1. Business Intelligence Systems Appendix J DAVID M. KROENKE and DAVID J. AUER DATABASE CONCEPTS, 6 th Edition

2. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America. Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

3. Chapter Objectives Learn the basic concepts of business intelligence (BI) systems Learn the basic concepts of reporting systems and data mining KROENKE and AUER - DATABASE CONCEPTS (6th Edition) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall J-3

4. Heather Sweeney Designs Review: Database Design KROENKE and AUER - DATABASE CONCEPTS (6th Edition) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall J-4

5. Heather Sweeney Designs Review: HSD Database Diagram in SQL Server 2012 KROENKE and AUER - DATABASE CONCEPTS (6th Edition) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall J-5

6. Business Intelligence Systems Business intelligence (BI) systems are information systems that –Assist managers and other professionals in the analysis of current and past activities and in the prediction of future events. –Do not support operational activities, such as the recording and processing of orders. These are supported by transaction processing systems. –Support management assessment, analysis, planning and control. BI systems fall into two broad categories: –Reporting systems that sort, filter, group, and make elementary calculations on operational data; and –Data mining applications that perform sophisticated analyses on data that usually involve complex statistical and mathematical processing. KROENKE and AUER - DATABASE CONCEPTS (6th Edition) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall J-6

7. The Relationship Among Operational and BI Applications KROENKE and AUER - DATABASE CONCEPTS (6th Edition) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall J-7 Figure J-1: Characteristic of Business Intelligence Systems

8. Characteristics of Business Intelligence Applications Review KROENKE and AUER - DATABASE CONCEPTS (6th Edition) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall J-8

9. Characteristics of a Data Warehouse KROENKE and AUER - DATABASE CONCEPTS (6th Edition) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall J-9 Figure J-2: Components of a Data Warehouse

10. Problems with Operational Data “Dirty Data” –Example: “G” for Gender –Example: “213” for Age Missing Values Inconsistent Data –Example: data that has changed, such as a customer’s phone number KROENKE and AUER - DATABASE CONCEPTS (6th Edition) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall J-10

11. Problems with Operational Data (Continued) Nonintegrated Data –Example: data from two or more sources that need to be combined Incorrect Format –Example: time data in hours when needed in minutes Too Much Data –Example: An excess number of columns KROENKE and AUER - DATABASE CONCEPTS (6th Edition) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall J-11

12. ETL Data Transformation Data may need to be transformed for use in a data warehouse –Example: {CountryCode  CountryName} “US”  “United States” –Example: Email address to Email domain joe@somewhere.com  “somewhere.com”joe@somewhere.com KROENKE and AUER - DATABASE CONCEPTS (6th Edition) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall J-12

13. Characteristics of a Data Mart KROENKE and AUER - DATABASE CONCEPTS (6th Edition) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall J-13 Figure J-3: Data Warehouses and Data Marts

14. Enterprise Data Warehouse (EDW) Architecture Combines the data warehouse structure and the data mart structures shown above Expensive to create, staff, and operate Smaller organizations use subsets of the EDW architecture. KROENKE and AUER - DATABASE CONCEPTS (6th Edition) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall J-14

15. Dimensional Database Review A non-normalized database structure used for data warehouses May use slowly changing dimensions –Values change infrequently Phone Number Address Use a Date or Time dimension KROENKE and AUER - DATABASE CONCEPTS (6th Edition) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall J-15

16. Star Schema Review KROENKE and AUER - DATABASE CONCEPTS (6th Edition) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall J-16

17. HSD-DW Star Schema Review KROENKE and AUER - DATABASE CONCEPTS (6th Edition) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall J-17

18. Conformed Dimensions and the Extended HSD-DW Schema KROENKE and AUER - DATABASE CONCEPTS (6th Edition) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall J-18 Figure J-4: Dimensional Databases and the Star Schema

19. The RFM Score Report KROENKE and AUER - DATABASE CONCEPTS (6th Edition) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall J-19 Figure J-5: The RFM Score Report

20. Reporting Systems KROENKE and AUER - DATABASE CONCEPTS (6th Edition) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall J-20 Figure J-6: Components of a Reporting System

21. Reporting Systems: RFM Analysis RFM Analysis—analyzes and ranks customers according to purchasing patterns: –R = Recent (most recent order) –F = Frequent (how often an order is made) –M = Money (dollar amount of orders) Customers are sorted into five groups, each containing 20% of the customers. Each group is given a numerical value: –1 = Top 20% –2, 3, 4 = Each 20% in between top and bottom 20% –5 = Bottom 20% KROENKE and AUER - DATABASE CONCEPTS (6th Edition) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall J-21

22. Reporting Systems: Report Characteristics KROENKE and AUER - DATABASE CONCEPTS (6th Edition) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall J-22 Figure J-7: Report Characteristics

23. Reporting Systems: Report System Functions Report Authoring: –Connect to data sources –Create the report structure –Format the report Report Management: –Defines who receives what reports when and by what means Report Delivery: –Push reports or allow them to be pulled KROENKE and AUER - DATABASE CONCEPTS (6th Edition) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall J-23

24. OLAP and Data Mining OnLine Analytical Processing (OLAP) is a technique for dynamically examining database data. –OLAP uses arithmetic functions such as Sum and Average. –Discussed in Chapter 8 Data Mining is a mathematically sophisticated technique for analyzing database data. –Data mining uses mathematical and statistical techniques. KROENKE and AUER - DATABASE CONCEPTS (6th Edition) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall J-24

25. OLAP OLAP systems produce an OLAP report, also know as an OLAP cube. The OLAP report uses inputs called dimensions. The OLAP report calculates outputs called measures. Excel PivotTables can be used to create OLAP reports. KROENKE and AUER - DATABASE CONCEPTS (6th Edition) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall J-25

26. Data Mining Applications: The Convergence of the Disciplines KROENKE and AUER - DATABASE CONCEPTS (6th Edition) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall J-26 Figure J-8: Convergence of Disciplines for Data Mining

27. Data Mining Applications: Popular Data Mining Techniques KROENKE and AUER - DATABASE CONCEPTS (6th Edition) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall Cluster analysis—Identifies groups of entities that have similar characteristics Decision tree analysis—Classifies entities into groups based on past history Logistic regression—Produces equations that offer probabilities that certain events will occur Neural Networks—Complex statistical prediction techniques Market Basket Analysis—Determines patterns of associated buying behavior J-27

28. Data Mining Applications: Cluster Analysis I KROENKE and AUER - DATABASE CONCEPTS (6th Edition) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall J-28 Figure J-9: The Microsoft Excel 2010 with the Microsoft SQL Server 2012 Data Mining Add-ins

29. Data Mining Applications: Cluster Analysis II KROENKE and AUER - DATABASE CONCEPTS (6th Edition) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall J-29 Figure J-10: The Cluster Analysis Results

30. Data Mining Applications: Cluster Analysis III KROENKE and AUER - DATABASE CONCEPTS (6th Edition) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall J-30 Figure J-11: The Cluster Analysis Results (cont’d)

31. Data Mining Applications: Market Basket Analysis KROENKE and AUER - DATABASE CONCEPTS (6th Edition) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall J-31 Figure J-12: A Market Basket Analysis Example

32. Business Intelligence Systems End of Presentation on Appendix J DAVID M. KROENKE and DAVID J. AUER DATABASE CONCEPTS, 6 th Edition