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What is Data Governance?

A Definition of Data Governance

Data governance is a term used to describe the overall, comprehensive process for controlling the integrity, use, availability, usability, and security of all data owned by or controlled by an enterprise. Often, enterprises appoint a team or council to oversee complex data governance programs. This strategy may include representatives from various departments to offer the multitude of perspectives that exist within most enterprises and enable the creation of data governance policies and procedures that are practical and address the needs of departments company-wide.

Data governance is such an important facet of modern enterprises that an entire organization exists solely dedicated to fostering ongoing knowledge and providing resources for enterprises implementing data governance programs. The Data Governance Institute defines data governance as “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.”

However, The Data Governance Institute further points out that the term data governance, depending on context, may be referring to a variety of different things, including organizational bodies, the rules, standards, and guidelines implemented by a company, decision rights or who has the authority to make decisions based on specific data sets, accountabilities, or enforcement methods. Therefore, the precise definition of data governance depends heavily on the context in which it is used.

Goals and Benefits of Data Governance

Data governance programs and initiatives are undertaken by enterprises with the goal of increasing revenue and profitability, enhancing the value of services, products, and decision-making, managing cost and complexity, and/or increasing awareness of risk and/or vulnerability. This may relate to regulatory compliance, security, privacy, and similar concerns impacting today’s organizations.

Implemented effectively, data governance provides numerous benefits for enterprises, including:

  • Facilitating effective decision-making
  • Protecting the interests of data stakeholders
  • Standardizing procedures and processes for streamlined repetition
  • Reducing costs and improving overall effectiveness

There are many other goals and objectives of enterprise data governance programs, ranging from broad, across-the-board objectives to industry-specific requirements and goals. Overall, data governance describes the set of people, processes, and technology that facilitates the most effective use of enterprise data as well as defines protocols and policies and implements systems to secure sensitive enterprise or consumer data.

When data governance programs are comprehensive and efficient, enterprises benefit from overall improved workflows, better decision making, reduced costs, improved performance, and greater transparency and accountability. In short, investments in data governance are typically returned multi-fold.

Data Governance Varies Widely in Practice

With so many possible objectives and outcomes of data governance programs, it’s not surprising that data governance often looks vastly different from one enterprise to the next. Data security and data management solutions play an important role in data governance, as well as other software solutions that aim to streamline data aggregation and analysis.

The final configuration of these systems, tools, and people make up data governance for any individual enterprise. The key to effective data governance lies in configuring the systems and solutions best-suited for your enterprise’s specific requirements and goals, as well as selecting the right software and solution partners capable of accommodating your enterprise data, without introducing unnecessary vulnerabilities and risks.

While there are clear benefits to be gleaned from effective data governance programs, aptly notes that data governance programs are most effective when used as a component of an overall IT governance strategy. This practice prevents potential obstacles in implementing data governance programs, such as teams or departments circumventing data governance policies in order to further their own objectives.