The Human Side of Data Governance: Why UX Thinking Changes Everything
Last week, I sat in a meeting watching Sarah, a marketing analyst, explain how she maintains customer segmentation data. “I know we have a data catalog,” she said, “but I keep my important segments in this spreadsheet because it’s just… easier.” Around the table, heads nodded in silent agreement.
This scene plays out in offices everywhere. Despite millions invested in data governance tools, ux data strategy, and frameworks, people consistently find ways around them. The reason? We’ve been solving the wrong problem.
When Good Intentions Meet Reality
Traditional data governance focuses on control. We build elaborate frameworks, implement expensive tools, and create detailed policies. Then we wonder why people don’t use them.
Think about James, a sales manager I worked with recently. His team needed to share customer feedback data. The “proper” process involved three different systems, two approval workflows, and a governance committee review. Instead, they used shared spreadsheets and Slack messages. Was James being deliberately difficult? No – he was just trying to get his job done.
Flipping the Script: Enter UX Thinking
What if we approached data governance the way UX designers approach product design? Start with the users, understand their needs, and build solutions that make their lives easier.
When we worked with a financial services firm last month, instead of starting with policies, we began by shadowing their analysts. We discovered something surprising: most data quality issues weren’t from lack of caring – they came from confusing interfaces and workflows that fought against natural working patterns.
What Does UX-Driven Governance Look Like?
Let me share a recent example. A healthcare provider was struggling with data classification compliance. Their traditional solution would have been more training and stricter policies. Instead, we watched how their staff actually worked.
We found that clinicians weren’t deliberately ignoring classification rules – the classification system simply didn’t match how they thought about patient data. By redesigning the classification options around their mental models and adding visual cues that made sense to them, compliance improved by 64% in just three weeks.
Making Quality Personal
Another revelation came when working with a retail company’s customer data team. Traditional data quality metrics (completeness, accuracy, etc.) meant nothing to the store managers entering customer information. But when we reframed quality in terms of “customers we can’t contact” or “lost sales opportunities,” suddenly everyone understood the impact.
The team redesigned their data entry screens to provide immediate, meaningful feedback. Instead of cryptic validation errors, they showed real-world implications: “43 customers won’t receive their birthday offers if this field isn’t filled correctly.” Engagement with data quality tools increased dramatically.
The Path Forward
This shift to human-centered governance isn’t just feel-good theory – it delivers real results. A manufacturing client saw their data quality scores improve by 47% after redesigning their governance processes around user needs. More importantly, people stopped viewing governance as an obstacle and started seeing it as a helpful tool.
Here’s what made the difference: They replaced their 50-page data governance policy with simple, contextual guidance where people actually needed it. Their data catalog evolved from a technical repository to an intuitive knowledge-sharing platform. Quality checks became immediate and meaningful, focusing on impact rather than abstract metrics.
Breaking Down the Wall
The most powerful moment in any governance transformation comes when you stop seeing it as “us vs. them” – governance teams against users – and start seeing it as a shared journey toward better data.
Last month, during a governance redesign workshop, a data steward had an breakthrough: “We’ve been treating governance like a security system, when we should have been treating it like a GPS – helping people get where they need to go.”
Looking Ahead
As AI and machine learning become more prevalent, good governance becomes even more critical. But adding more rules and restrictions isn’t the answer. The future of data governance lies in understanding and designing for human behavior.
The next time you’re frustrated by low adoption of governance policies or poor data quality, try asking different questions. Instead of “How do we make people follow the rules?” ask “How do we make the right way the easy way?”
After all, the best governance framework isn’t the one with the most comprehensive policies – it’s the one that people actually use.
Have thoughts about human-centered data governance? I’d love to hear your experiences. Reach out for a conversation about making your data governance more user-friendly here.
