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

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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?