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Chapter 9

Business Intelligence Systems

What is Business Intelligence?

Information that contains patterns, relationship, trends, etc.

Intelligent processing: The information needs to be found or produced

Challenge: There is not too much data for humans to analyze.

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Business Intelligence Tools

Reporting Tools – Wagemart Lab is a great example reduced a complex database into Total Cost and Average Rating

Data Mining Tools – Market Basket Lab 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 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?

Computers gather and store enormous amounts of data. 403 petabytes of new data were created in 2002.

An estimated 2,500 petabytes, or 2.5 exabytes of new data were generated in 2007.

Business intelligence is comprised of information that contains patterns, relationships, and trends about customers, suppliers, business partners, and employees.

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?

A BI tool is a computer program that implements the logic of a particular procedure or process.

A BI application uses BI tools on a particular type of data for a particular purpose.

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?

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?

Fig 9-5 Sales Data Filtered to Show Repeat Customers

This figure shows even better information that’s been filtered and formatted according to specific criteria.

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Q3 – What are typical reporting applications?

RFM Analysis 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?

Online Analytical Processing (OLAP) is more generic than RFM 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?

Fig 9-8 OLAP Product Family & Store Location by Store Type

This figure shows how you can alter the format of a report to provide users with the information they need to do their jobs.

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Q3 – What are typical reporting applications?

Fig 9-9 OLAP Product Family & Store Location by Store Type, Drilled Down to Show Stores in California

This figure shows how you can divide data into more detail by drilling down through the data.

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Q3 – What are typical reporting applications?

Fig 9-10 Role of OLAP Server & OLAP Database

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

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Q4 – What are typical data-mining applications?

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?

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?

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)

ABCD EF = P(ABCDEF)/P(ABCD)

decision tree

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Q4 – What are typical data-mining applications?

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.

Pivot Chart Lab combines Data Mining + OLAP

Pivot Chart is an OLAP report that helped us find important attributes, cutoffs and patterns

But eventually we used the results to make a hypothesis to help make predictions

Fig 9-14 Credit Score Decision Tree

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?

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

© Pearson Prentice Hall 2009

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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