Let's talk about the elephant in the data room: You can't purchase your way to clean data. No tool, platform, or governance framework will magically fix your data quality issues. Only doing the work will.
I've watched organizations pour thousands and even millions into cutting-edge data management tools and meticulously crafted governance frameworks. Yet years later, many are still grappling with the same problems: Data quality isn't where it needs to be. Data isn't documented. Data can't be connected.
Why? Because the proponents of tools and frameworks are missing a core truth: Data quality is a human challenge at its heart.
The real key to data quality lies in:
◾ How your teams communicate and collaborate and whether your departments even speak the same data language.
◾ How well your organization builds bridges between technical and business teams.
◾ Whether your employees understand why data quality matters and have meaningful incentives to care.
To be clear: tools can help. But they won't create good data entry practices, foster cross-departmental collaboration, or build a culture of data ownership. And they certainly can't replace human judgment, no matter how "AI-powered" they claim to be.
Real transformation begins with three fundamental questions:
1️⃣ Is the impact of data quality on the business understood in concrete terms, as in "value potential" and "value at risk" (not some abstract notion like "you need it for AI")?
2️⃣ Does everyone understand the impact of their role in data quality and the impact of data quality on their role? Again, this must be concrete and connected to daily work, not abstract like "it's important for the company."
3️⃣ Have you thoughtfully designed incentives for caring about data quality? (Or do you expect it to somehow emerge from everything else you're doing?)
Building a culture of data stewardship means more than giving a few people fancy titles and occasionally inviting them for pizza. And measuring true quality requires looking beyond metrics and KPIs (after all, it's human nature to find ways to meet metrics, whether or not that achieves the actual goal).
All too often, data quality is treated as "yes, it's important—among these other five priorities." That's a trap. It's either a priority or it isn't. The path to better data isn't paved with shortcuts. It requires rolling up your sleeves and doing the real work.
When it comes to data quality, stop chasing silver bullets. Start investing in what truly matters: your people and the culture of quality they create. Either way, the results will speak for themselves.