What is data governance and why does it matter?

What is data governance?

Data Governance is the sum of processes and practices utilized by an enterprise to ensure the formal management of data assets. In an enterprise, all assets need to be managed responsibly and effectively—and data is no different. With that in mind, data governance is the framework through which a company establishes data strategy, policy and objectives.

The goal of data governance is to ensure that data within the organization is trustworthy and consistent. In the modern world, we rely on data to drive innovation, particularly when it comes to optimizing the business or exploring new opportunities. Data insights help businesses improve decision-making, enter new markets and expose blind spots in current practices.

With new data regulations coming into effect every year, data governance also helps to protect data. Failure to adequately safeguard sensitive data can result in fines for non-compliance and can also be harmful for the reputation of the company.

According to the 2019 State of Data Management report, data governance was a top 5 strategic goal for organizations in 2019. Many companies are now focusing more heavily on data governance but are unsure how to structure their framework or what data governance should mean to their enterprise.

Why does data governance matter?

Business intelligence can only be effective when data is clean and consistent. Without robust data governance, erroneous, inconsistent or duplicated data can impede effective data analysis. For example, if customer data is siloed across different systems and there is no data governance to consolidate this data, then customer names, addresses or contact information might appear different to different teams. This data is subsequently no longer meaningful. The continued use of incorrect data can also cause friction between teams or with customers.

Well-managed data governance also increases efficiency. Good data governance paves the way for prime optimization. And simply by removing duplicate data, the risk of duplicated errors is also eliminated. Increased efficiency saves costs and the reduction of data errors reduces labor costs associated with fixing these errors.

Poor data governance is also inherently risky due to regulations. GDPR came into effect in the EU in 2018 and grants citizens more control over their data. Another example is the California Consumer Privacy Act (CCPA), also aimed at increasing data transparency and security. Noncompliance with these regulations can result in steep fines. Poorly structured data can be a security risk for the simple reason that it’s easier to see unusual activity in structured data.


Data governance is a complex topic that spans a wide range of industries, departments and stakeholders. In order to fully understand what it means, and how to practice good data governance, we have compiled a detailed and comprehensive guide answering the following questions:

What is data governance?
Why is data governance important?
What are the aims and benefits of data governance?
● Who oversees data governance in an organization?
What does a data governance framework look like?
How can enterprises implement data governance?
What are some data governance best practices?
What can NodeGraph do to help?