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

2. This Could Happen to You: “We’re Sitting on All This Data”
Anne proposes to combine membership data and publicly available data in order to better target marketing efforts for Fox Lake weddings.
Information will allow her classy promotions and increase wedding revenue.
Scenario video
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3. 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?
How does the knowledge in this chapter help Fox Lake and you?
Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall

4. Q1: Why Do Organizations Need Business Intelligence?
Businesses collect massive amounts of data
Reveal important patterns of relationships and valuable information buried in that data
Data communications and data storage essentially free
2 million s, 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
Such as, evidence that someone is going to default on a loan
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5. How Big Is an Exabyte?
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6. Q2: How Do Business Intelligence Systems Provide Competitive Advantages?
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7. Business Intelligence Tools vs. Business Intelligence Systems
Q2: How Do Business Intelligence Systems Provide Competitive Advantages? (cont’d)
Business Intelligence Tools vs Business Intelligence Systems
BI Tools (software)
Crystal Reports
SPSS
Clementine
SharePoint Server
BI Systems
Reporting System
Datamining System
Knowledge Mgmt System
Expert System
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8. Q3: What Problems Do Operational Data Pose for BI Systems?
Raw data usually unsuitable for sophisticated reporting or data mining
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9. Key Terms
Data granularity—degree of summarization or detail. Coarse data highly summarized; fine data precise details
Clickstream data—customers’ website clicking behavior
Curse of dimensionality—too much data (attributes/columns or rows)
Market-basket analysis—computes correlations of items on past orders to determine items frequently purchased together
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10. Q4: What Are the Purpose and Components of a Data Warehouse?
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11. Consumer Data Available for Purchase from Data Vendors
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12. Sources of Data for Data Warehouses
Internal operations systems
External data purchased from outside sources
Data from social networking, user-generated content applications
Metadata concerning data stored in data warehouse
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13. 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
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14. Components of a Data Mart
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15. 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
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16. Unsupervised Data Mining
Analysts do not create model before running analysis
Apply data-mining technique and observe results
Hypotheses created after analysis as explanation for results
Technique:
Cluster analysis to find groups with similar characteristics
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17. 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)
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18. Supervised Data Mining (cont’d)
Neural networks
Used for predicting values and making classifications
Complicated set of nonlinear equations
See to learn more
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19. Active Review 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?
How does the knowledge in this chapter help Fox Lake and you?
Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall