Transcript Slide 1

Chapter 9
Competitive Advantage
with Information
Systems for Decision
Making
© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
This Could Happen to You
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How can information systems improve decision
making?
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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?
© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
Study Questions
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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?
© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
How Big Is an Exabyte?
Figure 9-1
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© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
Why Does It Matter?
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Storage capacity is increasing as cost
decreases
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Over 2.5 exabytes of data have been created
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Nearly unlimited
Exponential growth both inside and outside of
organizations
Can be used to improve decision making
© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
Business Information (BI) Systems
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Provide information for improving decision
making
Primary systems:
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Reporting systems
Data-mining systems
Knowledge management systems
Expert systems
© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
Reporting Systems
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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
Data-Mining Systems
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Process data using statistical techniques
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Look for patterns and relationships to
anticipate events or predict outcomes
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Regression analysis
Decision tree analysis
Market-basket analysis
Predict donations
© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
Knowledge-Management Systems
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Create value from intellectual capital
Collects and shares human knowledge
Supported by the five components of the
information system
Fosters innovation
Increases organizational responsiveness
© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
Expert Systems
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Encapsulate experts’ knowledge
Produce If/Then rules
Improve diagnosis and decision making in
non-experts
© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
Problems with Operational Data
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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
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Curse of dimensionality
Too many rows
© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
Guide: Counting and Counting and
Counting
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Product managers wanted data miners to
analyze customer clicks on Web page
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Sampling is acceptable
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Determine preferences for product lines
Data miners wanted to sample; product managers
wanted all data
Would take days to calculate
Must be appropriate
Saves time and money
© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
Data Warehouse
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Used to extract and clean data from operational
systems
Prepares data for BI processing
Data-warehouse DBMS
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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
© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
Data Mart
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Data collection
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Created to address particular needs
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Smaller than data warehouse
Users may not have data management expertise
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Business function
Problem
Opportunity
Knowledgeable analysts for specific function
© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
Data Mining
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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:
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Unsupervised
Supervised
© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
Unsupervised Data Mining
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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
© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
Supervised Data Mining
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Model developed before analysis
Statistical techniques used to estimate
parameters
Examples:
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Regression analysis
Neural networks
© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
Ethics Guide: Data Mining Real
World
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Data mining is different from the way it is shown in
textbooks
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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
© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
Using This Knowledge to Close
the Gap
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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
© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke
Active Review?
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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?
© 2008 Pearson Prentice Hall, Experiencing MIS, David Kroenke