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MANAGING INFORMATION TECHNOLOGY FIFTH EDITION CHAPTER 7 MANAGERIAL SUPPORT SYSTEMS E. Wainright Martin Carol V. Brown Daniel W. DeHayes Jeffrey A. Hoffer William C. Perkins DECISION SUPPORT SYSTEMS Designed to assist decision makers with unstructured problems Usually interactive Incorporates data and models Data often comes from transaction processing systems or data warehouse © 2005 Pearson Prentice-Hall Chapter 7 - 2 Page 212 DECISION SUPPORT SYSTEMS Three major components © 2005 Pearson Prentice-Hall Chapter 7 - 3 Figure 7.1 Decision Support Systems Components Page 213 DECISION SUPPORT SYSTEMS Specific DSSs – actual DSS applications that directly assist in decision making DSS generator – a software package used to build a specific DSS quickly and easily Examples: Microsoft Excel or Lotus 1-2-3 DSS model 1 used to create DSS generator DSS model 2 DSS model 3 © 2005 Pearson Prentice-Hall Chapter 7 - 4 Page 212 How Big is an Exabyte? Kilobyte (KB) = 10 3 = 1,000 bytes 2 KB: a written page Megabyte (MB) = 10 6 = 1,000,000 bytes 1 MB: a small novel Gigabyte (GB) = 10 9 = 1,000,000,000 bytes 1 GB: a pickup truck filled with books 20 GB: a good collection of the works of Beethoven Terabyte (TB) = 10 12 = 1,000,000,000,000 bytes 2 TB: an academic research library 10 TB: the printed collections of the U.S. Library of Congress Petabyte (PB) = 10 = 1,000,000,000,000,000 bytes 20 PB: production of hard-disk drives in 1995 200 PB: all printed material Exabyte (EB) = 10 18 = 1,000,000,000,000,000,000 bytes 2 EB: total volume of information generated in 1999 15 Business Intelligence Reporting tools Reporting tools are programs that read data from a variety of sources, process that data, produce formatted reports, and deliver those reports to the users who need them. Reporting tools are used primarily for assessment to address questions like: What has happened in the past? What is the current situation? How does the current situation compare to the past? Reporting tools use simple operations like sorting, grouping, and summing. Business Intelligence Data-mining tools Data-mining tools process data using statistical techniques, many of which are sophisticated. Data mining involves searching for patterns and relationships among data. In most cases, data-mining tools are used to make predictions. For example, We can use one form of analysis to compute the probability that a customer will default on a loan. Trade Data for NDX.X (NASDAQ 100) Report based on Trade Data Components of a Reporting System Report Characteristics RFM Analysis RFM analysis is a way of analyzing and ranking customers according to their purchasing patterns. It considers how recently (R) a customer has ordered, how frequently (F) a customer orders, and how much money (M) the customer spends per order. To produce an RFM score, the program first sorts customer purchase records by the date of their most recent (R) purchase. The top 20% of the customers having the most recent orders are given an R score 1 (highest). The program then re-sorts the customers on the basis of how frequently they order. Finally the program sorts the customers again according to the amount spent on their orders. Example of RFM Score Data Online Analytical Processing (OLAP) OLAP provides the ability to sum, count, average, & perform other simple arithmetic operations on groups of data. The viewer of the report can change the report’s format, hence, the term online. An OLAP report has measures and dimensions. A measure is the data item of interest. The item is to be summed or averaged or otherwise processed in the OLAP report. A dimension is a characteristic of a measure. Examples are purchase data, customer type, customer location, and sales region OLAP Product Family & Store Location by Store Type Data Warehouses and Data Marts Basic reports and simple OLAP analyses can be made directly from operational data. For the most part, such reports display the current state of the business; and if there are a few missing values or small inconsistencies with the data, no one is too concerned. Operational data are unsuited to more sophisticated analyses, particularly, data-mining analyses that require high-quality input for accurate and useful results. Data Warehouses and Data Marts Many organizations choose to extract operational data into facilities called data warehouses and data marts for data mining and other analyses. Programs read operational data and extract, clean, and prepare that data for business intelligence processing. Data warehouses include data that are purchased from outside sources. The data-warehouse DBMS extracts and provides data to business intelligence tools such as datamining programs. Components of a Data Warehouse Problems of Using Transaction Data for Analysis and Data Mining Data Mart Examples Data Mining Data mining is the application of statistical techniques to find patterns and relationships among data and to classify and predict. With unsupervised data mining, analysts do not create a model or hypothesis before running the analysis. A common use for cluster analysis is to find groups of similar customers from customer order and demographic data. With supervised data mining, data miners develop a model prior to the analysis and apply statistical techniques to data to estimate parameters of the model. One such analysis is called a regression analysis. Market-Basket Analysis A market-basket analysis is a data-mining technique for determining sales patterns. A market-basket analysis shows the products that customers tend to buy together. You can expect market-basket analysis to become a standard CRM analysis during your career. Decision Trees A decision tree is a hierarchical arrangement of criteria that predict a classification or a value. Decision tree analyses are an unsupervised datamining technique. The analyst sets up the computer program and provides the data to analyze, and the decision tree program produces the tree. A common business application of decision trees is to classify loans by likelihood of default. Organizations analyze data from past loans to produce a decision tree that can be converted to loan-decision rules. DATA MINING Data Mining – uses different technologies to search for (mine) “nuggets” of information from data stored in a data warehouse Data mining software: Oracle 9i Data Mining and Oracle Data Mining Suite SAS Enterprise Miner IBM Intelligent Miner Modeling Angoss Software’s KnowledgeSEEKER, Knowledge Studio, and KnowledgeExcelerator Datamation’s Data Mining and Business Intelligence Product © 2005 Pearson Prentice-Hall Chapter 7 - 24 Page 215 DATA MINING Data Mining – uses different technologies to search for (mine) “nuggets” of information from data stored in a data warehouse Decision techniques used: Decision trees Linear and logistic regression Clustering for market segmentation Rule induction Nearest neighbor Genetic algorithms © 2005 Pearson Prentice-Hall Chapter 7 - 25 Page 215 DATA MINING Uses: Cross-selling Customer churn Customer retention Direct marketing Fraud detection Interactive marketing Market basket analysis Market segmentation Payment or default analysis Trend analysis © 2005 Pearson Prentice-Hall Chapter 7 - 26 see Table 7.1 Uses of Data Mining Page 216 GROUP SUPPORT SYSTEMS Type of DSS to support a group rather than an individual Specialized type of groupware Attempt to make group meetings more productive Now focus on supporting team in all its endeavors, including “different time, different place” mode – virtual teams © 2005 Pearson Prentice-Hall Chapter 7 - 27 Page 217-218 GROUP SUPPORT SYSTEMS Traditional “same time, same place” meeting layout © 2005 Pearson Prentice-Hall Chapter 7 - 28 Figure 7.2 Group Support System Layout Page 217 GEOGRAPHIC INFORMATION SYSTEMS GISs – systems based on manipulation of relationships in space that use geographic data Early GIS users: Natural resource management Public administration NASA and the military Urban planning Forestry Map makers © 2005 Pearson Prentice-Hall Chapter 7 - 29 Page 219 GEOGRAPHIC INFORMATION SYSTEMS Business Adopts Geographic Technologies Business uses: Determining site locations Market analysis and planning Logistics and routing Environmental engineering Geographic pattern analysis © 2005 Pearson Prentice-Hall Chapter 7 - 30 Page 219 (Reprinted courtesy of Environmental Systems Research Institute, Inc. Copyright © 2003 Environmental Systems Research Institute, Inc. All rights reserved.) © 2005 Pearson Prentice-Hall Chapter 7 - 31 Figure 7.3 Department Store Analysis Page 219 GEOGRAPHIC INFORMATION SYSTEMS What’s Behind Geographic Technologies Approaches to representing spatial data: Raster-based GISs – rely on dividing space into small, uniform cells (rasters) in a grid Vector-based GISs – associate features in the landscape with a point, line, or polygon Geodatabase model – uses object-oriented data concepts © 2005 Pearson Prentice-Hall Chapter 7 - 32 Page 220 GEOGRAPHIC INFORMATION SYSTEMS Coverage model uses different layers to represent similar types of geographic features in the same area © 2005 Pearson Prentice-Hall Chapter 7 - 33 Figure 7.4 Map Layers in a GIS Page 221 GEOGRAPHIC INFORMATION SYSTEMS What’s Behind Geographic Technologies Questions geographic analysis can answer: What is adjacent to this feature? Which site is the nearest one? What is contained within this area? Which features does this element cross? How many features are within a certain distance of a site? © 2005 Pearson Prentice-Hall Chapter 7 - 34 Page 221 EXECUTIVE INFORMATION SYSTEMS/BUSINESS INTELLIGENCE SYSTEMS EISs – a hands-on tool that focuses, filters, and organizes an executive’s information so he or she can make more effective use of it Where does EIS data come from? Filtered and summarized transaction data (internal) Collected competitive information (internal and external) © 2005 Pearson Prentice-Hall Chapter 7 - 35 Page 222-223 EXECUTIVE INFORMATION SYSTEMS/BUSINESS INTELLIGENCE SYSTEMS Executive information system (EIS): Delivers online current information about business conditions in aggregate form Easily accessible to senior executives and other managers Designed to be used without intermediary assistance Uses state-of-the-art graphics, communications and data storage methods © 2005 Pearson Prentice-Hall Chapter 7 - 36 Page 222-223 (Courtesy of Geac Computer Corporation Limited. Copyright © 2003 Geac Computer Corporation Limited.) © 2005 Pearson Prentice-Hall Chapter 7 - 37 Figure 7.5 Example Geac Performance Management Displays Page 225 (Courtesy of Geac Computer Corporation Limited. Copyright © 2003 Geac Computer Corporation Limited.) © 2005 Pearson Prentice-Hall Chapter 7 - 38 Figure 7.5 Example Geac Performance Management Displays Page 225 KNOWLEDGE MANAGEMENT SYSTEMS Knowledge management (KM): Set of practical and action-oriented management practices Involves strategies and processes of identifying, creating, capturing, organizing, transferring, and leveraging knowledge to help compete Relies on recognizing knowledge held by individuals and the firm © 2005 Pearson Prentice-Hall Chapter 7 - 39 Page 226 KNOWLEDGE MANAGEMENT SYSTEMS Knowledge management system (KMS): System for managing organizational knowledge Technology or vehicle that facilitates the sharing and transferring of knowledge so that valuable knowledge can be reused Enable people and organizations to enhance learning, improve performance, and produce longterm competitive advantage © 2005 Pearson Prentice-Hall Chapter 7 - 40 Page 226 ARTIFICIAL INTELLIGENCE AI – the study of how to make computers do things that are currently done better by people Six areas: © 2005 Pearson Prentice-Hall Natural languages Robotics Perceptive systems Genetic programming Expert systems Neural networks Chapter 7 - 41 Page 229 ARTIFICIAL INTELLIGENCE AI – the study of how to make computers do things that are currently done better by people Six areas: © 2005 Pearson Prentice-Hall Natural languages Robotics Perceptive systems Genetic programming Expert systems Neural networks Chapter 7 - 42 Most relevant for managerial support Page 229 EXPERT SYSTEMS Expert systems – attempt to capture the expertise of humans in a computer program Knowledge engineer: A specially trained systems analyst who works closely with one or more experts in the area of study Tries to learn about how experts make decisions Loads information (what learned) into module called knowledge base © 2005 Pearson Prentice-Hall Chapter 7 - 43 Page 229 EXPERT SYSTEMS © 2005 Pearson Prentice-Hall Chapter 7 - 44 Figure 7.6 Architecture of an Expert System Page 229 EXPERT SYSTEMS Obtaining an Expert System Approaches: Buy a fully developed system created for a specific application Develop using a purchased expert system shell (basic framework) and user-friendly special language Have knowledge engineers custom build using special-purpose language (such as Prolog or Lisp) © 2005 Pearson Prentice-Hall Chapter 7 - 45 Page 230 EXPERT SYSTEMS Examples of Expert Systems Standford University’s MYCIN – to diagnose and prescribe treatment for meningitis and blood diseases General Electric’s CATS-1 to diagnose mechanical problems in diesel locomotives AT&T’s ACE to locate faults in telephone cables Market Surveillance software – to detect insider trading FAST software – for credit analysis, used by banking industry Nestle Food’s developed system to provide employees information on pension fund status © 2005 Pearson Prentice-Hall Chapter 7 - 46 Page 230 NEURAL NETWORKS Neural networks – attempt to tease out meaningful patterns from vast amounts of data Process: 1. 2. 3. 4. 5. 6. Program given set of data Program analyzed data, works out correlations, selects variables to create patterns Pattern used to predict outcomes, then results compared to known results Program changes pattern by adjusting variable weights or variables themselves Repeats process over and over to adjust pattern When no further adjustment possible, ready to be used to make predictions for future cases © 2005 Pearson Prentice-Hall Chapter 7 - 47 Page 232 NEURAL NETWORKS © 2005 Pearson Prentice-Hall Chapter 7 - 48 Table 7.2 Uses of Neural Networks Page 232 VIRTUAL REALITY Virtual reality – use of a computer-based system to create an environment that seems real to one or more senses of users Non-entertainment categories: Training Design Marketing © 2005 Pearson Prentice-Hall Chapter 7 - 49 Page 233 VIRTUAL REALITY Training U.S. Army to train tank crews Amoco for training its drivers Duracell for training factory workers on using new equipment Design Design of automobiles Walk-throughs of air conditioning/ furnace units Marketing Interactive 3-D images of products (used on the Web) Virtual tours used by real estate companies or resort hotels © 2005 Pearson Prentice-Hall Chapter 7 - 50 Page 234-235 VIRTUAL REALITY Training U.S. Army to train tank crews Amoco for training its drivers Duracell for training factory workers on using new equipment Design Design of automobiles Walk-throughs of air conditioning/ furnace units Marketing Interactive 3-D images of products (used on the Web) Virtual tours used by real estate companies or resort hotels © 2005 Pearson Prentice-Hall Chapter 7 - 51 Page 234-235 VIRTUAL REALITY (Courtesy of Homestore, Inc. Copyright © 2004 Homestore, Inc.) © 2005 Pearson Prentice-Hall Chapter 7 - 52 Figure 7.7 Hometour 360o Virtual Tour of Living Room Page 235