Test-driven data preparation is primarily at home in the fields of Big Data and Smart Data, as well as artificial intelligence. It describes a method where data is prepared and checked in such a way that it can be reliably used for further analysis or machine learning.
Instead of simply gathering all the data, test-driven data preparation uses various tests early in the process. These tests automatically check whether the data is complete, correct, and usable. This ensures that errors or gaps are identified and rectified in a timely manner right from the start.
A practical example: A company wants to use artificial intelligence to predict which products will soon be in high demand. To do this, it needs clean, up-to-date sales data from various sources. Using test-driven data preparation, automated tests are first defined to check, for example, that no key figures are missing and that all data is in the correct format. Only once these tests have been passed is the data released for analysis.
This ensures greater reliability, saves time on later evaluations, and makes data projects more successful overall.
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