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ACCTG 6910
Building Enterprise &
Business Intelligence Systems
(e.bis)
From Information Management
to Knowledge Management
Olivia R. Liu Sheng, Ph.D.
Emma Eccles Jones Presidential Chair of Business
1
Business Intelligence
• We are drowning in data, but starving for
knowledge
• Business intelligence (BI) is knowledge
extracted from data to support better
business decision making.
2
Data, Information,
Knowledge
• Data is a set of discrete, objective facts about
events. E.g., Tony’s academic record:
– Fall 2001: 3 A’s
– Spring 2002: 3 A’s, 2 B’s and 1 C
– Fall 2002: 3 A’s and 4 C’s
• Information is meaningful data. E.g., how is
Tony doing academically?
• Knowledge is hidden patterns extracted from
data. E.g., how to improve Tony’s or any
one’s academic performance?
3
Data, Information,
Knowledge
Online bookstore Example:
July’s revenue is
$2 million
Information
July’s sale is
bad.
Knowledge
Suggest marketing
strategy to boost
sales
Data
4
Information Age :
What is lacking here
• The Reason: Operational vs. Strategic Use of Data
– Operational Use of Data:
• The goal is to automate the business processes.
• Major concern is speed and efficiency in transaction processing
– Strategic Use of Data:
• The goal is to collect, acquire, and use the knowledge extracted from
data
• Major concern is flexibility and responsiveness in decision support
5
Building BI Systems
• Data Warehouse: To organize, store and
publish integrated decision support data
• Data mining: Techniques to extract
hidden patterns from data.
6
What’s the Excitement About
Data Warehouse Technology?
The top three most important technologies ranked by
IT managers in 2000-2001 (Recent surveys by
The Data Warehousing Institute and Deloitte Research,
http::/www.sas.com/news/feature/21aug01/
dwdemand.html/)
1. Internet
2. Data warehousing
3. E-commernce
7
What’s the Excitement about
Data Mining Technology?
10 emerging technologies that will change the
world (MIT’s Magazine of Innovation, 2001
Annual Innovation Issue)
• Brain-machine
interfaces
• Flexible transistors
• Data mining
• Digital rights
management
• Biometrics
• Natural language
processing
• Microphotonics
• Untangling code
• Robot design
• Microfluidics
8
Many Applications
•
•
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•
•
•
•
•
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•
•
•
•
Target Marketing
Credit Scoring
Sales Forecasting
Promotion Analysis
Distribution Channel Analysis
Customer Profiling
Customer Profitability Analysis
Cross Sell/Up Sell
Help Desk Problem Resolution
Customer Service Automation
Network Forecasting
Tariff Modeling
E-government
•
•
•
•
•
•
•
•
•
•
•
•
Fraud detection
Security Management
Product/Product Line Profitability
Merchandise Planning
Resource Management
Operations Management
Capacity Management
Store/Branch Performance
Analysis
Store/Branch Site Selection
Diagnosis decision support
Gene and protein analysis
Homeland Security
9
Course Objectives
•
•
•
•
•
Basic concepts and techniques
GUI tools-oriented implementation
Real world oriented learning
A few managerial issues
Promoting data warehouse and data
mining career interests and opportunities
10
The Course
IS Not
• A general ebusiness
technology course
• A technical Java and
web programming
course
• A purely managerial
course
IS
• A course specifically on business
intelligence technologies for
ebusiness
• A hands-on course with DB and
data mining implementations
using tools
• A course emphasizing on
business and data analysis and
good project management
practices
11