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Information systems
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Not to be confused with Information system.
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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
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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
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of view. Tagged since November 2008.
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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
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15. ^ a b A.J. McGrail, E. Gulski et al.. "Data Mining Techniques to Asses the Condition of High
Voltage Electrical Plant". CIGRE WG 15.11 of Study Committee 15.
16. ^ R. Baker. "Is Gaming the System State-or-Trait? Educational Data Mining Through the MultiContextual Application of a Validated Behavioral Model". Workshop on Data Mining for User
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17. ^ J.F. Superby, J-P. Vandamme, N. Meskens. "Determination of factors influencing the
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18. ^ Xingquan Zhu, Ian Davidson (2007). Knowledge Discovery and Data Mining: Challenges and
Realities. Hershey, New York. pp. 163–189. ISBN 978-1-59904-252-7.
19. ^ Xingquan Zhu, Ian Davidson (2007). Knowledge Discovery and Data Mining: Challenges and
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20. ^ Yudong Chen, Yi Zhang, Jianming Hu, Xiang Li. "Traffic Data Analysis Using Kernel PCA and SelfOrganizing Map". Intelligent Vehicles Symposium, 2006 IEEE.
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Taylor and Francis).
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(London: Taylor & Francis).
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presented at Predictive Analytics World, Oct. 2010.
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Mining's Top Challenges.
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Mining Based on Sensor Grid in London". Sensors 8 (6): 3601. doi:10.3390/s8063601. edit
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Knowledge
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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
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40. ^ Stephen Haag et al. (2006). Management Information Systems for the information age.
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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
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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
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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.
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