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Data Warehouse Principles

Improve effectiveness with David Bowman’s information management guidelines for data warehouse principles

This site is designed for Information Technology professionals and management consultants who need to improve effectiveness and want information management guidelines.

It provides information management guidelines for data warehouse principles.

What are Data Principles?

Data principles are a set of principles, standards, best practices processes, procedures and metadata required to ensure effective and efficient data storage of information needed for business intelligence solutions.

Data is an Enterprise Asset

Data has often been viewed as belonging to particular individuals or as simply part of an application. It is important to note that while data is a shared asset, the Information Technology department should have organizational responsibility for managing the technology infrastructure that supports this asset.

This principle implies that an enterprise data model needs to be effectively shared and far greater rigor is required in integrating, managing, and cataloging data. 

The implications are far reaching, but include improvements in data quality, greater use of metadata, careful schema version management, and support for an enterprise data warehouse.

The Business Is The Guardian For Data

Data assets should have owners in the business. These owners are known as data guardians and data stewards.
This implies significant business involvement in developing business definitions for entities and attributes. Data stewards should have an active role in defining data quality specifications such as valid values, required relationships, etc. 

All of this metadata should be stored in a centrally managed “metadata repository”, an on line data dictionary, that describes the corporate data asset and the rules that protect it. 

Data stewards should also have a role in determining the appropriate security levels for the data assets

Data Should Be Secured Based On Risk Analysis
 

The appropriate level of security should be implemented for each data element within the enterprise.  A risk-based cost analysis should drive all security decisions. Stakeholders should balance the cost of strict security measures and the potential for blocking legitimate accesses against the potential risks presented by having less stringent policies in place.

This implies that the Information Technology department place sufficient security infrastructure around data assets, and that data guardians make well reasoned choices regarding legitimate access needs versus the cost of inadequately secured data.

Data Should Be Stored In Fewer Databases

It is better to maintain a few large multi-subject-area databases, rather than many application-specific databases;
Limit Dependence On Physical Data Structures

Ensure that business logic is insulated from the details of database structures and limit dependencies on data structures to a single, well managed code library.

Single Point For Data Manipulation

There should be a single application, function library, or component that manages all manipulation of data that is stored in systems of record

This principle recognizes that data quality should begin at the source system where data is created, updated and deleted.

Summary...

Data warehouse principles, standards, best practices processes, procedures and metadata are required to ensure effective, and efficient data storage of information needed for business intelligence solutions.

This site provided information management guidelines for data warehouse principles.