Data Warehouse

 
Philosophy
 

Business intelligence is an integrated framework of an information system that provides the business with a complete view of business activities and operations. Data warehousing is frequently used for decision support within a company or organization and provides the organization with organized, updated and coordinated data that provides a view of relationships between information obtained from different sections of the organization.

Our data warehousing design involves generating, manipulating and mapping models with business intelligence focus on an overall view through one front regardless of data storage style, number and locations. The models are physical, logical, and conceptual representations of the business data and end-user information requirements.

Creating a data warehouse requires the mapping of data between source to target models, capturing the details of the transformation in the data warehouse by utilization of tools that support these activities which are known as data warehouse mapping and transformation design tools.

By definition a "Data Warehouse" is a very massive database that frequently provides access to all of an organization's data (information). Data could be distributed over several databases on multiple computers and may contain information from numerous sources in different formats and varieties. The user data access to data source (regardless of location, format, type or hardware etc) should be transparent.

Data warehouses also contain information about how the warehouse is structured (it has data about the data stored in it) and organized, where the information is and the relations between the data.

Architecture

1) Data collection and injection
Gathering data from a wide variety of systems is a challenge, to simplify this task, it is best to make some form of mapping tool or to find an off the shelf one to fetch, transfer and load the data from the various applications. Such tools identify the information contained in data source systems and identify where that information needs to be loaded into the data warehouse.

  • Extract
    Function to get data from heterogeneous data sources and different systems.

  • Transform
    Function of transforming extracted data to be suitable for the warehouse.

  •  Load 
    Load the data to the warehouse 

2) Metadata Management
Tools necessary to locate, alter, create, and manage metadata objects--including technical, business intelligence, and operational metadata within the data Warehouse.

  • Metadata search
    Is required to enables you to perform searches on technical, operational, and business intelligence metadata components.

  •  Modification handling
    Assist you to maintain consistency when developing new components or reuse previously entered ones.

Covers data names, table names and what is in the database (information about the data kept in the database) etc.

3) Impact Analysis
Facilities that enable you to determine the interdependencies that a change to one data warehouse object may have on other related objects.

  • Objects related list 
    Provides Impact analysis of all objects so one can manage and inform others of potential impacts to their work.

Example:
A change to a data warehouse table may require change to the related tables, table maps, or data maps.

4) Information Access
Infrastructure for 360 information analysis to manage and explore: 

  • Strategic initiatives 

  • Cause of missed objectives

  • Generates operational reports

Example:
Use scorecards to view and manage strategic initiatives through client software or web portal. Plugged in analytical applications are used to provide immediate insight into data assets.

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