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Transcript of Chapter 9 Competitive Advantage with Information Systems for Decision Making © 2008 Pearson...
Chapter 9
Competitive Advantage with Information Systems for Decision Making
© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
9-2 © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
This Could Happen to You
How can information systems improve decision making?
– Business processes and decision making are closely allied– IS facilitate competitive strategy by adding value to or
reducing costs of processes– IS adds value or reduces costs by improving quality of
decisions
Can an information system assist in the selection of a vendor based on past performance?
9-3 © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
Study Questions
How big is an exabyte, and why does it matter? How do business intelligence systems 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? How does knowledge from this chapter help you at DSI?
9-4 © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
How Big Is an Exabyte?
Figure 9-1
9-5 © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
Why Does It Matter?
Storage capacity is increasing as cost decreases– Nearly unlimited
Over 2.5 exabytes of data have been created– Exponential growth both inside and outside of
organizations– Can be used to improve decision making
9-6 © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
Business Information (BI) Systems
Provide information for improving decision making
Primary systems:– Reporting systems– Data-mining systems– Knowledge management systems– Expert systems
9-7 © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
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
9-8 © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
Data-Mining Systems
Process data using statistical techniques– Regression analysis– Decision tree analysis
Look for patterns and relationships to anticipate events or predict outcomes– Market-basket analysis– Predict donations
9-9 © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
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
9-10 © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
Expert Systems
Encapsulate experts’ knowledge Produce If/Then rules Improve diagnosis and decision making in
non-experts
9-11 © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
Problems with Operational Data
Raw data usually unsuitable for sophisticated reporting or data mining
Dirty data Values may be missing Inconsistent data Data can be too fine or too coarse Too much data
– Curse of dimensionality– Too many rows
9-12 © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
Guide: Counting and Counting and Counting
Product managers wanted data miners to analyze customer clicks on Web page– Determine preferences for product lines– Data miners wanted to sample; product managers
wanted all data– Would take days to calculate
Sampling is acceptable– Must be appropriate– Saves time and money
9-13 © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
Data Warehouse
Used to extract and clean data from operational systems
Prepares data for BI processing Data-warehouse DBMS
– Stores data– May also include data from external sources– Metadata concerning data stored in data-warehouse meta
database– Extracts and provides data to BI tools
9-14 © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
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
9-15 © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
Data Mining
Application of statistical techniques to find patterns and relationships among data
Knowledge discovery in databases (KDD) Take advantage of developments in data
management Two categories:
– Unsupervised– Supervised
9-16 © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
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
Example: cluster analysis
9-17 © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
Supervised Data Mining
Model developed before analysis Statistical techniques used to estimate
parameters Examples:
– Regression analysis– Neural networks
9-18 © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
Ethics Guide: Data Mining Real World
Data mining is different from the way it is shown in textbooks
– Data is dirty– Values are missing or outside of ranges– Time value make no sense– You add parameters as you gain knowledge, forcing
reprocessing– Overfitting– Based on probabilities, not certainty– Seasonality problem
9-19 © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
Using This Knowledge to Close the Gap
Reporting system could process supplier information to rank quality
Data-mining system could search for patterns to predict delivery delays or quality problems
Knowledge management system could rank suppliers or share experiences
Expert system could contain rules for supplier selection
Data mart could maintain information on inbound logistics and manufacturing
9-20 © 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
Active Review?
How big is an exabyte, and why does it matter? How do business intelligence systems 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? How does knowledge from this chapter help you at DSI?