Newsroom

As organisations move from AI experimentation to full-scale production, many are discovering that trust in data, not models, is the real bottleneck. Traditional DataOps, designed for analytics, cannot support the continuous, governed and high-quality data that enterprise-scale AI demands.

Uma Ala, Data Engineering Practice Lead, explains that AI systems require data that is observable, traceable, automated and governed. Treating data as a product, with clear ownership, SLAs and quality standards, and adopting modern capabilities such as feature stores, metadata platforms and automated validation, helps build the trust layer AI depends on.

This perspective was previously published in Enterprise Times. Read the full article here.