Data Governance Framework: 5 Key Elements

By on
data governance framework

Like many roles and operations that have emerged in the digital age, Data Governance is often misunderstood. Some might confuse it with Data Management. Others might see it as an extension of IT. How can you communicate the role of Data Governance and ensure you implement it successfully? At DATAVERSITY’s Enterprise Data Governance Online event, data practitioner John Ladley shared five essential elements for a Data Governance framework.

Defining the Terrain for Your Data Governance Framework

First and foremost, you must clarify the roles and responsibilities of Data Governance. This will help prevent other Data Management activities from bleeding into its domain. Data Governance is fundamentally about “oversight and control,” said Ladley. Still, some people may recoil at the word “governance” and associate it with being told what to do.

An even bigger roadblock to good Data Governance? Ensuring the clear separation of duties found in most other organizational structures – but rarely within data culture.

“Any complicated system has checks and balances in this world,” noted Ladley, “until we get into our complicated data landscapes, and then the wheels wobble and everything goes pear-shaped.”

In many cases, business leaders expect Data Governance specialists to fix the nuts and bolts of operational problems. This, in turn, causes them to become bogged down in their execution. Instead, effective Data Governance should identify problems and allocate their solutions to the appropriate business area. This will help the right people get the right things done. While the Data Governance team should task itself with defining these “right things,” other departments should carry them out.

Aligning Data Governance with Data Strategy

Because Data Governance focuses on implementing long-term changes, it’s essential to align the framework with a company’s Data Strategy. The challenge? While most organizations employ some form of strategy, it may not be obvious. In some cases, leadership may even intentionally withhold it. If your company’s strategy is unavailable, check current business records or publications like the Wall Street Journal. Look for comparable industry norms and practices and gauge where your business is trying to head. These strategic intentions are what Ladley calls business capabilities.

By first defining business activities and initiatives, the Data Governance team can then derive the organization’s data capabilities. This will ensure that all data-related activities benefit the business rather than being the result of someone “giving you an order.” In short, don’t build a business glossary just because there’s a mandate for a business glossary, advised Ladley:

“Otherwise, you will be an order taker; you will have someone tell you, ‘Just give me the car keys – I’m driving this bus.’ Successful Data Governance efforts do things for the organization that you can measure.” 

Done successfully, Data Governance can help organizations:

  • Maintain good Data Quality
  • Comply with the latest data regulations
  • Ensure data privacy and security
  • Make better use of data for more informed decision-making

Taking a Thoughtful Approach to Your Organization’s Needs

A common misconception about Data Governance is that it is a one-and-done, top-down effort. Instead, Ladley recommends implementing governance gradually. A Data Governance framework must evolve as new data regulations are introduced and the organization’s goals and capabilities change. What’s more, the “level” of Data Governance will look different for different organizations. 

The most basic level of governance involves identifying what needs fixing – and determining where to start making things right. Then, the governance team can begin the process of what Ladley calls “formalizing the informal.” This involves identifying latent data assets within the company and demonstrating that these assets could add value over time.

When governance is scaled even more broadly, using reference data to support analytics, master data management will come into play. The next tier of problem-solving widens to include the more explicitly data-driven structures such as data hubs and data architectures. Finally, the most macro-scaled implementations of governance come in the form of external regulations, such as the General Data Protection Regulation (GDPR), which necessarily engage all aspects of the organization. 

From Small g to Big G

While evaluating an organization’s needs, it may not be necessary to start off small, but it’s crucial to always think incrementally. As the process edges from the more locally defined “small g” governance towards the more broadly organizational “big G” models, the nature of the models will also evolve.

At the most local level of governance, models tend to be reactive – that is, they respond to existing problems. Once governance steps into the initial phases of advanced implementation, governance shifts to be more proactive in oversight, as the day-to-day realities of the organization become baked into the paradigm. When a Data Governance program grows to full alignment with the strategy of the company, “it disappears,” said Ladley. “It becomes part of the fabric of the organization.”  

Emphasizing Data Literacy in a Data Governance Framework

The concept of Data Literacy refers to the ability to read, comprehend, interpret, and communicate with data, but being “data-literate” can mean different things to different people. For Ladley, a few guiding questions define the domain of literacy: Do you know how your business manages data and uses it along the entire supply chain? Do you understand the best ways to analyze the data, as well as the ramifications and risks of using the data?  

While the CEO and the executive board don’t need to be adept in executing metadata operations, they need to understand that data is never a fully local entity but is processed through a supply chain. The more data-oriented a business is, the more intimate top management will need to be with that chain.

“No one is excused from Data Literacy,” said Ladley, “but it has to start with leadership. A board that is overseeing an automobile manufacturer is rarely going to hire a CEO who knows nothing about automobiles.”

Ironically, Ladley posited that some of the biggest culprits of data illiteracy in the domain of governance are IT workers, even though they work with data every day. Indeed, development operations are sometimes obstacles to governance, even if they are crucial to data production. But Ladley sees data illiteracy as a function of corporate culture rather than any misunderstanding of roles and responsibilities: “The root cause of illiteracy is people just don’t seem to care. They don’t want to engage with this, but it really, really is important.”

Recognizing New Capabilities 

In the course of becoming a truly data-driven organization, it’s likely that novel business capabilities will emerge. For Data Governance, the trick is to recognize which are truly new – and which are merely old assets in new clothes. For example, monetizing data, entering into data agreements across the supply chain, or AI are all initiatives that create new capabilities and demands, not merely IT projects. However, innovations in data don’t require a different order of implementation or management than in the days of pre-digital operations.

“Organizations know how to do new,” Ladley reminded. “If your organization has adopted Lean Six Sigma, or did business reengineering, or did a merger or an acquisition, you know how to do new.”  


Many senior managers may resist the very concept of Data Governance, fearful that it represents a sort of revolution in technology. But that’s far from the truth, said Ladley. A Data Governance framework is simply the formal implementation of policies and processes that make most of modern life run smoothly – from banking to food safety to traffic regulation.

Almost all of the strategies and duties in governance have already been mastered in another field of company operations, making most changes more retrofits than reinventions. The key is to recognize what your company has already been doing right and go from there.

“None of this is rocket science,” quipped Ladley. “Don’t make this thing bigger than it is. You’re doing something good for your organization, but it’s not brand new and it’s not something to be afraid of.”

Want to learn more about DATAVERSITY’s upcoming events? Check out our current lineup of online and face-to-face conferences here.

Here is the video of the Enterprise Data Governance Online presentation:

Image used under license from