Managing data quality is a business priority. Poor data quality no longer just causes bad reports—it directly impacts product functionality and user experience in the age of AI. Bad quality data in the automotive industry could lead to self-driving cars misidentifying stop signs as speed limit signs, or faulty predictive maintenance systems incorrectly flagging healthy components as needing replacement.

The “Shift Left” approach ensures that data quality, governance, and accountability are built into processes from the start, rather than fixing issues later. I found this great article by Chad Sanderson “The Shift Left Data Manifesto” and can only ask you to read it!

By embedding data good practices early—like schema enforcement and version control—you reduce risks, save costs, and turn data into a competitive advantage. Bring the responsibility to the root where data is created: the source code.

This “Shift Left” approach in data management is similar to what we’ve seen in DevOps and DevSecOps. In DevOps for example, the focus is on integrating development and operations early in the software lifecycle to improve collaboration, speed, and quality.

Instead of buying yet another magic tool that promises to generate insights into your data swamps, start shifting left!”

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Recommended links to dive deeper:
https://www.gable.ai/blog/shift-left-data-manifesto
https://www.gable.ai/blog/how-a-simple-data-contract-could-have-saved-nasa-600m