Data Governance: Concept, Classifications, Applications
Data Governance (DG) and Data Management are two crucial components in the digital landscape, often confused due to their interconnected nature. However, according to leading authorities such as DAMA International, the Data Governance Institute, and Gartner Glossary, these two concepts have distinct roles.
Data Governance, as defined by Gartner Glossary, is the specification of decision rights and accountability framework to ensure the appropriate behaviour in the valuation, creation, consumption, and control of data and analytics. It is the exercise of authority and control over the management of data assets, setting the strategic framework, policies, standards, roles, and accountability to ensure data is accurate, secure, compliant, and treated as a valuable business asset.
On the other hand, Data Management focuses on the tactical execution—the day-to-day activities and processes that implement the governance framework. This includes the actual handling, storage, and processing of data according to the rules defined by governance. Data Management addresses the "how" of managing data assets, ensuring quality, accessibility, and operational use of data.
In a metaphor used by Gartner and others, Data Governance is like the constitution or laws that define how data should be treated, while Data Management is like the government or administration that carries out those daily operations in accordance with those laws.
Data Governance contributes to the growth of the enterprise's revenue by providing reliable data to business analysts, highlighting new services customers want and for which they would pay more money. It reduces the amount of duplicated and inaccurate data among divisions, lowering costs associated with other areas of Data Management. Moreover, it adapts to changing business circumstances, like new technology, through an agreed-upon model that evolves agilely.
Data Governance ensures regulators and employees have confidence that the company handles personally identifiable information legally, improving transparency within shared data activities. It standardizes enterprise-wide data, increasing its value for potential sale as training materials for AI. DG provides exact data lineage to regulators, improving accuracy in regulation and compliance activities.
Data Governance also provides data consumers with a consistent experience and a straightforward pathway to agree and resolve questions about shared data. It connects various components such as roles, processes, communications, metrics, and tools to ensure the right data flows to the right resources at the right time. Each DG element works with the others to guide shared data usage and access internally and with third parties.
In conclusion, Data Governance and Data Management are distinct but interdependent elements in the data management ecosystem. While Data Governance sets the strategic framework and policies, Data Management ensures their practical execution. By understanding this distinction, organisations can effectively manage their data assets, ensuring compliance, improving data quality, and driving business growth.
[1] DAMA International. (n.d.). Data Governance. Retrieved from https://dama.org/data-governance/ [3] Gartner. (2021). Data Governance. Retrieved from https://www.gartner.com/en/information-management/glossary/data-governance [5] The Data Governance Institute. (n.d.). What is Data Governance? Retrieved from https://www.datagovernance.com/what-is-data-governance/
- Data Governance (DG) dictates the strategic framework, policies, standards, roles, and accountability for data management, ensuring data is accurate, secure, compliant, and treated as a valuable business asset.
- Data Management, on the other hand, is responsible for the tactical execution of the governance framework, focusing on the day-to-day handling, storage, and processing of data according to the rules defined by governance.
- Technology plays a crucial role in both Data Governance and Data Management, as it enables the practical execution of data management processes and the implementation of the strategic decisions made by Data Governance in the data-and-cloud-computing environment.
