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Chapter 9
Business Intelligence Systems
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What is Business Intelligence?
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Information that contains patterns, relationship, trends, etc.
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Intelligent processing: The information needs to be found or
produced
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Challenge: There is not too much data for humans to analyze.
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Business Intelligence Tools
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Reporting Tools – Wagemart Lab is a great example
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Data Mining Tools – Market Basket Lab
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reduced a complex database into  Total Cost and Average Rating
Found association rules with the highest confidence and quality
Walmart likely has a Petabytes of data
1,000,000,000,000,000 bytes
Online Analytical Processing (OLAP) – Pivot Chart
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Sliced the data by dimension to find relationships
Drilled down to find more subtle patterns
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Q1 – Why do organizations need business intelligence?
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Computers gather and store enormous amounts of data. 403
petabytes of new data were created in 2002.
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An estimated 2,500 petabytes, or 2.5 exabytes of new data were
generated in 2007.
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Business intelligence is comprised of information that contains
patterns, relationships, and trends about customers, suppliers,
business partners, and employees.
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Business intelligence systems process, store, and provide useful
information to users who need it, when they need it.
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Q2 – What business intelligence systems are available?
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A BI tool is a computer program that implements the logic of a particular
procedure or process.
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A BI application uses BI tools on a particular type of data for a
particular purpose.
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A BI system is an information system that has all five components
(hardware, software, data, procedures, people) that delivers the results
of a BI application to users.
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Q3 – What are typical reporting applications?
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Basic reporting operations
include sorting, grouping,
calculating, filtering, and
formatting.
This figure shows raw data
before any reporting operations
are used.
Fig 9-2 Raw Sales Data
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Q3 – What are typical reporting applications?

This figure shows even better information that’s been filtered and
formatted according to specific criteria.
Fig 9-5 Sales Data Filtered to Show Repeat Customers
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Q3 – What are typical reporting applications?
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RFM Analysis
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R = how recently a
customer purchased your
products
F = how frequently a
customer purchases your
products
M = how much money a
customer typically spends
on your products
The lower the score, the
better the customer.
Fig 9-6 Example of RFM Score Data
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Q3 – What are typical reporting applications?
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Online Analytical Processing (OLAP) is more generic than RFM
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dynamic ability to sum, count, average
Reports, also called OLAP cubes, use
Dimensions which are characteristics of a measure. In the figure below a
dimension is Product Family.
Fig 9-7 OLAP Product Family by Store Type
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Q3 – What are typical reporting applications?

This figure shows how you can alter the format of a report to provide
users with the information they need to do their jobs.
Fig 9-8 OLAP Product Family & Store Location by Store Type
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Q3 – What are typical reporting applications?

This figure shows how you can divide data into more detail by drilling
down through the data.
Fig 9-9 OLAP Product Family & Store Location by Store Type, Drilled Down to Show
Stores in California
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Q3 – What are typical reporting applications?

OLAP servers are special products that read data from an
operational database, perform some preliminary calculations,
and then store the results in an OLAP database
Fig 9-10 Role of OLAP Server & OLAP Database
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Q4 – What are typical data-mining applications?
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Data Mining
statistical techniques to find patterns and relationships
classification and prediction.
Data mining techniques are a blend of statistics and mathematics,
and artificial intelligence and machine-learning.
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Q4 – What are typical data-mining applications?
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Unsupervised data-mining characteristics:
 No model or hypothesis exists before running the
analysis
 Analysts apply data-mining techniques and then
observe the results
 Analysts create a hypotheses after analysis is
completed
 Cluster analysis, a common technique in this
category groups entities together that have similar
characteristics
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Q4 – What are typical data-mining applications?

Supervised data-mining characteristics:
 Analysts develop a model prior to their analysis
 Apply statistical techniques to estimate parameters
of a model
 Regression analysis is a technique in this category
that measures the impact of a set of variables on
another variable
 Neural networks predict values and make
classifications
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Q4 – What are typical data-mining applications?
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Market-Basket Analysis is a data-mining tool for
determining sales patterns.
helps businesses create cross-selling opportunities.
 Support—the probability that two items will be
purchased together
 P(AB)
 Confidence—a conditional probability estimate
 A B
=
P(AB)/P(A)
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ABCD  EF = P(ABCDEF)/P(ABCD)
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decision tree
>
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Q4 – What are typical data-mining applications?
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A decision tree is a hierarchical arrangement of criteria that predicts
a classification or value.
It’s an unsupervised data-mining technique that selects the most
useful attributes for classifying entities on some criterion.
It uses if…then rules in the decision process.
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Pivot Chart Lab combines Data Mining + OLAP
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Pivot Chart is an OLAP report that helped us find important attributes,
cutoffs and patterns
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But eventually we used the results to make a hypothesis to help make
predictions
Fig 9-14 Credit Score Decision Tree
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Q5 – What is the purpose of data warehouses and data marts?
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Q5 – What is the purpose of data warehouses and data marts?

Here’s the difference between a
 data warehouse and a
 data mart
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Q6 – What are typical knowledge-management applications?
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The characteristics and goals of knowledge
management applications and systems are to
 Create value for an organization from its
intellectual capital
 Share knowledge among and between
employees, managers, suppliers, and
customers
 Include knowledge that is known to exist in
documents or employees’ brains
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Q6 – What are typical knowledge-management applications?

The characteristics and goals of knowledge
management applications and systems are to
 Foster innovation by encouraging the free
flow of ideas
 Improve customer service by streamlining
response times
 Boost revenues by getting products and
services to market faster
© Pearson Prentice Hall
2009
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