© 2008 pearson prentice hall, experiencing mis, david kroenke slide 1 chapter 9 competitive...
TRANSCRIPT
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 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 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
Mis_04.wmv
Mis_03.wmv
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
Slide 17© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
Data Warehouses Are Multidimensional
A Multidimensional Data Warehouse with Information from Multiple Operational Databases
Slide 18© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
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 21© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
Data Mining Caution ( privacy)
http://abcnews.go.com/Video/playerIndex?id=2803149