Data Governance is a collection of components – data, roles, processes, communications, metrics, and tools – that help organizations formally manage and gain better control over data assets. As a result, organizations can best balance security with accessibility and be compliant with standards and regulations while ensuring data assets go where the business needs them most.
Outcomes for better data control lead to efficient methods, technologies, and behaviors around the proper management of data, across all levels of the organization. From the senior leadership team to daily operations, governance ensures alignment by providing structure and services.
Data Governance often includes other concepts such as Data Stewardship and Data Quality. These bases help connect governance details with the data lifecycle, improving data integrity, usability, and integration. Both internal and external data flows, within an organization, fall under the jurisdiction of governance.
Other Definitions of Data Governance Include:
- “The exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets.” (DAMA International)
- “A system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.” (Data Governance Institute)
- “The specification of decision rights and accountability framework to ensure the appropriate behavior in the valuation, creation, consumption, and control of data and analytics.” (Gartner Glossary)
Use Cases Include:
Some Benefits Include:
- Lower costs associated with other areas of Data Management
- More accurate procedures around regulation and compliance activities
- Greater transparency within any data-related activities
- Help with instituting better training and educational practices around the management of data assets
- Increase in value of an organization’s data
- Ability to provide standardized data systems, data policies, data procedures, and data standards
- Better resolution of past and current data issues
- Improved monitoring and tracking mechanisms for Data Quality and other data-related activities
- Overall enterprise revenue growth
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