1. Introduction to Management Information Systems Chapter 9 Business Intelligence and Knowledge Management
HTM 304
Fall 07

2. 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?

3. 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

4. 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
Who gives defined the two types? US Census Bureau.
Drowning in data & starving for information!

5. 2.5 Exabyte by 2007!

6. 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)

7. BI Systems BI System: Purpose:
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

8. Two types of BI systems Reporting System Data-Mining 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

9. 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

10. Example: From Data to Report

11. Example of Online Report Systems

12. Components of a reporting system

13. Report Mode Push report Pull 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

14. 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 M F

15. 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)

16. Example of RFM Analysis output
Exercise:
Who should be your major marketing force target?
Write down your analysis to explain why.

17. 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
120
100 80
140 50 40 90 60
120
100 80
140 50 40 90 60
120
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140
East
Central
West
2005
2004
2003
Nuts Screws Bolts

18. 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

19. Example of Data Mining Customer Analysis ID Theft Risk:
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?
Buying LV handbag in Miami?