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

Slide 1© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke

Chapter 9

Competitive Advantage with Information Systems for Decision Making

Slide 2© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke

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?

Slide 3© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke

Business Intelligence (BI) Systems

Provide information for improving decision making hence competitive advantage

Primary systems:Reporting systemsData-mining systemsKnowledge management systemsExpert systems

Slide 4© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke

Reporting Systems

Integrate data from multiple sourcesProcess data by sorting, grouping, summing,

averaging, and comparingResults formatted into reportsImprove decision making by providing right

information to right user at right time

Slide 5© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke

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.

Slide 6© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke

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.

Slide 7© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke

Market-Basket AnalysisThis is the most widely used and, in many ways, most successful

data mining algorithm.

Determines sales patternsShows products that customers buy togetherProbability that two items will be bought togetherEstimate probability of customer purchaseStores 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.

Slide 8© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke

Decision Tree

Figure CE14-3

Slide 9© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke

Knowledge-Management Systems

Create value from intellectual capitalCollects and shares human knowledgeSupported by the five components of the

information systemFosters innovationIncreases organizational responsiveness by

getting products and services to market faster and reduce cost.

Slide 10© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke

Expert Systems

Encapsulate experts’ knowledgeProduce If/Then rulesImprove diagnosis and decision making in

non-expertsExample of a rule in medical diagnosis

system:

If patient_temperature >130, then initiate High_Fever_Procedure

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

Slide 12© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke

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, email address)

Values may be missing ( gender….)Inconsistent data ( time zone)Data can be too fine ( clickstream) or too

coarse (totals)

Slide 13© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke

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

Slide 14© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke

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

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Slide 15© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke

Components of a Data Warehouse

Slide 16© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke

Data purchased from outside source for Data Warehousing

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Data Warehouses Are Multidimensional

A Multidimensional Data Warehouse with Information from Multiple Operational Databases

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Data Mart

Data collectionCreated to address particular needs

Business functionProblemOpportunity

Smaller than data warehouseUsers may not have data management expertise

Knowledgeable analysts for specific function

Slide 19© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke

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.

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

Data Mart Examples

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Data Mining Caution ( privacy)

http://abcnews.go.com/Video/playerIndex?id=2803149

Slide 22© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke

videos

The Value of Business Intelligence - 5 min

Data Mining – 5 min

Data Mining Visual Analytics, Inc -9 min