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Chapter 9 Business Intelligence and Information Systems for Decision Making Study Questions Q1: Why do organizations need business intelligence? 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? Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall 9-2 Q1: Why Do Organizations Need Business Intelligence? Businesses collect massive amounts of data •Data communications and data storage essentially free •2 million emails, 31,000 text messages, and 162,000 instant messages transmitted every second (2007) •2010 total online computer storage about 600 exabytes •70 exabytes equivalent to 14 times total number of words ever spoken by humans Reveal important Such as, evidence that patterns of relationships someone is going to and valuable information default on a loan buried in that data Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall 9-3 How Big Is an Exabyte? Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall 9-4 Q2: How Do Business Intelligence Systems Provide Competitive Advantages? Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall 9-5 Q2: How Do Business Intelligence Systems Provide Competitive Advantages? (cont’d) Business Intelligence Tools vs. Business Intelligence Systems BI Tools (software) BI Systems Crystal Reports Reporting System SPSS Datamining System Clementine Knowledge Mgmt System SharePoint Server Expert System Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall 9-6 Q3: What Problems Do Operational Data Pose for BI Systems? • Raw data usually unsuitable for sophisticated reporting or data mining Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall 9-7 Q4: What Are the Purpose and Components of a Data Warehouse? Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall 9-8 Sources of Data for Data Warehouses Internal operations systems External data purchased from outside sources Data from social networking, usergenerated content applications Metadata concerning data stored in data warehouse Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall 9-9 Q5: What Is a Data Mart and How Does It Differ from a Data Warehouse? Data Mart => Collection of data created to address needs of a particular: •Business function •Problem •Opportunity Marts created from data extracted from data warehouse Data mart is like a retail store in a supply chain Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall 9-10 Components of a Data Mart Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall 9-11 Q6: What Are the Characteristics of Data-Mining Systems? Data mining—application of statistical techniques to find patterns and relationships in body of data for purpose of classifying and predicting Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall 9-12 Unsupervised Data Mining Analysts do not create model before running analysis Hypotheses created after analysis as explanation for results Apply data-mining technique and observe results Technique: •Cluster analysis to find groups with similar characteristics Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall 9-13 Supervised Data Mining Model developed before analysis • Statistical techniques used to estimate variable parameters • Regression analysis—measures impact of set of variables on one another Example: CellPhoneWeekendMinutes = 12 X (17.5 X CustomerAge) + (23.7 X NumberMonthsOfAccount) Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall 9-14 Supervised Data Mining (cont’d) Used for predicting values and making classifications See www.kdnuggets. com to learn more Neural networks Complicated set of nonlinear equations Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall 9-15