Data Warehouse

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Data Warehouse

• .
• Group 5
• Kacie Johnson
• Summer Bird
• Washington Farver
• Jonathan Wright
• Mike Muchane

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Outline

• I. Data warehouse definition and integrated
  technologies
• II. OLAP and OLTP
• III. The concept of data warehousing
• IV. How data warehouses are used by companies
• V. History of data warehousing
• VI. Advantages and Disadvantages
• VII. Future applications

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Definition

• A data warehouse is a logical collection of
  information gathered from many different
  operational databases used to create business
  intelligence that supports business analysis
  activities and decision-making tasks.

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Business Intelligence

• Business intelligence usually refers to the
  information that is available for the enterprise
  to make decisions on. A data warehousing (or data
  mart) system is the backend, or the
  infrastructural, component for achieving business
  intelligence

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Data Mart

• A database that has the same characteristics as a
  data warehouse, but is usually smaller and is
  focused on the data for one division or one
  workgroup within an enterprise.

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Data Mining Tools

• Data mining tools are Software tools used to
  query information in a data warehouse. Consist
  of

• Query-and-Reporting tools
• Intelligent Agents
• Multidimensional analysis tools (MDA)
• Statistical tools

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OLAP

• A data warehouse uses OLAP (On-Line Analytical
  Processing) to collect, organize, and make data
  available for the purpose of analysis - to give
  management the ability to access and analyze
  information about its business. This type of data
  can be called informational data.

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OLTP

• Most data is collected to handle a company's
  on-going business. This type of data can be
  called "operational data". The systems used to
  collect operational data are referred to as OLTP
  (On-Line Transaction Processing).

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Data Warehouse Is

• Subject Oriented
• Integrated
• Time Variant
• Nonvolatile Collection of Data for Managements
  Decisions

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Building Blocks

• Source Data
• Date Staging
• Data Storage
• Information Delivery
• Metadata
• Management and Control

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Design of DW

• Integration facilitates an overview and analysis
  in the data warehouse
• Separation operations used for reporting,
  decision support, analysis and controlling

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Dimensions and Measures

• Dimensions categorizes each item in a data set
  in non-overlapping regions.
• Measures a property that can be summed or
  averages using pre-computed aggregates.

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Types of Data Warehouse

• Financial
• Insurance
• Human Resources
• Global
• Data Mining/Data Mining and Exploration
• Telecommunications

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Before DW

• Executives and decision makers could get critical
  information that already existed on the
  organization
• The available data was exceedingly difficult to
  get (data in jail)
• Only a fraction of the data captured, processed
  and stored was actually available (data poor)

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DW In Companies

• Validation where users validate what they
  already believe to be true (45)
• Tactical Reporting where the user uses the data
  for tactical reasons (40)
• Exploration where the user searches for
  knowledge not already known (15)

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Why the volume of data is exploding

• DWs carry historical data
• DWs carry detailed data
• DWs carry data for which there is no known need
• DWs carry eCommerce data

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Advantages

• Cut costs
• Boost revenues
• Saves time
• Better customer service
• Avoids old data
• Queries or reports without impacting the
  performance of the operational systems
• Combines related data from separate sources
• Increased data consistency
• Improves access to a wide variety data

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Disadvantages

• Can complicate business processes.
• Data warehousing can have a learning curve that
  may be too long for impatient firms.
• Can require a great deal of "maintenance.
• The cost to capture data, clean it up, and
  deliver it .
• Inability to adapt quickly to changing business
  conditions or requirements.

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Future Developments

• Development of parallel DB servers with improved
  query engines will make it possible to access
  huge data bases in much less time
• Another new technology is data warehouses that
  allow for the mixing of traditional numbers, text
  and multi-media. The availability of improved
  tools for data visualization (business
  intelligence) will allow users to see things that
  could never be seen before.

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Any Questions?