A lot of data quality projects kick off in the quest for root-cause discovery.

Sometimes they’ll get lucky and find a coding error or some data entry ‘finger flubs’ that are the culprit. Of course, data quality tools can help a great deal in speeding up this process by automating the data assessment phase and putting in place routine monitoring for future detection.

However, are we using these tools to their full capacity?

In most cases, when you take a much higher ‘birds-eye view’ of the problem, the real cause is often due to a lack of knowledge about the underlying data and processes that support the services of the business.

This lack of knowledge isn't as attractive as a single root cause or something that can be instantly fixed, so it’s often overlooked in favor of something more tangible.

The reality is that we should be using data quality tools far more wisely to solidify our knowledge about information chains, data structures, business rules, data quality rules, interfaces and relationships within the data. It’s here where assumptions are made and mistakes lead to endless data defects. Yes, you can cure the defects by fixing at source, but what is really the source of the problem?

How can you truly prevent defects at data entry if you don’t understand the information chain that stems from that information collection point?

How can you improve a calculation process if you don’t know the source of all the data flowing into each variable?

Before you start looking for the perceived root cause take a step back and assess whether you have the relevant knowledge first, you may just find this is the ultimate root cause of the issue.