What is Data Governance
- devinnturner
- Dec 1, 2025
- 4 min read
Intro
Our world runs on data...
Data governance is the practice of ensuring that the policies, processes, roles and standards are in place for data within a group or organization. In simpler terms, its setting up rules and holding oneself accountable for how, and by whom, data is used.
Why does this matter?
It matters because data has a value, but that value is not always easily accessible. Good data governance practices allows you to identify the value of the data that you ingest, but also the data that you produce. It ensures that the data is handled properly so that it cannot be abused or manipulated by bad actors, and that its accessible to those who need it.
While there is some overlap, it is worth differentiating between data governance and data management. Data management is about the day-to-day handling of data. It is focused primarily on how data moves and is handled. Data governance is more about the processes and accountability structures around data.
Accessibility and Quality
The data is accurate, and those who need it can access it.
This ensures that the right people can find and use the data that they need, and can be assured that the data is accurate, complete, and up-to-date.
Example: A business is trying to forecast their inventory needs for the next quarter. If their CRM has duplicated customer records and missing order history, their analysis will be skewed, possibly leading to over or under-stocking.
Transparency and Integrity
The data is valid and verifiable.
This ensures that the data isn't manipulated or corrupted, and openness regarding how the data was collected.
Example: A business has conducted a customer satisfaction survey. If they were only to report on the positive feedback and were unclear about how the data would be used where surveying, they will be undermining the data, rendering it useless.
Security and Privacy
Sensitive or private data is not exposed.
This ensures that our individual privacy rights are properly safeguarded.
Example: A healthcare non-profit has collected participant information for a community wellness program. If a participant contact spreadsheet was accessed by a bad actor, sensitive data could be leaked and participants could be harmed.
Accountability and Compliance
Data is a responsibility.
Data governance only really works when there checks and balances, and people who are responsible for it.
Example: A regional manufacturer is subject to strict supply chain reporting. If no one is clearly responsible for maintaining accurate supplier data, reporting deadlines could be missed and agreements could be violated.
Frameworks
There are a number of different frameworks designed for different types of organizations.
DAMA-DMBOK
First, there is the DAMA-DMBOK (Data Management Body of Knowledge), a tool-agnostic comprehensive framework. It covers a wide-range of topics, from architecture to security to metadata to operations, making it particularly attractive to larger enterprises. Being a framework that predates cloud and modern AI technologies, it lacks some agility and automation, but it is still widely used and effective.
DGI Framework
DGI (the Data Governance Institute) has their own value-driven framework. It works by establishing a Data Governance program within an organization, with its own mission, vision, and measurable outcomes. The program produces data products, which are subject to controls, accountabilities, decision rights, policies, rules and tools. This makes it useful for organizations that have a strong understanding of the business value of data, but not a clear path forward.
COBIT 2019
COBIT 2019 is a governance framework designed specifically for IT assets, however it is often used as the under-pinning for data governance as well. It is designed to be interoperable with other frameworks and standards, such as ISO and ITIL. This is particularly useful for highly-regulated fields, where asset auditability is a requirement.
DMM Model
Finally, there are the more general DMM (Data Management Maturity) Models. Rather than an over-arching framework for data governance, the DMM Models are an applied practice measuring the maturity of data management within the organization, and can be applied generally to the organization or specifically to a process within an organization. Based on the maturity-level, recommendations are made. This makes it very useful for small organizations with informal standards, but it can be applied in parallel with other frameworks for larger organizations. There are many different maturity models available, and they all have their own strengths and weaknesses.
Conclusion
Data governance isn't just a corporate buzzword, its the foundation of how our increasingly data-driven world operates. Data has a value, and good data governance practices helps us unlock that value while also protecting us from liability and harm.
If you are a small business owner, non-profit leader, or public servant, now should be the time you start asking:
- Do I trust the data I use for decision making?
- Do we manage data securely and responsibly?
- Do my team members have the proper access to the data they need?
These questions are the first step in strengthening your data governance posture. If we treat data with care, we will see the returns in our business decisions, our communities, and our future.
Disclaimer: This article was written by a human author. AI tools may have been used to support research and preparation, but all final writing and ideas are human-created.
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