Data Governance As Data Quality Improvement Vehicle

By Anne Smith

Data quality is like the weather: many people talk about it but few address its challenges.  Data governance can play an active role in the improvement of an organization’s data quality and develop a permanent role in the management of the organization’s data.

Poor data quality abounds; every organization can recite many instances where a data quality problem caused extra work, lost revenue, higher costs, production problems, etc… However, many companies struggle to address and resolve their data quality issues from a data management perspective.  “If you talk about the data, you’re talking about the integrity of the business,” says Philip Say, an SAP director of solution marketing for ERP and finance applications. “Companies are grappling with understanding what they have really created — what’s really running their business.”  Awareness of bad data is on the rise, and the practices needed to address data integrity issues across the enterprise are now available.  Using these practices can enable an organization to face their data challenges and improve the quality and usability of their data permanently.

Historically, companies have allowed IT to shoulder the burden of correcting data quality problems, despite the fact that many data quality issues arise in the business and not from information technology.  Many companies “solve” the data quality problem by having large numbers of staff extract data from various sources, parse it and re-key it into spreadsheets so management could have some analytical data.  This process does not resolve any data quality problems, and may create additional errors made in the parsing and re-keying of the source data.

As compliance regulations, such as the Sarbanes-Oxley Act and HIPPA, have emerged, the ultimate responsibility for accurate data has shifted from IT to business leaders.  The development of the practice called data governance provides the organization with a format for fixing the data quality problems of the past and avoiding new data quality errors.

When organizations examine data management and data governance issues, they discover two major problem areas: responsibility and management.  Data governance and the data stewards that form the foundation of the governance program are responsible for the data instances (values) and the meta data for that data – the two parts of data quality management.  Therefore, it is important that any data governance program include the development and implementation of a data quality improvement component.

The change in responsibility for data quality is also a change to the organizational culture, driven from the executive layer. Executives and business managers must understand the scope of the data quality problem and support the development of a governance process. Once executives are committed, the entire organization must be made aware of the governance effort, its focus on data quality and each staff members’ responsibility for data quality improvement. Bad or missing data elements create chaos and poor results, and possibly economic disaster.

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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