Data governance is a term used to refer to the management of processes, roles, policies, standards, and metrics related to business data. Organizations rely on data governance and governance tools to manage and gain better control over data assets. Data governance solutions support organizations in balancing data security with accessibility and are designed to comply with different standards and regulations.
In this definition...
What Is Data Governance and Why Is It Important?
Effective data governance ensures that data is trustworthy, consistent, and used correctly. It also speeds up project implementation. Many business projects rely on well-managed data, especially those that address compliance requirements or push enterprise-wide business process integration.
What Role Does Data Governance Play in an Organization?
Data governance forms a foundation for sustainable data quality improvements in an organization. It enhances data integrity and accuracy and generates returns through issue prevention and automation. Data governance policies can increase data transparency and increase stakeholders’ trust in the data.
Strong data governance best practices also increase data’s value. It ensures data is “fit-for-purpose” and can be used in business processes, decision-making, and business models.
What Happens When Data Governance Is Done Wrong?
Bad data governance results in bad data. Bad data can lead to inaccurate analysis, uninformed business decisions, and poor customer relationship management. Another significant consequence of bad data is the wastage of resources. For example, if marketing materials don’t reach the intended audience, a business loses money.
And finally, bad or mismanaged data can lead to noncompliance with various regional and industry-specific data regulations; noncompliance can result in hefty fines and other legal consequences. According to Gartner, bad data costs businesses $15 million per year in losses.
What Are Data Governance Best Practices?
Build a simple governance system
A data governance system should be built linearly so data governance can happen from a centralized place. A simple system is easier to manage. Also, data governance rules should be defined in very few locations. Tools that enable the centralization of governed business logic are easier to maintain than those that require the same concepts to be defined in different places.
Focus on the people
Data governance combines people, processes, and technology. The right people are required to develop the right processes before technology is incorporated. Without the right people, it’s difficult to build the processes required for the technical implementation of data governance. Organizations should hire professionals who will develop the processes, source the technology, and act with integrity.
Use metrics to measure data governance success
Data governance can be difficult to measure because it involves the use of different tools, new processes, and newly defined roles. But there are several strategic metrics organizations should use to measure the value and overall success of a data governance program.
The top four metrics to measure are:
- Improvements in data quality scores over time
- Decreases in data rectification costs
- Enhancements in data risk management
- Overall adherence to data management standards
What Kinds of Tools Are Used in Data Governance?
Some of the most widely used data governance tools are Alation Data Catalog, Ataccama, IBM Data Governance, Atlan, and Informatica’s various data governance solutions.
Other enterprise software and SaaS solutions offer support for data governance as well, but it’s important to discuss with vendors what regulations and strategic data governance initiatives their platforms natively support.
Want to learn more about your data governance options? Read this guide on the Best Data Governance Tools & Software.