Here's what keeps data leaders up at night: your CEO just announced an ambitious AI strategy, your engineering team is spinning up models faster than you can track them. Now there's this thing called the EU AI Act that apparently makes you responsible for everything.
Spoiler alert: This isn't your villain origin story, it's your hero moment.
The simple truth behind Article 10
Starting in August 2026, any organization deploying high-risk AI in the EU must demonstrate that its data is trustworthy. Not "pretty good" or "good enough for now"— it must be trustworthy.
Article 10 of the EU AI Act requires your AI training and production data to be:
Relevant and representative, avoiding “ cherry-picked” datasets
Error-free and unbiased, as much as humanly possible
Statistically sound with bulletproof documentation
Think of it as a data audit where "we'll fix it later" isn't an acceptable answer.
Why everyone's doing AI Governance backwards
Most organizations are building elaborate AI governance frameworks, like ethics committees, model monitoring dashboards, and risk assessment matrices. It's like installing a state-of-the-art security system on a house with no foundation.
According to ISACA, data quality under the EU AI Act must cover accuracy, completeness, consistency, uniqueness, relational integrity, and timeliness. That's not just a checklist, it's a complete rethinking of how most companies handle data.
As SourcingSpeak puts it, "the scope of data governance processes at most companies does not encompass the very data these AI systems prefer to consume." In other words, we may be governing the wrong things.
The GX way: build your foundation first
At GX, we've seen this movie before. Organizations that start with data governance don't just survive regulatory changes; they thrive. Here's how we help data leaders build that foundation:
1. Know what you have
Audit your existing datasets. Not just the clean ones you show everyone, but the messy operational data your AI actually consumes.
2. Measure everything that matters
Implement quality metrics across accuracy, completeness, and consistency. If you can't measure it, you can't manage it.
3. Build data quality into your pipelines
Integrate Expectations, logging, and versioning into every data workflow. Use ExpectAI (AI-recommended rules) to perform a deep analysis of a given Data Asset, setting expectations based on patterns in the data and ensuring quality that is impossible to skip.
4. Monitor continuously
As your models evolve, so too must your data quality standards. This isn't a one-and-done project.
Your 18-month opportunity
August 2026 sounds far away, but it's not. Organizations that wait until 2025 to start will be retrofitting data controls under regulatory pressure—expensive, stressful, and often ineffective.
Innovative data leaders are using this window to build the data foundation their company should have had all along. They're not just preparing for compliance; they're positioning themselves as the strategic leaders who made AI work.
The real opportunity
The EU AI Act isn't just regulatory overhead—it's validation. For years, you've been the voice saying, "We need better data practices." Now you have regulatory backing, executive attention, and budget support. You're not the bottleneck preventing AI innovation. You're the foundation that makes it possible.
The question isn't whether you'll need to address data quality for AI governance. The question is whether you'll lead the charge or get dragged along.
Start with trust. Start with data quality. And show everyone what happens when AI is built on solid ground.
Try GX Cloud today to find out how to turn compliance into a competitive edge.
