Evolution of Decision Support Systems

Data warehouseData 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

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 Detail, day to day, current valued, high probability of

access, application oriented Atomic/data warehouse

Most granular, time variant, integrated, subject oriented, some summary

Departmental 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 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