1. Chapter 9 Business Intelligence and Information Systems for Decision Making

2. Q1:How big is an exabyte, and why does it matter? Q2:How do business intelligence (BI) systems provide competitive advantages? Q3:What problems do operational data pose for BI systems? Q4:What are the purpose and components of a data warehouse? Q5:What is a data mart, and how does it differ from a data warehouse? Q6:What are the characteristics of data-mining systems? Q7: What are OLAP reports? Study Questions Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall 9-2

3. Q1: How Big Is an Exabyte? 9-3 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

4. Businesses collect massive amounts of data –Storage capacity is increasing as cost is decreasing –Storage capacity is becoming almost unlimited, so businesses collect more at little extra cost Buried in that data are important patterns of relationships that can yield valuable information to help businesses make better decisions Q1: Why Does an Exabyte Matter? 9-4 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

5. Primary BI systems: 1.Reporting systems Integrate data from multiple systems Sorting, grouping, summing, averaging, comparing data 2.Data-mining systems Use sophisticated statistical techniques, regression analysis, and decision tree analysis Used to discover hidden patterns and relationships Market-basket analysis –purchasing patterns Q2: How Do Business Intelligence (BI) Systems Provide Competitive Advantages? 9-5 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

6. 3.Knowledge management systems (KMs) Create value by collecting and sharing human knowledge about products, products uses, best practices, other critical knowledge Used by employees, managers, customers, suppliers, others who need access to company knowledge 4.Expert systems (ES) Encapsulates knowledge in form of “If/Then” rules –If Patient_Temp > 103, Then start High_Fever_Procedure ES can improve diagnostic and decision quality of non- experts Q2: How Do Business Intelligence (BI) Systems Provide Competitive Advantages? (cont’d) 9-6 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

7. Dirty data. –Values may be missing –Inconsistent data –Non-integrated data –Wrong granularity (Coarse vs. Fine) Too much data causes: 1.Curse of dimensionality Problem caused by the exponential increase in volume associated with adding extra dimensions to a (mathematical) space. 2.Too many rows or data points Q3: What Problems Do Operational Data Pose for BI Systems? 9-7 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

8. Purpose: –To extract and clean data from various operational systems and other sources –To store and catalog data for BI processing Q4: What are the Purpose and Components of a Data Warehouse? 9-8 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

9. Data warehouse architecture consists of the following layers: Operational database layer - The source data for the data warehouse - An organization's Enterprise Resource Planning systems fall into this layer. Data access layer - The interface between the operational and informational access layer - Tools to extract, transform, load (ETL) data into the warehouse fall into this layer. Metadata layer - The data directory - This is usually more detailed than an operational system data directory. Informational access layer - The reporting and analyzing tools. Business intelligence tools fall into this layer. Q4: What are the Purpose and Components of a Data Warehouse?(cont.) 9-9 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

10. Components of a Data Warehouse 9-10 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

11. Created to address particular needs –Business function –Problem –Opportunity Smaller than data warehouse Data extracted from data warehouse for a functional area Q5: What Is a Data Mart, and How Does It Differ from a Data Warehouse? 9-11 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

12. Components of a Data Mart 9-12 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

13. Data mining—application of statistical techniques to find patterns and relationships in body of data for purpose of classifying and predicting Q6: What Are the Characteristics of Data-Mining Systems? 9-13 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

14. Analysts do not create model before running analysis Apply data-mining technique and observe results Hypotheses created after analysis as explanation for results Common statistical technique used: –Cluster analysis to identify groups with similar characteristics Unsupervised Data Mining 9-14 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

15. Model developed before analysis Statistical techniques used to estimate parameters Examples: –Regression analysis—measures impact of set of variables on one another –Used for making predictions Supervised Data Mining 9-15 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

16. Neural networks Used for predicting values and making classifications Complicated set of nonlinear equations Supervised Data Mining (cont’d) 9-16 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

17. OnLine Analytical Processing –is an approach to quickly answer multi- dimensional analytical queries –Dynamic online view based on Measures –Data item to be manipulated – total sales, average cost Dimensions –Characteristic of measure – purchase date, customer type, location, sales region Q7: What are OLAP reports? Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall CE17-17

18. OLAP cube –Presentation of measure with associated dimensions (a.k.a. OLAP report) Users can alter format Users can drill down into data –Divide data into more detail Q7: What are OLAP reports? (cont.) Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall CE17-18

19. Figure CE17-12 Role of OLAP Server and OLAP Database CE17-19 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

20. Q1:How big is an exabyte, and why does it matter? Q2:How do business intelligence (BI) systems provide competitive advantages? Q3:What problems do operational data pose for BI systems? Q4:What are the purpose and components of a data warehouse? Q5:What is a data mart, and how does it differ from a data warehouse? Q6:What are the characteristics of data-mining systems? Q7: What are OLAP reports? Active Review Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall 9-20

21. Chapter Extension 16 Database Marketing

22. Q1:What is a database marketing opportunity? Q2: How does RFM analysis classify customers? Q3: How does market-basket analysis identify cross-selling opportunities? Study Questions CE16-22 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

23. Database marketing –Application of business intelligence systems for planning and executing marketing programs –Databases are a key component –Data-mining techniques also important Process of sorting through large amounts of data and picking out relevant information Q1: What Is a Database Marketing Opportunity? CE16-23 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

24. RFM –RFM program analyzes and ranks customers according to their purchase patterns –How recently (R) a customer has ordered? –How frequently (F) a customer has ordered? –How much money (M) a customer has spent per order? Divides customers into five groups and assigns a score R score 1 = top 20% in most recent orders R score 5 = bottom 20% (longest since last order) F score 1 = top 20% in most frequent orders F score 5 = bottom 20% least frequent orders M score 1 = top 20% in most money spent M score 5 = bottom 20% in amount of money spent Q2: How Does RFM Analysis Classify Customers? CE16-24 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

25. Figure CE16-1 Example of RFM Score Data CE16-25 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

26. Market-basket analysis is a data-mining technique for determining sales patterns –Uses statistical methods to identify sales patterns in large volumes of data –Shows which products customers tend to buy together –Helps identify cross-selling opportunities "Customers who bought book X also bought book Y” Q3: How Does Market-Basket Analysis Identify Cross-Selling Opportunities? CE16-26 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

27. Figure CE16-2 Market-Basket Example CE16-27 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

28. Support  Probability that two items will be bought together –Fins and masks purchases together 150 times, thus support for fins and a mask is 150/1,000, or 15% –Support for fins and weights is 60/1,000, or 6% –Support for fins along with a second pair of fins is 10/1,000, or 1% Market-Basket Terminology CE16-28 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

29. Confidence  What proportion of the customers who bought a mask also bought fins? –Conditional probability estimate –Example: »Probability of buying fins = 28% (280/1000) »Probability of buying swim mask = 27% (270/1000) –After buying Mask, »Probability of buying Fins = 150/270 or 55.56%  Likelihood that a customer will also buy fins almost doubles, from 28% to 55.56%. Thus, all sales personnel should try to sell fins to anyone buying a mask Market-Basket Terminology (cont’d) CE16-29 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

30. Lift  Ratio of confidence to base probability of buying item –Shows how much base probability increases or decreases when other products are purchased Example: –Lift of fins and a mask is confidence of fins given a mask, divided by the base probability of fins. –Lift of fins and a mask is.5556/.28 = 1.98 Market-Basket Terminology (cont’d) CE16-30 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

31. Common business application –Classify loan applications by likelihood of default –Rules identify loans for bank approval –Identify market segment –Structure marketing campaign –Predict problems Too bad the Banks didn’t use decision trees!! Decision Tree for Loan Evaluation CE16-31 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

32. Q1:What is a database marketing opportunity? Q2: How does RFM analysis classify customers? Q3: How does market-basket analysis identify cross-selling opportunities? Active Review CE16-32 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

33. Chapter 9 Ch Ext.16 Business Intelligence and Information Systems for Decision Making