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Chapter 9
Business Intelligence and
Information Systems for
Decision Making
Study Questions
Q1: Why do organizations need business intelligence?
Q2: How do business intelligence (BI) systems provide
competitive advantages?
Q3: What problems do operational data pose for BI
systems?
Q4: What are the purpose and components of a data
warehouse?
Q5: What is a data mart and how does it differ from a data
warehouse?
Q6: What are the characteristics of data-mining systems?
Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall
9-2
Q1: Why Do Organizations Need Business
Intelligence?
Businesses
collect
massive
amounts of
data
•Data communications and data storage
essentially free
•2 million emails, 31,000 text messages,
and 162,000 instant messages
transmitted every second (2007)
•2010 total online computer storage about
600 exabytes
•70 exabytes equivalent to 14 times total
number of words ever spoken by humans
Reveal important
Such as, evidence that
patterns of relationships
someone is going to
and valuable information
default on a loan
buried in that data
Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall
9-3
How Big Is an Exabyte?
Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall
9-4
Q2: How Do Business Intelligence Systems
Provide Competitive Advantages?
Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall
9-5
Q2: How Do Business Intelligence Systems
Provide Competitive Advantages? (cont’d)
Business
Intelligence
Tools
vs.
Business
Intelligence
Systems
BI Tools
(software)
BI Systems
Crystal
Reports
Reporting
System
SPSS
Datamining
System
Clementine
Knowledge
Mgmt System
SharePoint
Server
Expert System
Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall
9-6
Q3: What Problems Do Operational Data
Pose for BI Systems?
•
Raw data usually unsuitable for sophisticated
reporting or data mining
Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall
9-7
Q4: What Are the Purpose and Components
of a Data Warehouse?
Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall
9-8
Sources of Data for Data Warehouses
Internal operations systems
External data purchased from
outside sources
Data from social networking, usergenerated content applications
Metadata concerning data stored in
data warehouse
Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall
9-9
Q5: What Is a Data Mart and How Does It
Differ from a Data Warehouse?
Data Mart => Collection of data created to address
needs of a particular:
•Business function
•Problem
•Opportunity
Marts created from data extracted from data
warehouse
Data mart is like a retail store in a supply chain
Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall
9-10
Components of a Data Mart
Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall
9-11
Q6: What Are the Characteristics of
Data-Mining Systems?
Data mining—application of statistical techniques to find patterns and
relationships in body of data for purpose of classifying and predicting
Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall
9-12
Unsupervised Data Mining
Analysts do not
create model before
running analysis
Hypotheses created
after analysis as
explanation for
results
Apply data-mining
technique and
observe results
Technique:
•Cluster analysis to
find groups with
similar characteristics
Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall
9-13
Supervised Data Mining
Model developed before analysis
• Statistical techniques used to estimate
variable parameters
• Regression analysis—measures impact of
set of variables on one another
Example:
CellPhoneWeekendMinutes =
12 X (17.5 X CustomerAge) +
(23.7 X NumberMonthsOfAccount)
Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall
9-14
Supervised Data Mining (cont’d)
Used for predicting
values and making
classifications
See
www.kdnuggets.
com to learn
more
Neural
networks
Complicated
set of nonlinear
equations
Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall
9-15