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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”
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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)
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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
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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.
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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
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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
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Data Warehouse Functionality
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Data Warehouse Functionality
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Evolution architecture of
data warehouse• Top-Down Architecture
•Bottom-Up Architecture
•Enterprise Data Mart Architecture
•Data Stage/Data Mart Architecture
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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
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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)
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Cautions & Measures
• You are building a HIGH maintenancesystem
•Slow and tedious expansion process
•Necessary training to employees
•Prototyping before implementation
•Data integrity checks
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T HA N K Y
O U ! !
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Business intelligence and datawarehousing
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Reference Diagram
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Few Interesting Facts
• Over the last 20 years, $1 trillionhas been invested in new computersystems to gain competitive
advantage
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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.