Evolution of Decision Support
Systems
Data warehouse
 Data
warehouse ?
 Why Data warehouse ?
 What for the Data warehouse ?
The Evolution

1960 (the world of computation consisted of
creating individual applications that were run
using master files)
 1965 (complexity of maintenance and
development,synchronization of data,
hardware)
 1970 (database-a single source of data of all
processing)
 1975 (online,high performance transaction
processing)
 1980 (Pcs, 4GL technology)
Problems with the Naturally
Evolving Architecture

Data Credibility

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No time basis of data
The algorithmic differential of data
The levels of extraction
The problem of external data
No Common source of data from the beginning
Productivity
 Inability to transform data into information
The Architected Environment

Level of the architecture

Operational


Atomic/data warehouse


Most granular, time variant, integrated, subject oriented,
some summary
Departmental

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Detail, day to day, current valued, high probability of
access, application oriented
Parochial, some derived; some primitive, typical depts
(acct, marketing, engineering, actuarial, manufacturing)
Individual

Temporary, ad hoc, heuristic, non repetitive, pc,
workstation based
Who is the user?
 What
is the different between users of
the data warehouse and users of
operational environment
 Why need DSS analyst ?
The Development Life Cycle


The different between classical SDLC and Data Warehouse
SDLC
Classical SDLC
 Requirement gathering
 Analysis
 Design
 Programming
 Testing
 Integration
 Implementation

Data warehouse SDLC
 Implement warehouse
 Integrate data
 Test for bias
 Program against data
 Design DSS system
 Analyze results
 Understand
requirements
Patterns of Hardware
Utilization

Major difference between the operational and
the data warehouse environments is the
pattern of hardware utilization that occurs in
each environment.
 There are peaks and valleys in operational
processing, but ultimately there is a relatively
static and predictable pattern of hardware
utilization.
 There is an essentially different pattern of
hardware utilization in the data warehouse
environment
Setting the stage for
Reengineering

A very beneficial side effect of going from the
production environment to the architected, data
warehouse environment.
Monitoring the data
Warehouse Environment

Two operating components are monitored on a regular
basis : the data residing in the data warehouse and usage
of the data.
 Some of the important results that are achieved by
monitoring
 Identifying what growth is occurring, where the growth is
occcurring and at what rate the growth is occuring

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Identifying what data is being used
Calculating what response time the user is getting
Determining who is actually using the data warehouse
Specifying how much of the data warehouse end users are
using
Pinpointing when the data warehouse is being used
Recognizing how much of the data ware house is being used
Examining the level of usage of the data warehouse