Popular data validation techniques for analytics, and why you need them
Iteratively Blog
DECEMBER 13, 2020
At the end of the day, your data analytics needs to be tested like any other code. If you don’t validate this code—and the data it generates—it can be costly (like $9.7-million-dollars-per-year To avoid this fate, companies and their engineers can leverage a number of proactive and reactive data validation techniques.
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