This document discusses decision support systems (DSS) and online analytical processing (OLAP). It defines DSS as interactive computer systems that help managers make decisions, using tools like analytical models, databases, and modeling processes. OLAP enables examining and manipulating large amounts of consolidated data from different perspectives. Both DSS and OLAP support analysis of operational data, markets, sales, and customers to help with decisions around pricing, forecasting, and risk.

1. DECISION SUPPORT
SYSTEM
and
OLAP
PRESENTED BY:
BHUWNESHWAR
PANDAYA

2. DEFINITION
 DSS give direct computer support to
managers during the decision-making
process.
 DECISION SUPPORT SYSTEM
provides interactive ad hoc support for
the decision making process of
managers and other business
professionals.
 EXAMPLES: product pricing, profitability
forecasting, and risk analysis systems
etc.

3. ……..
 DECISION SUPPORT SYSTEMS are
computer-based information systems that
provide interactive information support to
managers and business professionals
during the decision making process.
 DSSs use:
1) Analytical models,
2) Specialized databases,
3) A decision makers own insights and
judgments,
4) An interactive, computer based modeling
process to support semi structured
business decisions.

4. Components of DSS
Legacy Web Other
software browser softwares
 ..
User Interface Functions
Hyperlinked Multimedia, 3D Visualization
Model Management Functions
Analytical Modeling, Statistical Analysis
Data Management Functions
Data extraction, validation, Sanitation, integration, and
replication
Operation Market Sales Customer
al Data Data Data a/c data

5. Difference b/w DSS & MIS
MIS DSS
Provide information about Provide information and
•Decision support the performance of the decision support
provided organization techniques to analyze
specific problems.
Periodic, exception, Interactive inquiries and
• Information form demand, push reports and responses
and frequency responses
Prespecified, fixed format Ad hoc, flexible and
• Information format adoptable format
Information Information produced by Information produced by
processing extraction and manipulation analytical modeling of
methodology of business data business data.

6. Using DSS:
 A decision support system involves an
interactive analytical modeling process.
 There are four basic types of analytical
modeling activities are involved in using
a DSS:
I. What-if analysis ,
II. Sensitivity analysis,
III. Goal-seeking analysis, and
IV. Optimization analysis.

7. ….
Type of Analytical
Modeling
Activities and examples
Observing how changes to selected variables
affect other variables.
What-if analysis Example: what if we cut advertising by 10%?
What would happen to sales.
Observing how repeated changes to a single
variable affect other variables.
Sensitivity analysis Example: Let’s cut advertising by 100%
repeatedly so we can see its relationship to
sales.
Making repeated changes to selected variables
Goal-seeking analysis until a chosen variable reaches to a target value.
Example: Let’s try increases in advertising until
sales reach
Finding an optimum value for selected
variables, given certain constraints.
Optimization analysis Example: What’s the best amount of advertising
to have, our budget and choice of media?

8. Online Analytical Processing [OLAP]
 OLAP enables managers to examine and
manipulate large amounts of detailed and
consolidated data from many perspectives.
 OLAP involves analyzing complex
relationship among thousands or even
millions of data items stores in a data marts,
data warehouses and multidimensional
databases to discover patterns and trends
etc.
 It provides fast answers to complex queries

9. ….
.
OLAP server
Corporate
Client PCs databases
Multi-
Dimensional
databases
Spreadsheet data are retrieved Operational databases
Statistical packages from corporate databases Data marts
WEB-enabled and staged in an OLAP Data warehouse
OLAP software

10. OLAP involves –
 Consolidation: the aggregation of data.
e.g. Data about sales offices can be rolled up to the
district level, and district level data can be rolled up
to provide state-level perspective.
 Drill-down: the reverse direction and automatically
display.
e.g. The sales by individual products or sales reps
that make up a region’s sales totals could be easily
accessed.
 Slicing and Dicing: ability to look at the database
from different viewpoints. Slicing and dicing is often
performed along a time axis to analyze trends and
find time based patterns in the data.

11. OLAP examples….
Common business areas where OLAP can
solve complex problems include:
 Marketing and sales analysis
 Database marketing
 Budgeting
 Financial reporting and consolidation
 Profitability analysis
 Quality analysis etc.

12. .
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