Data Management for Decision Support

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Data Management for Decision Support

• Session - 1
• Prof. Bharat Bhasker

2

• Course Outline
• Assignments 50
• Surprises!
• HW
• Papers/ Cases (10 points)
• Final Exam 50
• Whats Expected?
• Multiple Books ( DBMS, Data Warehousing, Computer
  Architecture)
• Research Papers ( ToDS, Computing Surveys)

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Productivity Paradox

• In 1994, US 464 Billion were spent on IT, but
  ROI didnt measure up.gt
• Automation of Clerical tasks
• Improving efficiency of existing processes
• Gathering Collection of Data went up, but no
  significant changes in deployment of the
  technology to extract value

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Potential for ROI

• Management of data offers potential ROI
• Ability to focus on business processes, perform
  complete financial analysis and make decisions
  based on understanding of the entire system
• Ability to organize data for both macro and micro
  level analysis
• Dynamic drill down through the reports generated
  via EIS
• Quick responses to ad-hoc queries for testing
  hypothesis
• Correlate current and historical data
• IDC survey showed average 3 year return reached
  400 for data warehousing!!

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Why Manage the Accumulated data

• Information needs of Knowledge workers,
  Strategic
• planners, Customers Vendors can be met from the
• accumulated data.
• Business Perspectivegt
• Quick and correct decision making using all
  available data
• Users are domain expert, not software pros
• Data double every 18 months, effects response
  time and comprehension
• Business Intelligence the new emerging tool

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Why Manage the Accumulated data

• Technology Perspective
• Price of processing power in MIPS declines,
  while power of microprocessor double every 18
  months.
• The prices of digital storage continues to drop
• The environment is heterogeneous is terms of H/W
  and S/W
• Legacy systems can be integrated with new
  applications
• N/W b/w is increasing, cost is declining

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Introduction- Shifting Paradigm

• Computing Paradigm

Users
Networks
Computers
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Introduction- Shifting Paradigm

• Business Paradigm

New Enterprise BPR Led, Paperless Office, Fully
networked, distributed , Knowledge enabled
Open Systems, C/S, Distributed Computers
Automated Enterprise Back office Automation Front
office Automation
Proprietary Systems on Mainframes
Legacy Enterprise Manual Back office Manual front
office
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Problem Heterogeneous Information Sources
Heterogeneities are everywhere
Personal Databases
World Wide Web
Scientific Databases
Digital Libraries

• Different interfaces
• Different data representations
• Duplicate and inconsistent information

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Problem Data Management in Large Enterprises

• Vertical fragmentation of informational systems
  (vertical stove pipes)
• Result of application (user)-driven development
  of operational systems

Sales Planning
Suppliers
Num. Control
Stock Mngmt
Debt Mngmt
Inventory
...
...
...
Sales Administration
Finance
Manufacturing
...
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Goal Unified Access to Data
Personal Databases
Digital Libraries
Scientific Databases

• Collects and combines information
• Provides integrated view, uniform user interface
• Supports sharing

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The Traditional Research Approach

• Query-driven (lazy, on-demand)

Clients
Metadata
Integration System
. . .
Wrapper
Wrapper
Wrapper
. . .
Source
Source
Source
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Disadvantages of Query-Driven Approach

• Delay in query processing
• Slow or unavailable information sources
• Complex filtering and integration
• Inefficient and potentially expensive for
  frequent queries
• Competes with local processing at sources
• Hasnt caught on in industry

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The Warehousing Approach

• Information integrated in advance
• Stored in wh for direct querying and analysis

Clients
Data Warehouse
Metadata
Integration System
. . .
Extractor/ Monitor
Extractor/ Monitor
Extractor/ Monitor
. . .
Source
Source
Source
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Advantages of Warehousing Approach

• High query performance
• But not necessarily most current information
• Doesnt interfere with local processing at
  sources
• Complex queries at warehouse
• OLTP at information sources
• Information copied at warehouse
• Can modify, annotate, summarize, restructure,
  etc.
• Can store historical information
• Security, no auditing
• Has caught on in industry

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Not Either-Or Decision

• Query-driven approach still better for
• Rapidly changing information
• Rapidly changing information sources
• Truly vast amounts of data from large numbers of
  sources
• Clients with unpredictable needs

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What is a Data Warehouse?A Practitioners
Viewpoint

• A data warehouse is simply a single, complete,
  and consistent store of data obtained from a
  variety of sources and made available to end
  users in a way they can understand and use it in
  a business context.
• -- Barry Devlin, IBM Consultant