What steps and tools do you need to audit data quality and accuracy?

I have created a Data Quality Framework as a part of a project in one of the firms & this was an excellent example which helped the team to identify gaps & opportunities in a few report creation methods revolving around some major KPIs tracked. Sometimes we do certain processes manually which might lead to some human errors & this is where data quality & accuracy comes into play!

Six criteria ensure data quality. Accuracy is only one of them. 1) Accurate: correct data / no mistake / true 2) Valid: what is measures is what we want to measure. Ex: if we want to measure income per family and we consider up to two incomes, we assume that there is never more than two incomes; It might not be valid. 3) Reliable: if the study was to be done a 2nd time, we expect the same data points. Ex: when we measure a weight, the scale should indicate twice the same value. 4) Timeliness / up to date: no outdated information 5) Relevant / Consistent: specific information for the context/ Ex: you don't need shoe size if you work on flu. 6) Complete: no missing data.

Establishing clear criteria for data quality and accuracy is similar to establishing a solid foundation for your data analysis project. It's not just about setting metrics, it's about understanding the nature of the data you're working with and how it aligns with your project goals. When determining these criteria, it is wise to involve stakeholders from relevant departments to ensure a comprehensive understanding and agreement on what constitutes "quality" and "accuracy" in the context of your project. Additionally, aligning your criteria with industry standards or best practices can also provide a strong framework for your audit.

"Garbage in, garbage out" is the concept all data professionals learn earlier in their career: if your input data for arriving at a choice is flawed then so too will the output. Decisions based off inconsistent, inaccurate, and/or incomplete data can jeopardize to achieve the intended results. A common challenge I've seen is for third party data sets that update quarterly being used as inputs for inhouse models refreshed monthly. The lag isn't a deal breaker since the dataset has a consistent distribution and coverage, but it's a known characteristic that must be taken into consideration when monitoring for model performance drift across months.

Reports should be constructed with three audiences in mind. It leads to three parts: a global summary with the main findings and associated limitations that might be seen as an executive summary about quality. A detailed explanation of sources or errors and recommendations for improvement. It is designed for department's heads. The third level is a a very detailed explanation of each type of error, how it might be discovered and prevented. This part is designed to be shared inside each department/service.