Why Data Governance Is Failing (And What We Can Do About It)

I remember the first time I heard a senior executive say, “We’re rolling out data governance to revolutionise our operations—just watch this space!” It was a bold promise made with genuine enthusiasm. Yet, a year later, we were back in a conference room, sifting through spreadsheets of conflicting numbers and arguing about which system held the correct records. In hindsight, it’s no surprise that so many data governance initiatives fail.

I’ve seen it happen across multiple sectors—from local authorities trying to integrate housing data with social services to financial services firms drowning in compliance requirements but lacking real oversight of their data. Each story is unique, but the problems often relate to the same issues.

1. Treating Governance as a Paper Exercise

We often discuss data governance as a set of policies and frameworks that should keep everything neat. The reality? Organisations create reams of documentation—policies that look amazing on paper, but nobody reads or follows.

Take the example of a housing association in the UK. They painstakingly crafted a 40-page data governance manual covering everything from handling tenant complaints to tracking maintenance records. The catch? Their frontline staff (who manage the data) had zero input in creating this manual. The result was predictable: no one on the ground found it practical. Within months, they reverted to old, familiar (and often flawed) workarounds because they were faster and easier.

Lesson: If your employees aren’t involved and your governance policies don’t reflect real-world tasks, then data governance turns into a dusty guide nobody uses.

2. Lack of Real Leadership Buy-In

Senior leaders might rave about data as a “strategic asset.” But that praise can ring hollow if it’s not backed up by real commitment, especially regarding budget and resources. Data governance programmes are often starved of the time and people they need to succeed.

I once saw a compliance team rushing around in panic mode in financial services. Why? Their data was disjointed, contradictory, and downright messy. Yet they had a beautifully worded data governance policy. The problem? Nobody in the C-suite prioritised implementing it. So, requests for system integrations or hiring extra data stewards sat gathering dust on some agenda. When a compliance incident eventually erupted, it cost the firm thousands in fines—and much trust.

Lesson: Leaders can’t just talk; they must walk the walk—by providing the budget, setting realistic timelines, and empowering teams to drive change.

3. Focusing on Technology Over People

It’s tempting to think data governance is all about shiny new tools—master data management systems, AI-driven dashboards, you name it. Tools are helpful, but people are the ones who input, interpret, and act on data. They’re also the ones who resist change if they feel undermined or overwhelmed.

In one local authority, I saw millions spent on a comprehensive data platform intended to unify citizen records. The software was cutting-edge, but staff on the ground found it cumbersome. They continued using spreadsheets and ad-hoc databases they’d built themselves. So now the council had data in two places: the official system (mostly ignored) and the old shadow systems (still riddled with errors). In the end, data quality got worse.

Lesson: No piece of technology can rescue a failing data governance effort unless you involve your people in the change. Focus on training, empathy, and usability.

4. Forgetting That Culture Takes Time to Evolve

Data governance is, at its core, a culture change. It forces teams to share data openly, follow consistent processes, and take collective responsibility for data quality, which is especially challenging in organisations where siloed working is the norm.

Often, leaders expect immediate results. “We invested in data governance—why aren’t we seeing outcomes this quarter?” But culture shifts don’t happen overnight. After all, you ask people to unlearn habits they’ve perfected. Without patience and a long-term view, data governance initiatives get labelled “failures” before they have a chance to flourish.

Lesson: Treat data governance like a marathon, not a sprint. Celebrate small successes and keep momentum going over time.

5. Lack of Meaningful Metrics

If you can’t measure it, you can’t manage it. Yet many data governance efforts kick off without setting concrete goals or metrics. A local authority might say, “We want better data quality,” but what does that mean? Are they aiming to cut duplicate tenant records by 50%? Are they seeking a 95% accuracy rate in council tax files?

Clear, quantifiable targets help teams see exactly what they’re working towards. They also help you track progress and justify continued investment.

Lesson: Define tangible metrics—like a drop in duplicate records or an increase in data accuracy rates—and measure them regularly to prove the value of data governance.

A Real-World Failure: “Financial Future Bank”

To paint a clearer picture, let’s look at a realistic example: “Financial Future Bank.” They wanted to bolster their data governance practices to stay on top of complex regulations and reduce operational errors.

• What Went Wrong? They published a sleek 30-page governance policy without involving the compliance or frontline teams that manage client data. Executive backing was half-hearted. The board lauded data governance in quarterly reviews but never allocated enough budget to hire additional stewards. They rolled out a fancy data integration tool without properly training the operations staff, leading to confusion and heavy reliance on old, unofficial systems. All this was expected to magically show ROI within six months—far too short a timeframe for entrenched cultural and process changes.
• The result is Delays in meeting regulatory deadlines, mounting frustrations in the operations team, and a near miss with a major compliance breach.

How They Fixed It

1. Involving Frontline Teams: They reconvened with compliance officers, data entry clerks, and operational heads to rewrite the data policies in plain language, making it far more practical.
2. Clear Leadership Commitment: The CFO championed the programme, ring-fencing the budget and providing monthly status checks to ensure it remained on the radar.
3. Focused Technology Rollout: After retraining staff, the bank gradually replaced the new integration tool department by department so employees had time to adapt.
4. Patient, Realistic Timelines: They stretched their initial goal (improving data accuracy by 20%) over a year rather than six months, which gave everyone time to adjust and see measurable progress.

Another Real-World Failure: “Meadowbridge Housing Authority”

Let’s look at a housing-focused scenario that feels all too familiar: “Meadowbridge Housing Authority.” Tasked with providing affordable housing and ensuring tenant well-being, they recognised the importance of high-quality data to coordinate maintenance requests, manage rent payments, and comply with local regulations.

• What Went Wrong? Overly Ambitious Policy Manual: Meadowbridge produced a lengthy governance document but never validated its practicalities with the frontline maintenance and customer service teams.
• Top-Down Approach: Senior leadership prioritised data governance but offered no channels for feedback or real collaboration.
• Siloed Systems: Maintenance requests were tracked in one outdated tool, tenant information sat in another, and rent records lived on a separate platform. Nobody owned the data end-to-end.
• Short-Lived Training: A single training session was offered when the new policy was launched, after which staff were left to figure things out independently.
• The result was Conflicting tenant records, duplicated requests, and a backlog of unresolved maintenance issues because employees couldn’t agree on whose data was correct. Complaints skyrocketed, and the housing authority faced multiple investigations for failing to maintain accurate records.

How They Fixed It

1. Ground-Up Involvement: They formed a steering group with representatives from frontline maintenance, customer service, finance, and IT. These individuals shaped a slimmer, more relevant governance policy.
2. Executive Champion: A senior official from the local authority stepped in to endorse data governance, unlocking the budget needed to integrate systems.
3. System Consolidation: They rolled out a single platform to handle tenant, maintenance, and payment information—but only after thorough pilot programmes and iterative staff training.
4. Ongoing Support & Reinforcement: Monthly “data huddles” allowed staff to discuss issues, refine processes, and keep each other accountable.

The Outcome: A gradual but steady improvement in data accuracy, fewer tenant complaints, and a notable reduction in manual, repetitive tasks. Staff morale also rose as they felt more confident in the quality of the information they relied on.

So, What Can We Do About It?

If you find your data governance efforts struggling, don’t lose hope. Here are a few pointers:

• Involve the People Who Do the Work: Bring in frontline teams early, listen to their pain points, and shape your governance policies around real-world tasks.
• Secure Genuine Executive Backing: Leaders must provide not just words but also tangible resources, such as staff, budget, and time.
• Prioritise Culture, Not Just Tech: Even the best tool is useless if your workforce hates using it. Focus on training, user-friendliness, and continual engagement.
• Be Patient: Meaningful change won’t happen in a single quarter. Make it clear that data governance is a journey.
• Measure, Track, and Celebrate Wins: Define clear metrics, celebrate every improvement, and keep the momentum going.

About the Author: Dr Joshua Depiver

Dr Joshua Depiver holds a PhD in Mechanical, Manufacturing, and Electronics Engineering and is a DAMA Certified Data Management Professional (CDMP Practitioner). Over the years, he has advised global organisations—in financial services,?housing,?local authorities,?energy, and?automotive sectors—on crafting sustainable, real-world data governance strategies and frameworks that truly stick. His passion lies in helping businesses transform “paper exercises” into living, breathing initiatives that improve operations, reduce risk, and deliver genuine value. Dr Depiver firmly believes that technology alone cannot solve data problems: it takes strong leadership, cultural change, and practical, people-focused solutions to succeed.

In the end, data governance isn’t failing because the concept is flawed—it’s failing because we often treat it as a box-ticking exercise or an IT project rather than a culture shift. Turn those attitudes around, and you’ll discover that the journey to robust, trusted data is worth it for your bottom line and peace of mind.

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