Tackling the greatest challenges when striving for data quality

All business leaders can agree on some level that data quality is important to them - in order to fully understand their marketplace and their workflow, companies need data, and before they can trust that data, they need to make sure it's accurate. For this reason, one of the main challenges that information management leaders face is that of coming up with a data quality strategy.

There are a lot of different approaches they can take. They can verify information immediately at the point of collection, or they can screen it as it's moved from one internal system to another. Alternatively, they could run periodic spot-checks every few weeks or months to look for inaccuracies. In any event, it's important for all businesses to look diligently for strategies that work for them.

According to TechTarget, the first step in this process is for a business to consider its mission and its overall goals. This is a big question, but it's an essential one for any company that cares about accurate data, explains Laura Sebastian-Coleman, data quality architect at Optum Insight.

"Addressing such challenges requires strategy - that is, an intentional plan for success," Sebastian-Coleman said. "The goal of a data strategy cannot be to fix everything, but to fix the things that matter and establish better ways to manage data. It should answer one big question: What do we need to do to have the data we need, in the condition it needs to be in, to support the mission of our organization over time?"

Business leaders need to consider how their data-driven plans align with their overall goals. If they can do this, they'll be well on their way, as the first step is to figure out why data matters and how it can help. Companies aren't just striving for quality with their data - they're looking for the right kind of quality.