Meeting a Business Need

Chapter 2

Overview

Defining DW Concepts& Terminology

PlanningFor a

SuccessfulWarehouse

Project Management(Methodology, Maintaining Metadata)

Meeting aBusiness

Need

Choosing aComputingArchitecture

ModelingThe Data

Warehouse

AnalyzingUser Query

Needs

PlanningWarehouse

Storage

ETT(Building

TheWarehouse)

ETT(Building

TheWarehouse)

SupportingEnd UserAccess

ManagingThe Data

Warehouse

Characteristics of OLTP Systems

Level of analytical requirement

Screens

Amount of data per transaction

Data level

Age of data

Orientation

Low

Unchanging

Small

Detailed

Current

Records

Characteristic OLAP

Typical operation Update

Why OLTP Is Not Suitable for Complex Analysis

OLAP

Information to support day-to-day service

Data stored at transactionlevel

Database design: Normalized

Complex Analysis

Historical informationto analyze

Data needs to be integrated

Database design:Denormalized, star schema

Management Information Systems and Decision Support

MIS systems provided business dataReports were developed on requestReports provided little analysis capabilityDecision support tools gave personal ad

hoc access to data

Operational reports Decision makers

Ad hoc accessProductionplatforms

Analyzing Data from Operational SystemsData structures are complexSystems are designed for high

performance and throughputData is not meaningfully represented Data is dispersedOLTP systems may be unsuitable for

intensive queriesProduction platforms

Operational reports

Data Extract Processing

End user computing offloaded from the operational environment

User’s own data

Operational systems Extracts Decision makers

Management Issuess

Operational systems Extracts Decision makers

Extract explosion

Productivity Issues

Duplicated effort Multiple technologies Obsolete reports No metadata

Data Quality IssuesNo common time basisDifferent calculation algorithmsDifferent levels of extractionDifferent levels of granularityDifferent data field namesDifferent data field meaningsMissing informationNo data correction rulesNo drill-down capability

From Extract to Warehouse DSS

ControlledReliableQuality informationSingle source of data

Internal and external systems

Data warehouse Decision makers

Advantages of Warehouse Processing Environment

No duplication of effortNo need for tools to support many

technologiesNo disparity in data, meaning, or

representationNo time period conflictNo algorithm confusionNo drill-down restrictions

Business MotivatorsKnow the businessReinvent to face new challengesInvest in productsInvest in customersRetain customersInvest in technologyImprove access to business informationBe profitableProvide superior services and products

Business Motivators

Provides supporting information systems

Get quality information - Reduce costs - Streamline the business - Improve margins

Technological Advances

Parallelism - Hardware - Operating system - Query - Index - ApplicationsLarge database

64-bit architectureIndexing techniquesAffordable, cost-

effectiveOpen systemsRobust warehouse

toolsSophisticated end user tools

Growth Motivators and Inhibitors

Successful implementationsDecreased riskRobust extraction software Improving price to performance ratios Improved staff training

Year 2000 complianceSkills shortageLack of integrated metadataData cleaning cost

Typical Uses of Data Warehouse

AirlineBankingHealth CareInvestmentInsurance

RetailTelecommunicationsManufacturingCredit card suppliersClothing distributors

SummaryThis 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