Data Warehouse i.e. DWH

7/27/2019 Data Warehouse i.e. DWH

http://slidepdf.com/reader/full/data-warehouse-ie-dwh 1/17

Management Information System

SIMSREE MMS-2012-14

TOPIC:“Data Warehousing”



What is Data Warehouse?

• Focuses on the modeling and analysis of data for decision makers, not

on daily operations or transaction processing

• Support information processing by providing a solid platform of

consolidated data for analysis

• Subject Oriented: Provide a simple and concise view around particular

subject issues

• Integrated: Multiple ,heterogeneous data sources are integrated.

• The time horizon for the data warehouse is significantly longer than that

of operational systems

– Operational database: current value data

– Data warehouse data: provide information from a historical

perspective (e.g., past 5-10 years)



From Tables and Spreadsheets to Data Cubes

• A data warehouse is

based on a

multidimensional data

model which views

data in the form of adata cube

• A data cube, such as

sales, allows data to

be modeled andviewed in multiple

dimensions



Business advantages• Customer-centric view of the company’s heterogeneous

data by integrating data from sales, service,manufacturing and distribution, and other customer-related business systems.

• Provides added value to the company’s customers byallowing them to access information in better way whendata warehousing is coupled with internet technology.

• Provides a repository of all customer contacts forsegmentation modeling, customer retention planning, andcross sales analysis.

• It reports on trends across multidivisional, multinationaloperating units, including trends or relationships in areassuch as merchandising, production planning etc.



Strategic uses of data warehousing

Industry Functional areas of use

Strategic use

Airline Operations; marketing Crew assignment, analysis of routeprofitability, frequent flyer programpromotions

Banking Product development;Operations; marketing

Customer service, trend analysis, product andservice promotions, reduction of ISexpenses

Credit card Product development;

marketing

Customer service, fraud detection

Health care Operations Reduction of operational expenses

Investment andInsurance

Product development;Operations; marketing

Risk management, market movementsanalysis, customer tendencies analysis,portfolio management

Retail chain Distribution; marketing Trend analysis, buying pattern analysis,pricing policy, inventory control, salespromotions, optimal distribution channel

Telecommunications Product development;Operations; marketing

New product and service promotions,profitability analysis

Personal care Distribution; marketing Distribution decisions, product promotions,sales decisions, pricing policy

Public sector Operations Intelligence gathering



Data Warehouse Components

Staging AreaA preparatory repository where transaction data can betransformed for use in the data warehouse

Data Mart

Traditional dimensionally modeled set of dimension and

fact tablesMeta data

Meta data is data about data that describes the datawarehouse. It is used for building, maintaining, managingand using the data warehouse

End-User Access toolsUsed for performing operation by managers on DWH



Data Warehouse Functionality



Data Warehouse Functionality



Evolution architecture of

data warehouse• Top-Down Architecture

•Bottom-Up Architecture

•Enterprise Data Mart Architecture

•Data Stage/Data Mart Architecture



Very Large Data Bases

• Terabytes -- 10^12 bytes:

• Petabytes -- 10^15 bytes:

• Exabytes -- 10^18 bytes:

• Zettabytes -- 10^21 bytes:

• Zottabytes -- 10^24 bytes:

Wal-Mart -- 24 Terabytes

Geographic Information

SystemsNational Medical Records

Weather images

Intelligence AgencyVideos



Complexities of Creating a

Data Warehouse•Incomplete errors

1. Missing Fields (incomplete sourcing)

2. Records or Fields are not Being Recorded (faulty systemdesigned for sourcing/ cleaning data)

•Incorrect errors

1. Wrong Calculations, Aggregations (storing error)

2. Duplicate Records (transforming error)3. Wrong Information Entered into Source System (sourcing

error)



Cautions & Measures

• You are building a HIGH maintenancesystem

•Slow and tedious expansion process

•Necessary training to employees

•Prototyping before implementation

•Data integrity checks



T HA N K Y

O U ! !



Business intelligence and datawarehousing



Reference Diagram



Few Interesting Facts

• Over the last 20 years, $1 trillionhas been invested in new computersystems to gain competitive

advantage



Differences

Operational Data Warehouse

Holds current data Holds historic data

Data is dynamic Data is largely static

Read/Write accesses Read only accesses

Repetitive processing Adhoc complex queries

Transaction driven Analysis driven

Application oriented Subject oriented

Used by clerical staff for day-to-dayoperations

Used by top managers for analysis

Normalized data model (ER model) Denormalized data model (Dimensional

model)Must be optimized for writes andsmall queries.

Must be optimized for queries involving alarge portion of the warehouse.

Documents

Data Warehouse i.e. DWH