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Meeting a Business Need. Chapter 2. Overview. Planning Warehouse Storage. Meeting a Business Need. Defining DW Concepts & Terminology. Choosing a Computing Architecture. ETT (Building The Warehouse). Managing The Data Warehouse. Modeling The Data Warehouse. Planning

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overview
Overview

Planning

Warehouse

Storage

Meeting a

Business

Need

Defining

DW Concepts

& Terminology

Choosing a

Computing

Architecture

ETT

(Building

The

Warehouse)

Managing

The Data

Warehouse

Modeling

The Data

Warehouse

Planning

For a

Successful

Warehouse

Analyzing

User Query

Needs

Supporting

End User

Access

Project Management

(Methodology, Maintaining Metadata)

characteristics of oltp systems
Characteristics of OLTP Systems

Characteristic

OLAP

Update

Typical operation

Level of analytical requirement

Low

Screens

Unchanging

Small

Amount of data per transaction

Detailed

Data level

Age of data

Current

Records

Orientation

why oltp is not suitable for complex analysis
Why OLTP Is Not Suitable for Complex Analysis

OLAP

Complex Analysis

Information to support

day-to-day service

Historical information

to analyze

Data stored at transaction

level

Data needs to be integrated

Database design:

Denormalized, star schema

Database design: Normalized

management information systems and decision support
Management Information Systems and Decision Support

Ad hoc access

  • MIS systems provided business data
  • Reports were developed on request
  • Reports provided little analysis capability
  • Decision support tools gave personal ad hoc access to data

Production

platforms

Operational reports

Decision makers

analyzing data from operational systems
Analyzing Data from Operational Systems
  • Data structures are complex
  • Systems are designed for high performance and throughput
  • Data is not meaningfully represented
  • Data is dispersed
  • OLTP systems may be unsuitable for intensive queries

Production

platforms

Operational reports

data extract processing
Data Extract Processing
  • End user computing offloaded from the operational environment
  • User’s own data

Operational systems

Extracts

Decision makers

management issuess
Management Issuess

Decision makers

Operational systems

Extracts

Extract explosion

productivity issues
Productivity Issues
  • Duplicated effort
  • Multiple technologies
  • Obsolete reports
  • No metadata
data quality issues
Data Quality Issues
  • No common time basis
  • Different calculation algorithms
  • Different levels of extraction
  • Different levels of granularity
  • Different data field names
  • Different data field meanings
  • Missing information
  • No data correction rules
  • No drill-down capability
from extract to warehouse dss
From Extract to Warehouse DSS
  • Controlled
  • Reliable
  • Quality information
  • Single source of data

Data warehouse

Decision makers

Internal and

external systems

advantages of warehouse processing environment
Advantages of Warehouse Processing Environment
  • No duplication of effort
  • No need for tools to support many technologies
  • No disparity in data, meaning, or representation
  • No time period conflict
  • No algorithm confusion
  • No drill-down restrictions
business motivators
Business Motivators
  • Know the business
  • Reinvent to face new challenges
  • Invest in products
  • Invest in customers
  • Retain customers
  • Invest in technology
  • Improve access to business information
  • Be profitable
  • Provide superior services and products
business motivators1
Business Motivators
  • Provides supporting information systems
  • Get quality information

- Reduce costs

- Streamline the business

- Improve margins

technological advances
Technological Advances
  • 64-bit architecture
  • Indexing techniques
  • Affordable, cost-effective
  • Open systems
  • Robust warehouse tools
  • Sophisticated end user

tools

  • Parallelism

- Hardware

- Operating

system

- Query

- Index

- Applications

  • Large database
growth motivators and inhibitors
Growth Motivators and Inhibitors
  • Successful implementations
  • Decreased risk
  • Robust extraction software
  • Improving price to performance ratios
  • Improved staff training
  • Year 2000 compliance
  • Skills shortage
  • Lack of integrated metadata
  • Data cleaning cost
typical uses of data warehouse
Typical Uses of Data Warehouse
  • Airline
  • Banking
  • Health Care
  • Investment
  • Insurance
  • Retail
  • Telecommunications
  • Manufacturing
  • Credit card suppliers
  • Clothing distributors
summary
Summary

This lesson covered the following topics:

  • Describing why an online transaction processing(OLTP) systems is not suitable for complex analysis
  • Describing how extracting processing for decision support querying led to data warehouse solutions employed today
  • Explaining why businesses are driven to employ data warehouse technology
  • Identifying some of the industries that employ data warehouses