Page 93 - DCAP208_Management Support Systems

Management Support Systems

Notes 6.4.1 Business Requirements Definition

The Business Requirements Definition process defines the requirements, clarifies the scope, and
establishes the implementation roadmap for the data warehouse. With the direction of the client
organization, strategic business goals and initiatives are established and used to direct the
strategies, purpose and goals for each phase of the data warehouse solution. Early in the process,
the focus is on the enterprise aspects of the data warehouse such as enterprise information
requirements, subject areas, an implementation roadmap, and a business case for the data
warehouse. As the process continues, Business Requirements Definition focuses on determining
the specifics of the solution to be developed and delivered, identifying the client’s information
needs, and modeling the requirements.

6.4.2 Data Acquisition

The objective of the Data Acquisition process is to identify, extract, transform, and transport the
various source data necessary for the operation of the data warehouse. Data Acquisition is
performed between several components of the warehouse, including operational and external
data sources to data warehouse, data warehouse to data mart, and data mart to individual marts.
Early in the Data Acquisition process, data sources are identified and evaluated against the
subject areas, and a gap analysis is conducted to verify that data is available to support the
information requirements.
The Data Acquisition Strategy is also developed to outline the approach for extraction,
transformation, and transportation of the source data to the data warehouse. The strategy includes
selecting a tool or set of tools as the data pump or defining the specifications of one that must be
built. If tools are to be utilized, high-level tool requirements, tool evaluations, and tool
recommendations are also addressed.

6.4.3 Architecture

The Data Warehouse Architecture provides an integrated data warehouse environment while
delivering incremental solutions. The architectural design focuses on the application of a
centralized data warehouse, data marts, individual marts, metadata repositories, and incremental
solution architectures. As the process continues, the development and execution of the integration
plans are completed and the compliance of incremental solutions with the strategic architecture
is validated.

6.4.4 Data Quality

The objective of the Data Quality process is to provide consistent, reliable, and accurate data in
the warehouse. The Data Quality Strategy is developed based upon a clear understanding of
which data cleansing and integrity functions meet the needs of the customer. To facilitate timely
and reliable resolution of data issues, Data Management Procedures are also defined. Early in
the process, data quality tools are also evaluated and recommended. The Data Quality process
identifies the business rules for error exception handling, data cleansing, and audit and control.
In addition, any variations in business rules for error handling between initial loads and
subsequent updates to the data warehouse are defined. Utilizing the strategy, procedures, and
tools, modules are developed to support the requirements for data quality. In order to populate
the data warehouse with reliable data, the Data Quality modules are integrated with the Data
Acquisition modules to check that the quality functions are properly sequenced within the
overall transformation of source data to the target environment.

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