Source: Featured Blogs – Forrester
As generative and agentic AI systems proliferate across organizations, data quality has shifted from a back-office concern to a front-line business risk—poor data directly undermines the reliability of AI outputs and erodes stakeholder trust. Enterprises can no longer treat data governance as separate from AI strategy; platforms that combine quality monitoring with AI-specific validation are becoming table stakes for scaling AI safely. This represents a fundamental architectural change where data pipelines must be as robust as the models they feed, making data quality solutions a competitive necessity rather than an optional layer.