Why Data Warehouse?

ABC Pvt. Ltd is a company with branches at Mumbai, Delhi, Chennai and Bangalore.

The Sales Manager wants quarterly sales report.

Each branch has a separate operational system.

Mumbai

Delhi

Chennai

Banglore

SalesManager

Sales per item type per branchfor first quarter.

Solution 1:ABC Pvt Ltd.

Extract sales information from each database.

Store the information in a common repository at a single site.

Mumbai

Delhi

Chennai

Banglore

DataWarehouse

SalesManager

Query &Analysis tools

Report

One Stop Shopping Super Market has huge operational database.Whenever Executives wants some report the OLTP system becomes slow and data entry operators have to wait for some time.

OperationalDatabase

Data Entry Operator

Data Entry Operator

ManagementWait

Report

Solution 2

Extract data needed for analysis from operational database.

Store it in another system, the data warehouse.

Refresh warehouse at regular intervals so that it contains up to date information for analysis.

Warehouse will contain data with historical perspective.

Operationaldatabase

DataWarehouse

Extractdata

Data EntryOperator

Data EntryOperator

ManagerTransaction

Report

Cakes & Cookies is a small, new company. The chairman of this company wants his company to grow. He needs information so that he can make correct decisions.

Solution 3Improve the quality of data before loading it

into the warehouse.Perform data cleaning and transformation

before loading the data.Use query analysis tools to support adhoc

queries.

Query & Analysis

tool

Chairman

Expansion

Improvement

sales

time

DataWarehouse

Summing up?Why do you need a warehouse?

Operational systems could not provide strategic information

Executive and managers need such strategic information for Making proper decision Formulating business strategies Establishing goals Setting objectives Monitoring results

Why operational data is not capable of producing valuable information?Data is spread across incompatible structures

and systemsNot only that, improvements in technology

had made computing faster, cheaper and available

FAILURES OF PAST DECISION-SUPPORT SYSTEMS

OLTP systems

Decision support systems

Operational and informational

Functional definition of a DWThe data warehouse is an informational

environment that Provides an integrated and total view of the

enterprise Makes the enterprise’s current and historical

information easily available for decision making Makes decision-support transactions possible

without hindering operational systems Renders the organization’s information consistent Presents a flexible and interactive source of

strategic information

Questions????Describe five differences between operational

systems and informational systemsA data warehouse in an environment, not a

product. Discuss.

A data warehouse is- subject-oriented,- integrated,- time-variant,- nonvolatile

collection of data in support of management’sdecision making process.

Subject-oriented

Data warehouse is organized around subjects such as sales, product, customer.

It focuses on modeling and analysis of data for decision makers.

Excludes data not useful in decision support process.

Integration

Data Warehouse is constructed by integrating multiple heterogeneous sources.

Data Preprocessing are applied to ensure consistency.RDBMS

LegacySystem

DataWarehouse

Flat File Data ProcessingData Transformation

IntegrationIn terms of data.

encoding structures.

Measurement ofattributes.

physical attribute. of data

naming conventions.

Data type format

remarks

Time-variant

Provides information from historical perspective, e.g. past 5-10 years

Every key structure contains either implicitly or explicitly an element of time, i.e., every record has a timestamp.

The time-variant nature in a DWAllows for analysis of the pastRelates information to the presentEnables forecasts for the future

Non-volatile

Data once recorded cannot be updated.Data warehouse requires two

operations in data accessingInitial loading of dataIncremental loading of data

load

access

Data GranularityIn an operational system, data is usually kept at the

lowest level of detail.In a DW, data is summarized at different levels.

Three data levels in a banking data warehouse

Daily Detail Monthly Summary Quaterly Summary

Account Account Account

Activity Date Month Month

Amount No. of transactions No. of transactions

Deposit/ Withdraw Withdrawals Withdrawals

Deposits Deposits

Beginning Balance Beginning Balance

Ending Balance Ending Balance

Operational v/s Information SystemFeatures Operational Information

Characteristics Operational processing Informational processing

Orientation Transaction Analysis

User Clerk,DBA,database professional

Knowledge workers

Function Day to day operation Decision support

Data Content Current Historical, archived, derived

View Detailed, flat relational Summarized, multidimensional

DB design Application oriented Subject oriented

Unit of work Short ,simple transaction Complex query

Access Read/write Read only

Operational v/s Information System

Features Operational Information

Focus Data in Information out

No. of records accessed

tens/ hundreds millions

Number of users thousands hundreds

DB size 100MB to GB 100 GB to TB

Usage Predictable, repetitive Ad hoc, random, heuristic

Response Time Sub-seconds Several seconds to minutes

Priority High performance,high availability

High flexibility,end-user autonomy

Metric Transaction throughput Query throughput

Two approaches in designing a DWTop-down approach Bottom-up approach

Enterprise view of data Narrow view of data

Inherently architected Inherently incremental

Single, central storage of data Faster implementation of manageable parts

Centralized rules and control Each datamart is developed independently

Takes longer time to build Comparatively less time than a DW

Higher risk to failure Less risk of failure

Needs higher level of cross-functional skills

Unmanageable interfaces

Bottom Up Approach

Top Down Approach

A Practical Approach-Kimball1. Plan and Define requirements2. Create a surrounding architecture3. Conform and Standardize the data Content4. Implement Data Warehouse as series of

super-mart one at a time.

Individual Architected Data Marts

Common Logical Subject Area ERD

Common Business Dimensions

Common Business Rules

Common Business Metrics

Glossary

SalesDistribution

Product

Marketing Customer Accounts

Finance Inventory Vendors

An Incremental Approach

ArchitectedArchitectedEnterpriseEnterpriseFoundationFoundation

SalesDistribution

Product

Marketing Customer Accounts

Finance Inventory Vendors

Enterprise Data Warehouse

The Eventual Result

Data Warehouse:

Holds multiple subject areasHolds very detailed informationWorks to integrate all data sourcesDoes not necessarily use a dimensional model but feeds dimensional models.Data Mart

Often holds only one subject area- for example, Finance, or SalesMay hold more summarised data (although many hold full detail)Concentrates on integrating information from a given subject area or set of source systemsIs built focused on a dimensional model using a star schema.

Data Warehouse verses data marts