Data governance

Data governance is a control that ensures that the data entry by an operations team member or by automated processes meets precise standards, such as a business rule, a data definition and data integrity constraints in the data model. The data governor uses data quality monitoring against production data to communicate errors in data back to operational team members, or to the technical support team, for corrective action. Data governance is used by organizations to exercise control over processes and methods used by their data stewards and data custodians in order to improve data quality.

Data governance is a set of processes that ensures that important data assets are formally managed throughout the enterprise. Data governance ensures that data can be trusted and that people can be made accountable for any adverse event that happens because of low data quality. It is about putting people in charge of fixing and preventing issues with data so that the enterprise can become more efficient. Data governance also describes an evolutionary process for a company, altering the company’s way of thinking and setting up the processes to handle information so that it may be utilized by the entire organization. It’s about using technology when necessary in many forms to help aid the process. When companies desire, or are required, to gain control of their data, they empower their people, set up processes and get help from technology to do it.

According to one vendor, data governance is a quality control discipline for assessing, managing, using, improving, monitoring, maintaining, and protecting organizational information. It is 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 encompasses the people, processes, and information technology required to create a consistent and proper handling of an organization's data across the business enterprise. Goals may be defined at all levels of the enterprise and doing so may aid in acceptance of processes by those who will use them. Some goals include

These goals are realized by the implementation of Data governance programs, or initiatives using Change Management techniques

While data governance initiatives can be driven by a desire to improve data quality, they are more often driven by C-Level leaders responding to external regulations. Examples of these regulations include Sarbanes-Oxley, Basel I, Basel II, HIPAA, GDPR and a number of data privacy regulations. To achieve compliance with these regulations, business processes and controls require formal management processes to govern the data subject to these regulations. Successful programs identify drivers meaningful to both supervisory and executive leadership.

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