Download HTM304-9-BIKM

Introduction to Management Information Systems
Chapter 9 Business Intelligence and
Knowledge Management
HTM 304
Fall 07
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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Business Problem
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
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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 information
Reporting 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 tools
Reporting 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 Systems
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
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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
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equation to enable bankers to assess new loan applicants
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
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R
F
M
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”
East
Central
West
50 40 90
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50 40 90
60 60 120
60 60 120
60 60 120
100 80 140
100 80 140
100 80 140
2005
2004
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Nuts Screws Bolts
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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 Analysis
Group 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?
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Buying LV handbag in Miami?