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DATA WAREHOUSE

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INTORDUCTION DATA WAREHOUSE Mastering Data Warehouse and Design Relation and Dimensional Techniques Claudia Imhoff, Nicholas Galemmo,Jonathan G. Geiger – PowerPoint PPT presentation

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Title: DATA WAREHOUSE


1
DATA WAREHOUSE
  • INTORDUCTION

Mastering Data Warehouse and Design Relation and
Dimensional Techniques Claudia Imhoff, Nicholas
Galemmo,Jonathan G. Geiger Wiley Publishing, Inc.
2003,
2
Business Intelligence (BI)
BI, in the context of the data warehouse, is the
ability of an enterprise to study past behaviors
and actions in order to understand where the
organization has been, determine its current
situation, and predict or change what will happen
in the future
3
(continue)
  • Data warehousing is another good example of the
    adoption curve.
  • Today, the value of a sophisticated decision
    support environment is widely recognized
    throughout the business.

4
(continue)
As an example, an effective customer relationship
management program could not exist without
strategic (data warehouse with associated marts)
and a tactical (operational data store and oper
mart) decision-making capabilities.
5
BI Architecture (EIS approach)
6
(continue)
  • Two major groupings of components and processes
  • Getting data in consists of the processes and
    databases involved in acquiring data from the
    operational systems, integrating it, cleaning it
    up, and putting it into a database for easy
    usage. The components of the CIF that are found
    in this function
  • The operational system databases (source systems)
  • The data warehouse
  • The operational data
  • Data acquisition

7
(continue)
  • Getting information out consists of the processes
    and databases involved in delivering BI to the
    ultimate business consumer or analyst. The
    components of the CIF that are found in this
    function
  • The data marts
  • The oper marts are derivatives of the Operational
    Data Store (ODS)
  • Data delivery is the process that moves data from
    the data warehouse into data and oper marts.

8
Role and Purpose of the Data Warehouse
The data warehouse has been a part of the BI
architecture from the very beginning.
9
(continue)
  • Different methodologies and data warehouse have
    given this component various names such as
  • A staging area. A variation on the data warehouse
    is the back office staging area where data from
    the operational systems is first brought
    together.
  • The information warehouse. This was an early name
    for the data warehouseused by IBM and other
    vendors.

10
The Corporate Information Factory
  • These information stores support three high-level
    organizational processes
  • Business operations are concerned with the
    ongoing day-to-day operations of the business.
  • Business intelligence is concerned with the
    ongoing search for a better understanding of the
    company, of its products, and of its customers.
  • Business management is the function in which the
    knowledge

11
The Multipurpose Nature of the Data Warehouse
  • It should be enterprise focused.
  • Its design should be as resilient to change as
    possible.
  • It should be designed to load massive amounts of
    data in very short amounts of time.
  • It should be designed for optimal data extraction
    processing by the data delivery programs.
  • Its data should be in a format that supports any
    and all possible BI analyses in any and all
    technologies.

12
Types of Data Marts Supported
  • OLAP data mart.
  • Exploration warehouse.
  • Data-mining or statistical warehouse.
  • Customizable analytical applications.

13
Types of BI Technologies Supported
Business requirementsdata mart design matrix.
14
Recommended Readings
Chapter 1
Mastering Data Warehouse and Design Relation and
Dimensional Techniques Claudia Imhoff, Nicholas
Galemmo,Jonathan G. Geiger Wiley Publishing, Inc.
2003,
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