Beyond "Govern Everything:" Why CDEs Are the Secret to Trusted AI

Across the financial services landscape, a striking paradox has emerged. On one hand, executive teams are charging toward enterprise-scale AI with unprecedented speed. On the other hand, the data teams tasked with fueling those ambitions are hitting a wall.

At Alation, we call this the ambition gap: the distance between the desire for sophisticated, high-value AI models and the foundational reality of the data stack. As Diego de Aragão, SVP at Citigroup, notes in our whitepaper with Corinium, this disconnect is one of the most significant hurdles facing modern institutions.

To close this gap, we have to stop treating data governance as a bureaucratic "check-the-box" exercise and start seeing it for what it truly is: the critical foundation for trusted AI.

The high cost of the "govern everything" trap

For years, the prevailing wisdom was to govern everything. If it’s data, document it. If it’s a system, audit it. But in the era of AI, this "horizontal" approach is breaking down. When you try to apply the same level of rigor to every byte of data in your organization, you don’t get more control—you get more noise.

The numbers bear this out. Gartner predicts that through 2026, 80% of organizations seeking to scale digital business will fail because they do not take a modern approach to data and analytics governance. Furthermore, IDC research reveals that 29% of enterprises cite data quality and availability as the single biggest barrier to realizing AI value.

How do you know if your program is falling into this trap? Here are five warning signs that your governance is stalling:

1. Documentation grows, but trust shrinks: You are checking off metadata boxes, yet executives still spend meetings debating which department’s "total revenue" figure is actually correct.

2. The "groundhog day" discovery: Your data scientists are forced to restart the search-and-validate process for every new project because there is no "gold standard" foundation they can reuse.

3. The 80/20 time drain: Your most expensive talent (AI builders and data scientists) spend 80% of their time as "data janitors" rather than building models.

4. Ownership is a ghost town: Roles are assigned in a spreadsheet, but when an AI model begins to drift or a data pipeline fails, no one feels empowered or accountable to fix the source.

5. Pilot purgatory: You have dozens of successful AI prototypes that never reach production because the underlying data isn't stable or defensible enough for real-world use.

We found that starting with Critical Data Elements was the logical way to break governance into manageable pieces, and it gave us something tangible to work with as we operationalised data governance across the business.

Natalie Hogan

Senior Manager Data Quality & Enablement, Aware Super

Escaping the trap: The power of critical data elements (CDEs)

The solution isn't to work harder; it’s to work with more intent. This is where critical data elements (CDEs) come in. CDEs are the surgical tool that allows you to bypass the "govern everything" trap.

A CDE strategy forces your organization to prioritize. By identifying the small percentage of data that drives the highest percentage of business value, you create a "fast track" for AI-readiness. Instead of boiling the ocean, you’re building a reinforced bridge.

For regulated industries, three "anchors" help these organizations define what truly matters:

• Anchor 1: Regulatory-critical data: The attributes tied to capital adequacy and supervisory scrutiny. In FSI, these are non-negotiable for defensibility.

• Anchor 2: Executive decision data: The metrics used for forecasting and performance management. If the C-suite doesn’t trust it, they won’t trust the AI built upon it.

• Anchor 3: High-value business outcomes: Data that enables measurable operational results, such as underwriting efficiency or customer churn prediction.

While these anchors will depend on your specific business goals as they relate to data, the strategy comes down to one of aggressive prioritization. Ask: Which data, if it were to disappear, would seriously disrupt business operations? This is your critical data, demanding greater investment and opportunity.

The Brambles lesson: Not all data is created equal

To see this in practice, look at Brambles Ltd, a global leader in supply chain logistics. With billions of data points moving through their ecosystem, they realized early on that trying to govern every piece of data with the same intensity was a losing battle.

Brambles adopted a "pyramid" framework, built on the reality that not all data is created equal. A pallet ID in a specific logistics hub might be important, but it doesn't merit the same level of investment or rigor as a global financial reporting attribute.

By using Alation to focus on 10 high-impact data domains, they were able to apply intense quality checks and automated policies only where they would move the needle. This tiered approach allowed them to move faster, reduce governance fatigue, and ensure their data management was directly supporting their AI and business objectives.

Translating intent into continuous enforcement 

The industry is now shifting toward governing by business and policy intent. At enterprise scale, governance must enable AI, not inhibit it. Success is no longer measured by the volume of data under management, but by how reliably your most important data stands up to scrutiny.

This is why we built Alation CDE Manager. It’s designed to bridge the gap between "CDE theory" and operational reality by leveraging organizational standards to turn intent into a continuously enforced system of governance. 

With features like our AI CDE drafting agent, we help you use AI to find the data you need for AI, accelerating the path to a trusted foundation. Meanwhile, continuous compliance provides a unified view of your data health, so you can prove to both regulators and the C-suite that your models are built on a bedrock of automated, persistent standards

Closing the gap

The ambition gap is real, but it is not insurmountable. By narrowing your focus to what truly matters and leveraging automation to govern those elements consistently, you can build a data culture that doesn't just manage risk—it drives innovation.

Ready to start governing what matters? Download the full Alation & Corinium whitepaper to see how other FSI leaders are navigating this shift.