How to Create a Data Quality Game Plan

We all need to be a little more proactive and less reactive.

What happens when your data arrives later than expected? Or the values in a column that should be populated are all NULL?

Data quality issues like these are often lagging indicators that something in your data pipeline went wrong.

By the time they occur, the damage has already been done, and you need to find a solution to prevent issues like these from ever getting into production again.

While the impact on the business can range from insignificant to detrimental, no matter the magnitude of the issue, trust will be lost every time something goes wrong.

It’s an unfortunate thing we need to deal with as data practitioners.

To help reduce the amount of errors that make it to production and to keep trust in your data high, you must create a data quality framework.

Data quality frameworks help you foresee any problems before they happen and disrupt workflows. Proactive > reactive. They also reduce the time you spend troubleshooting problems when things (inevitably) do go wrong.

Cause/effect of common data quality issues

What is a data quality framework?

A data quality framework is a tool for organizations and data teams to use to determine the most impactful data quality characteristics so that they can then develop systems and processes to improve overall data quality.