Toptal

Data Quality (DQ) in data warehouse systems is getting more and more important. Increasing regulatory requirements, but also the growing complexity of data warehouse solutions, force companies to intensify (or start) a data quality initiative.

This article’s main focus will be on “traditional” data warehousing, but data quality is also an issue in more “modern” concepts such as data lakes. It will show some main points to consider and also some common pitfalls to avoid when implementing a data quality strategy. It does not cover the part on choosing the right technology/tool to build a DQ framework.

One of the most obstructive problems of a DQ project is the fact that at first sight, it creates a lot of work for the business units without providing any extra functionality. A data quality initiative usually only has strong proponents if:

There are data quality issues with a severe impact

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