Issue #47 – The Misjudged (Yet Integral) Role of Data Governance
Eight minutes to redefine what you thought you knew about Data Governance
Read time: 8 minutes
If there is one data domain that organisations undervalue, it is Data Governance.
I mean, nobody is truly enamoured with the idea of government and bureaucracy. You receive complaints about it being inefficient, creating red tape, and taxing you for your hard-earned money, among other issues.
So why add governance into our corporate structure?
Does the data industry really need it?
I mean, especially since Data & AI has a future-forward and cutting-edge reputation, how can we build fast and break things with governance? We need to invest to stay ahead of the curve and get started immediately without delay!
And governance gives off the opposite reputation: it's a bureaucratic barrier to progress; it is required for risk purposes; it’s hard to execute against and do properly.
However, this thinking is completely misguided.
While many people perceive Data Governance as bureaucratic red tape, meant only for highly regulated industries, it is actually a hidden driver to enable effective, organisational-wide data use. Granted, it is challenging to implement properly, but the investment in finding the right resources and approach will pay dividends across every aspect of your data ecosystem.
These misconceptions around Data Governance run deep, creating a vicious cycle where organisations avoid investing unless required for compliance or risk reasons. This is short-sighted, and this, along with my following two articles, are intended to demonstrate why.
The Misperception of Data Governance
Before we explore what Data Governance really is and what it accomplishes, it's worth dispelling the myths that make (in my opinion) DG the most undervalued domain in the Data Ecosystem.
These misperceptions stem from three persistent beliefs that have long plagued the data industry. I can guarantee you that if you chat with your data or business leader, one of these reasons is probably why you don’t have a DG team (or your team is made up of one person).
Myth 1: Data Governance is Bureaucratic and Slows Progress
Governance is often perceived as a hindrance to innovation and progress. This stems from traditional views of governance as a slow, committee-driven process that requires endless approvals and documentation before anything gets done
Compare this to the favourite quote in data: "build fast and break things". So when data leaders hear the term ‘governance’, they imagine months of policy writing, committee meetings, and red tape that will slow down their urgent AI initiatives or critical dashboard projects (rather than building fast)
In reality, governance should enable business and data teams to use data more effectively. Governance may add some checks and balances, but it should do so in the name of long-term progress and proactively addressing potential issues. Poor implementation of a DG team without a clear Data Strategy also slows progress, as the DG team is not aligned to deliver value through its work
Myth 2: Data Governance is Primarily for Risk and Compliance
Risk, security and compliance. These three things tend to be what people think of when considering the Data Governance remit, existing solely to help the company meet regulatory requirements, such as GDPR. This places the DG team as a necessary evil rather than a business enabler
This view also suggests that governance is only relevant for heavily regulated industries, like finance, healthcare, or government. A survey in 2019 (so a few years old) showed that compliance is the main driver for data governance for 56% of companies, with this figure rising to 64% in Europe
The problem with this perception is that while protecting against security breaches, addressing regulatory requirements or safeguarding data privacy is important (especially today), governance should be an enabler. With properly governed data, stakeholders (both business and technical) understand who owns the data, how it is structured within the organisation (e.g., data domains/lineage), and how it can be utilised. Trust me when I say this, but these three things are some of the biggest things slowing down companies’ data & AI progress, and a lot of this comes back to poorly governed data
Myth 3: Data Governance is Too Hard to Deliver
Finally, the idea that Data Governance is not worth the effort because it is too difficult. This one has the most merit: many organisations struggle to find executive sponsors, knowledgeable leaders, and dedicated resources. Moreover, the setup time is viewed as substantial, making it difficult to justify an investment case, especially with tight data budgets. A lot of companies just come to the conclusion that their technology and good data engineering can solve for governance (please don’t do this)
These issues boil down to a lack of obvious value seen by the leadership team, which leads to a low budget. Without the budget, it becomes hard to find good, high-quality DG analysts (especially because they are few and far between)
While I do admit DG is hard to deliver on, it isn’t impossible. Companies often try to tackle cross-organisational governance when they should start small. Focus on a priority data project, test what works, and expand to other higher-priority initiatives. Moreover, when it comes to people, don’t just expect Data Governance analysts to learn and execute it all. This type of thinking should be instilled in the role of Data Engineers or Analysts, as they need to build ownership and governance policies into the data workflows that the organisation runs on.
Understanding these myths is the first step in gaining buy-in and securing a budget for a DG team. It is also good to know where they originate from:
Poor Understanding of What Governance Actually Is – Many organisations confuse governance with data management or treat it as "just" data quality and master data management. This narrow view misses governance's strategic potential
Immature Data Practices – Organisations without strong foundational data capabilities often implement governance as an afterthought or bypass it for other ‘higher value’ domains like analytics or Data Science
Technology-First Thinking – Companies often believe that a strong data stack can compensate for a lack of governance, leading to technical solutions without organisational alignment
Bad Implementation Experiences – Previous failed governance initiatives create scepticism, making future efforts more difficult to launch and sustain
Lack of Data Governance Purpose – Misalignment on what Data Governance is there to do is a non-starter. Without a clear role and path to value, nobody will be bought in
So now that we have set the foundation for the myths and why people think this way, it’s time to get into what DG really means.
Defining Data Governance as a Business Enabler
Despite these persistent myths, organisations are starting to recognise governance's true importance. Just as countries grew large enough to require some sort of governance (instead of anarchy), organisational data ecosystems are hitting that same scale, and companies are realising governance is essential to manage data properly to create the foundation for value-enabling analytical activities.
So a lot of companies are slowly shifting toward business-enabling Data Governance practices that prioritise value creation.
McKinsey research backs this up, demonstrating leading firms have "eliminated millions of dollars in cost from their data ecosystems and enabled digital and analytics use cases worth millions or even billions of dollars. Data governance is one of the top three differences between firms that capture this value and firms that don't."
Let’s redefine Data Governance in a more business-enabling way. See my definition below:
Data Governance establishes clear ownership and processes that make data trustworthy, accessible, and valuable across the organisation, ensuring data drives business value rather than business risk.
I’m going to reference back to two of my past articles to explain how this comes together: (1) Data Quality Issues in Today’s Ecosystem and (2) The Data Product Supply Chain.
In the first article, I referenced the top five issues I see in organisations right now regarding data quality. A lack of accountability, unclear standards and definitions, and increasing complexity are three of those challenges, all of which data governance can help address.
However, governance can only be effective if it is aligned with the overall strategic direction, particularly when it comes to the data products the organisation needs to develop to meet its business needs. As you can see in the graphic below, Data Governance encompasses the ownership and compliance processes that support the delivery of data products. It enables the data and technology infrastructure to operate under strategic direction and guides the execution of data management required to maintain high-quality data.
Given all this, the definition laid out above positions governance as an enabler rather than a barrier. Within the Data Ecosystem, this establishes Data Governance as a linchpin between different domains, enabling their success by:
Establishing Guidelines for Managing Data and Technology Systems – Governance ensures enterprise-wide adherence to who owns what data and who is responsible for managing it. From a systems perspective, DG principles outline risk and compliance processes for technology owners to follow, specifically concerning how tools interact with data
Collaborating with Data Engineering and Quality Practices – Modern governance ensures business teams work with technical teams on backend foundational decisions. This ensures the data foundations align with business and compliance needs and prevents the disconnect that usually exists between the backend and frontend business requirements
Building Trust in Data Products – Establishing accountability for datasets and creating clear lineage documentation helps governance establish trust that business stakeholders often lack. Users can also be assured that the data they use is secure and doesn’t breach any regulatory requirements or create inherent risks. This is necessary for widespread analytics adoption and for business users to be confident in the insights they're seeing
Empowering AI and ML Initiatives – Especially with AI, poor governance will lead to ‘garbage in, garbage out’ scenarios. Data Governance is the proactive approach to ensuring data quality is managed prior to entering the AI supply chain
Cultivating Data Literacy and Culture – Data Governance is fundamentally about creating alignment on how data can be used to drive value. Therefore, these programs should include training, stakeholder management and getting buy-in, which does a lot better for data literacy and culture than releasing an unsuspected tool on the business teams
Now, before we go any further, I do want to clear up some confusion around how governance differs from related disciplines:
Management – Data management focuses on the technical aspects of handling data throughout its lifecycle, whereas governance defines the organisational policies and frameworks that guide those activities. Think of governance as the blueprint, and data management as the executional activities
Data Security – Platform and data security is a component of governance, outlining the actual protection policies and technical access controls for data and systems. Governance provides the broader framework for managing data securely and identifying the level of security each dataset needs to have and the type of role that should have access
Compliance – Regulatory compliance extends beyond data. In that sense, compliance works closely with DG teams to ensure the data adheres to those rules/ regulations, and there is an owner in case any compliance risks come about
Overall, we can see that taking a business-enabling perspective on Data Governance puts the domain in a very important role in creating sustainable business value from data.
What to Take Away About Governance
I would argue that the journey to understand what Data Governance needs to represent is one of the most important shifts an organisation can take.
So many of the challenges we discuss in today's data world—building data culture, succeeding with AI, creating ownership across your analytical data, establishing solid data foundations—come back to the role of governance.
Organisations that recognise this connection and invest in business-enabling governance capabilities will be the ones that unlock the most value from their existing data and any investments they’ve made in the area.
But understanding the role governance plays is only the first step. In our next two articles, we'll dive deeper into both the strategy and implementation of Data Governance in an organisation. First, we will examine how to approach governance strategically and propose a framework to organise that domain. We will then evaluate the tactical elements of implementing governance successfully, from securing sponsorship to managing the cultural transformation that effective governance entails.
I hope you enjoyed this article and that it has prompted you to think about Data Governance in a different way. Have a great Sunday and see you next week!
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Great read! Thanks for referencing some of my articles 😊
Can’t wait to next parts