Ep2 | Data quality: How AI can turn data quality from a challenge into a competitive advantage

Guests: Philippe Boulanger and Dylan Anderson - Director of Data Strategy, Analytics & AI at Atombit, creator of “The Data Ecosystem”

In Episode 2, Philippe Boulanger and Dylan Anderson explore why AI has become the most powerful companion to data, and why it raises the stakes on data quality like never before. Where business intelligence and analytics once required specialist skills, long lead times, and retrospective insights, AI now puts answers directly into the hands of business users.

But this new accessibility comes with a paradox: AI is only as good as the data it consumes. Poorly structured or low-quality data doesn’t just produce wrong answers, it actively misleads AI systems and amplifies errors. The episode explains why structured, contextualised data and smaller, specialised AI models grounded in a company’s own information outperform broad, generic approaches, reducing hallucinations and increasing reliability at scale.

The conversation then tackles the root of the problem: decades of accumulated “data quality debt.” Many organisations treated data quality as a nice-to-have, leaving gaps in ownership, governance, and processes that AI is now exposing. Dylan outlines how leading companies turn this challenge into a competitive advantage by investing in people, processes, and design. The episode also highlights how AI itself can help repay that debt, from deduplication and anomaly detection to standardising data entry and improving compliance. 

The takeaway is clear: companies that fix their data foundations first are the ones that can build trustworthy AI, move faster, and make success repeatable.

Brought to you by DQE - Data Quality Everywhere.

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