Why is data governance so important?
What exactly is data governance?
Data Governance can be broadly defined as the processes set internally by a company to ensure data quality and how it is handled. Data governance strategies will include (but not be limited to) policies, standards, and systems in place that will align with the company’s data goals. The key targets of a data governance strategy will often be to maintain data security, validity, and reliability.
A solid data governance strategy will always clearly define everything that needs to be done to achieve the objectives that have been laid out. It will establish everyone’s roles, responsibilities, and accountability to ensure transparency and clarity. It will also outline quantitative indicators and metrics for a clear strategic direction. An example could be the length of time that an organization will retain a customer’s personal information. This, of course, is not a figure that can be plucked from the air but will be influenced by specific laws and regulatory bodies.
Data governance is, nowadays, a critical component of any company handling big data. They can help companies avoid dangers and security risks and extract more value from big data.
Ways to strengthen a data governance system
Data catalogs are organized inventories of company metadata assets. They are an excellent and, nowadays, vital part of any well-functioning data governance system. Data catalogs provide up-to-date, comprehensive information on metadata assets, making them extremely helpful to data stewards in identifying suitable and valid data. This results in more robust and more resolute data governance as the risk of utilizing data that contradicts compliance standards is minimized.
Data lineage visualizes data flow through an organization, documenting every part of its journey. The background information it provides can be invaluable to data stewards in determining data safety, validity, and reliability. Therefore, it is often considered an indispensable tool for shoring up data governance practices, as the information it provides can help ensure the standards remain adhered to.
Data Quality and data governance are very often referred to as one entity. While the two concepts overlap in many ways, they are different. Data quality refers to a piece of data’s overall grade based on how well it meets specific criteria (including reliability, accuracy, validity, completeness, etc.) Data quality is often quantified following various metrics and visualized in graphs, scorecards, and the like. Organizations regularly invest heavily in protecting, maintaining, and upgrading their data quality management processes. Data governance is the set of practices, policies, etc., to ensure that data quality, among other areas, is maintained. In other words, data quality is one of the core drivers behind a data governance strategy. This connection would likely explain why the two terms are often used interchangeably.
Advantages of data governance
Compliance with internally set company standards and external laws and regulations is undoubtedly a significant reason for investing in solid data governance. When a company has decided on its standards, it is essential to ensure they are followed throughout the organization. Data governance is crucial in maintaining a consistent, informed approach among all data users. It is equally vital to ensure that international and regional laws, regulations, and requirements are being adhered to.
Data democratization refers to the increased accessibility of data within an organization. Rather than being kept for the select few within the tech department, employees from any department are encouraged and taught more about how to use data appropriately. Data democratization is naturally going to increase in tandem with data governance. This is because more roles and responsibilities are defined to delegate more effective handling of data across the organization. This, in turn, results in a greater level of education on data standards and more employees benefiting from it. Companies can ensure improved data quality management practices by increasing enterprise-wide understanding of how data works and how it should be used.