Interested to know how much MDG tooling drives governance frameworks and practices?

Where Do They Intersect? Share your thought? 👇

From this article. On August 2, 2026, the EU AI Act's high-risk provisions become enforceable. While boards are obsessing over model compliance, they are missing the real operational threat: Article 10. It mandates that training, validation, and testing datasets must be relevant, representative, error-free, and complete. Regulators are no longer just auditing your AI; they are auditing the underlying data architecture. The brutal reality from a recent Cloudera/HBR report is clear: only 7% of enterprises believe their data foundation is completely ready for AI. The other 93% are accelerating blindly into a regulatory wall. The Verdict: You cannot bolt compliance onto a messy data swamp. If your data governance practices—like lineage tracking, bias detection, and data preparation—aren't systematically documented and enforced "by design," your high-risk AI systems will become immediate legal liabilities by August. The fix isn't deploying more AI tools; it's enforcing rigorous, unglamorous data architecture. Let's Discuss: 💬 The Readiness Gap: Are your AI initiatives building on a governed data foundation that can withstand a rigorous regulatory audit, or is your organization part of the 93% crossing their fingers for a grace period? 💬 The Article 10 Challenge: When the auditor knocks, who in your C-Suite is actually on the hook for proving your datasets are "free of errors and complete"—the CDO, the Legal team, or the AI engineers left holding the bag?