Using MIS 2e Chapter 9: Business Intelligence Systems David Kroenke

1. Using MIS 2e Chapter 9: Business Intelligence Systems David Kroenke

2. Chapter 9: Business Intelligence Systems Study Questions Q1 � Why do organizations need business intelligence? Q2 � What business intelligence systems are available? Q3 � What are typical reporting applications? Q4 � What are typical data-mining applications? Q5 � What is the purpose of data warehouses and data marts? Q6 � What are typical knowledge-management applications? Q7 � How are business intelligence applications delivered? Security Guide: Semantic Security 9-2

3. Chapter 9: Business Intelligence Systems Q1 � Why do organizations need business intelligence? 9-3

4. Chapter 9: Business Intelligence Systems Q2 � What business intelligence systems are available (intelligence tools)? Business intelligence tools search data to find meaningful information; they fall into two classifications � reporting tools and data-mining tools Reporting tools read data from a variety of sources, process that data, and produce formatted reports Use simple techniques; e.g., sorting, selecting and grouping to calculate totals and averages. Used primarily for assessment; e.g., What has happened in the past? What is the current situation? and how does the current situation compare to the past? Data-mining tools process data using statistical techniques, many of which are mathematically complex. Data mining involves searching for patterns and relationships among data. In most cases, data-mining tools are used to make predictions; e.g., what is the probability that a customer will default on a loan? 9-4

5. Chapter 9: Business Intelligence Systems Q2 � What business intelligence systems are available (tools versus systems)? The purpose of a business intelligence system is to provide the right information, to the right user, at the right time. A tool is a computer program An information system is a collection of hardware, software, data, procedures, and people A reporting tool can generate a report that shows a customer has canceled an important order. A reporting system, however, alerts that customer�s salesperson with this unwanted news, and does so in time for the salesperson to try to alter the customer�s decision. A data-mining tool can create an equation that computes the probability that a customer will default on a loan. A data-mining system uses that equation to enable banking personnel to assess new loan applications. 9-5

6. Chapter 9: Business Intelligence Systems Q3 � What are typical reporting applications (report characteristics)? Reports may be classified in different ways: Static reports are prepared once from the underlying data and do not change; ;e.g., a report of past year�s sales Dynamic reports: the reporting system reads the most current data and generates the report using that data; e.g., a report on today�s sales or current stock prices The report mode is either �push� or �pull�: A push report is sent to users according to a preset schedule; users receive the report without any activity on their part. A pull report must be requested by the user; the user goes to a Web portal or digital dashboard and clicks a link or button to cause the reporting system to produce and deliver the report. All reports can be delivered via different media including paper, e-mail alerts, Web sites and/or a digital dashboard. 9-6

7. Chapter 9: Business Intelligence Systems Q3 � What are typical reporting applications (basic operations) 9-7

8. Chapter 9: Business Intelligence Systems Q3 � What are typical reporting applications (basic operations) 9-8

9. Chapter 9: Business Intelligence Systems Q3 � What are typical reporting applications (RFM analysis)? RFM analysis ranks customers according to purchasing patterns. It is a simple technique that considers: How recently (R) a customer has ordered How frequently (F) a customer orders, 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 program then divides the customers into five groups giving each group a score of 1 to 5. The group with the most recent orders is given an R score 1 (highest). The program then resorts the customers on the basis of frequency and creates five groups with scores of 1 to 5 And finally the program resorts customers on the basis of how much money was spent, once again creating five groups with scores of 1 to 5 9-9

10. Chapter 9: Business Intelligence Systems Q3 � What are typical reporting applications (RFM analysis)? 9-10

11. Chapter 9: Business Intelligence Systems Q3 � What are typical reporting applications (OLAP processing) Online analytical processing (OLAP) provides the ability to sum, count, average, and perform other arithmetic operations on groups of data. OLAP reports are dynamic and are easily changed online. An OLAP report has measures and dimensions. A measure is the data item of interest. It is the item that is to be summed or averaged or otherwise processed in the OLAP report. A dimension is a characteristic of a measure. Purchase data, customer type, customer location, and sales region are all examples of dimension. OLAP reports enable the user to �drill down�; i.e. to divide the data into more detail. The OLAP cube is analogous to an Excel pivot table; OLAP output may be directed to Excel. 9-11

12. Chapter 9: Business Intelligence Systems Q3 � What are typical reporting applications (OLAP processing) 12

13. Chapter 9: Business Intelligence Systems Q3 � What are typical reporting applications (OLAP processing) 9-13

14. Chapter 9: Business Intelligence Systems Q4 � What are typical data-mining applications? 9-14

15. Chapter 9: Business Intelligence Systems Q4 � What are typical data-mining applications? Data mining techniques fall into two broad categories, unsupervised and supervised With unsupervised data mining, analysts do not create a model or hypothesis before running the analysis. Instead, they apply the data-mining technique to the data, observe the results, and create hypotheses after the analysis to explain the patterns found. One common unsupervised technique is cluster 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 before the analysis and apply statistical techniques to data to estimate parameters of the model. Regression analysis is a common supervised technique that measures impact of a set of independent variables on another dependent variable. 9-15

16. Chapter 9: Business Intelligence Systems Q4 � What are typical data-mining applications (market basket analysis)? Fig 9-12 Market-Basket Example 9-16

17. Chapter 9: Business Intelligence Systems Q4 � What are typical data-mining applications (decision trees)? 9-17

18. Chapter 9: Business Intelligence Systems Q4a � What are other examples of data-mining (Benford�s Law)? 9-18

19. Chapter 9: Business Intelligence Systems Q4a � What are other examples of data-mining? (Pareto Principal)? 9-19

20. Chapter 9: Business Intelligence Systems Q5 � What is the purpose of data warehouses and data marts? Operational data are often unsuited to more sophisticated analyses that require high-quality input for accurate and useful results. Thus many organizations extract operational data into facilities called data warehouses and data marts. The data warehouse cleans and processes operational or purchased data, and then stores the data on the �shelves� of the data warehouse. Metadata concerning the data, its source, format, assumptions, constraints, and other facts about the data is kept in a data-warehouse metadata database. A data mart is a data collection, smaller than the data warehouse, that addresses a particular component or functional area of the business. Users in the data mart obtain data that pertain to a particular business function from the warehouse. 9-20

21. Chapter 9: Business Intelligence Systems Q5 � What is the purpose of data warehouses and data marts (data warehouse)? 9-21

22. Chapter 9: Business Intelligence Systems Q5 � What is the purpose of data warehouses and data marts (data mart) 9-22

23. Chapter 9: Business Intelligence Systems Q6 � What are typical knowledge-management applications? Knowledge Management (KM) is the sharing of knowledge that already exists, be it in libraries, documents, in the heads of employees, or elsewhere It is the process of creating value from intellectual capital and sharing that knowledge with managers, employees, suppliers, customers, and others who need that capital. In other words, someone, some where has the answer; KM seeks to get the right information to the right person at the right time so that he/she can do the job more effectively. KM is different from data mining which relies on statistical techniques to acquire information from hidden patterns KM is supported by the five components of an information system; the emphasis is on people, their knowledge, and effective means for sharing that knowledge with others. KM preserves organizational memory by storing the lessons learned and best practices of key employees. 9-23

24. Chapter 9: Business Intelligence Systems Q6 � What are typical knowledge-management applications? Content management systems are information systems that track organizational documents, graphics, Web pages, and related materials; they are a subset of KMS. Such systems differ from operational document systems in that they do not directly support business operations. Typical users of content management systems are companies that sell complicated products and want to share their knowledge of those products with employees and customers. Content management functions are very complicated. Most content databases are huge; some have thousands of individual documents, pages, and graphics. Documents may refer to one another or multiple documents may refer to the same product or procedure. When one of them changes, others must change as well. Document contents are perishable. Documents become obsolete and need to be altered, removed, or replaced. Multinational companies have to ensure language translations. 9-24

25. Chapter 9: Business Intelligence Systems Q6 � What are typical knowledge-management applications? 9-25

26. Chapter 9: Business Intelligence Systems Q6 � What are typical knowledge-management applications? Almost all users of content management systems pull (i.e., request) the contents. Users cannot pull content if they do not know it exists. The content must be arranged and indexed, and a facility for searching the content devised. Documents that reside behind a corporate firewall, however, are not publicly accessible and will not be reachable by Google or other search engines. Organizations must index their own proprietary documents and provide their own search capability for them. Nothing is more frustrating for a manager to contemplate than the situation in which one employee struggles with a problem that another employee knows how to solve easily. KM systems are concerned with the sharing not only of content, but with sharing of knowledge among humans. Collaboration systems include video conferencing and net presentations Human knowledge-sharing systems use portals, bulletin boards, and email to facilitate knowledge interchange. 9-26

27. Chapter 9: Business Intelligence Systems Q7 � How are business intelligence applications delivered? 9-27

28. Chapter 9: Business Intelligence Systems Security Guide�Semantic Security 28

29. Chapter 9: Business Intelligence Systems Security Guide�Semantic Security (Continued) 29

30. Chapter 9: Business Intelligence Systems Summary Enormous amounts of data are generated each year. Business intelligence (BI) tools search these increasing amounts of data for useful information. Reporting tools tend to be used for assessment and use simple calculations such as sums and averages. Data-mining tools, tend to be used for prediction and process data using sophisticated statistical and mathematical techniques. Benford�s Law and the Pareto Principle are two widely applicable data patterns. Reporting systems create meaningful information from disparate data sources and deliver that information to the proper user on a timely basis. RFM and OLAP are two examples of report applications. 9-30

31. Chapter 9: Business Intelligence Systems Summary (Continued) Decision trees are used to construct �If�Then�� rules for predicting classifications. Data warehouses and data marts are facilities that clean and store data for data mining and other analyses. Knowledge management is the process of creating value from intellectual capital and sharing that knowledge with employees, managers, suppliers, customers, and others who need that capital. Content management is extremely complex. Human knowledge-sharing systems use portals, bulletin boards, and email to facilitate knowledge interchange. Collaboration systems include net conferencing, video conferencing, and expert systems. 9-31

32. Chapter 9: Business Intelligence Systems Review: Select the appropriate term for each item A way of analyzing and ranking customers according to their purchasing pattern RFM analysis One thousand gigabytes Terabyte The search for relationships among data Data mining Electronic display customized for a user Digital dashboard Analogous to an Excel pivot table OLAP cube Report sent according to a preset schedule Push report Report requested by the user Pull report Hierarchical arrangement of criteria that predict a value or classification Decision Tree A data collection smaller than a data warehouse Data mart Concerned with the unintended release of protected information via a combination of reports Semantic Security 9-32