Data governance ensures that roles and responsibilities regarding data are properly vested in the organization. Processes and consultation structures are set up for this, so that information management and data management are structurally secured. In addition, a number of policy principles are drawn up top-down, and these are elaborated into rules. Auditing takes place on this, so that people within an organization know for sure that these rules are also being followed. External regulators or clients may also perform these audits. Monitoring data quality management also falls under data governance.
Within data management, the responsibility for the data is further elaborated. Components of data management are metadata management, master data management, data quality management and proper handling of privacy rules regarding the use of data. Metadata management is about recording the meaning and use of data. Master data management is about dealing with a specific group of data, namely the important data of an organization that is used in multiple processes, such as product data and customer data. Data quality management provides a structural way of dealing with errors in data, and solving them, by adapting systems or by manual or automated repair work.
Why data governance and data management?
Many regulations require companies to have their data in order. Think, for example, of the obligations arising from the European privacy legislation from 2018 “General Data Protection Regulation” (GDPR or AVG). Organizations often set up a compliance process and organization to ensure compliance with laws and regulations. Additional obligations often apply to banks and insurers with regard to risk management, information provision and data governance. This can go so far that banks must be able to demonstrate how information on ECB reports is derived and constructed from data in source systems and what the quality of this underlying data is. There can be hefty fines for non-compliance with regulations. Another risk concerns the image damage that organizations may suffer if they do not comply with the guidelines, for example because data ends up on the street. A use case arising from a compliance finding often leads to the obligation to structurally set up data governance.
Reducing project risks
When data governance and data management are properly set up, this leads to a better insight into the quality of the data. Project teams can then take appropriate measures at an early stage and thus significantly reduce the risk of a disappointing result.
Increasing commercial effectiveness
Strategic value analysis
More confidence in reports
When line managers are aware that structural attention is paid to the origin of information on reports and that specific work is done on data quality, this leads to more confidence. It often happens that information in different reports is not reconcilable. This leads to a lot of research and can encourage managers to create their own reports. This takes a lot of time and capacity, which can be better used to work on process improvement based on the right information.
Preventing steering errors
In the example above, the manager notes that information is inconsistent and possibly incorrect. But what if it isn’t? If the turnover is accidentally allocated in the wrong way and is thus directed on the basis of incorrect information? That can lead to wrong priorities. Reliable information is the basis for better predictions that help an organization stay ahead of the competition.
Communication between IT and business