Transcript
BAILEY LAPOINTEBOB SWAIN
KIMBERLY TREMBLAY
NOVEMBER 30, 2009BUS 345
Business Intelligence
Outline
A brief historyData MartData MiningBusiness Intelligence ToolsSASBusiness UseDepartment Use
History of BI
1958
• BI defined by Hans Peter Luhn
1960s
• Data ware-housing terms defined
1970s
• Beginning of the End-User Computer Era
1980
• Data Mining emerges
•Definition of BI - "the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal."
History of BI
1983
• Teradata intoduces a database management system
1980s
• Concept of Data Warehouse intoduced
1989
• Definition of BI adapting to changes
1990
• Red Brick Systems introduces database management specifically for data warehousing
•BI as an Umbrella Term - "concepts and methods to improve business decision making by using fact-based support systems."
History of BI
1991
• Prism Solutions introduces software for developing a data warehouse
1995
• The Date Warehousing Institute is founded
1996• The Data
Warehouse Tool Kit is published
1997
• Oracle 8 is released
•The Oracle Database is a relational database management system produced and marketed by Oracle Corporation. As of 2009, Oracle remains a major presence in database computing.
Data Mart
“a data mart is a data repository that may derive from a data warehouse or not and
that emphasizes ease of access and usability for a particular designed purpose.”
-SearchSQLServer.com
Data Mining
Customer RelationsHuman Resources
MarketingFraud Detection
Business Intelligence Tools
Many different companiesStatistically driven
Accessible GUIs
Sample SAS Code
Copyright © 2008, SAS Institute Inc.
Business Use
Business intelligence is exclusive to a company. Large or small Various industries
Example: Tom’s of Maine, 150 employees, 90 oral & body care products, 40,000 retail outlets worldwide
Source: http://www.tomsofmaine.com/
Department Use
Sales Department Sales trends Potential customers Profitable employees Cost efficiency Strategy success
Department Use
Marketing Department Demographic customer profile Plan effectiveness Efficient budget use Alignment to customer
Department Use
Research and Development Evaluate Risk Identify opportunities Manage projects Predict pay-off yield
Department Use
Supply Chain Management Relative product cost Inventory level Receivables Payables Network efficiency
Department Use
Benchmarking Standardization Goals
Developing Monitoring
Competition
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