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Data Warehouse Principles

Need to understand data warehouse principles for an information management project?

Data Warehousing is a sub-set of data management, which in turn is aData Warehouse principles sub-set of information management that governs organization and control of the structure and design, storage, movement, security and quality of information.
What Is Data Storage?

Data storage is 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.

Typical data warehouse principles
  • Data is an enterprise asset and has too 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 IT department should have organizational responsibility for managing the technology infrastructure that supports this asset.
This principle implies that the 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 and 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 IT 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 since it is better to maintain a few large multi-subject-area databases, rather than many application-specific databases;
  • Limit dependence on physical data structures and ensure that business logic is insulated from the details of database structures; and
  • Single point for data manipulation implies there should be a single application, function library, or component that manages all manipulation of data that is stored in systems of record (SOR’s). 
This principle recognizes that data quality should begin at the source system where data is created, updated and deleted.

Typical software components include:
  • Relational data base management software (RDBMS), which stores the data within the respective environments;
  • Extract transform and load software (ETL), which moves the data between environments; and
  • Business intelligence reporting/analytical tools, which provide reporting and decision support capabilities to end-users
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.

Standards and best practices are required to ensure rapid project delivery and optimal return on information management investment.