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Business Intelligence
What is BI?
• Business Intelligence is a broad category
of applications and technologies for
gathering, storing, analyzing, and
providing access to data to help managers
make better business decisions
BI Evolution
Group Memory
Corporate Intranets & Decision Support Portals
Extranets & Interenterprise portals
E-Commerce & Click Stream Analysis
Business Intelligence
Business Intelligence
Technologies
Increasing potential
to support
business decisions
Decision
Making
Data Presentation
Visualization Techniques
Data Mining
Information discovery
End User
Business
Analyst
Data
Analyst
Data Exploration
OLAP, DSS, EIS, Querying and Reporting
Data Warehouses / Data Marts
DB
Admin
Data Sources
Paper, Files, Information Providers, Database Systems, OLTP
Business Processes
(E.g. Banking)
(agreement with a credit card)
(grant a loan)
(transaction on
bank account
decisional
processes
management
processes
operational
processes
DSS vs. EIS
• Decision Support Systems (DSS) and Executive
Information Systems (EIS): information systems
designed to help managers in making decisions.
• Different, yet interrelated applications
• A DSS focuses on a particular decision, whereas
an EIS provides a much wider range of
information (e.g., information on financials, on
production history, and on external events).
• DSSs appeared in the 1970s
• EISs appeared in the 1980s.
Where does an EIS or DSS get its
data?
• The EISs and DSSs often lacked a strong
database component.
• Most organizational information gathering was
(and is) directed to maintaining current (preferably
on-line) information about individual transactions
and customers.
• Managerial decision making requires
consideration of the past and the future, not just
the present.
• New databases, called data warehouses, were
created specifically for analytic use
Data Warehousing
• The process of collecting and storing large
quantities of historical data from different
sources in one CENTRAL location
• Data mining is the analysis of data to
establish relationships and identify
patterns
A Data Warehouse is ...
A data warehouse is a
– subject-oriented,
– integrated,
– time-variant, and
– nonvolatile
collection of data in support
of management’s decisions
… subject-oriented ...
• The data in the warehouse is
defined and organized in business
terms, and is grouped under
business-oriented subject
headings, such as
– customers
– products
– sales
rather than individual transactions.
• Normalization is not relevant.
… integrated ...
• The data warehouse contents are defined such
that they are valid across the enterprise and its
operational and external data sources
Data
warehouse

Operational
systems
The data in the warehouse should be



clean
validated
properly integrated
… time-variant ...
• All data in the data warehouse is
time-stamped at time of entry into
the warehouse or when it is
summarized within the warehouse.
• This chronological recording of data
provides historical and trend
analysis possibilities.
• On the contrary, operational data is
overwritten, since past values are
not of interests.
… nonvolatile ...
• Once loaded into the data
warehouse, the data is not updated.
• Data acts as a stable resource for
consistent reporting and
comparative analysis.
• This is different to operational data
that is updated (inserted, deleted,
modified).
Which Data in the Warehouse?
• A data warehouse contains five
types of data:
– Current detail data
– Old detail data
– Lightly summarized data
– Highly summarized data
– Metadata
How does data get into a data
warehouse?
Checking Account System
Jane Doe (name)
Female (gender)
Bounced check #145 on 1/5/95
Opened account 1994
Savings Account System
Jane Doe
F (gender)
Opened account 1992
Investment Account System
Jane Doe
Owns 25 Shares Exxon
Opened account 1995
 Operational
data
Customer
Jane Doe
Female
Bounced check #145
Married
Owns 25 Shares Exxon
Customer since 1992
 data
warehouse
Cost and Size of a Data
Warehouse
• Data warehouses are expensive
undertakings (mean cost: $2.2
million).
• Since a data warehouse is
designed for the enterprise it has a
typical storage size running from 50
Gb to over a Terabite.
The Data Mart
• A lower-cost, scaled-down version of the
data warehouse designed for the
strategic business unit (SBU) or
department level.
• An excellent first step for many
organizations.
• Main problem: data marts often differ
from department to department.
• Two approaches:
– data marts  enterprise-wide system
– data warehouse  data marts
An Architecture for Data Warehousing
metadata
EIS
DSS
external sources
.
OLAP
data
warehouse
data
mining
query
operational
databases
data mart
On-Line Analytical Processing
(OLAP)
• Decision support software that allows the user to
quickly analyze information that has been
summarized into multidimensional views and
hierarchies.
• E.g., OLAP tools can be used to perform trend
analysis on sales and financial information. They
enable users to drill down into masses of sales
statistics in order to isolate products that are the
most volatile.
On-Line Analytical Processing
(OLAP)
Market
• Basic idea: users should be able to
manipulate enterprise data models
across many dimensions to
understand changes that are
occurring.
• Data used in OLAP should be in the
form of a multi-dimensional cube.
Product
Dimensional Hierarchies
• Each dimension can have a hierarchy
Year
Country
Type of product
Month
State
Product
Week
City
Item
Day
Store
Star Schema
Fact Table
Market Dimension
STORE KEY
Store Desc.
City
State
District ID
District Desc.
Region ID
Region Desc.
Regional Mgr.
Level
STORE KEY
PRODUCT KEY
PERIOD KEY
Dollars
Units
Price
Product Dimension
PRODUCT KEY
Product Desc.
Brand
Color
Size
Manufacturer
Time Dimension
PERIOD KEY
Period Desc.
Year
Quarter
Month
Day
Journal Question 8
Do you think that business managers in
Doha are embracing Business Intelligence
applications? Why or why not?