Introduction to Management Information

Systems

Chapter 9 Business Intelligence and

Knowledge Management

HTM 304

Fall 07

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Business Intelligence System

Chapter 7 & 8: Operational data and information. Information Flow

designed to facilitate corporate daily operation

Tracking orders, inventories, and shipments

Managing account receivables, payables

Storing employee information, addresses, HR benefits

Chapter 9: Systems that takes daily operational data as input and

produce higher level “business intelligence”

Analyzing order patterns, data relationships, clusters for strategic

planning and forecasting

Analyzing customer relationships, identifying potential business problems

and business opportunities

GPS for citation appeal?

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Carbon Creek Gardens

Mary Keeling retails trees,

plants, flowers, soil,

fertilizer, etc.

Ran into a good customer –

hasn’t shopped in a year

Salesperson was rude

Mary realizes she needs

better information

Business Problem

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Challenge of Data Analysis

Data Volume Facts:

Study at UC-Berkeley: Total of 403 petabytes new data created in 2002

403 petabytes = all printed material ever written

Printed collection of Library of Congress = 0.01 petagytes

400 petabytes ~ Collection of 40,000 Library (size of LOC)

Directly related to Moore’s Law

Today, storage nearly unlimited

Drowning in data & starving for information!

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2.5 Exabyte by 2007!

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Business Intelligence Tools

BI tools: search data to find patterns or informationReporting tools:

Read and process data, produce and deliver reports

Used primarily for assessing the past and current situation

Data-Mining tools: Process data using sophisticated statistical techniques

Searching for patterns and relationships among data

In more cases, used to predict (give probabilities of loan default, id theft, etc.)

Differences of reporting and data-mining toolsReporting tools use simple operations like sorting, group, and summing to provide description of existing data (mainly descriptive statistics)

Data-mining tools use sophisticated techniques (including inferential statistics)

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BI System:

The IS that incorporates BI tools

Purpose:

to provide the right information,

to the right user,

at the right time.

Help user accomplish goals and objectives by producing

insights that lead to actions

BI Systems

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Two types of BI systems

Reporting System

Use reporting tool to produce status report: generate report

showing customer cancelled important order

Deliver the report to the right person at the right time: alerts

salesperson with bad customer news in time to try to alter the

customer’s decision

Data-Mining System

Use data-mining tool to predict the events and probabilities:

Create equation to compute the probability that customer will

default on loan

Deliver the probability to the right person at the right time: Use

equation to enable bankers to assess new loan applicants

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Reporting System

Purpose:

To create meaningful information from disparate data sources

and deliver information to proper user on timely basis.

Reporting system normally generate information from

data through 4 operations

Filtering

Sorting

Grouping

Making simple calculations

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Example: From Data to Report

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Example of Online Report Systems

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Components of a reporting system

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Report Mode

Push report

Organizations send push report to users according to preset

schedule

Users receive report automatically

Pull report

Requested by user

User goes to Web portal or digital dashboard and clicks button to

have reporting system produce and deliver report

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One Solution to Carbon Creek Gardens

RFM Analysis report: analyzing and ranking customers

according to purchasing patterns

Simple technique considers how

-- how recently (R) customer ordered

-- how frequently (F) customer orders

-- how much money (M) customer spends per order

R F M

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RFM Analysis

To produce RFM score, program first sorts customer

purchase records by date of most

recent (R) purchase

Divides customers into five groups and

scores customers 1-5

Top 20% of recent orders given R score 1 (highest)

Re-sorts customers on order frequency

Top 20% of most frequent given F score of 1 (highest)

Sorts customers according to amount spent

20% of biggest spenders given M score of 1 (highest)

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Example of RFM Analysis output

Exercise: Who should be your major marketing force target? Write down your analysis to explain why.

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Data Warehouses & Data Marts

Data Warehouses and Data Marts:Prepare, store & manage data for data mining and other analyses

Report systems report up-to-date status information

Cumulative reports stored in warehouse can be used for further analysis.

multi-dimensional “data cube”50 40 90

60 60 120

100 80 140

50 40 90

60 60 120

100 80 140

50 40 90

60 60 120

100 80 140

Nuts Screws Bolts

East

Central

West 2005 20042003

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Data-Mining Systems

Application of Statistical Techniques

to find patterns and relationships

among data

to classify and predict.

Represents a convergence of Disciplines

Statistics

Mathematics

Artificial Intelligence

Machine-learning fields in Computer Science

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Example of Data Mining

Customer AnalysisGroup 1 – average age 33, owns at least 1 laptop, 1 PDA, drives high-end SUV, buys expensive children’s playing equipment

Group 2 – average age 64, owns vacation property, plays golf, buys expensive wines and designer children’s clothing

ID Theft Risk:good credit rating

live in San Diego

outstanding home loan mortgage

rarely travel, grocery shopping weekend, weekly gas refill

Alert? When

Hotel check-in at Las Vegas?

Buying LV handbag in Miami?