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Organizational data complexity is increasing. So are the number and complexity of regulations that governments worldwide are placing on that data — as of 2025, 82% of the world’s population are protected by some sort of data privacy legislation. How do data leaders deal with this dual dilemma? The solution to both of these issues can be found in a well-defined data governance strategy, an increasingly crucial component needed for turning data into value in the modern enterprise.
A data governance strategy doesn’t just help organizations meet compliance demands, it’s also useful for improving the quality of your data, which in turn supports your business objectives — including unlocking the power of generative AI. In this article, we’ll explore the nine steps needed to implement a successful data governance strategy, how to overcome any obstacles that emerge along the way and more.
Data governance defines a structured approach to managing, organizing and controlling data assets within an organization. As such, a data governance strategy defines a framework for this approach, setting guidelines and procedures that ensure data is high in quality and secure, while enabling compliance with relevant rules and regulations. The ultimate goal of the strategy is to streamline all of an organization’s data management operations, which in turn increases the value of that organization’s data.
The core components of a data governance strategy include:
Roles and responsibilities of various stakeholders
Rules and policies that help cultivate a data-centric business culture and define data operations
Standards that define how to store and use data, designed to support robust security and privacy practices
Tools and technologies that can make all of the above possible — typically through a centralized platform designed with data governance in mind
Working together, these components allow the organization to manage enterprise data effectively in a way that is as secure and compliant as possible.
Data governance doesn’t live in a vacuum. It’s an issue that spans the enterprise. Without a strong strategy to manage it, data governance problems can quickly rear their heads. These include a higher risk of compliance and regulatory violations, a decline in the organization’s data quality, the emergence of data silos that can erode confidence in data accuracy, and overall reduced operational efficiency.
Strong organizations know that data is a critical asset and that better business decision-making happens by treating that asset with the appropriate level of respect. A strong data governance strategy is the first step on that path.
After building a strong understanding of the value of a data governance strategy, it’s time to get to work and actually implement one. This 9-step process can help get you on the right path to develop and implement a comprehensive one in your organization.
What is the condition of your organization’s data from the beginning of the project? Having an understanding of your existing data quality, the locations of that data and the classification (structured or unstructured) of that data is an essential first step. A metadata management process, in which data is more formally described and cataloged, is also part of this initial evaluation.
What is the goal of your data governance strategy? Do you anticipate major regulatory changes that require heightened compliance obligations? Are you worried about a decline in data quality? Are you looking to better monetize your existing data? All of the above? Determine and formalize specific, quantifiable goals at this stage as part of the strategy and scope.
Without broad support from the C-suite and stakeholders across the enterprise, your data governance strategy is likely to fail before it ever gets off the ground. A Chief Data Officer (CDO) can be critical for getting buy-in from other leaders in the enterprise and helping secure appropriate funding and support for the strategy.
The meat of the data governance strategy development begins here, as the actual governance framework comes into focus. Stakeholders will need to work together to align the goals outlined in Step 2 with broader business priorities, using this information to establish the specific rules and processes that will become the tactical elements of the data governance strategy.
Who will do which work in your governance operations? A Chief Data Officer may oversee strategic efforts, but various data stewards will be instrumental in managing data assets from day to day, validating quality, accessibility and security. Each data steward may be responsible for managing a certain data catalog or data-related process, serving as a liaison between IT teams and end users who need access to the data.
With processes and roles defined, you can now settle on a backbone for your data governance initiatives. A primary goal of most data governance efforts is to reduce or eliminate data silos, which leads most enterprises to strongly consider centralized data management platforms that encourage data sharing and improve security.
Implementation does not need to happen all at once, and a staged rollout is often preferable, focusing on a single data catalog or process at a time to avoid overwhelming users and data stewards. This also helps encourage early wins that can be instrumental in driving continued support for the project.
Every data governance strategy needs to be monitored via metrics that show whether the project is meeting the intended goals. If stakeholders designed these goals as quantifiable objectives, determining the appropriate metrics with which to measure them should be simple. Measure metrics early and often, and track their progress over time.
A data governance strategy is a living organism that should change as the needs of the business change. Metrics should show continuous improvement; consider new components of the strategy as the business environment evolves.
Surprise! Implementing a data governance strategy probably won’t be an overnight success. Obstacles are common, but they don’t have to derail your efforts. Here are a few of the most common barriers you’re likely to encounter along the way (and how to overcome them).
Without a strong leader to champion the project, any technology initiative has the potential for failure. Data governance is no different, and your data governance strategy will likely require support from the highest levels in order to stay on track. An effective CDO can be a key catalyst for the project, championing data governance efforts across the organization.
As with any job, data stewards and other data operators need to know their part. Poorly defined roles can lead to duplication of effort and crossed signals which create conflicting results. Make sure roles are appropriately defined at the outset and monitor those roles to ensure staff are indeed remaining in their lanes.
Stories of under-budgeted technology projects that don’t get off the ground due to a lack of funding are as old as time. Again, part of the CDO’s job is to set appropriate budget expectations at the outset of the project, and to include budget reporting as a regularly measured metric.
Few organizations are staffed by workers who love having “the old way of doing things” upended by new processes. Following change management best practices is as essential for a data governance strategy rollout as it is for any other business initiative.
Naturally, it’s important to settle on a strong platform for your data governance initiatives, as starting over from scratch is rarely an option for any organization. Don’t rush the technology evaluation step; thoroughly consider all of your options before finalizing the purchase decision.
Looking to get a jump start on your data governance strategic initiatives? Consider these best practices gleaned from years of expertise in launching successful data governance strategies.
Think of it this way: Communications are just another form of data. If your teams can’t effectively govern their own communications, they won’t be able to govern the data for the organization at large.
The amount of data at most organizations is massive — and growing. There’s no way to effectively manage that data via manual processes, so look to automated solutions early and often to streamline operations.
For many stakeholders, data governance will be an entirely new concept. Ease the transition to new tools and policies and encourage buy-in with effective training programs that demonstrate the value the program provides.
You won’t know how successful your data governance strategy is unless you measure it. These metrics can also be instrumental in identifying gaps in the strategy and areas where your processes need rethinking.
By taking the above steps to implement a data governance strategy and honing in on the best practices to make sure the plan gets the support it needs, your organization will be in good shape to comply with relevant legislation, keep data secure and ultimately be prepared for all the fluctuations of the data landscape.
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