Data Governance: A Human Challenge, Not Just a Tech Issue

We’ve all heard it before: Data is the new oil. But if that’s true, then most enterprises are still digging with spoons. Despite massive investments in cloud platforms, analytics tooling and artificial intelligence (AI) readiness, the promise of data-driven transformation continues to slip through the cracks. Silos persist. Data quality issues remain rampant. And trust? Often missing in action.

Here’s the hard truth: the biggest barrier to data value isn’t technological — it’s human.

We often treat data governance like a tech project — deploy a catalog, implement policies, buy a tool, done. But governance isn’t a piece of software you install. It’s an organizational capability you activate. That activation requires a shift in mindset, behavior and culture.

True data governance is about enabling value and building data agility, not enforcing control. It’s how we ensure the right people have the right data at the right time, with confidence that it’s accurate, secure and usable. When done right, governance accelerates business. It’s the foundation of:

●         Faster, trusted decision-making enabled by consistent data quality
●         Operational efficiency by reducing data friction and time lost on cleanup
●         Risk management and compliance that lets business move faster with confidence
●         Accelerated innovation (advanced analytics, machine learning and AI) supported by high-quality, accessible data

This isn’t theory. Poor data quality alone costs businesses billions annually. And governance, properly activated, is the remedy.

Despite good intentions, data governance programs often stall. Why? Because we approach them as top-down IT initiatives, not as transformations that require cultural change.

Here's the pattern:

●         Governance is branded as a compliance effort.
●         Business units see it as extra work.
●         Data stewards are underfunded and unsupported, and they often have little data knowledge.
●         Momentum fizzles.
●         Another governance reboot begins next year. The business groans.

Sound familiar? To break this cycle, we need to stop asking, “What tool should we implement?” And start asking, “How do we change behaviors at scale?” That’s where organizational change management (OCM) comes in.

If governance is the engine, OCM is the ignition. It’s the structured, people-first approach to helping your organization move from current state to future state, with less friction and greater success.

Projects that incorporate strong change management are up to seven times more likely to succeed. That’s not just theory. That’s the difference between a data governance platform gathering dust and a data culture that drives competitive advantage and innovation.

Here are the most common failure modes we’ve seen across industries and how change management corrects them.

It’s not enough to buy a tool or write a policy. You need a structured approach. John Kotter is a Harvard professor and the foremost expert on organizational science. Blending his eight-step approach to successful OCM with Prosci’s ADKAR change management framework (awareness, desire, knowledge, ability, reinforcement) provides a strong roadmap for guiding the organization through to a new culture of data.

Here’s what that looks like in practice:

1. Create urgency: Quantify the financial impact of poor data quality, illustrate hours wasted by high-value employees and discuss missed strategic opportunities due to a lack of trusted data. Show the business what’s at stake.
   
   
2. Build a coalition: Form a data governance council that includes influential leaders from key business units. Always have a senior leader as a key sponsor. Make it clear that data governance is part of business success.
   
   
3. Craft a clear vision: Develop a simple, strategic and relatable vision that’s free of jargon. For example, “Empower faster, smarter decisions with trusted data.” 
   
   
4. Mobilize your change agents: Identify natural stewards and subject-matter experts who can drive grassroots adoption. You already know the go-to people for data in the business. Invite them to be stewards.
   
   
5. Remove barriers: Kill the outdated processes and incentives that block progress. Collaborate between data, security and the business. 
   
   
6. Deliver quick wins: Don’t try to "boil the ocean." Focus on a pilot project in one or two high-impact, high-visibility data domains. Start small, succeed fast and scale momentum.
   
   
7. Celebrate and reinforce success: Publicly celebrate these wins to demonstrate value and provide positive reinforcement that the change is working.
   
   
8. Sustain through communication and training: Build understanding, confidence and skill at every level. Establish an ongoing communication plan and develop role-based training programs.

Organizations want to leverage and scale AI, automation and analytics, but you can’t do that if your foundation is shaky. A strong foundation isn’t just infrastructure — it’s trust. That trust isn’t created by tools — it’s created by people, culture and a shared understanding of data’s role.

Data governance isn’t a checkbox — it’s a force multiplier that turns raw data into strategic advantage, fueling everything from digital transformation to AI maturity. Looking ahead, governance will be augmented by AI, decentralized through data mesh and embedded in the operating model of every business domain. But none of that matters without activation.

OCM is the mechanism that activates this multiplier effect. Activation is human. Change is human. And if you want to win in the data economy, you must lead like it.