HTM 304 Fall 07

1. Introduction to Management Information SystemsChapter 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 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: • 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 • 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 • 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 M R 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” 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 • 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?