There is a lot of discussion happening about data governance these days. Data governance is the overall management of the availability, usability, integrity, and security of data used in an enterprise. A sound data governance program includes a governing body or council, a defined set of procedures and a plan to execute those procedures.
In order to get past the confusion of rapidly evolving types, formats, risks, and tools, first identify the most important information and data assets for your organization and start treating them like assets. These assets may already be known but not documented, or identifying them may require chartering and funding a project.
Critical information and data assets vary widely across organizations and departments. They need to be based on the core products, expertise, and risks of an organization, which also may need to be identified. The identification and listing of important information and data assets should include brief descriptions, the most recent owner, and a relative value.
Focusing on the business intelligence marketplace provides insight into some of the common challenges organizations face in relation to their data management strategies. Whether implementing self-service dashboards, developing reporting processes to meet regulatory compliance, or defining business intelligence strategy, one common challenge seems to arise: governance.
The questions around data governance are not surprising and not unusual. How can we ensure that source data can be trusted? How do we develop strong data quality parameters that are consistent and repeatable? Can our current data support better customer experience initiatives? How can we leverage analytics to provide a comprehensive view of our business?
When organizations begin to understand the relevance of these questions to their overall information management strategy, they are ready to start developing a strong data governance initiative that combines governance requirements with analytics.
For most organizations, a single data warehouse is not a reality. Big data sources, increasing complexities, operational intelligence, and information diversity creates an environment that requires a consistent and thorough data management strategy. Added complexities mean more moving parts and a requirement to understand the intricacies of each data process flow.
Although very technological and data focused, strong governance affects business in a variety of ways. Better customer lists and insight into demographics, products, suppliers, partners, etc. create more visibility. This visibility, when used right, helps organizations manage their data more effectively and helps identify potential opportunities and manage performance.
In most cases, it will be beneficial to incorporate principles, standards, and best practices from a variety of complementary disciplines which have found successful ways to deal with the issues – records management, information science, library science, ISO, ANSI, related industries, project management, organizational change, and COBIT and ITIL frameworks for IT governance and management.
Most organizations should have some form of data governance to prevent sensitive information from getting into the wrong hands. However, large companies and regulated industries, such as healthcare or banking, have the most at stake. The amount of effort and expense spent on data governance should be commensurate with the amount of risk.
Melody K. Smith
Sponsored by Data Harmony, a unit of Access Innovations, the world leader in indexing and making content findable.
