online updating.[2] The term is a buzzword, and is frequently misused to mean any form of large-scale data or information processing (collection, extraction, warehousing, analysis and statistics) but also generalized to any kind of computer decision support system including artificial intelligence, machine learning and business intelligence. In the proper use of the word, the key term is discovery, commonly defined as "detecting something new". Even the popular book "Data mining: Practical machine learning tools and techniques with Java"[4] (which covers mostly machine learning material) was originally to be named just "Practical machine learning", and the term "data mining" was only added for marketing reasons.[5] Often the more general terms "(large scale) data analysis" or "analytics" or when referring to actual methods, artificial intelligence and machine learning are more appropriate. The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection) and dependencies (association rule mining). This usually involves using database techniques such as spatial indexes. These patterns can then be seen as a kind of summary of the input data, and used in further analysis or for example in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation nor result interpretation and reporting are part of the data mining step, but do belong to the overall KDD process as additional steps. The related terms data dredging, data fishing and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations. Contents [hide] 1 Background o 1.1 Research and evolution 2 Process o 2.1 Pre-processing o 2.2 Results validation 3 Standards 4 Notable uses o 4.1 Games o 4.2 Business o 4.3 Science and engineering o 4.4 Human rights o 4.5 Spatial data mining 4.5.1 Challenges 4.5.2 Sensor data mining o 4.6 Visual data mining o 4.7 Music data mining o 4.8 Surveillance 4.8.1 Pattern mining 4.8.2 Subject-based data mining o 4.9 Knowledge grid 5 Reliability 6 Privacy concerns and ethics 7 Software o 7.1 Free libre open-source data-mining software and applications o 7.2 Commercial data-mining software and applications o 7.3 Marketplace surveys 8 See also 9 References 10 Further reading 11 External links [edit] Background The manual extraction of patterns from data has occurred for centuries. Early methods of identifying patterns in data include Bayes' theorem (1700s) and regression analysis (1800s). The proliferation, ubiquity and increasing power of computer technology has increased data collection, storage and manipulations. As data sets have grown in size and complexity, direct hands-on data analysis has increasingly been augmented with indirect, automatic data processing. This has been aided by other discoveries in computer science, such as neural networks, cluster analysis, genetic algorithms (1950s), decision trees (1960s) and support vector machines (1990s). Data mining is the process of applying these methods to data with the intention of uncovering hidden patterns[6] in large data sets. It bridges the gap from applied statistics and artificial intelligence (which usually provide the mathematical background) to database management by exploiting the way data is stored and indexed in databases to execute the actual learning and discovery algorithms more efficiently, allowing such methods to be applied to larger data sets. [edit] Research and evolution The premier professional body in the field is the Association for Computing Machinery's Special Interest Group on knowledge discovery and Data Mining (SIGKDD). Since 1989 they have hosted an annual international conference and published its proceedings,[7] and since 1999 have published a biannual academic journal titled "SIGKDD Explorations".[8] Computer science conferences on data mining include: CIKM – ACM Conference on Information and Knowledge Management DMIN – International Conference on Data Mining DMKD – Research Issues on Data Mining and Knowledge Discovery ECDM – European Conference on Data Mining ECML-PKDD – European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases EDM – International Conference on Educational Data Mining ICDM – IEEE International Conference on Data Mining KDD – ACM SIGKDD Conference on Knowledge Discovery and Data Mining MLDM – Machine Learning and Data Mining in Pattern Recognition PAKDD – The annual Pacific-Asia Conference on Knowledge Discovery and Data Mining PAW – Predictive Analytics World SDM – SIAM International Conference on Data Mining (SIAM) SSTD – Symposium on Spatial and Temporal Databases Data mining topics are present on most data management / database conferences. [edit] Process The knowledge discovery in databases (KDD) process is commonly defined with the stages (1) Selection (2) Preprocessing (3) Transformation (4) Data Mining (5) Interpretation/Evaluation.[1] It exists however in many variations of this theme such as the CRoss Industry Standard Process for Data Mining (CRISP-DM) which defines six phases: (1) Business Understanding, (2) Data Understanding, (3) Data Preparation, (4) Modeling, (5) Evaluation, and (6) Deployment or a simplified process such as (1) Pre-processing, (2) Data mining, and (3) Results validation. [edit] Pre-processing Before data mining algorithms can be used, a target data set must be assembled. As data mining can only uncover patterns actually present in the data, the target dataset must be large enough to contain these patterns while remaining concise enough to be mined in an acceptable timeframe. A common source for data is a data mart or data warehouse. Pre-processing is essential to analyze the multivariate datasets before data mining. The target set is then cleaned. Data cleaning removes the observations containing noise and those with missing data. Data mining involves six common classes of tasks:[1] Anomaly detection (Outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors and require further investigation. Association rule learning (Dependency modeling) – Searches for relationships between variables. For example a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis. Clustering – is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data. Classification – is the task of generalizing known structure to apply to new data. For example, an email program might attempt to classify an email as legitimate or spam. Regression – Attempts to find a function which models the data with the least error. Summarization – providing a more compact representation of the data set, including visualization and report generation. [edit] Results validation This section is missing information about non-classification tasks in data mining, it only covers machine learning. This concern has been noted on the talk page where whether or not to include such information may be discussed. (September 2011) The final step of knowledge discovery from data is to verify the patterns produced by the data mining algorithms occur in the wider data set. Not all patterns found by the data mining algorithms are necessarily valid. It is common for the data mining algorithms to find patterns in the training set which are not present in the general data set. This is called overfitting. To overcome this, the evaluation uses a test set of data on which the data mining algorithm was not trained. The learned patterns are applied to this test set and the resulting output is compared to the desired output. For example, a data mining algorithm trying to distinguish spam from legitimate emails would be trained on a training set of sample emails. Once trained, the learned patterns would be applied to the test set of emails on which it had not been trained. The accuracy of these patterns can then be measured from how many emails they correctly classify. A number of statistical methods may be used to evaluate the algorithm such as ROC curves. If the learned patterns do not meet the desired standards, then it is necessary to reevaluate and change the pre-processing and data mining. If the learned patterns do meet the desired standards then the final step is to interpret the learned patterns and turn them into knowledge. [edit] Standards There have been some efforts to define standards for the data mining process, for example the 1999 European Cross Industry Standard Process for Data Mining (CRISP-DM 1.0) and the 2004 Java Data Mining standard (JDM 1.0). Development on successors of these processes (CRISPDM 2.0 and JDM 2.0) was active in 2006, but has stalled since. JDM 2.0 was withdrawn without reaching a final draft. For exchanging the extracted models – in particular for the use in predictive analytics – the key standard is the Predictive Model Markup Language (PMML), which is an XML-based language developed by the Data Mining Group (DMG) and supported as exchange format by many data mining applications. As the name suggests it only covers prediction models, a particular data mining task of high importance to business applications, however extensions to for example cover subspace clustering have been proposed independently of the DMG.[9] [edit] Notable uses See also Category: Applied data mining This section may need to be rewritten entirely to comply with Wikipedia's quality standards. You can help. The discussion page may contain suggestions. (September 2011) [edit] Games Since the early 1960s, with the availability of oracles for certain combinatorial games, also called tablebases (e.g. for 3x3-chess) with any beginning configuration, small-board dots-andboxes, small-board-hex, and certain endgames in chess, dots-and-boxes, and hex; a new area for data mining has been opened. This is the extraction of human-usable strategies from these oracles. Current pattern recognition approaches do not seem to fully acquire the high level of abstraction required to be applied successfully. Instead, extensive experimentation with the tablebases, combined with an intensive study of tablebase-answers to well designed problems and with knowledge of prior art, i.e. pre-tablebase knowledge, is used to yield insightful patterns. Berlekamp in dots-and-boxes etc. and John Nunn in chess endgames are notable examples of researchers doing this work, though they were not and are not involved in tablebase generation. [edit] Business Data mining in customer relationship management applications can contribute significantly to the bottom line.[citation needed] Rather than randomly contacting a prospect or customer through a call center or sending mail, a company can concentrate its efforts on prospects that are predicted to have a high likelihood of responding to an offer. More sophisticated methods may be used to optimize resources across campaigns so that one may predict to which channel and to which offer an individual is most likely to respond—across all potential offers. Additionally, sophisticated applications could be used to automate the mailing. Once the results from data mining (potential prospect/customer and channel/offer) are determined, this "sophisticated application" can either automatically send an e-mail or regular mail. Finally, in cases where many people will take an action without an offer, uplift modeling can be used to determine which people will have the greatest increase in responding if given an offer. Data clustering can also be used to automatically discover the segments or groups within a customer data set. Businesses employing data mining may see a return on investment, but also they recognize that the number of predictive models can quickly become very large. Rather than one model to predict how many customers will churn, a business could build a separate model for each region and customer type. Then instead of sending an offer to all people that are likely to churn, it may only want to send offers to loyal customers. Finally, it may want to determine which customers are going to be profitable over a window of time and only send the offers to those that are likely to be profitable. In order to maintain this quantity of models, they need to manage model versions and move to automated data mining. Data mining can also be helpful to human-resources departments in identifying the characteristics of their most successful employees. Information obtained, such as universities attended by highly successful employees, can help HR focus recruiting efforts accordingly. Additionally, Strategic Enterprise Management applications help a company translate corporatelevel goals, such as profit and margin share targets, into operational decisions, such as production plans and workforce levels.[10] Another example of data mining, often called the market basket analysis, relates to its use in retail sales. If a clothing store records the purchases of customers, a data-mining system could identify those customers who favor silk shirts over cotton ones. Although some explanations of relationships may be difficult, taking advantage of it is easier. The example deals with association rules within transaction-based data. Not all data are transaction based and logical or inexact rules may also be present within a database. Market basket analysis has also been used to identify the purchase patterns of the Alpha consumer. Alpha Consumers are people that play a key role in connecting with the concept behind a product, then adopting that product, and finally validating it for the rest of society. Analyzing the data collected on this type of user has allowed companies to predict future buying trends and forecast supply demands.[citation needed] Data mining is a highly effective tool in the catalog marketing industry.[citation needed] Catalogers have a rich history of customer transactions on millions of customers dating back several years. Data mining tools can identify patterns among customers and help identify the most likely customers to respond to upcoming mailing campaigns. Data mining for business applications is a component which needs to be integrated into a complex modelling and decision making process. Reactive business intelligence (RBI) advocates a holistic approach that integrates data mining, modeling and interactive visualization, into an end-to-end discovery and continuous innovation process powered by human and automated learning.[11] In the area of decision making the RBI approach has been used to mine the knowledge which is progressively acquired from the decision maker and self-tune the decision method accordingly.[12] Related to an integrated-circuit production line, an example of data mining is described in the paper "Mining IC Test Data to Optimize VLSI Testing."[13] In this paper the application of data mining and decision analysis to the problem of die-level functional test is described. Experiments mentioned in this paper demonstrate the ability of applying a system of mining historical die-test data to create a probabilistic model of patterns of die failure. These patterns are then utilized to decide in real time which die to test next and when to stop testing. This system has been shown, based on experiments with historical test data, to have the potential to improve profits on mature IC products. [edit] Science and engineering In recent years, data mining has been used widely in the areas of science and engineering, such as bioinformatics, genetics, medicine, education and electrical power engineering. In the study of human genetics, sequence mining helps address the important goal of understanding the mapping relationship between the inter-individual variation in human DNA sequences and variability in disease susceptibility. In lay terms, it is to find out how the changes in an individual's DNA sequence affect the risk of developing common diseases such as cancer. This is very important to help improve the diagnosis, prevention and treatment of the diseases. The data mining method that is used to perform this task is known as multifactor dimensionality reduction.[14] In the area of electrical power engineering, data mining methods have been widely used for condition monitoring of high voltage electrical equipment. The purpose of condition monitoring is to obtain valuable information on the insulation's health status of the equipment. Data clustering such as self-organizing map (SOM) has been applied on the vibration monitoring and analysis of transformer on-load tap-changers (OLTCS). Using vibration monitoring, it can be observed that each tap change operation generates a signal that contains information about the condition of the tap changer contacts and the drive mechanisms. Obviously, different tap positions will generate different signals. However, there was considerable variability amongst normal condition signals for exactly the same tap position. SOM has been applied to detect abnormal conditions and to estimate the nature of the abnormalities.[15] Data mining methods have also been applied for dissolved gas analysis (DGA) on power transformers. DGA, as a diagnostics for power transformer, has been available for many years. Methods such as SOM has been applied to analyze data and to determine trends which are not obvious to the standard DGA ratio methods such as Duval Triangle.[15] A fourth area of application for data mining in science/engineering is within educational research, where data mining has been used to study the factors leading students to choose to engage in behaviors which reduce their learning[16] and to understand the factors influencing university student retention.[17] A similar example of the social application of data mining is its use in expertise finding systems, whereby descriptors of human expertise are extracted, normalized and classified so as to facilitate the finding of experts, particularly in scientific and technical fields. In this way, data mining can facilitate Institutional memory. Other examples of applying data mining method applications are biomedical data facilitated by domain ontologies,[18] mining clinical trial data,[19] traffic analysis using SOM,[20] et cetera. In adverse drug reaction surveillance, the Uppsala Monitoring Centre has, since 1998, used data mining methods to routinely screen for reporting patterns indicative of emerging drug safety issues in the WHO global database of 4.6 million suspected adverse drug reaction incidents.[21] Recently, similar methodology has been developed to mine large collections of electronic health records for temporal patterns associating drug prescriptions to medical diagnoses.[22] [edit] Human rights Data mining of government records, particularly records of the justice system (courts, prisons), enables the discovery of systemic human rights violations - related to generation and publication of invalid or fraudulent legal records by various government agencies. [23] [24] [edit] Spatial data mining Spatial data mining is the application of data mining methods to spatial data. Spatial data mining follows along the same functions in data mining, with the end objective to find patterns in geography. So far, data mining and Geographic Information Systems (GIS) have existed as two separate technologies, each with its own methods, traditions and approaches to visualization and data analysis. Particularly, most contemporary GIS have only very basic spatial analysis functionality. The immense explosion in geographically referenced data occasioned by developments in IT, digital mapping, remote sensing, and the global diffusion of GIS emphasizes the importance of developing data driven inductive approaches to geographical analysis and modeling. Data mining, which is the partially automated search for hidden patterns in large databases, offers great potential benefits for applied GIS-based decision-making. Recently, the task of integrating these two technologies has become critical, especially as various public and private sector organizations possessing huge databases with thematic and geographically referenced data begin to realize the huge potential of the information hidden there. Among those organizations are: offices requiring analysis or dissemination of geo-referenced statistical data public health services searching for explanations of disease clusters environmental agencies assessing the impact of changing land-use patterns on climate change geo-marketing companies doing customer segmentation based on spatial location. [edit] Challenges Geospatial data repositories tend to be very large. Moreover, existing GIS datasets are often splintered into feature and attribute components, that are conventionally archived in hybrid data management systems. Algorithmic requirements differ substantially for relational (attribute) data management and for topological (feature) data management.[25] Related to this is the range and diversity of geographic data formats, that also presents unique challenges. The digital geographic data revolution is creating new types of data formats beyond the traditional "vector" and "raster" formats. Geographic data repositories increasingly include ill-structured data such as imagery and geo-referenced multi-media.[26] There are several critical research challenges in geographic knowledge discovery and data mining. Miller and Han[27] offer the following list of emerging research topics in the field: Developing and supporting geographic data warehouses – Spatial properties are often reduced to simple aspatial attributes in mainstream data warehouses. Creating an integrated GDW requires solving issues in spatial and temporal data interoperability, including differences in semantics, referencing systems, geometry, accuracy and position. Better spatio-temporal representations in geographic knowledge discovery – Current geographic knowledge discovery (GKD) methods generally use very simple representations of geographic objects and spatial relationships. Geographic data mining methods should recognize more complex geographic objects (lines and polygons) and relationships (non-Euclidean distances, direction, connectivity and interaction through attributed geographic space such as terrain). Time needs to be more fully integrated into these geographic representations and relationships. Geographic knowledge discovery using diverse data types – GKD methods should be developed that can handle diverse data types beyond the traditional raster and vector models, including imagery and geo-referenced multimedia, as well as dynamic data types (video streams, animation). In four annual surveys of data miners,[28] data mining practitioners consistently identified that they faced three key challenges more than any others: Dirty Data Explaining Data Mining to Others Unavailability of Data / Difficult Access to Data In the 2010 survey data miners also shared their experiences in overcoming these challenges.[29] [edit] Sensor data mining Wireless sensor networks can be used for facilitating the collection of data for spatial data mining for a variety of applications such as air pollution monitoring.[30] A characteristic of such networks is that nearby sensor nodes monitoring an environmental feature typically register similar values. This kind of data redundancy due to the spatial correlation between sensor observations inspires the techniques for in-network data aggregation and mining. By measuring the spatial correlation between data sampled by different sensors, a wide class of specialized algorithms can be developed to develop more efficient spatial data mining algorithms.[31] [edit] Visual data mining The process of turning from analogical into digital, large data sets have been generated, collected and stored discovering statistical patterns, trends and information which is hidden in data, in order to build predictive patterns. A study found that Visual Data Mining is faster and much more intuitive than traditional data mining.[32][33] [edit] Music data mining Data mining techniques and in particular co-occurrence analysis has been used be used to discover relevant similarities among music corpora (radio list, CD databases) for the purpose of classifying music into genres in an objective manner.[34] [edit] Surveillance Prior data mining to stop terrorist programs under the U.S. government include the Total Information Awareness (TIA) program, Secure Flight (formerly known as Computer-Assisted Passenger Prescreening System (CAPPS II)), Analysis, Dissemination, Visualization, Insight, Semantic Enhancement (ADVISE),[35] and the Multi-state Anti-Terrorism Information Exchange (MATRIX).[36] These programs have been discontinued due to controversy over whether they violate the US Constitution's 4th amendment, although many programs that were formed under them continue to be funded by different organizations, or under different names.[37] Two plausible data mining methods in the context of combating terrorism include "pattern mining" and "subject-based data mining". [edit] Pattern mining "Pattern mining" is a data mining method that involves finding existing patterns in data. In this context patterns often means association rules. The original motivation for searching association rules came from the desire to analyze supermarket transaction data, that is, to examine customer behavior in terms of the purchased products. For example, an association rule "beer ⇒ potato chips (80%)" states that four out of five customers that bought beer also bought potato chips. In the context of pattern mining as a tool to identify terrorist activity, the National Research Council provides the following definition: "Pattern-based data mining looks for patterns (including anomalous data patterns) that might be associated with terrorist activity — these patterns might be regarded as small signals in a large ocean of noise."[38][39][40] Pattern Mining includes new areas such a Music Information Retrieval (MIR) where patterns seen both in the temporal and non temporal domains are imported to classical knowledge discovery search methods. [edit] Subject-based data mining "Subject-based data mining" is a data mining method involving the search for associations between individuals in data. In the context of combating terrorism, the National Research Council provides the following definition: "Subject-based data mining uses an initiating individual or other datum that is considered, based on other information, to be of high interest, and the goal is to determine what other persons or financial transactions or movements, etc., are related to that initiating datum."[39] [edit] Knowledge grid Knowledge discovery on the Grid typically refers to conducting knowledge discovery in an open environment using Grid computing concepts, allowing users to integrate data from various online data sources as well make use of remote resources for executing their data mining tasks. The earliest examples was the Discovery Net [41][42] developed at Imperial College London which won the “Most Innovative Data Intensive Application Award” at the ACM SC02 (Supercomputing 2002) conference and exhibition, based on a demonstration of a fully interactive distributed knowledge discovery application for a bioinformatics application. Other examples also include work conducted by researchers at the University of Calabria who developed a Knowledge Grid architecture for distributed knowledge discovery, based on grid computing.[43][44] [edit] Reliability Data mining can be misused, and can unintentionally produce results which appear significant but do not actually predict future behavior and cannot be reproduced on a new sample of data. See Data snooping, Data dredging. [edit] Privacy concerns and ethics Some people believe that data mining itself is ethically neutral.[45] It is important to note that the term data mining has no ethical implications. The term is often associated with the mining of information in relation to peoples' behavior. However, data mining is a statistical method that is applied to a set of information, or a data set. Associating these data sets with people is an extreme narrowing of the types of data that are available in today's technological society. Examples could range from a set of crash test data for passenger vehicles, to the performance of a group of stocks. These types of data sets make up a great proportion of the information available to be acted on by data mining methods, and rarely have ethical concerns associated with them. However, the ways in which data mining can be used can raise questions regarding privacy, legality, and ethics.[46] In particular, data mining government or commercial data sets for national security or law enforcement purposes, such as in the Total Information Awareness Program or in ADVISE, has raised privacy concerns.[47][48] Data mining requires data preparation which can uncover information or patterns which may compromise confidentiality and privacy obligations. A common way for this to occur is through data aggregation. Data aggregation is when the data are accrued, possibly from various sources, and put together so that they can be analyzed.[49] This is not data mining per se, but a result of the preparation of data before and for the purposes of the analysis. The threat to an individual's privacy comes into play when the data, once compiled, cause the data miner, or anyone who has access to the newly compiled data set, to be able to identify specific individuals, especially when originally the data were anonymous. It is recommended that an individual is made aware of the following before data are collected: the purpose of the data collection and any data mining projects, how the data will be used, who will be able to mine the data and use them, the security surrounding access to the data, and in addition, how collected data can be updated.[49] In the United States, privacy concerns have been somewhat addressed by their congress via the passage of regulatory controls such as the Health Insurance Portability and Accountability Act (HIPAA). The HIPAA requires individuals to be given "informed consent" regarding any information that they provide and its intended future uses by the facility receiving that information. According to an article in Biotech Business Week, "In practice, HIPAA may not offer any greater protection than the longstanding regulations in the research arena," says the AAHC. More importantly, the rule's goal of protection through informed consent is undermined by the complexity of consent forms that are required of patients and participants, which approach a level of incomprehensibility to average individuals."[50] This underscores the necessity for data anonymity in data aggregation practices. One may additionally modify the data so that they are anonymous, so that individuals may not be readily identified.[49] However, even de-identified data sets can contain enough information to identify individuals, as occurred when journalists were able to find several individuals based on a set of search histories that were inadvertently released by AOL.[51] [edit] Software See also Category: Data mining and machine learning software [edit] Free libre open-source data-mining software and applications 1. Carrot2 – Text and search results clustering framework. 2. Chemicalize.org – A chemical structure miner and web search engine. 3. ELKI – A university research project with advanced cluster analysis and outlier detection methods written in the Java language. 4. GATE – Natural language processing and language engineering tool. 5. JHepWork – Java cross-platform data analysis framework developed at ANL. 6. KNIME – The Konstanz Information Miner, a user friendly and comprehensive data analytics framework. 7. NLTK or Natural Language Toolkit – A suite of libraries and programs for symbolic and statistical natural language processing (NLP) for the Python language. 8. Orange – A component-based data mining and machine learning software suite written in the Python language. 9. R – A programming language and software environment for statistical computing, data mining and graphics. It is part of the GNU project. 10. RapidMiner – An environment for machine learning and data mining experiments. 11. UIMA – The UIMA (Unstructured Information Management Architecture) is a component framework for analyzing unstructured content such as text, audio and video, originally developed by IBM. 12. Weka – A suite of machine learning software written in the Java language. In 2010, the open source R language overtook other tools to become the tool used by more data miners (43%) than any other.[28] [edit] Commercial data-mining software and applications IBM InfoSphere Warehouse – in-database data mining platform provided by IBM. IBM SPSS Modeler – data mining software provided by IBM. KXEN Infinite Insight - data mining software provided by KXEN Microsoft Analysis Services - data mining software provided by Microsoft SAS Enterprise Miner – data mining software provided by the SAS Institute. STATISTICA Data Miner – data mining software provided by StatSoft. Oracle Data Mining - data mining software by Oracle. LIONsolver - an integrated software for data mining, business intelligence, and modeling implementing the Learning and Intelligent OptimizatioN approach According to Rexer's Annual Data Miner Survey in 2010, IBM SPSS Modeler, STATISTICA Data Miner and R received the strongest satisfaction ratings.[28] [edit] Marketplace surveys Several researchers and organizations have conducted reviews of data mining tools and surveys of data miners. These identify some of the strengths and weaknesses of the software packages. They also provide an overview of the behaviors, preferences and views of data miners. Some of these reports include: Annual Rexer Analytics Data Miner Surveys.[28] Forrester Research 2010 Predictive Analytics and Data Mining Solutions report.[52] Gartner 2008 "Magic Quadrant" report.[53] Haughton et al.'s 2003 Review of Data Mining Software Packages in The American Statistician.[54] Robert A. Nisbet's 2006 Three Part Series of articles "Data Mining Tools: Which One is Best For CRM?"[55] 2011 Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery in [56] [edit] See also Methods Anomaly / outlier / change detection Association rule learning Classification Cluster analysis Decision trees Factor analysis Neural Networks Regression analysis Structured data analysis Sequence mining Text mining Application domains Analytics Bioinformatics Business intelligence Data analysis Data warehouse Decision support system Drug Discovery Exploratory data analysis Predictive analytics Web mining Application examples See also Category: Applied data mining Customer analytics Data mining in agriculture National Security Agency Police-enforced ANPR in the UK Quantitative structure–activity relationship Data mining in meteorology Educational data mining Surveillance / Mass surveillance (e.g., Stellar wind) Related topics Data mining is about analyzing data; for information about extracting information out of data, see: Information extraction Information integration Web scraping Named-entity recognition Data integration Data transformation Profiling practices [edit] References 1. ^ a b c Fayyad, Usama; Gregory Piatetsky-Shapiro, and Padhraic Smyth (1996). 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S. and Raper, J., (eds.), 1999, Spatial Multimedia and Virtual Reality, (London: Taylor and Francis). 27. ^ Miller, H. and Han, J., (eds.), 2001, Geographic Data Mining and Knowledge Discovery, (London: Taylor & Francis). 28. ^ a b c d Karl Rexer, Heather Allen, & Paul Gearan (2010) 2010 Data Miner Survey Summary, presented at Predictive Analytics World, Oct. 2010. 29. ^ Karl Rexer, Heather Allen, & Paul Gearan (2010) 2010 Data Miner Survey – Overcoming Data Mining's Top Challenges. 30. ^ Ma, Y.; Richards, M.; Ghanem, M.; Guo, Y.; Hassard, J. (2008). "Air Pollution Monitoring and Mining Based on Sensor Grid in London". Sensors 8 (6): 3601. doi:10.3390/s8063601. edit 31. ^ Ma, Y.; Guo, Y.; Tian, X.; Ghanem, M. (2011). "Distributed Clustering-Based Aggregation Algorithm for Spatial Correlated Sensor Networks". IEEE Sensors Journal 11 (3): 641. doi:10.1109/JSEN.2010.2056916. edit 32. ^ ZHAO Kaidi and LIU Bing (University of Illinois), M. TIRPARK Thomas and WEIMIN Xiao (Motorola Labs): A Visual Data Mining Framework for Convenient Identification of Useful Knowledge 33. ^ A. KEIM Daniel, Information Visualization and Visual Data Mining 34. ^ Pachet, F., Westermann, G. and Laigre, D. Musical Data Mining for Electronic Music Distribution, Proceedings of the 1st WedelMusic Conference, pp. 101-106, Firenze, Italy, 2001. 35. ^ Government Accountability Office, Data Mining: Early Attention to Privacy in Developing a Key DHS Program Could Reduce Risks, GAO-07-293, Washington, D.C.: February 2007. 36. ^ Secure Flight Program report, MSNBC. 37. ^ "Total/Terrorism Information Awareness (TIA): Is It Truly Dead?". Electronic Frontier Foundation (official website). 2003. Retrieved 2009-03-15. 38. ^ R. Agrawal et al., Fast discovery of association rules, in Advances in knowledge discovery and data mining pp. 307–328, MIT Press, 1996. 39. ^ a b National Research Council, Protecting Individual Privacy in the Struggle Against Terrorists: A Framework for Program Assessment, Washington, DC: National Academies Press, 2008. 40. ^ Stephen Haag et al. (2006). Management Information Systems for the information age. Toronto: McGraw-Hill Ryerson. p. 28. ISBN 0-07-095569-7. OCLC 63194770. 41. ^ Ghanem, M.; Guo, Y.; Rowe, A.; Wendel, P. (2002). "Grid-based knowledge discovery services for high throughput informatics". Proceedings 11th IEEE International Symposium on High Performance Distributed Computing. pp. 416. doi:10.1109/HPDC.2002.1029946. ISBN 0-76951686-6. edit 42. ^ Ghanem, M.; Curcin, V.; Wendel, P.; Guo, Y. (2009). "Building and Using Analytical Workflows in Discovery Net". Data Mining Techniques in Grid Computing Environments. pp. 119. doi:10.1002/9780470699904.ch8. ISBN 9780470699904. edit 43. ^ Mario Cannataro; Domenico Talia (January 2003). "The knowledge grid: An Architecture for Distributed Knowledge Discovery". Communications of the ACM 46 (1): 89–93. doi:10.1145/602421.602425. Retrieved October 17, 2011. 44. ^ Domenico Talia; Paolo Trunfio (July 2010). "How distributed data mining tasks can thrive as knowledge services". Communications of the ACM 53 (7): 132–137. doi:10.1145/1785414.1785451. Retrieved October 17, 2011. 45. ^ William Seltzer. The Promise and Pitfalls of Data Mining: Ethical Issues. 46. ^ Chip Pitts (March 15, 2007). "The End of Illegal Domestic Spying? Don't Count on It". Washington Spectator. 47. ^ K.A. Taipale (December 15, 2003). "Data Mining and Domestic Security: Connecting the Dots to Make Sense of Data". Columbia Science and Technology Law Review 5 (2). OCLC 45263753. SSRN 546782. 48. ^ John Resig, Ankur Teredesai (2004). "A Framework for Mining Instant Messaging Services". In Proceedings of the 2004 SIAM DM Conference. 49. ^ a b c Think Before You Dig: Privacy Implications of Data Mining & Aggregation, NASCIO Research Brief, September 2004. 50. ^ Biotech Business Week Editors. (June 30, 2008). BIOMEDICINE; HIPAA Privacy Rule Impedes Biomedical Research. Biotech Business Week. Retrieved 17 Nov 2009 from LexisNexis Academic. 51. ^ AOL search data identified individuals, SecurityFocus, August 2006. 52. ^ James Kobielus (1 July 2008) The Forrester Wave: Predictive Analytics and Data Mining Solutions, Q1 2010, Forrester Research. 53. ^ Gareth Herschel (1 July 2008) Magic Quadrant for Customer Data-Mining Applications, Gartner Inc. 54. ^ Dominique Haughton, Joel Deichmann, Abdolreza Eshghi, Selin Sayek, Nicholas Teebagy, & Heikki Topi (2003) A Review of Software Packages for Data Mining, The American Statistician, Vol. 57, No. 4, pp. 290–309. 55. ^ Robert Nisbet (2006) Data Mining Tools: Which One is Best for CRM? Part 1, Information Management Special Reports, January 2006. 56. ^ Ralf Mikut; Markus Reischl (September/October 2011). "Data Mining Tools". Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1 (5): 431–445. doi:10.1002/widm.24. Retrieved October 21, 2011. [edit] Further reading This section may require copy-editing for wikification, too much literature, most covering subdomains only. Literature spam. Cabena, Peter, Pablo Hadjnian, Rolf Stadler, Jaap Verhees and Alessandro Zanasi (1997). Discovering Data Mining: From Concept to Implementation. Prentice Hall, ISBN 0-13-743980-6. Feldman, Ronen and James Sanger. The Text Mining Handbook. Cambridge University Press, ISBN 978-0-521-83657-9. Guo, Yike and Robert Grossman, editors (1999). High Performance Data Mining: Scaling Algorithms, Applications and Systems. Kluwer Academic Publishers. Hastie, Trevor, Robert Tibshirani and Jerome Friedman (2001). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, ISBN 0-387-95284-5. Liu, Bing (2007). Web Data Mining: Exploring Hyperlinks, Contents and Usage Data. Springer, ISBN 3-540-37881-2. Murphy, Chris (May 16, 2011). "Is Data Mining Free Speech?". InformationWeek (UMB): 12. Nisbet, Robert, John Elder, Gary Miner (2009). Handbook of Statistical Analysis & Data Mining Applications. Academic Press/Elsevier. ISBN 978-0-12-374765-5 Poncelet, Pascal, Florent Masseglia and Maguelonne Teisseire, editors (October 2007). "Data Mining Patterns: New Methods and Applications", Information Science Reference. ISBN 978-159904-162-9. Pang-Ning Tan, Michael Steinbach and Vipin Kumar (2005). Introduction to Data Mining. ISBN 0321-32136-7 Sergios Theodoridis, Konstantinos Koutroumbas (2009). Pattern Recognition, 4th Edition. Academic Press. ISBN 978-1-59749-272-0. Weiss and Indurkhya. Predictive Data Mining. Morgan Kaufmann. Ian H. Witten; Eibe Frank; Mark A. Hall (30 January 2011). Data Mining: Practical Machine Learning Tools and Techniques (3 ed.). Elsevier. ISBN 978-0-12-374856-0. (See also Free Weka software.) Ye, N. (2003). The Handbook of Data Mining. Mahwah, New Jersey: Lawrence Erlbaum. Predictive analytics From Wikipedia, the free encyclopedia Jump to: navigation, search This article needs additional citations for verification. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. (June 2011) Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, data mining and game theory that analyze current and historical facts to make predictions about future events. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions. Predictive analytics is used in actuarial science, financial services, insurance, telecommunications, retail, travel, healthcare, pharmaceuticals and other fields. One of the most well-known applications is credit scoring, which is used throughout financial services. Scoring models process a customer’s credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time. A well-known example would be the FICO score. Contents [hide] 1 Definition 2 Types o 2.1 Predictive models o 2.2 Descriptive models o 2.3 Decision models 3 Applications o 3.1 Analytical customer relationship management (CRM) o 3.2 Clinical decision support systems o 3.3 Collection analytics o 3.4 Cross-sell o 3.5 Customer retention o 3.6 Direct marketing o 3.7 Fraud detection o 3.8 Portfolio, product or economy level prediction o 3.9 Risk Management o 3.10 Underwriting 4 Statistical techniques o 4.1 Regression Models 4.1.1 Linear regression model o 4.2 Discrete choice models 4.2.1 Logistic regression 4.2.2 Multinomial logistic regression 4.2.3 Probit regression 4.2.4 Logit versus probit o 4.3 Time series models o 4.4 Survival or duration analysis o 4.5 Classification and regression trees o 4.6 Multivariate adaptive regression splines o 4.7 Machine learning techniques 4.7.1 Neural networks 4.7.2 Radial basis functions 4.7.3 Support vector machines 4.7.4 Naïve Bayes 4.7.5 k-nearest neighbours 4.7.6 Geospatial predictive modeling 5 Tools 6 See also 7 References [edit] Definition Predictive analytics is an area of statistical analysis that deals with extracting information from data and using it to predict future trends and behavior patterns. The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting it to predict future outcomes. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions. [edit] Types Generally, the term predictive analytics is used to mean predictive modeling, "scoring" data with predictive models, and forecasting. However, people are increasingly using the term to describe related analytical disciplines, such as descriptive modeling and decision modeling or optimization. These disciplines also involve rigorous data analysis, and are widely used in business for segmentation and decision making, but have different purposes and the statistical techniques underlying them vary. [edit] Predictive models Predictive models analyze past performance to assess how likely a customer is to exhibit a specific behavior in the future in order to improve marketing effectiveness. This category also encompasses models that seek out subtle data patterns to answer questions about customer performance, such as fraud detection models. Predictive models often perform calculations during live transactions, for example, to evaluate the risk or opportunity of a given customer or transaction, in order to guide a decision. With advancement in computing speed, individual agent modeling systems can simulate human behavior or reaction to given stimuli or scenarios. The new term for animating data specifically linked to an individual in a simulated environment is avatar analytics. [edit] Descriptive models Descriptive models quantify relationships in data in a way that is often used to classify customers or prospects into groups. Unlike predictive models that focus on predicting a single customer behavior (such as credit risk), descriptive models identify many different relationships between customers or products. Descriptive models do not rank-order customers by their likelihood of taking a particular action the way predictive models do. Descriptive models can be used, for example, to categorize customers by their product preferences and life stage. Descriptive modeling tools can be utilized to develop further models that can simulate large number of individualized agents and make predictions. [edit] Decision models Decision models describe the relationship between all the elements of a decision — the known data (including results of predictive models), the decision and the forecast results of the decision — in order to predict the results of decisions involving many variables. These models can be used in optimization, maximizing certain outcomes while minimizing others. Decision models are generally used to develop decision logic or a set of business rules that will produce the desired action for every customer or circumstance. [edit] Applications Although predictive analytics can be put to use in many applications, we outline a few examples where predictive analytics has shown positive impact in recent years. [edit] Analytical customer relationship management (CRM) Analytical Customer Relationship Management is a frequent commercial application of Predictive Analysis. Methods of predictive analysis are applied to customer data to pursue CRM objectives which is to have a holistic view of the customer no matter where their information resides in the company or the department involved. CRM uses predictive analysis in applications for marketing campaigns, sales, and customer services to name a few. These tools are required in order for a company to posture and focus their efforts effectively across the breadth of their customer base. They must analyze and understand the products in demand or have the potential for high demand, predict customer's buying habits in order to promote relevant products at multiple touch points, and proactively identify and mitigate issues that have the potential to lose customers or reduce their ability to gain new ones. [edit] Clinical decision support systems Experts use predictive analysis in health care primarily to determine which patients are at risk of developing certain conditions, like diabetes, asthma, heart disease and other lifetime illnesses. Additionally, sophisticated clinical decision support systems incorporate predictive analytics to support medical decision making at the point of care. A working definition has been proposed by Dr. Robert Hayward of the Centre for Health Evidence: "Clinical Decision Support systems link health observations with health knowledge to influence health choices by clinicians for improved health care." [edit] Collection analytics Every portfolio has a set of delinquent customers who do not make their payments on time. The financial institution has to undertake collection activities on these customers to recover the amounts due. A lot of collection resources are wasted on customers who are difficult or impossible to recover. Predictive analytics can help optimize the allocation of collection resources by identifying the most effective collection agencies, contact strategies, legal actions and other strategies to each customer, thus significantly increasing recovery at the same time reducing collection costs. [edit] Cross-sell Often corporate organizations collect and maintain abundant data (e.g. customer records, sale transactions) and exploiting hidden relationships in the data can provide a competitive advantage to the organization. For an organization that offers multiple products, an analysis of existing customer behavior can lead to efficient cross sell of products. This directly leads to higher profitability per customer and strengthening of the customer relationship. Predictive analytics can help analyze customers’ spending, usage and other behavior, and help cross-sell the right product at the right time. [edit] Customer retention With the number of competing services available, businesses need to focus efforts on maintaining continuous consumer satisfaction. In such a competitive scenario, consumer loyalty needs to be rewarded and customer attrition needs to be minimized. Businesses tend to respond to customer attrition on a reactive basis, acting only after the customer has initiated the process to terminate service. At this stage, the chance of changing the customer’s decision is almost impossible. Proper application of predictive analytics can lead to a more proactive retention strategy. By a frequent examination of a customer’s past service usage, service performance, spending and other behavior patterns, predictive models can determine the likelihood of a customer wanting to terminate service sometime in the near future. An intervention with lucrative offers can increase the chance of retaining the customer. Silent attrition is the behavior of a customer to slowly but steadily reduce usage and is another problem faced by many companies. Predictive analytics can also predict this behavior accurately and before it occurs, so that the company can take proper actions to increase customer activity. [edit] Direct marketing When marketing consumer products and services there is the challenge of keeping up with competing products and consumer behavior. Apart from identifying prospects, predictive analytics can also help to identify the most effective combination of product versions, marketing material, communication channels and timing that should be used to target a given consumer. The goal of predictive analytics is typically to lower the cost per order or cost per action. [edit] Fraud detection Fraud is a big problem for many businesses and can be of various types. Inaccurate credit applications, fraudulent transactions (both offline and online), identity thefts and false insurance claims are some examples of this problem. These problems plague firms all across the spectrum and some examples of likely victims are credit card issuers, insurance companies, retail merchants, manufacturers, business-to-business suppliers and even services providers. A predictive model can help weed out the “bads” and reduce a business's exposure to fraud. Predictive modeling can also be used to detect financial statement fraud in companies, allowing auditors to gauge a company's relative risk, and to increase substantive audit procedures as needed. The Internal Revenue Service (IRS) of the United States also uses predictive analytics to try to locate tax fraud. Recent[when?] advancements in technology have also introduced predictive behavior analysis for Web fraud detection. This type of solutions utilizes heuristics in order to study normal web user behavior and detect anomalies indicating fraud attempts. [edit] Portfolio, product or economy level prediction Often the focus of analysis is not the consumer but the product, portfolio, firm, industry or even the economy. For example a retailer might be interested in predicting store level demand for inventory management purposes. Or the Federal Reserve Board might be interested in predicting the unemployment rate for the next year. These type of problems can be addressed by predictive analytics using Time Series techniques (see below). They can also be addressed via machine learning approaches which transform the original time series into a feature vector space, where the learning algorithm finds patterns that have predictive power.[1][2] [edit] Risk Management When employing risk management techniques the results are always to predict and benefit from a future scenario. The Capital asset pricing model (CAP-M) “predicts” the best portfolio to maximize return, Probabilistic Risk Assessment (PRA)-when combined with mini-Delphi Techniques and statistical approaches yields accurate forecasts and RiskAoA is a stand-alone predictive tool [3]. These are three examples of approaches that can extend from project to market, and from near to long term. Underwriting (see below) and other business approaches identify risk management as a predictive method. [edit] Underwriting Many businesses have to account for risk exposure due to their different services and determine the cost needed to cover the risk. For example, auto insurance providers need to accurately determine the amount of premium to charge to cover each automobile and driver. A financial company needs to assess a borrower’s potential and ability to pay before granting a loan. For a health insurance provider, predictive analytics can analyze a few years of past medical claims data, as well as lab, pharmacy and other records where available, to predict how expensive an enrollee is likely to be in the future. Predictive analytics can help underwriting of these quantities by predicting the chances of illness, default, bankruptcy, etc. Predictive analytics can streamline the process of customer acquisition, by predicting the future risk behavior of a customer using application level data. Predictive analytics in the form of credit scores have reduced the amount of time it takes for loan approvals, especially in the mortgage market where lending decisions are now made in a matter of hours rather than days or even weeks. Proper predictive analytics can lead to proper pricing decisions, which can help mitigate future risk of default. [edit] Statistical techniques The approaches and techniques used to conduct predictive analytics can broadly be grouped into regression techniques and machine learning techniques. [edit] Regression Models Regression models are the mainstay of predictive analytics. The focus lies on establishing a mathematical equation as a model to represent the interactions between the different variables in consideration. Depending on the situation, there is a wide variety of models that can be applied while performing predictive analytics. Some of them are briefly discussed below. [edit] Linear regression model The linear regression model analyzes the relationship between the response or dependent variable and a set of independent or predictor variables. This relationship is expressed as an equation that predicts the response variable as a linear function of the parameters. These parameters are adjusted so that a measure of fit is optimized. Much of the effort in model fitting is focused on minimizing the size of the residual, as well as ensuring that it is randomly distributed with respect to the model predictions. The goal of regression is to select the parameters of the model so as to minimize the sum of the squared residuals. This is referred to as ordinary least squares (OLS) estimation and results in best linear unbiased estimates (BLUE) of the parameters if and only if the Gauss-Markov assumptions are satisfied. Once the model has been estimated we would be interested to know if the predictor variables belong in the model – i.e. is the estimate of each variable’s contribution reliable? To do this we can check the statistical significance of the model’s coefficients which can be measured using the t-statistic. This amounts to testing whether the coefficient is significantly different from zero. How well the model predicts the dependent variable based on the value of the independent variables can be assessed by using the R² statistic. It measures predictive power of the model i.e. the proportion of the total variation in the dependent variable that is “explained” (accounted for) by variation in the independent variables. [edit] Discrete choice models Multivariate regression (above) is generally used when the response variable is continuous and has an unbounded range. Often the response variable may not be continuous but rather discrete. While mathematically it is feasible to apply multivariate regression to discrete ordered dependent variables, some of the assumptions behind the theory of multivariate linear regression no longer hold, and there are other techniques such as discrete choice models which are better suited for this type of analysis. If the dependent variable is discrete, some of those superior methods are logistic regression, multinomial logit and probit models. Logistic regression and probit models are used when the dependent variable is binary. [edit] Logistic regression For more details on this topic, see logistic regression. In a classification setting, assigning outcome probabilities to observations can be achieved through the use of a logistic model, which is basically a method which transforms information about the binary dependent variable into an unbounded continuous variable and estimates a regular multivariate model (See Allison’s Logistic Regression for more information on the theory of Logistic Regression). The Wald and likelihood-ratio test are used to test the statistical significance of each coefficient b in the model (analogous to the t tests used in OLS regression; see above). A test assessing the goodness-of-fit of a classification model is the "percentage correctly predicted." [edit] Multinomial logistic regression An extension of the binary logit model to cases where the dependent variable has more than 2 categories is the multinomial logit model. In such cases collapsing the data into two categories might not make good sense or may lead to loss in the richness of the data. The multinomial logit model is the appropriate technique in these cases, especially when the dependent variable categories are not ordered (for examples colors like red, blue, green). Some authors have extended multinomial regression to include feature selection/importance methods such as Random multinomial logit. [edit] Probit regression Probit models offer an alternative to logistic regression for modeling categorical dependent variables. Even though the outcomes tend to be similar, the underlying distributions are different. Probit models are popular in social sciences like economics. A good way to understand the key difference between probit and logit models, is to assume that there is a latent variable z. We do not observe z but instead observe y which takes the value 0 or 1. In the logit model we assume that y follows a logistic distribution. In the probit model we assume that y follows a standard normal distribution. Note that in social sciences (e.g. economics), probit is often used to model situations where the observed variable y is continuous but takes values between 0 and 1. [edit] Logit versus probit The Probit model has been around longer than the logit model. They behave similarly, except that the logistic distribution tends to be slightly flatter tailed. One of the reasons the logit model was formulated was that the probit model was computationally difficult due to the requirement of numerically calculating integrals. Modern computing however has made this computation fairly simple. The coefficients obtained from the logit and probit model are fairly close. However, the odds ratio is easier to interpret in the logit model. Practical reasons for choosing the probit model over the logistic model would be: There is a strong belief that the underlying distribution is normal The actual event is not a binary outcome (e.g., bankruptcy status) but a proportion (e.g., proportion of population at different debt levels). [edit] Time series models Time series models are used for predicting or forecasting the future behavior of variables. These models account for the fact that data points taken over time may have an internal structure (such as autocorrelation, trend or seasonal variation) that should be accounted for. As a result standard regression techniques cannot be applied to time series data and methodology has been developed to decompose the trend, seasonal and cyclical component of the series. Modeling the dynamic path of a variable can improve forecasts since the predictable component of the series can be projected into the future. Time series models estimate difference equations containing stochastic components. Two commonly used forms of these models are autoregressive models (AR) and moving average (MA) models. The Box-Jenkins methodology (1976) developed by George Box and G.M. Jenkins combines the AR and MA models to produce the ARMA (autoregressive moving average) model which is the cornerstone of stationary time series analysis. ARIMA(autoregressive integrated moving average models) on the other hand are used to describe non-stationary time series. Box and Jenkins suggest differencing a non stationary time series to obtain a stationary series to which an ARMA model can be applied. Non stationary time series have a pronounced trend and do not have a constant long-run mean or variance. Box and Jenkins proposed a three stage methodology which includes: model identification, estimation and validation. The identification stage involves identifying if the series is stationary or not and the presence of seasonality by examining plots of the series, autocorrelation and partial autocorrelation functions. In the estimation stage, models are estimated using non-linear time series or maximum likelihood estimation procedures. Finally the validation stage involves diagnostic checking such as plotting the residuals to detect outliers and evidence of model fit. In recent years time series models have become more sophisticated and attempt to model conditional heteroskedasticity with models such as ARCH (autoregressive conditional heteroskedasticity) and GARCH (generalized autoregressive conditional heteroskedasticity) models frequently used for financial time series. In addition time series models are also used to understand inter-relationships among economic variables represented by systems of equations using VAR (vector autoregression) and structural VAR models. [edit] Survival or duration analysis Survival analysis is another name for time to event analysis. These techniques were primarily developed in the medical and biological sciences, but they are also widely used in the social sciences like economics, as well as in engineering (reliability and failure time analysis). Censoring and non-normality, which are characteristic of survival data, generate difficulty when trying to analyze the data using conventional statistical models such as multiple linear regression. The normal distribution, being a symmetric distribution, takes positive as well as negative values, but duration by its very nature cannot be negative and therefore normality cannot be assumed when dealing with duration/survival data. Hence the normality assumption of regression models is violated. The assumption is that if the data were not censored it would be representative of the population of interest. In survival analysis, censored observations arise whenever the dependent variable of interest represents the time to a terminal event, and the duration of the study is limited in time. An important concept in survival analysis is the hazard rate, defined as the probability that the event will occur at time t conditional on surviving until time t. Another concept related to the hazard rate is the survival function which can be defined as the probability of surviving to time t. Most models try to model the hazard rate by choosing the underlying distribution depending on the shape of the hazard function. A distribution whose hazard function slopes upward is said to have positive duration dependence, a decreasing hazard shows negative duration dependence whereas constant hazard is a process with no memory usually characterized by the exponential distribution. Some of the distributional choices in survival models are: F, gamma, Weibull, log normal, inverse normal, exponential etc. All these distributions are for a non-negative random variable. Duration models can be parametric, non-parametric or semi-parametric. Some of the models commonly used are Kaplan-Meier and Cox proportional hazard model (non parametric). [edit] Classification and regression trees Main article: decision tree learning Classification and regression trees (CART) is a non-parametric decision tree learning technique that produces either classification or regression trees, depending on whether the dependent variable is categorical or numeric, respectively. Decision trees are formed by a collection of rules based on variables in the modeling data set: Rules based on variables’ values are selected to get the best split to differentiate observations based on the dependent variable Once a rule is selected and splits a node into two, the same process is applied to each “child” node (i.e. it is a recursive procedure) Splitting stops when CART detects no further gain can be made, or some pre-set stopping rules are met. (Alternatively, the data are split as much as possible and then the tree is later pruned.) Each branch of the tree ends in a terminal node. Each observation falls into one and exactly one terminal node, and each terminal node is uniquely defined by a set of rules. A very popular method for predictive analytics is Leo Breiman's Random forests or derived versions of this technique like Random multinomial logit. [edit] Multivariate adaptive regression splines Multivariate adaptive regression splines (MARS) is a non-parametric technique that builds flexible models by fitting piecewise linear regressions. An important concept associated with regression splines is that of a knot. Knot is where one local regression model gives way to another and thus is the point of intersection between two splines. In multivariate and adaptive regression splines, basis functions are the tool used for generalizing the search for knots. Basis functions are a set of functions used to represent the information contained in one or more variables. Multivariate and Adaptive Regression Splines model almost always creates the basis functions in pairs. Multivariate and adaptive regression spline approach deliberately overfits the model and then prunes to get to the optimal model. The algorithm is computationally very intensive and in practice we are required to specify an upper limit on the number of basis functions. [edit] Machine learning techniques Machine learning, a branch of artificial intelligence, was originally employed to develop techniques to enable computers to learn. Today, since it includes a number of advanced statistical methods for regression and classification, it finds application in a wide variety of fields including medical diagnostics, credit card fraud detection, face and speech recognition and analysis of the stock market. In certain applications it is sufficient to directly predict the dependent variable without focusing on the underlying relationships between variables. In other cases, the underlying relationships can be very complex and the mathematical form of the dependencies unknown. For such cases, machine learning techniques emulate human cognition and learn from training examples to predict future events. A brief discussion of some of these methods used commonly for predictive analytics is provided below. A detailed study of machine learning can be found in Mitchell (1997). [edit] Neural networks Neural networks are nonlinear sophisticated modeling techniques that are able to model complex functions. They can be applied to problems of prediction, classification or control in a wide spectrum of fields such as finance, cognitive psychology/neuroscience, medicine, engineering, and physics. Neural networks are used when the exact nature of the relationship between inputs and output is not known. A key feature of neural networks is that they learn the relationship between inputs and output through training. There are three types of training in neural networks used by different networks, supervised and unsupervised training, reinforcement learning,with supervised being the most common one. Some examples of neural network training techniques are backpropagation, quick propagation, conjugate gradient descent, projection operator, Delta-Bar-Delta etc. Some unsupervised network architectures are multilayer perceptrons, Kohonen networks, Hopfield networks, etc. [edit] Radial basis functions A radial basis function (RBF) is a function which has built into it a distance criterion with respect to a center. Such functions can be used very efficiently for interpolation and for smoothing of data. Radial basis functions have been applied in the area of neural networks where they are used as a replacement for the sigmoidal transfer function. Such networks have 3 layers, the input layer, the hidden layer with the RBF non-linearity and a linear output layer. The most popular choice for the non-linearity is the Gaussian. RBF networks have the advantage of not being locked into local minima as do the feed-forward networks such as the multilayer perceptron. [edit] Support vector machines Support Vector Machines (SVM) are used to detect and exploit complex patterns in data by clustering, classifying and ranking the data. They are learning machines that are used to perform binary classifications and regression estimations. They commonly use kernel based methods to apply linear classification techniques to non-linear classification problems. There are a number of types of SVM such as linear, polynomial, sigmoid etc. [edit] Naïve Bayes Naïve Bayes based on Bayes conditional probability rule is used for performing classification tasks. Naïve Bayes assumes the predictors are statistically independent which makes it an effective classification tool that is easy to interpret. It is best employed when faced with the problem of ‘curse of dimensionality’ i.e. when the number of predictors is very high. [edit] k-nearest neighbours The nearest neighbour algorithm (KNN) belongs to the class of pattern recognition statistical methods. The method does not impose a priori any assumptions about the distribution from which the modeling sample is drawn. It involves a training set with both positive and negative values. A new sample is classified by calculating the distance to the nearest neighbouring training case. The sign of that point will determine the classification of the sample. In the knearest neighbour classifier, the k nearest points are considered and the sign of the majority is used to classify the sample. The performance of the kNN algorithm is influenced by three main factors: (1) the distance measure used to locate the nearest neighbours; (2) the decision rule used to derive a classification from the k-nearest neighbours; and (3) the number of neighbours used to classify the new sample. It can be proved that, unlike other methods, this method is universally asymptotically convergent, i.e.: as the size of the training set increases, if the observations are independent and identically distributed (i.i.d.), regardless of the distribution from which the sample is drawn, the predicted class will converge to the class assignment that minimizes misclassification error. See Devroy et al. [edit] Geospatial predictive modeling Conceptually, geospatial predictive modeling is rooted in the principle that the occurrences of events being modeled are limited in distribution. Occurrences of events are neither uniform nor random in distribution – there are spatial environment factors (infrastructure, sociocultural, topographic, etc.) that constrain and influence where the locations of events occur. Geospatial predictive modeling attempts to describe those constraints and influences by spatially correlating occurrences of historical geospatial locations with environmental factors that represent those constraints and influences. Geospatial predictive modeling is a process for analyzing events through a geographic filter in order to make statements of likelihood for event occurrence or emergence. [edit] Tools There are numerous tools available in the marketplace which help with the execution of predictive analytics. These range from those which need very little user sophistication to those that are designed for the expert practitioner. The difference between these tools is often in the level of customization and heavy data lifting allowed. In an attempt to provide a standard language for expressing predictive models, the Predictive Model Markup Language (PMML) has been proposed. Such an XML-based language provides a way for the different tools to define predictive models and to share these between PMML compliant applications. PMML 4.0 was released in June, 2009. [edit] See also Criminal Reduction Utilising Statistical History Data mining Learning analytics Odds algorithm Pattern recognition This article includes a list of references, related reading or external links, but its sources remain unclear because it lacks inline citations. Please improve this article by introducing more precise citations. (October 2011) [edit] References 1. ^ Dhar, Vasant (April 2011). "Prediction in Financial Markets: The Case for Small Disjuncts". ACM Transactions on Intelligent Systems and Technologies 2 (3). 2. ^ Dhar, Vasant; Chou, Dashin and Provost Foster (October 2000). "Discovering Interesting Patterns in Investment Decision Making with GLOWER – A Genetic Learning Algorithm Overlaid With Entropy Reduction". Data Mining and Knowledge Discovery 4 (4). 3. ^ https://acc.dau.mil/CommunityBrowser.aspx?id=126070 Agresti, Alan (2002). Categorical Data Analysis. Hoboken: John Wiley and Sons. ISBN 0-47136093-7. Coggeshall, Stephen, Davies, John, Jones, Roger., and Schutzer, Daniel, "Intelligent Security Systems," in Freedman, Roy S., Flein, Robert A., and Lederman, Jess, Editors (1995). Artificial Intelligence in the Capital Markets. Chicago: Irwin. ISBN 1-55738-811-3. L. Devroye, L. Györfi, G. Lugosi (1996). A Probabilistic Theory of Pattern Recognition. New York: Springer-Verlag. Enders, Walter (2004). Applied Time Series Econometrics. Hoboken: John Wiley and Sons. ISBN 052183919X. Greene, William (2000). Econometric Analysis. Prentice Hall. ISBN 0-13-013297-7. Guidère, Mathieu; Howard N, Sh. Argamon (2009). Rich Language Analysis for Counterterrrorism. Berlin, London, New York: Springer-Verlag. ISBN 978-3-642-01140-5. Mitchell, Tom (1997). Machine Learning. New York: McGraw-Hill. ISBN 0-07-042807-7. Tukey, John (1977). Exploratory Data Analysis. New York: Addison-Wesley. ISBN 0201076160. Purchase order request From Wikipedia, the free encyclopedia Jump to: navigation, search A purchase order request or purchase requisition is a request sent internally within a company to obtain purchased goods and services, including stock. The request is a document which tells the purchasing department or manager exactly what items and services are requested, the quantity, source and associated costs. A Purchase Requisition Form (PRF) is filled out prior to purchasing goods as a form of tangible authorisation. Purchase request forms are often used in smaller business who do not have a computer-based system. However, many computer (included web-based solution) systems are available on the market that can facilitate the capture of purchase request information. Purchase order requests can also be passed to the purchasing department via a management information system. A PRF may contain budget and purchase values to make the individual aware of the annual and remaining budget before a purchase is made. Such a system is there to guarantee that goods and services are purchased with the consent of the line manager and that a sufficient budget is available. Enterprise architecture From Wikipedia, the free encyclopedia (Redirected from Enterprise Architecture) Jump to: navigation, search Enterprise architecture (EA) is the process of translating business vision and strategy into effective enterprise change by creating, communicating and improving the key requirements, principles and models that describe the enterprise's future state and enable its evolution.[1] Practitioners of EA call themselves enterprise architects. An enterprise architect is a person responsible for performing this complex analysis of business structure and processes and is often called upon to draw conclusions from the information collected. By producing this understanding, architects are attempting to address the goals of Enterprise Architecture: Effectiveness, Efficiency, Agility, and Durability. [2] Contents [hide] 1 Definition 2 Scope 3 Developing an Enterprise Level Architectural Description 4 Using an enterprise architecture 5 The growing use of enterprise architecture 6 Relationship to other disciplines 7 Published examples 8 Academic qualifications 9 See also 10 References 11 External links o 11.1 University and college programs [edit] Definition Enterprise architecture is an ongoing business function that helps an 'enterprise' figure out how to best execute the strategies that drive its development. The MIT Center for Information Systems Research (MIT CISR) defines enterprise architecture as the specific aspects of a business that are under examination: Enterprise architecture is the organizing logic for business processes and IT infrastructure reflecting the integration and standardization requirements of the company's operating model. The operating model is the desired state of business process integration and business process standardization for delivering goods and services to customers.[3] The United States Government classifies enterprise architecture as an Information Technology function, and defines the term not as the process of examining the enterprise, but rather the documented results of that examination. Specifically, US Code Title 44, Chapter 36, defines it as a 'strategic information base' that defines the mission of an agency and describes the technology and information needed to perform that mission, along with descriptions of how the architecture of the organization should be changed in order to respond to changes in the mission.[4] [edit] Scope The term enterprise is used because it is generally applicable in many circumstances, including Public or private sector organizations An entire business or corporation A part of a larger enterprise (such as a business unit) A conglomerate of several organizations, such as a joint venture or partnership A multiply outsourced business operation The term enterprise includes the whole complex, socio-technical system,[5] including: people information technology business (e.g. operations) Defining the boundary or scope of the enterprise to be described is an important first step in creating the enterprise architecture. Enterprise as used in enterprise architecture generally means more than the information systems employed by an organization.[6] [edit] Developing an Enterprise Level Architectural Description Enterprise architects use various methods and tools to capture the structure and dynamics of an enterprise. In doing so, they produce taxonomies, diagrams, documents and models, together called artifacts. These artifacts describe the logical organization of business functions, business capabilities, business processes, people organization, information resources, business systems, software applications, computing capabilities, information exchange and communications infrastructure within the enterprise. A collection of these artifacts, sufficiently complete to describe the enterprise in useful ways, is considered by EA practitioners an 'enterprise' level architectural description, or enterprise architecture, for short. The UK National Computing Centre EA best practice guidance[7] states Normally an EA takes the form of a comprehensive set of cohesive models that describe the structure and functions of an enterprise. and continues The individual models in an EA are arranged in a logical manner that provides an everincreasing level of detail about the enterprise: its objectives and goals; its processes and organization; its systems and data; the technology used and any other relevant spheres of interest. This is the definition of enterprise architecture implicit in several EA frameworks including the popular TOGAF architectural framework. An enterprise architecture framework bundles tools, techniques, artifact descriptions, process models, reference models and guidance used by architects in the production of enterprisespecific architectural description. Several enterprise architecture frameworks break down the practice of enterprise architecture into a number of practice areas or domains. See the related articles on enterprise architecture frameworks and domains for further information. In 1992, Steven Spewak described a process for creating an enterprise architecture that is widely used in educational courses.[8] [edit] Using an enterprise architecture Describing the architecture of an enterprise aims primarily to improve the effectiveness or efficiency of the business itself. This includes innovations in the structure of an organization, the centralization or federation of business processes, the quality and timeliness of business information, or ensuring that money spent on information technology (IT) can be justified. [2] One method of using this information to improve the functioning of a business, as described in the TOGAF architectural framework, involves developing an "architectural vision": a description of the business that represents a "target" or "future state" goal. Once this vision is well understood, a set of intermediate steps are created that illustrate the process of changing from the present situation to the target. These intermediate steps are called "transitional architectures" by TOGAF. Similar methods have been described in other enterprise architecture frameworks. [edit] The growing use of enterprise architecture Documenting the architecture of enterprises is done within the U.S. Federal Government[9] in the context of the Capital Planning and Investment Control (CPIC) process. The Federal Enterprise Architecture (FEA) reference models guides federal agencies in the development of their architectures.[10] Companies such as Independence Blue Cross, Intel, Volkswagen AG[11] and InterContinental Hotels Group[12] also use enterprise architecture to improve their business architectures as well as to improve business performance and productivity. [edit] Relationship to other disciplines Enterprise architecture is a key component of the information technology governance process in organizations such as Dubai Customs and AGL Energy who have implemented a formal enterprise architecture process as part of their IT management strategy. While this may imply that enterprise architecture is closely tied to IT, this should be viewed in the broader context of business optimization in that it addresses business architecture, performance management and process architecture as well as more technical subjects. Depending on the organization, enterprise architecture teams may also be responsible for some aspects of performance engineering, IT portfolio management and metadata management. Recently, protagonists like Gartner and Forrester have stressed the important relationship of Enterprise Architecture with emerging holistic design practices such as Design Thinking and User Experience Design.[13] The following image from the 2006 FEA Practice Guidance of US OMB sheds light on the relationship between enterprise architecture and segment (BPR) or Solution architectures. (This figure demonstrates that software architecture is truly a solution architecture discipline, for example.) Activities such as software architecture, network architecture, and database architecture are partial contributions to a solution architecture. [edit] Published examples This unreferenced section requires citations to ensure verifiability. It is uncommon for a commercial organization to publish rich detail from their enterprise architecture descriptions. Doing so can provide competitors information on weaknesses and organizational flaws that could hinder the company's market position. However, many government agencies around the world have begun to publish the architectural descriptions that they have developed. Good examples include the US Department of the Interior, US Department of Defense Business Enterprise Architecture, or the 2008 BEAv5.0 version. [edit] Academic qualifications Enterprise Architecture was included in the Association for Computing Machinery (ACM) and Association for Information Systems (AIS)’s Curriculum for Information Systems as one of the 6 core courses.[14] There are several universities that offers enterprise architecture as a fourth year level course or part of a master's syllabus. The Center for Enterprise Architecture [15] at the Penn State University is one of these institutions that offer EA courses. It is also offered within the Masters program in Computer Science at The University of Chicago. In 2010 resarchers at the Meraka Institute, Council for Scientific and Industrial Research, in South Africa organized a workshop and invited staff from computing departments in South African higher education institutions. The purpose was to investigate the current status of EA offerings in South Africa. A report was compiled and is available for download at the Meraka Institute.[16] [edit] See also Book: Enterprise Architecture Wikipedia books are collections of articles that can be downloaded or ordered in print. Architectural pattern (computer science) Enterprise Architect Enterprise Architecture Assessment Framework Enterprise Architecture framework Enterprise Architecture Planning Enterprise engineering Enterprise Life Cycle Enterprise Unified Process GINA : Global Information Network Architecture Information Architecture Open Source Enterprise Architecture Tools [edit] References 1. ^ Definition of Enterprise Architecture, Gartner[1] 2. ^ a b Pragmatic Enterprise Architecture Foundation, PEAF Foundation - Vision[2] 3. ^ MIT Center for Information Systems Research, Peter Weill, Director, as presented at the Sixth e-Business Conference, Barcelona Spain, 27 March 2007, [3] 4. ^ U.S.C. Title 44, Chap. 36, § 3601[4] 5. ^ Giachetti, R.E., Design of Enterprise Systems, Theory, Architecture, and Methods, CRC Press, Boca Raton, FL, 2010. 6. ^ [5] 7. ^ Jarvis, R, Enterprise Architecture: Understanding the Bigger Picture - A Best Practice Guide for Decision Makers in IT, The UK National Computing Centre, Manchester, UK 8. ^ Spewak, Steven H. and Hill, Steven C. , Enterprise Architecture Planning - Developing a Blueprint for Data Applications and Technology,(1992), John Wiley 9. ^ Federal Government agency success stories, (2010), whitehouse.gov 10. ^ FEA Practice Guidance Federal Enterprise Architecture Program Management Office OMB, (2007), whitehouse.gov 11. ^ "Volkswagen of America: Managing IT Priorities," Harvard Business Review, October 5, 2005, Robert D. Austin, Warren Ritchie, Greggory Garrett 12. ^ ihg.com 13. ^ Leslie Owens, Forrester Blogs - Who Owns Information Architecture? All Of Us., (2010), blogs.forrester.com 14. ^ ACM and AIS Curriculum for Information Systems acm.org 15. ^ Center for Enterprise Architecture, Penn State University, ea.ist.psu.edu 16. ^ hufee.meraka.org.za [edit] External links Wikiquote has a collection of quotations related to: Enterprise architecture Wikimedia Commons has media related to: Enterprise architecture Professional Practice Guide for Enterprise Architects Gartner Magic Quadrant on Enterprise Architecture Tools - Report [edit] University and college programs Pennsylvania State University Carnegie Mellon National University Kent State University Griffith University Royal Melbourne Institute of Technology Temple University, Fox School of Business University of Utrecht, Dept of Information and Computing Sciences [hide] v t e Software engineering Fields Requirements analysis Systems analysis Software design Computer programming Formal methods Software testing Software deployment Software maintenance Concepts Data modeling Enterprise architecture Functional specification Modeling language Programming paradigm Software Software architecture Software development methodology Software development process Software quality Software quality assurance Software archaeology Structured analysis Orientations Agile Aspect-oriented Object orientation Ontology Service orientation SDLC Development models Agile Iterative model RUP Scrum Spiral model Waterfall model XP V-Model Incremental model Prototype model Other models Automotive SPICE CMMI Data model Function model Information model Metamodeling Object model Systems model View model Modeling languages IDEF UML Models Software engineers Kent Beck Grady Booch Fred Brooks Barry Boehm Peter Chen Ward Cunningham Ole-Johan Dahl Tom DeMarco Martin Fowler C. A. R. Hoare Watts Humphrey Michael A. Jackson Ivar Jacobson James Martin Bertrand Meyer David Parnas Winston W. Royce Colette Rolland James Rumbaugh Niklaus Wirth Edward Yourdon Victor Basili Related fields Computer science Computer engineering Enterprise engineering History Management Project management Quality management Software ergonomics Systems engineering Category Commons View page ratings Rate this page What's this? 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Enterprise planning system From Wikipedia, the free encyclopedia (Redirected from Enterprise planning systems) Jump to: navigation, search An enterprise planning system covers the methods of planning for the internal and external factors that affect an enterprise. These factors generally fall under PESTLE. PESTLE refers to political, economic, social, technological, legal and environmental factors. Regularly addressing PESTLE factors fall under operations management. Meanwhile, addressing any event, opportunity or challenge in any one or many factors for the first time will involve project management. As opposed to enterprise resource planning (ERP), enterprise planning systems have broader coverage. Enterprise planning systems address the resources that are available or not available to an enterprise and its ability to produce products or resources and/or provide services. It also considers those factors that will positively or negatively affect the firm's ability to run these actions. Enterprise planning systems will tend to vary and are flexible. These are due to the periodic and adaptive nature of strategy formation. These will also have tactical aspects. Typically, enterprise planning systems are part of a firm's knowledge base or corporate structure whether it formally identified and structured or simply executed these when the need appeared. Contents [hide] 1 Purposes o 1.1 Survival o 1.2 Competition o 1.3 Opportunities o 1.4 Vulnerabilities 2 Strategic planning o 2.1 Strategy via analysis o 2.2 Strategy via geography o 2.3 Strategy via projects integration 3 Planning and budgeting o 3.1 Classifications 4 Group planning 5 Transition plan 6 Planning software 7 See also 8 References [edit] Purposes An enterprise planning system will address at least three basic purposes to help the enterprise: survive compete thrive [edit] Survival An enterprise will plan for tactical moves for quick reaction to the PESTLE threats that affect its survival. For instance, right after Japan's Fukushima nuclear power plant has experienced explosions due to the earthquake and the tsunami that followed, several enterprises (within and outside of Japan) have publicly announced their course of actions to address the emergency.[1] [edit] Competition Meanwhile, an enterprise will plan for longer term strategic actions to address its competition or improve its competitiveness. For instance, enterprises will plan for, set budgets, implement and use strategic information systems as “information systems or information technology investments can be a source of competitive advantage”.[2] [edit] Opportunities Most significantly, an enterprise will plan for using the PESTLE opportunities that are available to it. The profit and benefit motives justify most enterprise planning systems.[3] [edit] Vulnerabilities A fourth noteworthy purpose for enterprise planning systems is preparedness against terrorist attacks. As noted in the US Presidential Directive for Critical infrastructure protection, terrorist groups are likely to attack commercial infrastructure for economic sabotage. Enterprises that are providing products or services that are critical to the economic system of a nation are potential targets of extremists. [edit] Strategic planning Two major characteristics of enterprise planning systems are: these are varied and flexible. For instance, technological risks abound as even enterprise software are prone to obsolescence and disruptive innovations. Technology is not stagnant. Thus, systems variety and flexibility work to the advantage of a strategically adaptive or agile enterprise as PESTLE conditions change. To illustrate this some more, ERP software prescribes processes to realize its promised benefits. However, compliance to these rigid, prescribed processes is often assumed rather than real. In many cases, the ERP software is accepted but the practices within the enterprise reflect inconsistencies with the prescribed processes of the software. In a sense, variety and flexibility in a standard ERP implementation will still manifest in many ways such as "workarounds, shadow systems, various forms of unintended improvisations, and organizational 'drift'" as the knowledge workers in the enterprise adapt to the realities of daily activities.[4] With changing real world conditions, at least three components can structure enterprise strategy. These are: analytical frameworks for the evaluation of PESTLE data at a given time geographic coverage of operations to manage risks or maximize benefits from macroeconomic forces or government regulations projects integration to efficiently support enterprise operations [edit] Strategy via analysis Frameworks of analysis usually drive a firm's strategy. These enable the firm to cope with the actions of its competitors, demands of its consumers or clients, nature of its operating environments, effects of government regulations in the places where it does business, or opportunities that are available among other factors.[5] Here, team planning is crucial. One group will normally specialize in one aspect like operations or government regulations. Managing the interrelation of PESTLE factors requires team work in the enterprise planning process. A sample framework for general analysis is the SWOT analysis. Another is the Balanced Scorecard for performance measurement analysis.[6] [edit] Strategy via geography Enterprise strategy can also refer to the mix of structured actions that address the political, economic, social, technological, legal and environmental factors that affect a business or firm. These structured actions can be local, transnational, global or combination of local, transnational or global.[7] Hence, enterprises can have any of the following geographic strategies in their plans: local strategy regional strategy (Europe, North America, Asia-Pacific, etc.) international strategy global strategy global and local strategy[8] [edit] Strategy via projects integration Moreover, since management actions occur simultaneously in an enterprise, strategic planners can consider operations or projects portfolio management (PPM) as crucial elements in an enterprise's strategic planning guide. For instance, the need to have strategic priorities across many projects in companies with multiple product development projects have made executives borrow principles from investment portfolio management to better manage the distribution of resources compared with the assessed risks for each project.[9] Thus, PESTLE factors lead to strategy formation that will enable the enterprise to adapt to changing conditions. Meanwhile, the strategies that have been formed from the analytical framework processes of evaluating an enterprise's condition will lead to detailed plans which could be part of a firm's manual of operations or projects portfolio thrusts for funding and execution across the units or geographic coverage of the enterprise. [edit] Planning and budgeting Enterprise planning and budgeting go hand-in-hand as the wherewithal to execute plans will determine the success or failure of an enterprise strategy. In another light, expanding or limiting the budget for a particular operations aspect of the enterprise or an ongoing project in favor of another will signal changes to an enterprise's strategy.[10] Hence, planning and budgeting are integral parts of any enterprise planning systems as these impact the strategic directions of the enterprise. For instance, enterprise projects tend to be mutually dependent with other projects to leverage a firm's engineering, financial and technology resources.[11] A market research project will trigger a research, development and engineering (RD&E) project for a new product. In turn, this RD&E project could trigger a production strategy project to manufacture the new product at the most efficient locations to bring it closer to its target consumers.[12] Hence, cutting the RD&E project budget in half or increasing it twice will have profound effects in the long term direction of an enterprise as this will affect the other units of the firm undertaking projects that are linked to the RD&E project. [edit] Classifications Enterprise planning and budgeting can be generally classified into: centralized devolved hybrid Centralized. Headquarters or executive management directs all planning and budgets from the top then downwards in the organization hierarchy. It will closely follow Frederick Winslow Taylor's Principles of Scientific Management. Devolved. Middle managers set plans effectively steering the enterprise's strategic direction. Executive management takes into account that the enterprise has knowledge workers that are experts in their respective fields. The Management Board approves the proposed strategic direction under certain financial constraints such as expected returns on investment or equity. Hybrid. Executive management determines and sets the strategic direction of the enterprise based on the inputs of middle managers and the rank and file. In this set up, plans and budgets are negotiated. Essentially, enterprise plans and budgets can be detailed in a top-down approach, generalized in a bottom-up approach, or combined in a top-down and bottom-up approach. [edit] Group planning Enterprise group planning will typically refer to the involvement of the major units of an enterprise such as the finance, marketing, production or technology departments. It can also refer to the involvement of the geographic units of a transnational or global firm. Some enterprises also involve external parties in their group planning where inputs from the crucial parts of the supply chain, cooperation and collaboration, or outsiders-looking-in are part of the firm's strategy.[13][14] Enterprise group planning will usually manifest in regular board of directors' or management committee' meetings with varying frequencies such as monthly, quarterly or annually. Traditional meetings have required the physical presences of representatives from the various business units of the enterprise. With improvements in telecommunications, enterprise group planning can be conducted through video conferencing where participants may be dispersed geographically. However, video conferencing still appears to be an inadequate substitute when warm, interpersonal relations are part of the firm's culture. Yet for fast-paced events like natural disasters or a meltdown of the financial markets that require immediate action from the enterprise, video conferencing might be the only option. Troubleshooting that requires the major resources of the enterprise will also entail enterprise group planning. Here, enterprise planning systems take a tactical form rather than a strategic focus to preserve the stability or ensure the survival of the enterprise. [edit] Transition plan Enterprise transition plans will generally refer to change management-related actions in the case of mergers or in the implementation of an enterprise-wide project. The transition plan will cover the elimination of redundant functions in the case of a merger or the incorporation of new processes into business operations in the case of a technology project. [edit] Planning software Enterprise planning software will have varied or depth of coverage but will not essentially refer to enterprise resource planning software. This will include planning-centric software and the tools to support strategic planning for and across the enterprise, such as: strategy formation software performance measurement and evaluation software project management software scenario planning software data warehouse or business intelligence software [edit] See also Business intelligence Business process management Enterprise relationship management Enterprise Information System Enterprise system Management information system Supply chain management [edit] References 1. ^ Adam Gabbatt, Richard Adams and Ben Quinn. "Japan tsunami and nuclear alert - Monday 14 March part two". guardian.co.uk. Guardian News and Media Limited. Retrieved 30 April 2011. 2. ^ John Ward and Joe Peppard (2002). Strategic Planning for Information Systems 3rd Edition. West Sussex PO19 1UD, England: John Wiley & Sons Ltd. pp. 23–43. ISBN 0-470-84147-8. 3. ^ Massood Samii. "Project Financial Evaluation". Lecture Notes MIT OpenCourseWare. Massachusetts Institute of Technology. Retrieved 30 April 2011. 4. ^ Berente, Nicholas; Danail Ivanov and Betty Vandenbosch (2007). "Process Compliance and Enterprise System Implementation". IEEE Computer Society 40th Hawaii International Conference on System Sciences: 1530–1605. 5. ^ Williams, Kevan. Strategic Management. 375 Hudson Street, New York, New York 10014, United States of America: DK Publishing. pp. 16–32. ISBN 978-0-7566-4859-6. 6. ^ Kaplan, Robert S.; David P. Norton (January–February 1996). "Using the Balanced Scorecard as a Strategic Management System". Harvard Business Review. 7. ^ Mellahi, K., Frynas, J.G. & Finlay, P. (2005). Global Strategic Management. New York: Oxford University Press. pp. 7–18. ISBN 9780199266159. 8. ^ HSBC. "The world's local bank". 2011. HSBC Holdings plc 2011. Retrieved March 3, 2011. 9. ^ McGrath, Michael E. (2004). Next Generation Product Development: How to Increase Productivity, Cut Costs, and Reduce Cycle Times. United States of America: McGraw-Hill. pp. 229–232, Chapter 17. ISBN 0071435123. 10. ^ Fraser, et al., Xiall M. (2009). Global Engineering Economics: Financial Decision Making for Engineers. Toronto, Ontario: Pearson Education Canada. pp. 90–110. ISBN 9780132071611. 11. ^ Kentaro Nobeoka; Michael A. Cusumano (11). "Multi-Project Management: Strategy and Organization in Automobile Product Development". WP-3609-93 BPS. MIT Sloan School of Management. Retrieved April 27, 2011. 12. ^ Fujimoto, Takahiro. "Production Strategy". Department of Economics, University of Tokyo. Retrieved 16 March 2011. 13. ^ Usoro, Abel; Abbas Abid, Matthew Kuofie (December 2008). "Scales Construction for Organisational Variables that Influence the Use Of ICT for Global Planning". International Journal of Global Business. 1 1: 242. 14. ^ Koch, Richard (2001). The Natural Laws of Business: How to Harness the Power of Evolution, Physics, and Economics to Achieve Business Success. New York, USA: Doubleday, Random House Inc.. pp. 107–108. ISBN 0385501595. Corporate governance of information technology From Wikipedia, the free encyclopedia (Redirected from Information technology governance) Jump to: navigation, search Information Technology Governance, IT Governance is a subset discipline of Corporate Governance focused on information technology (IT) systems and their performance and risk management. The rising interest in IT governance is partly due to compliance initiatives, for instance Sarbanes-Oxley in the USA and Basel II in Europe, but more so because of the need for greater accountability for decision-making around the use of IT in the best interest of all stakeholders. IT capability is directly related to the long term consequences of decisions made by top management. Traditionally, board-level executives deferred key IT decisions to the company's IT professionals. This cannot ensure the best interests of all stakeholders unless deliberate action involves all stakeholders. IT governance systematically involves everyone: board members, executive management, staff and customers. It establishes the framework (see below) used by the organization to establish transparent accountability of individual decisions, and ensures the traceability of decisions to assigned responsibilities. Contents [hide] 1 Definitions 2 Background 3 Problems with IT governance 4 Frameworks 5 Professional certification 6 See also 7 Further reading 8 References 9 Footnotes 10 External links [edit] Definitions There are narrower and broader definitions of IT governance. Weill and Ross focus on "Specifying the decision rights and accountability framework to encourage desirable behavior in the use of IT."[1] In contrast, the IT Governance Institute expands the definition to include foundational mechanisms: "… the leadership and organisational structures and processes that ensure that the organisation’s IT sustains and extends the organisation’s strategies and objectives."[2] Van Grembergen and De Haes (2009) focus on enterprise governance of IT and define this as "an integral part of corporate governance and addresses the definition and implementation of processes, structures and relational mechanisms in the organization that enable both business and IT people to execute their responsibilities in support of business/IT alignment and the creation of business value from IT enabled investments". While AS8015, the Australian Standard for Corporate Governance of ICT, defines Corporate Governance of ICT as "The system by which the current and future use of ICT is directed and controlled. It involves evaluating and directing the plans for the use of ICT to support the organisation and monitoring this use to achieve plans. It includes the strategy and policies for using ICT within an organisation." [edit] Background The discipline of information technology governance first emerged in 1993 as a derivative of corporate governance and deals primarily with the connection between strategic objectives and IT management of an organization. It highlights the importance of IT-related matters in contemporary organizations and states that strategic IT decisions should be owned by the corporate board, rather than by the chief information officer or other IT managers. The primary goals for information technology governance are to (1) assure that the investments in IT generate business value, and (2) mitigate the risks that are associated with IT. This can be done by implementing an organizational structure with well-defined roles for the responsibility of information, business processes, applications, ICT infrastructure, etc. Accountability is the key concern of IT governance. After the widely reported collapse of Enron in 2000 and the alleged problems within Arthur Andersen and WorldCom, the duties and responsibilities of auditors and the boards of directors for public and privately held corporations were questioned. As a response to this, and to attempt to prevent similar problems from happening again, the US Sarbanes-Oxley Act was written to stress the importance of business control and auditing. Although not directly related to IT governance, Sarbanes-Oxley and Basel-II in Europe have influenced the development of information technology governance since the early 2000s. Following corporate collapses in Australia around the same time, working groups were established to develop standards for corporate governance. A series of Australian Standards for Corporate Governance were published in 2003, these were: Good Governance Principles (AS8000) Fraud and Corruption Control (AS8001) Organisational Codes of Conduct (AS8002) Corporate Social Responsibility (AS8003) Whistle Blower protection programs (AS8004) AS8015 Corporate Governance of ICT was published in January 2005. It was fast-track adopted as ISO/IEC 38500 in May 2008.[3] [edit] Problems with IT governance Is IT governance different from IT management and IT controls? The problem with IT governance is that often it is confused with good management practices and IT control frameworks. ISO 38500 has helped clarify IT governance by describing it as the management system used by directors. In other words, IT governance is about the stewardship of IT resources on behalf of the stakeholders who expect a return from their investment. The directors responsible for this stewardship will look to the management to implement the necessary systems and IT controls. Whilst managing risk and ensuring compliance are essential components of good governance, it is more important to be focused on delivering value and measuring performance. [edit] Frameworks There are quite a few supporting references that may be useful guides to the implementation of information technology governance. Some of them are: AS8015-2005 Australian Standard for Corporate Governance of Information and Communication Technology. AS8015 was adopted as ISO/IEC 38500 in May 2008 ISO/IEC 38500:2008 Corporate governance of information technology,[4] (very closely based on AS8015-2005) provides a framework for effective governance of IT to assist those at the highest level of organizations to understand and fulfill their legal, regulatory, and ethical obligations in respect of their organizations’ use of IT. ISO/IEC 38500 is applicable to organizations from all sizes, including public and private companies, government entities, and not-for-profit organizations. This standard provides guiding principles for directors of organizations on the effective, efficient, and acceptable use of Information Technology (IT) within their organizations. Control Objectives for Information and related Technology (COBIT) is regarded as the world's leading IT governance and control framework. CobiT provides a reference model of 34 IT processes typically found in an organization. Each process is defined together with process inputs and outputs, key process activities, process objectives, performance measures and an elementary maturity model. Originally created by ISACA, COBIT is now the responsibility of the ITGI[5] (IT Governance Institute). The IT Infrastructure Library[6] (ITIL) is a high-level framework with information on how to achieve a successful operational Service management of IT, developed and maintained by the United Kingdom's Office of Government Commerce, in partnership with the IT Service Management Forum. While not specifically focused on IT governance, the process related information is a useful reference source for tackling the improvement of the service management function. Others include: ISO27001 - focus on Information Security CMM - The Capability Maturity Model: focus on software engineering TickIT - a quality-management certification program for software development CARE[7] - Comprehensive Architecture Rationalization and Engineering: a prescriptive method to perform a systematic assessment of information systems applications in an application/project portfolio[8] Non-IT specific frameworks of use include: The Balanced Scorecard (BSC) - method to assess an organization’s performance in many different areas. Six Sigma - focus on quality assurance TOGAF - The Open Group Architectural Framework - methodology to align business and IT, resulting in useful projects and effective governance. [edit] Professional certification Certified in the Governance of Enterprise Information Technology (CGEIT) is an advanced certification created in 2007 by the Information Systems Audit and Control Association (ISACA). It is designed for experienced professionals, who can demonstrate 5 or more years experience, serving in a managing or advisory role focused on the governance and control of IT at an enterprise level. It also requires passing a 4-hour test, designed to evaluate an applicant's understanding of enterprise IT management. The first examination was held in December 2008. [edit] See also Data governance Enterprise architecture Information Technology Infrastructure Library Information technology management ISACA ISO/IEC 38500 IT portfolio management IT service management Project governance Val IT Website governance [edit] Further reading This article's further reading may not follow Wikipedia's content policies or guidelines. Please improve this article by removing excessive, less relevant or many publications with the same point of view; or by incorporating the relevant publications into the body of the article through appropriate citations. (August 2010) Lutchen, M. (2004). Managing IT as a business : a survival guide for CEOs. Hoboken, N.J., J. Wiley., ISBN 0-471-47104-6 Van Grembergen W., Strategies for Information technology Governance, IDEA Group Publishing, 2004, ISBN 1-59140-284-0 Van Grembergen, W., and S. De Haes, Enterprise Governance of IT: Achieving Strategic Alignment and Value, Springer, 2009. W. Van Grembergen, and S. De Haes, “A Research Journey into Enterprise Governance of IT, Business/IT Alignment and Value Creation”, International Journal of IT/Business Alignment and Governance, Vol. No. 1, 2010, pp. 1–13. S. De Haes, and W. Van Grembergen, “An Exploratory Study into the Design of an IT Governance Minimum Baseline through Delphi Research”, Communications of AIS, No. 22, 2008, pp. 443–458. S. De Haes, and W. Van Grembergen, “An Exploratory Study into IT Governance Implementations and its Impact on Business/IT Alignment”, Information Systems Management, Vol. 26, 2009, pp. 123–137. S. De Haes, and W. Van Grembergen, “Exploring the relationship between IT governance practices and business/IT alignment through extreme case analysis in Belgian mid-to-large size financial enterprises”, Journal of Enterprise Information Management, Vol. 22, No. 5, 2009, pp. 615–637. Georgel F., IT Gouvernance : Maitrise d'un systeme d'information, Dunod, 2004(Ed1) 2006(Ed2), 2009(Ed3), ISBN 2-10-052574-3. "Gouvernance, audit et securite des TI", CCH, 2008(Ed1) ISBN 978-2-89366-577-1 See also the bibliography sections of IT Portfolio Management and IT Service Management Renz, Patrick S. (2007). "Project Governance." Heidelberg, Physica-Verl. (Contributions to Economics) ISBN 978-3-7908-1926-7 Weill, P. and Ross, J.W. (2004). IT Governance: How Top Performers Manage IT Decision Rights for Superior Results, Boston, MA, Harvard Business School Publishing, ISBN 1-59139-253-5 Wood, David J., 2011. "Assessing IT Governance Maturity: The Case of San Marcos, Texas". Applied Research Projects, Texas State University-San Marcos. (This paper applies a modified COBIT framework to a medium sized city.) [edit] References This article uses bare URLs for citations. Please consider adding full citations so that the article remains verifiable. Several templates and the Reflinks tool are available to assist in formatting. (Reflinks documentation) (March 2012) 1. ^ Weill, P. & Ross, J. W., 2004, IT Governance: How Top Performers Manage IT Decision Rights for Superior Results", Harvard Business School Press, Boston. 2. ^ "Board Briefing on IT Governance, 2nd Edition". IT Governance Institute. 2003. Retrieved January 18, 2006. 3. ^ Introduction to ISO 38500 4. ^ http://www.iso.org/iso/pressrelease.htm?refid=Ref1135 5. ^ itgi.org 6. ^ itil.co.uk 7. ^ http://www.igi-global.com/chapter/comprehensive-architecture-rationalizationengineering/23687 8. ^ Tony Shan and Winnie Hua. "Comprehensive Architecture Rationalization and Engineering - Information Technology Governance and Service Management: Frameworks and Adaptations". Igi-global.com. Retrieved August 8, 2008. Knowledge management From Wikipedia, the free encyclopedia Jump to: navigation, search Knowledge management (KM) comprises a range of strategies and practices used in an organization to identify, create, represent, distribute, and enable adoption of insights and experiences. Such insights and experiences comprise knowledge, either embodied in individuals or embedded in organizations as processes or practices. An established discipline since 1991 (see Nonaka 1991), KM includes courses taught in the fields of business administration, information systems, management, and library and information sciences (Alavi & Leidner 1999). More recently, other fields have started contributing to KM research; these include information and media, computer science, public health, and public policy. Many large companies and non-profit organizations have resources dedicated to internal KM efforts, often as a part of their business strategy, information technology, or human resource management departments (Addicott, McGivern & Ferlie 2006). Several consulting companies also exist that provide strategy and advice regarding KM to these organizations. Knowledge management efforts typically focus on organizational objectives such as improved performance, competitive advantage, innovation, the sharing of lessons learned, integration and continuous improvement of the organization. KM efforts overlap with organizational learning, and may be distinguished from that by a greater focus on the management of knowledge as a strategic asset and a focus on encouraging the sharing of knowledge. Contents [hide] 1 History 2 Research o 2.1 Dimensions o 2.2 Strategies o 2.3 Motivations o 2.4 Technologies o 2.5 Knowledge Managers 3 Knowledge Management System o 3.1 Benefits & Issues of knowledge management 4 See also 5 Notes 6 References 7 External links [edit] History KM efforts have a long history, to include on-the-job discussions, formal apprenticeship, discussion forums, corporate libraries, professional training and mentoring programs. More recently, with increased use of computers in the second half of the 20th century, specific adaptations of technologies such as knowledge bases, expert systems, knowledge repositories, group decision support systems, intranets, and computer-supported cooperative work have been introduced to further enhance such efforts.[1] In 1999, the term personal knowledge management was introduced which refers to the management of knowledge at the individual level (Wright 2005). In terms of the enterprise, early collections of case studies recognized the importance of knowledge management dimensions of strategy, process, and measurement (Morey, Maybury & Thuraisingham 2002). Key lessons learned included: people and the cultural norms which influence their behaviors are the most critical resources for successful knowledge creation, dissemination, and application; cognitive, social, and organizational learning processes are essential to the success of a knowledge management strategy; and measurement, benchmarking, and incentives are essential to accelerate the learning process and to drive cultural change. In short, knowledge management programs can yield impressive benefits to individuals and organizations if they are purposeful, concrete, and action-oriented. More recently with the advent of the Web 2.0, the concept of Knowledge Management has evolved towards a vision more based on people participation and emergence. This line of evolution is termed Enterprise 2.0 (McAfee 2006). However, there is an ongoing debate and discussions (Lakhani & McAfee 2007) as to whether Enterprise 2.0 is just a fad that does not bring anything new or useful or whether it is, indeed, the future of knowledge management (Davenport 2008). [edit] Research KM emerged as a scientific discipline in the earlier 1990s. It was initially supported solely by practitioners, when Skandia hired Leif Edvinsson of Sweden as the world’s first Chief Knowledge Officer (CKO). Hubert Saint-Onge (formerly of CIBC, Canada), started investigating various sides of KM long before that. The objective of CKOs is to manage and maximize the intangible assets of their organizations. Gradually, CKOs became interested in not only practical but also theoretical aspects of KM, and the new research field was formed. The KM ideas taken up by academics, such as Ikujiro Nonaka (Hitotsubashi University), Hirotaka Takeuchi (Hitotsubashi University), Thomas H. Davenport (Babson College) and Baruch Lev (New York University). In 2001, Thomas A. Stewart, former editor at FORTUNE Magazine and subsequently the editor of Harvard Business Review, published a cover story highlighting the importance of intellectual capital of organizations. Since its establishment, the KM discipline has been gradually moving towards academic maturity. First, there is a trend towards higher cooperation among academics; particularly, there has been a drop in single-authored publications. Second, the role of practitioners has changed. Their contribution to academic research has been dramatically declining from 30% of overall contributions up to 2002, to only 10% by 2009 (Serenko et al. 2010). A broad range of thoughts on the KM discipline exist; approaches vary by author and school. As the discipline matures, academic debates have increased regarding both the theory and practice of KM, to include the following perspectives[citation needed]: Techno-centric with a focus on technology, ideally those that enhance knowledge sharing and creation. Organizational with a focus on how an organization can be designed to facilitate knowledge processes best. Ecological with a focus on the interaction of people, identity, knowledge, and environmental factors as a complex adaptive system akin to a natural ecosystem. Regardless of the school of thought, core components of KM include people, processes, technology (or) culture, structure, technology, depending on the specific perspective (Spender & Scherer 2007). Different KM schools of thought include various lenses through which KM can be viewed and explained, to include: community of practice (Wenger, McDermott & Synder 2001)[2] social network analysis[3] intellectual capital (Bontis & Choo 2002)[4] information theory[5] (McInerney 2002) complexity science[6][7] constructivism[8] (Nanjappa & Grant 2003) The practical relevance of academic research in KM has been questioned (Ferguson 2005) with action research suggested as having more relevance (Andriessen 2004) and the need to translate the findings presented in academic journals to a practice (Booker, Bontis & Serenko 2008). [edit] Dimensions Different frameworks for distinguishing between different 'types of' knowledge exist. One proposed framework for categorizing the dimensions of knowledge distinguishes between tacit knowledge and explicit knowledge. Tacit knowledge represents internalized knowledge that an individual may not be consciously aware of, such as how he or she accomplishes particular tasks. At the opposite end of the spectrum, explicit knowledge represents knowledge that the individual holds consciously in mental focus, in a form that can easily be communicated to others.[9] (Alavi & Leidner 2001). Similarly, Hayes and Walsham (2003) describe content and relational perspectives of knowledge and knowledge management as two fundamentally different epistemological perspectives. The content perspective suggest that knowledge is easily stored because it may be codified, while the relational perspective recognizes the contextual and relational aspects of knowledge which can make knowledge difficult to share outside of the specific location where the knowledge is developed.[10] Organizational Learning and Knowledge Management Ref : NITC SOMS The Knowledge Spiral as described by Nonaka & Takeuchi. Early research suggested that a successful KM effort needs to convert internalized tacit knowledge into explicit knowledge in order to share it, but the same effort must also permit individuals to internalize and make personally meaningful any codified knowledge retrieved from the KM effort. Subsequent research into KM suggested that a distinction between tacit knowledge and explicit knowledge represented an oversimplification and that the notion of explicit knowledge is self-contradictory. Specifically, for knowledge to be made explicit, it must be translated into information (i.e., symbols outside of our heads) (Serenko & Bontis 2004). Later on, Ikujiro Nonaka proposed a model (SECI for Socialization, Externalization, Combination, Internalization) which considers a spiraling knowledge process interaction between explicit knowledge and tacit knowledge (Nonaka & Takeuchi 1995). In this model, knowledge follows a cycle in which implicit knowledge is 'extracted' to become explicit knowledge, and explicit knowledge is 're-internalized' into implicit knowledge. More recently, together with Georg von Krogh, Nonaka returned to his earlier work in an attempt to move the debate about knowledge conversion forwards (Nonaka & von Krogh 2009). A second proposed framework for categorizing the dimensions of knowledge distinguishes between embedded knowledge of a system outside of a human individual (e.g., an information system may have knowledge embedded into its design) and embodied knowledge representing a learned capability of a human body’s nervous and endocrine systems (Sensky 2002). A third proposed framework for categorizing the dimensions of knowledge distinguishes between the exploratory creation of "new knowledge" (i.e., innovation) vs. the transfer or exploitation of "established knowledge" within a group, organization, or community. Collaborative environments such as communities of practice or the use of social computing tools can be used for both knowledge creation and transfer.[11] [edit] Strategies Knowledge may be accessed at three stages: before, during, or after KM-related activities. Different organizations have tried various knowledge capture incentives, including making content submission mandatory and incorporating rewards into performance measurement plans. Considerable controversy exists over whether incentives work or not in this field and no consensus has emerged. One strategy to KM involves actively managing knowledge (push strategy). In such an instance, individuals strive to explicitly encode their knowledge into a shared knowledge repository, such as a database, as well as retrieving knowledge they need that other individuals have provided to the repository.[12] This is also commonly known as the Codification approach to KM. Another strategy to KM involves individuals making knowledge requests of experts associated with a particular subject on an ad hoc basis (pull strategy). In such an instance, expert individual(s) can provide their insights to the particular person or people needing this (Snowden 2002). This is also commonly known as the Personalization approach to KM. Other knowledge management strategies and instruments for companies include: rewards (as a means of motivating for knowledge sharing) storytelling (as a means of transferring tacit knowledge) cross-project learning after action reviews knowledge mapping (a map of knowledge repositories within a company accessible by all) communities of practice expert directories (to enable knowledge seeker to reach to the experts) best practice transfer knowledge fairs competence management (systematic evaluation and planning of competences of individual organization members) proximity & architecture (the physical situation of employees can be either conducive or obstructive to knowledge sharing) master-apprentice relationship collaborative technologies (groupware, etc.) knowledge repositories (databases, bookmarking engines, etc.) measuring and reporting intellectual capital (a way of making explicit knowledge for companies) knowledge brokers (some organizational members take on responsibility for a specific "field" and act as first reference on whom to talk about a specific subject) social software (wikis, social bookmarking, blogs, etc.) Inter-project knowledge transfer [edit] Motivations A number of claims exist as to the motivations leading organizations to undertake a KM effort.[13] Typical considerations driving a KM effort include: Making available increased knowledge content in the development and provision of products and services Achieving shorter new product development cycles Facilitating and managing innovation and organizational learning Leveraging the expertise of people across the organization Increasing network connectivity between internal and external individuals Managing business environments and allowing employees to obtain relevant insights and ideas appropriate to their work Solving intractable or wicked problems Managing intellectual capital and intellectual assets in the workforce (such as the expertise and know-how possessed by key individuals) Debate exists whether KM is more than a passing fad, though increasing amount of research in this field may hopefully help to answer this question, as well as create consensus on what elements of KM help determine the success or failure of such efforts (Wilson 2002).[14] [edit] Technologies Early KM technologies included online corporate yellow pages as expertise locators and document management systems. Combined with the early development of collaborative technologies (in particular Lotus Notes), KM technologies expanded in the mid-1990s. Subsequent KM efforts leveraged semantic technologies for search and retrieval and the development of e-learning tools for communities of practice[15] (Capozzi 2007). Knowledge management systems can thus be categorized as falling into one or more of the following groups: Groupware, document management systems, expert systems, semantic networks, relational and object oriented databases, simulation tools, and artificial intelligence [16] (Gupta & Sharma 2004) More recently, development of social computing tools (such as bookmarks, blogs, and wikis) have allowed more unstructured, self-governing or ecosystem approaches to the transfer, capture and creation of knowledge, including the development of new forms of communities, networks, or matrixed organizations. However such tools for the most part are still based on text and code, and thus represent explicit knowledge transfer. These tools face challenges in distilling meaningful re-usable knowledge and ensuring that their content is transmissible through diverse channels[17](Andrus 2005). Software tools in knowledge management are a collection of technologies and are not necessarily acquired as a single software solution. Furthermore, these knowledge management software tools have the advantage of using the organization existing information technology infrastructure. Organizations and business decision makers spend a great deal of resources and make significant investments in the latest technology, systems and infrastructure to support knowledge management. It is imperative that these investments are validated properly, made wisely and that the most appropriate technologies and software tools are selected or combined to facilitate knowledge management. Knowledge management has also become a cornerstone in emerging business strategies such as Service Lifecycle Management (SLM) with companies increasingly turning to software vendors to enhance their efficiency in industries including, but not limited to, the aviation industry.[18] [edit] Knowledge Managers This unreferenced section requires citations to ensure verifiability. "Knowledge manager" is a role and designation that has gained popularity over the past decade. The role has evolved drastically from that of one involving the creation and maintenance of knowledge repositories to one that involves influencing the culture of an organization toward improved knowledge sharing, reuse, learning, collaboration and innovation. Knowledge management functions are associated with different departments in different organizations. It may be combined with Quality, Sales, HR, Innovation, Operations etc. and is likely to be determined by the KM motivation of that particular organization. Knowledge managers have varied backgrounds ranging from Information Sciences to Business Management. An effective knowledge manager is likely to be someone who has a versatile skills portfolio and is comfortable with the concepts of organizational behavior/culture, processes, branding & marketing and collaborative technology. [edit] Knowledge Management System Knowledge Management System (KM System) refers to a (generally generated via or through to an IT based program/department or section) system for managing knowledge in organizations for supporting creation, capture, storage and dissemination of information. It can comprise a part (neither necessary nor sufficient) of a Knowledge Management initiative. The idea of a KM system is to enable employees to have ready access to the organization's documented base of facts, sources of information, and solutions. For example a typical claim justifying the creation of a KM system might run something like this: an engineer could know the metallurgical composition of an alloy that reduces sound in gear systems. Sharing this information organization wide can lead to more effective engine design and it could also lead to ideas for new or improved equipment. Knowledge Management framework Ref: School Of management Studies, NIT Calicut A KM system could be any of the following: 1. Document based i.e. any technology that permits creation/management/sharing of formatted documents such as Lotus Notes, SharePoint, web, distributed databases etc. 2. Ontology/Taxonomy based: these are similar to document technologies in the sense that a system of terminologies (i.e. ontology) are used to summarize the document e.g. Author, Subj, Organization etc. as in DAML & other XML based ontologies 3. Based on AI technologies which use a customized representation scheme to represent the problem domain. 4. Provide network maps of the organization showing the flow of communication between entities and individuals 5. Increasingly social computing tools are being deployed to provide a more organic approach to creation of a KM system. KMS systems deal with information (although Knowledge Management as a discipline may extend beyond the information centric aspect of any system) so they are a class of information system and may build on, or utilize other information sources. Distinguishing features of a KMS can include: 1. Purpose: a KMS will have an explicit Knowledge Management objective of some type such as collaboration, sharing good practice or the like. 2. Context: One perspective on KMS would see knowledge is information that is meaningfully organized, accumulated and embedded in a context of creation and application. 3. Processes: KMS are developed to support and enhance knowledge-intensive processes, tasks or projects of e.g., creation, construction, identification, capturing, acquisition, selection, valuation, organization, linking, structuring, formalization, visualization, transfer, distribution, retention, maintenance, refinement, revision, evolution, accessing, retrieval and last but not least the application of knowledge, also called the knowledge life cycle. 4. Participants: Users can play the roles of active, involved participants in knowledge networks and communities fostered by KMS, although this is not necessarily the case. KMS designs are held to reflect that knowledge is developed collectively and that the “distribution” of knowledge leads to its continuous change, reconstruction and application in different contexts, by different participants with differing backgrounds and experiences. 5. Instruments: KMS support KM instruments, e.g., the capture, creation and sharing of the codifiable aspects of experience, the creation of corporate knowledge directories, taxonomies or ontologies, expertise locators, skill management systems, collaborative filtering and handling of interests used to connect people, the creation and fostering of communities or knowledge networks. A KMS offers integrated services to deploy KM instruments for networks of participants, i.e. active knowledge workers, in knowledge-intensive business processes along the entire knowledge life cycle. KMS can be used for a wide range of cooperative, collaborative, adhocracy and hierarchy communities, virtual organizations, societies and other virtual networks, to manage media contents; activities, interactions and work-flows purposes; projects; works, networks, departments, privileges, roles, participants and other active users in order to extract and generate new knowledge and to enhance, leverage and transfer in new outcomes of knowledge providing new services using new formats and interfaces and different communication channels. The term KMS can be associated to Open Source Software, and Open Standards, Open Protocols and Open Knowledge licenses, initiatives and policies. [edit] Benefits & Issues of knowledge management Some of the advantages claimed for KM systems are: 1. 2. 3. 4. 5. Sharing of valuable organizational information throughout organizational hierarchy. Can avoid re-inventing the wheel, reducing redundant work. May reduce training time for new employees Retention of Intellectual Property after the employee leaves if such knowledge can be codified. time management Knowledge Sharing remains a challenging issue for knowledge management, and while there is no clear agreement barriers may include time issues for knowledge works, the level of trust, lack of effective support technologies and culture (Jennex 2008). [edit] See also Knowledge community Knowledge ecosystem Knowledge engineering Knowledge management software Knowledge transfer Legal case management Journals: Electronic Journal of Knowledge Management Journal of Knowledge Management Journal of Knowledge Management Practice [edit] Notes 1. ^ "Introduction to Knowledge Management". Unc.edu. Retrieved 15 January 2010. 2. ^ (PDF). http://www.crito.uci.edu/noah/HOIT/HOIT%20Papers/TeacherBridge.pdf. Retrieved 15 January 2010. 3. ^ (PDF). http://www.ischool.washington.edu/mcdonald/ecscw03/papers/groth-ecscw03-ws.pdf. Retrieved 15 January 2010. 4. ^ Secretary of Defense Corporate Fellows Program; Observations in Knowledge Management: Leveraging the Intellectual Capital of a Large, Global Organization with Technology, Tools and Policies. IBM, Global Business Services. 2002. Retrieved 15 January 2010. 5. ^ "Information Architecture and Knowledge Management". Iakm.kent.edu. Archived from the original on June 29, 2008. Retrieved 15 January 2010. 6. ^ Snowden, Dave (2002). "Complex Acts of Knowing – Paradox and Descriptive Self Awareness". Journal of Knowledge Management, Special Issue 6 (2): 100 – 111. 7. ^ SSRN-Knowledge Ecosystems: A Theoretical Lens for Organizations Confronting Hyperturbulent Environments by David Bray. Papers.ssrn.com. Retrieved 15 January 2010. 8. ^ http://citeseer.ist.psu.edu/wyssusek02sociopragmatic.html 9. ^ "SSRN-Literature Review – Knowledge Management Research at the Organizational Level by David Bray". Papers.ssrn.com. Retrieved 15 January 2010. 10. ^ Hayes, M.; Walsham, G. (2003). Knowledge sharing and ICTs: A relational perspective In M. Easterby- Smith & M. A. Lyles (Eds.), The Blackwell handbook of organizational learning and knowledge management. Malden, MA: Blackwell. pp. 54–77. ISBN 978-0-631-22672-7. 11. ^ "SSRN-Exploration, Exploitation, and Knowledge Management Strategies in Multi-Tier Hierarchical Organizations Experiencing Environmental Turbulence by David Bray". Papers.ssrn.com. Retrieved 15 January 2010. 12. ^ (PDF). http://www.cs.fiu.edu/~chens/PDF/IRI00_Rathau.pdf. Retrieved 15 January 2010. 13. ^ http://tecom.cox.smu.edu/abasu/itom6032/kmlect.pdf 14. ^ (PDF). http://myweb.whitman.syr.edu/yogesh/papers/WhyKMSFail.pdf. Retrieved 15 January 2010.[dead link] 15. ^ "p217-ricardo.pdf" (PDF). Retrieved 15 January 2010. 16. ^ Gupta, Jatinder; Sharma, Sushil (2004). Creating Knowledge Based Organizations. Boston: Idea Group Publishing. ISBN 1591401631. 17. ^ "Knowledge Management". www.systems-thinking.org. Retrieved 26 February 2009. 18. ^ Aviation Industry Group. "Service life-cycle management"[dead link], Aircraft Technology: Engineering & Maintenance, February–March, 2005. [edit] References This article is based on material taken from the Free On-line Dictionary of Computing prior to 1 November 2008 and incorporated under the "relicensing" terms of the GFDL, version 1.3 or later. Addicott, Rachael; McGivern, Gerry; Ferlie, Ewan (2006). "Networks, Organizational Learning and Knowledge Management: NHS Cancer Networks". Public Money & Management 26 (2): 87– 94. doi:10.1111/j.1467-9302.2006.00506.x. Alavi, Maryam; Leidner, Dorothy E. (1999). "Knowledge management systems: issues, challenges, and benefits". Communications of the AIS 1 (2). Alavi, Maryam; Leidner, Dorothy E. (2001). "Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues". 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Benbya, H (2008). Knowledge Management Systems Implementation: Lessons from the Silicon Valley. Oxford, Chandos Publishing. Langton, N & Robbins, S. (2006). Organizational Behaviour (Fourth Canadian Edition). Toronto, Ontario: Pearson Prentice Hall. Maier, R (2007): Knowledge Management Systems: Information And Communication Technologies for Knowledge Management. 3rd edition, Berlin: Springer. Rhetorical Structure Theory (assumed from the reference of RST Theory above) http://acl.ldc.upenn.edu/W/W01/W01-1605.pdf Rosner, D.., Grote, B., Hartman, K, Hofling, B, Guericke, O. (1998) From natural language documents to sharable product knowledge: a knowledge engineering approach. in Borghoff Uwe M., and Pareschi, Remo (Eds.). Information technology for knowledge management. Springer Verlag, pp 35–51. The RST site at http://www.sfu.ca/rst/ run by Bill Mann Jennex, M. E. (2008). Knowledge Management: Concepts, Methodologies, Tools, and Applications (pp. 1–3808).