Data Quality Fundamentals

Chapter 8. Democratizing Data Quality

“Hey—is this good data?”

“Can I trust this dashboard?”

“Who owns this data set?”

If you’ve heard these questions—and many others like them—from business analysts and other data consumers at your company, congratulations! The onus of data trust falls on your shoulders.

As companies ingest more and more data and analytics becomes part and parcel of every organizational strategy, the need for high-quality data only increases, putting pressure on data engineers, analytics engineers, and even data analysts to take ownership of this important, but challenging task.

Still, it doesn’t matter how many data quality tests you run—data trust can only be achieved when the entire company buys into it. Despite the data-driven nature of nearly all teams, data organizations often shoulder the brunt of the work when it comes to tracking, enforcing, and scaling data quality initiatives.

After all, data quality isn’t just about building more reliable data pipelines and setting service-level agreements (SLAs) for data freshness. It’s also about education and communication. In fact, data quality is just as much a technical process as it is a cultural one. And very often, it’s not about having fully accurate data—it’s about understanding to what extent we can trust it.

In an interview with the authors, Cindi Howson, Chief Data Strategy Officer at ThoughtSpot and former VP at Gartner, summarized it best:

You build trust when people understand where the data comes ...