Data quality assessment pitfalls

How you can avoid the risks of a poor outcome when measuring the quality of your data

Data quality assessments establish if the data is fit for its purpose. It is the foundation for improving data quality. In this article, we will look at what you should not do if you want to get a better understanding of the quality of your data.

A data quality assessment is a process of evaluating data and measuring it against selected quality criteria such as completeness and validity. It also includes analysing the cause and impact of quality problems and sharing the findings. In order to tackle data quality problems in the right way, you need a robust and sound assessment process.

If your assessment is not good enough, you may think that data is good quality when it is not or vice versa. Making the wrong assessment may lead to the wrong actions being taken and that will have an impact on your organisational outcomes.

Your data quality assessment will be more reliable if it is carefully planned. It is important to involve people with the right skill set and knowledge to carry out the assessment. Technical specialists and process specialists should work in close co-operation to identify, process, test, and refine business rules that are used to confirm and measure the quality of the data.

Before the actual assessment takes place, make sure that you have a good understanding of the data. It is helpful to review any documentation that exists about the data set to be assessed as this will speed up the assessment process.

What you learn from the assessment will be very valuable. It is important that this is documented and saved for future reference. Plan for your assessment to be reproducible. Keeping a record of the assessment process and its findings will allow you to repeat the process. It will become an invaluable source of information for future data quality improvements. This will also maintain your team’s knowledge, provide continuity, and minimise risks.