© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Slide 1 Chapter 9 Competitive Advantage with Information Systems for Decision Making

© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Slide 2 Agenda How do business intelligence systems (BI) provide competitive advantages? What problems do operational data pose for BI systems? What are the purpose and components of a data warehouse? What is a data mart, and how does it differ from a data warehouse? What are the characteristics of data-mining systems ?

© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Slide 3 Business Intelligence (BI) Systems Provide information for improving decision making hence competitive advantage Primary systems: Reporting systems Data-mining systems Knowledge management systems Expert systems

© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Slide 4 Reporting Systems Integrate data from multiple sources Process data by sorting, grouping, summing, averaging, and comparing Results formatted into reports Improve decision making by providing right information to right user at right time

© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Slide 5 Data-Mining Systems Process data using statistical techniques like regression analysis and decision tree analysis Look for patterns and relationships to anticipate events or predict outcomes Example: Market-basket analysis – computes correlation of items on past orders to determine items that are frequently purchased together.

© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Slide 6 Beer and Diapers There is a story that a large supermarket chain, usually Wal-Mart, did an analysis of customers' buying habits and found a statistically significant correlation between purchases of beer and purchases of diapers. It was theorized that the reason for this was that fathers were stopping off at Wal-Mart to buy diapers for their babies, and since they could no longer go down to the pub as often, would buy beer as well. As a result of this finding, the supermarket chain is alleged to have the diapers next to the beer, resulting in increased sales of both.Wal-Mart

© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Slide 7 Market-Basket Analysis This is the most widely used and, in many ways, most successful data mining algorithm. Determines sales patterns Shows products that customers buy together Probability that two items will be bought together Estimate probability of customer purchase Stores can use this information to place these products in the same area. Direct marketers can use this information to determine which new products to offer to their current customers.

© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Slide 8 Decision Tree Figure CE14-3

© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Slide 9 Knowledge-Management Systems Create value from intellectual capital Collects and shares human knowledge Supported by the five components of the information system Fosters innovation Increases organizational responsiveness by getting products and services to market faster and reduce cost.

© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Slide 10 Expert Systems Encapsulate experts’ knowledge Produce If/Then rules Improve diagnosis and decision making in non-experts Example of a rule in medical diagnosis system: If patient_temperature >130, then initiate High_Fever_Procedure

© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Slide 11

© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Slide 12 Problems with Operational Data for BI Raw data usually unsuitable for sophisticated reporting or data mining ( lack of demographic data) Dirty data ( gender, age, phone #, misspelling, address) Values may be missing ( gender….) Inconsistent data ( time zone) Data can be too fine ( clickstream) or too coarse (totals)

© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Slide 13 What are Data Warehouses? A logical collection of information Gathered from many different operational databases Used to create business intelligence that supports business analysis activities and decision-making tasks. Used to extract and clean data from operational systems Prepares data for BI processing

© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Slide 14 What are Data Warehouses? Data-warehouse DBMS Stores data May also include data from external sources Metadata concerning data, its source, its format and its constraints, stored in data-warehouse meta database Extracts and provides data to BI tools

© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Slide 15 Components of a Data Warehouse

© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Slide 16 Data purchased from outside source for Data Warehousing

© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Slide 17 Data Warehouses Are Multidimensional A Multidimensional Data Warehouse with Information from Multiple Operational Databases

© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Slide 18 Data Mart Data collection Created to address particular needs Business function Problem Opportunity Smaller than data warehouse Users may not have data management expertise Knowledgeable analysts for specific function

© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Slide 19 Data Marts – Smaller Data Warehouses Data mart - a subset of a data warehouse in which only a focused portion of the data warehouse information is kept.

© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Slide 20 Data Mart Examples

© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Slide 21 Data Mining Caution ( privacy)

© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke Slide 22 videos The Value of Business Intelligence - 5 min Data Mining – 5 min Data Mining Data Mining Visual Analytics, Inc -9 min Data Mining Visual Analytics, Inc