Data Governance As Part Of A Data Warehouse Initiative

By Anne Smith

Many corporations are experiencing significant business benefits using data warehouse technology.  Users report gains in market competitiveness (increased revenue) and information management (reduced costs).

A data warehouse is a separate architecture used to maintain critical historical data that has been extracted from operational data storage and transformed into formats understandable to and usable by the organization’s analytical community and management.

Data integrity is a major issue within most organizations, and the development of a data warehouse is frequently used as a vehicle to improve data quality significantly.  Accuracy in data can mean realized savings of thousands of dollars in areas such as marketing, customer service, and finance.  Many studies by organizations such as Gartner Group and Innovative Systems point to the savings obtained from a 4% increase in the integrity of data in many diverse companies.

A data warehouse serves as the focus for analytical and decision making querying and reporting, and, therefore, needs the attention to data requirements across the enterprise that a robust data governance program would provide. Data warehouse initiatives require organizations to make many decisions that involve data from several sources, to enable the cross-application analysis that is one of the reasons data warehouses are constructed.  In addition to these foundational data challenges, a data governance program for a data warehouse also can provide analysis for external data that is brought into the warehouse, and can offer the oversight to enforce standards and rules after the decision support system becomes operational.

The principles that drive a data governance effort usually involve components such as data integrity, standardization, standardized change management, and audit capabilities.  These components are especially important in any cross-organizational effort such as a data warehouse.

Although any data governance initiative starts with the same foundational principles that have been the topics for previous articles in this series, developing a data warehouse-oriented data governance program may focus on the following points:

  • Identification of the data warehouse’s stakeholders and their decision-oriented data requirements.
  • Analysis of data integration needs to achieve the cross-application / cross-functional decision-making expected in a data warehouse.
  • Improvement of data quality and data integrity, including standardization of data elements for the data stored in the warehouse.
  • Creation of data definitions for master data (common data) and development of standard codes sets for common (master) data used in the warehouse, including appropriate algorithms and calculations.
  • Creation of common reporting requirements for warehouse data, based on stakeholder requirements.
  • Creation and organizational enforcement of data creation / modification and deletion standards for warehouse data.
  • Development of recommendations to reduce data redundancy and encourage appropriate data reuse.
  • Oversight of management and development of meta data repositories.
  • Development and enforcement of data quality metrics for warehouse data.
  • Analysis of whether data is fit for its intended use, including completeness and business-rule compliance.
  • Implementation of processes to cleanse, transform, integrate and enrich fresh data across subject areas.
  • Development of security and privacy requirements across integrated subject areas in the warehouse.
  • Reporting results on warehouse data management to appropriate senior management.

Many organizations start a data warehouse effort to examine and improve data quality for critical decision-oriented data.  These warehouses will have a governance program that is focused on managing the improvement of the data quality and controlling risks to its continued degradation. These controls may be preventive or investigative, and they may be completely automated or consist of technology-enabled manual processes, or a combination of both approaches.  There are many products available to examine and monitor data quality, both in transaction and decision support systems, and many processes for improving data quality.

Data marts need data governance as much as data warehouses; in fact, the creation of one or more data marts shows the need for an organizational approach to data governance, to provide the oversight and guidance for decision-oriented data across business units, to enable data mart data to be useful across those divisions.  As with many robust data governance initiatives, a decision-support program may start small (a small warehouse or a small set of integrated marts) and grow to an enterprise approach as data governance is accepted and sustained across the organization.

A data governance program can help ensure valid data is in the hands of business users in every department and business function. The results for a data governance program as part of a data warehouse initiative include: more informed decisions; reduced redundant data and colliding definition / calculations; statutory and regulatory reporting using accurate and consistent data and an integrated approach to data management and usage throughout the enterprise.

About the Author

Anne Marie Smith is a leading consultant in Information Management and is a frequent contributor to various IS publications. Anne Marie has over 20 years experience in information management for several corporate entities and has successfully led the development of data resource management departments within corporations and consulting organizations. Anne Marie is active in the local chapter of DAMA and serves on the board of directors of DAMA International, and is an advisor to the DM Forum. She has been an instructor of Management Information Systems (MIS) with Philadelphia, PA area colleges and universities. Anne Marie has taught topics such as: data stewardship and governance, data warehousing, business requirements gathering and analysis, metadata management and metadata strategy, information systems and data warehouse project management. Anne Marie’s areas of consulting expertise include metadata management including data stewardship and governance, information systems planning, systems analysis and design, project management, data warehouse systems assessment and development, information systems process improvement and information resource management/data resource management. Anne Marie holds the degrees Bachelor of Arts and a Master's of Business Administration in Management Information Systems from La Salle University; she has earned a PhD in MIS at Northcentral University. She is a certified logical data and process modeler and holds project management certification. Anne Marie can be reached at

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