1. © Pearson Prentice Hall 2009 9-1 Using MIS 2e Chapter 9 Business Intelligence Systems David Kroenke

2. © Pearson Prentice Hall 2009 9-2 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?

3. © Pearson Prentice Hall 2009 9-3 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?

4. © Pearson Prentice Hall 2009 9-4 Q1 – Why do organizations need business intelligence? Computers gather and store enormous amounts of data. 403 petabytes of new data were created in 2002. An estimated 2,500 petabytes, or 2.5 exabytes of new data were generated in 2007. Business intelligence is comprised of information that contains patterns, relationships, and trends about customers, suppliers, business partners, and employees. Business intelligence systems process, store, and provide useful information to users who need it, when they need it.

5. © Pearson Prentice Hall 2009 9-5 Q1 – Why do organizations need business intelligence? Fig 9-1 How Big is an Exabyte? This chart explains the names and amounts of computer data measurements.

6. © Pearson Prentice Hall 2009 9-6 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?

7. © Pearson Prentice Hall 2009 9-7 Q2 – What business intelligence systems are available? A business intelligence (BI) system is an information system that employs business intelligence tools to produce and deliver information. Business intelligence tools are computer programs that implement a particular BI technique. The techniques are categorized three ways:  Reporting tools read data, process them, and format the data into structured reports that are delivered to users. They are used primarily for assessment.  Data-mining tools process data using statistical techniques, search for patterns and relationships, and make predictions based on the results  Knowledge-management tools store employee knowledge, make it available to whomever needs it. These tools are distinguished from the others because the source of the data is human knowledge.

8. © Pearson Prentice Hall 2009 9-8 Q2 – What business intelligence systems are available? It’s important that you understand the difference between these business intelligence components:  A BI tool is a computer program that implements the logic of a particular procedure or process.  A BI application uses BI tools on a particular type of data for a particular purpose.  A BI system is an information system that has all five components (hardware, software, data, procedures, people) that delivers the results of a BI application to users.

9. © Pearson Prentice Hall 2009 9-9 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?

10. © Pearson Prentice Hall 2009 9-10 Q3 – What are typical reporting applications? Reporting applications input data from a source(s) and apply a reporting tool to the data to produce information. The reporting system delivers the information to users. Basic reporting operations include sorting, grouping, calculating, filtering, and formatting. This figure shows raw data before any reporting operations are used. Fig 9-2 Raw Sales Data

11. © Pearson Prentice Hall 2009 9-11 Q3 – What are typical reporting applications? The figure on the left shows the raw sales data sorted by customer names. The figure on the right shows data that’s been sorted and grouped. Fig 9-3 Sales Data Sorted by Customer Name Fig 9-4 Sales Data, Sorted by Customer Name & Grouped by Number of Orders & Purchase Amount

12. © Pearson Prentice Hall 2009 9-12 Q3 – What are typical reporting applications? Fig 9-5 Sales Data Filtered to Show Repeat Customers This figure shows even better information that’s been filtered and formatted according to specific criteria.

13. © Pearson Prentice Hall 2009 9-13 Q3 – What are typical reporting applications? RFM Analysis allows you to analyze and rank customers according to purchasing patterns as this figure shows.  R = how recently a customer purchased your products  F = how frequently a customer purchases your products  M = how much money a customer typically spends on your products The lower the score, the better the customer. Fig 9-6 Example of RFM Score Data

14. © Pearson Prentice Hall 2009 9-14 Q3 – What are typical reporting applications? Online Analytical Processing (OLAP) is more generic than RFM and provides you with the dynamic ability to sum, count, average, and perform other arithmetic operations on groups of data. Reports, also called OLAP cubes, use  Measures which are data items of interest. In the figure below a measure is Store Sales Net.  Dimensions which are characteristics of a measure. In the figure below a dimension is Product Family. Fig 9-7 OLAP Product Family by Store Type

15. © Pearson Prentice Hall 2009 9-15 Q3 – What are typical reporting applications? Fig 9-8 OLAP Product Family & Store Location by Store Type This figure shows how you can alter the format of a report to provide users with the information they need to do their jobs.

16. © Pearson Prentice Hall 2009 9-16 Q3 – What are typical reporting applications? Fig 9-9 OLAP Product Family & Store Location by Store Type, Drilled Down to Show Stores in California This figure shows how you can divide data into more detail by drilling down through the data.

17. © Pearson Prentice Hall 2009 9-17 Q3 – What are typical reporting applications? Fig 9-10 Role of OLAP Server & OLAP Database OLAP servers are special products that read data from an operational database, perform some preliminary calculations, and then store the results in an OLAP database

18. © Pearson Prentice Hall 2009 9-18 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?

19. © Pearson Prentice Hall 2009 9-19 Q4 – What are typical data-mining applications? Fig 9-11 Convergence Disciplines for Data Mining Businesses use statistical techniques to find patterns and relationships among data and use it for classification and prediction. Data mining techniques are a blend of statistics and mathematics, and artificial intelligence and machine-learning.

20. © Pearson Prentice Hall 2009 9-20 Q4 – What are typical data-mining applications? There are two types of data-mining techniques:  Unsupervised data-mining characteristics: No model or hypothesis exists before running the analysis Analysts apply data-mining techniques and then observe the results Analysts create a hypotheses after analysis is completed Cluster analysis, a common technique in this category groups entities together that have similar characteristics  Supervised data-mining characteristics: Analysts develop a model prior to their analysis Apply statistical techniques to estimate parameters of a model Regression analysis is a technique in this category that measures the impact of a set of variables on another variable Neural networks predict values and make classifications

21. © Pearson Prentice Hall 2009 9-21 Q4 – What are typical data-mining applications? Fig 9-12 Market-Basket Example Market-Basket Analysis is a data-mining tool for determining sales patterns. It helps businesses create cross-selling opportunities. Two terms used with this type of analysis, and shown in the figure, are:  Support—the probability that two items will be purchased together  Confidence—a conditional probability estimate

22. © Pearson Prentice Hall 2009 9-22 Q4 – What are typical data-mining applications? A decision tree is a hierarchical arrangement of criteria that predicts a classification or value. It’s an unsupervised data-mining technique that selects the most useful attributes for classifying entities on some criterion. It uses if…then rules in the decision process. Here are two examples. Fig 9-13 Grades of Students from Past MIS Class (Hypothetical Data) Fig 9-14 Credit Score Decision Tree

23. © Pearson Prentice Hall 2009 9-23 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?

24. © Pearson Prentice Hall 2009 9-24 Q5 – What is the purpose of data warehouses and data marts? Fig 9-15 Components of a Data Warehouse Data warehouses and data marts address the problems companies have with missing data values and inconsistent data. They also help standardize data formats between operational data and data purchased from third-party vendors. These facilities prepare, store, and manage data specifically for data mining and analyses.

25. © Pearson Prentice Hall 2009 9-25 Q5 – What is the purpose of data warehouses and data marts? Figure 9-16, left, lists some of the data that’s readily available for purchase from data vendors Some of the problems companies experience with operational data are shown in figure 9-17 below. Granularity refers to whether data are too fine or too coarse. Clickstream data refers to the clicking behavior of customers on Web sites. The phenomenon called the curse of dimensionality—just because you have more attributes doesn’t mean you have a more worthwhile predictor.

26. © Pearson Prentice Hall 2009 9-26 Q5 – What is the purpose of data warehouses and data marts? Fig 9-18 Data Mart Examples Here’s the difference between a data warehouse and a data mart:  A data warehouse stores operational data and purchased data. It cleans and processes data as necessary. It serves the entire organization.  A data mart is smaller than a data warehouse and addresses a particular component or functional area of an organization.

27. © Pearson Prentice Hall 2009 9-27 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?

28. © Pearson Prentice Hall 2009 9-28 Q6 – What are typical knowledge-management applications? The characteristics and goals of knowledge management applications and systems are to  Create value for an organization from its intellectual capital  Share knowledge among and between employees, managers, suppliers, and customers  Include knowledge that is known to exist in documents or employees’ brains  Foster innovation by encouraging the free flow of ideas  Improve customer service by streamlining response times  Boost revenues by getting products and services to market faster  Enhance employee retention rates by recognizing the value of employees’ knowledge and rewarding them for it  Streamline operations and reduce costs by eliminating redundant or unnecessary processes  Preserve organizational memory by capturing and storing lessons learned and the best practices of key employees. The three major categories of knowledge assets are data, documents, and employees.

29. © Pearson Prentice Hall 2009 9-29 Q6 – What are typical knowledge-management applications? Two key technologies for sharing content in KM systems are:  Indexing—the single most important content function in KM applications. It’s an easily accessible and robust means of determining if content exists and includes a link to obtain the content. It’s used in conjunction with search functions.  RSS, Real Simple Syndication—a standard for subscribing to content sources on Web sites. It uses an RSS Reader program that helps users subscribe to content sources. periodically check sources for new or updated content through RSS feeds. place content summaries in an RSS inbox with a link to the full content.

30. © Pearson Prentice Hall 2009 9-30 Q6 – What are typical knowledge-management applications? Fig 9-19 Interface of a Typical RSS Reader This figure shows a typical RSS reader. The left pane shows RSS sources. Entries are grouped into categories predetermined by the user.

31. © Pearson Prentice Hall 2009 9-31 Q6 – What are typical knowledge-management applications? Fig 9-20 Blog Posts of SharePoint Team Member Blogs provide an easy way to share knowledge as seen in this figure. You can use RSS feeds to subscribe to thousands of blogs.

32. © Pearson Prentice Hall 2009 9-32 Q6 – What are typical knowledge-management applications? Another form of knowledge management are expert systems. Here are characteristics about them along with some of their problems:  They capture human expertise and format it for use by nonexperts.  They are rule-based systems that use if…then rules.  They gather data from people rather than using data-mining techniques.  They are difficult and expensive to develop.  They are difficult to maintain because the rules are constantly changing.  They have been unable to live up to the high expectations set by their name.

33. © Pearson Prentice Hall 2009 9-33 Q6 – What are typical knowledge-management applications? Fig 9-21 Alert from Pharmacy Clinical Decision Support System This is an example of the output from a medical expert system that is part of a decision support system. Based on the system’s rules, an alert is issued if the system detects a problem with a patient’s prescriptions.

34. © Pearson Prentice Hall 2009 9-34 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?

35. © Pearson Prentice Hall 2009 9-35 Q7 – How are business intelligence applications delivered? Fig 9-22 Components of Generic Business Intelligence System This figure shows the components of a generic BI system. A BI application server delivers results in a variety of formats to devices for consumption by BI users. A BI server provides two functions: management and delivery.

36. © Pearson Prentice Hall 2009 9-36 Q7 – How are business intelligence applications delivered? The management function of a BI server maintains metadata about the authorized allocation of BI results to users. It tracks what results are available, who is authorized to view them, and when the results are provided to users. Here are options for managing BI results:  Users can pull their results from a Web site using a portal server with a customizable user interface.  A server can automatically push information to users through alerts which are messages announcing events as they occur.  A report server, a special server dedicated to reports, can supply users with information.

37. © Pearson Prentice Hall 2009 9-37 Q7 – How are business intelligence applications delivered? Fig 9-23 Sample Portal, Provided by iGoogle This figure shows a portal that provides common data to users. It can be used to help companies manage their knowledge.

38. © Pearson Prentice Hall 2009 9-38 Q7 – How are business intelligence applications delivered? Here are the characteristics of the delivery function of a BI server:  It tracks authorized users.  It tracks the schedule for providing results to users.  It uses exception alerts that notify users of an exceptional event.  The procedures used depends on the nature of the BI system.  Procedures tend to be more flexible than those in an operational system because users of a BI system tend to be engaged in work that is neither structured nor routine.  Procedures are determined by unique requirements of users.