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Data Warehouse

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Data Warehouse. Group 5 Kacie Johnson Summer Bird Washington Farver Jonathan Wright Mike Muchane – PowerPoint PPT presentation

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Title: Data Warehouse


1
Data Warehouse
  • .
  • Group 5
  • Kacie Johnson
  • Summer Bird
  • Washington Farver
  • Jonathan Wright
  • Mike Muchane

2
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

3
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.

4
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

5
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.

6
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

7
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.

8
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).

9
Data Warehouse Is
  • Subject Oriented
  • Integrated
  • Time Variant
  • Nonvolatile Collection of Data for Managements
    Decisions

10
Building Blocks
  • Source Data
  • Date Staging
  • Data Storage
  • Information Delivery
  • Metadata
  • Management and Control

11
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12
Design of DW
  • Integration facilitates an overview and analysis
    in the data warehouse
  • Separation operations used for reporting,
    decision support, analysis and controlling

13
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.

14
Types of Data Warehouse
  • Financial
  • Insurance
  • Human Resources
  • Global
  • Data Mining/Data Mining and Exploration
  • Telecommunications

15
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)

16
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)

17
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

18
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

19
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.

20
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.

21
Any Questions?
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