Data quality is your moat, this is your guide

While data quality is important to measure and maintain throughout the data lifecycle, we’ll focus on three stages where ensuring high data quality is particularly critical. Why these three stages? Companies can get away with less-than-ideal data quality monitoring systems until:

• Bad data hits production, affecting all downstream processes and dependencies, and their users. 
• They undergo a migration and need to prove that data in the two databases are identical—anything less erodes trust.
• There are critical outages of databases across different regions, and the company needs to be able to access the data somewhere, somehow.

In our experience, this is usually when data practitioners start searching for better tools and processes to proactively identify and resolve sources of poor data quality in their workflows. 

For each stage, we’ll go over the core problems you should be aware of and present a range of solutions, from stopgap fixes to more advanced setups, that you can adopt based on your specific business context, tech stack, and resource constraints.