In many ways, we’re in a golden age of data collection. With so many new tools and technologies available, and so much data to capture, organizations are enjoying an unprecedented level of information right at their fingertips.
Unfortunately, not all of that data is accurate or well-managed. Bad data is such a big problem that it’s estimated to cost the economy upwards of $3 trillion per year. Why? Because a business that acts on low-quality information makes low-quality decisions, and those are seldom cheap to correct.
For any organization serious about using data to grow and develop, it’s critical to be sure the data in question is solid. One of the best ways to do so is through a data quality assessment that measures your data against four key benchmarks: completeness, validity, timeliness, and consistency. There are plenty of data assessments out there, but a well-rounded, high-quality assessment has these qualities.
Assessing the Completeness of Your Data
Let’s say you have a database that’s supposed to include the addresses of your customers. Going through it all by hand would take a lot of time and effort. A quality data assessment can help you verify whether all the necessary information is actually present (and it will do it a lot faster, too).
That might sound pretty basic, but skipping this step can have disastrous effects on future decisions. For this example, imagine trying to decide where to open your newest retail location. If you have incomplete data on where your customers actually live, that new storefront will be built in a suboptimal place—a potentially serious waste of time and money.
By confirming your data is actually complete, you and your organization can make better choices more efficiently. Just as important, you’ll also do a better job of helping the people you serve.
Determining Whether Your Data Is Valid
Sometimes data that appears complete is not actually valid. This is often the result of human error, but if it’s not caught in time it can lead to some pretty serious consequences.
“Validity” here means that the data fits the parameters defined for it. For example, if the parameter is “Customer First Name” and the field contains someone’s phone number, that piece of data would not be considered valid. Again, imagine checking and verifying all of that information manually.
A quality data assessment can show you not only how complete your data is but how much of that data is valid. Knowing where you stand with the accuracy of your data is crucial to know before you begin analytics.
Is Your Data Still Timely?
Another important factor to consider is the timeliness of your data. In other words, is the data you have up to date? Is the right information available when you need it or is your database more of an inconvenience than a tool?
It’s entirely possible to have complete, valid information that is no longer timely. If a customer moves, for instance, and his address hasn’t been updated in the system, that piece of data may pass the first two hurdles but fall this one. That’s why a quality data assessment factors in timeliness as well. An assessment worth its weight should act as a benchmark so you know exactly what needs to be improved in your analytics.
How Consistent Is Your Data?
When working with data, small, simple differences can lead to inaccurate analysis. Consistency is key to avoiding this problem. By recording data in the same format no matter where it’s logged, you can help ensure better results. But if this hasn’t been done in the database, a well-rounded data assessment can help uncover where those inconsistencies are hiding, paint a solid picture of where your data stands, and can help develop your future roadmap.
Taken together, these four metrics—completeness, validity, timeliness, and consistency—are key to ensuring your business strategy is based on a firm foundation. While most organizations are gathering more data than ever before, only the best know the right way to use it. Most likely, a large part of their advantage began with a high-quality data assessment.
We help organizations develop a well-rounded, actionable data strategy. Learn more.