254900I43CTC59RFW495: Practicing What We Preach—We Have an LEI and You Should Too

That 20-character code in the title? It's the Data Foundation's Legal Entity Identifier (LEI). We're a nonprofit think tank advocating for better data infrastructure across government. We're not required to have an LEI. We don't trade derivatives or operate as a financial institution. But we got one anyway—and here's why that matters.

Data standards only work when organizations actually use them. They can't remain theoretical frameworks debated in policy papers or technical specifications gathering dust. If we're going to advocate for standardized entity identifiers, semantic interoperability, and machine-readable data across federal agencies, we need to demonstrate that adoption is practical, affordable, and valuable.

The registration process was straightforward. The cost was minimal. The time investment was modest. And the benefits for organizational transparency and stakeholder engagement are real: increased visibility and credibility, extending our reach across the globe and online. Our firsthand experience reinforces what we know from research: the biggest barriers to standards adoption aren't technical or financial—they're rooted in fear of change and assumptions about complexity that often prove unfounded.

The Real Cost of Not Having Standards

At the XBRL US Data Forum in December 2025, I joined leaders from the Global Legal Entity Identifier Foundation (GLEIF), EDM Council, XBRL US, the SEC's Office of Structured Data, and FASB to address a pressing question for supporters of digital reporting standards for business and government: why is regulatory adoption of data standards so challenging?

John Bottega from EDM Association—a former federal Chief Data Officer who has held the CDO role in both public and private sectors—offered a stark statistic: when he worked on the LEI initiative at Treasury, they identified over two dozen different entity identifier standards across just 12 federal agencies. The Data Foundation's own research has identified over 50 unique identifier systems in use across the United States. We don't have too few standards—we have too many, and far too often they aren’t harmonized to talk to each other.

The costs of this fragmentation are staggering:

• COVID relief fraud: Hundreds of billions lost because fictitious entities received payouts
• Duplicative systems: $30 million annually in governmental costs for the UEI identifier that essentially duplicates the LEI—and that doesn't even factor in the private sector costs of maintaining yet another identifier
• Financial crisis aftermath: Years spent unwinding Lehman Brothers without a way to identify connected subsidiaries and counterparties

Peter Warms from GLEIF shared a powerful counter-example: when the Commodity Futures Trading Commission encouraged LEI reporting, one bank expected massive disruption. The actual implementation timeline? Two weeks.

Fear of change is often far worse than the reality.

What Works: International Lessons

The Data Foundation’s recent research on Standard Business Reporting examined successful implementations internationally:

• The Netherlands reduced 200,000 data elements to approximately 4,500 through a common taxonomy over 20 years, achieving over 80% market adoption. The Dutch started small with pilots and built incrementally.
• Australia generated $1.1 billion in compliance savings by reducing reporting fields by 80%, proving that agencies often collect more duplicative data than necessary.

By contrast, the European Union provides a cautionary tale: fragmented, voluntary adoption resulted in slow progress despite technical feasibility.

The takeaway: mandates matter.

The FDTA Opportunity

The Financial Data Transparency Act (FDTA)—supported by our Data Coalition members—represents a key building block for broader Standard Business Reporting adoption. If implemented with proper coordination across U.S. financial regulatory agencies, it can establish true "submit once, use many" architecture.

The risk? If each agency implements FDTA in isolation, building separate taxonomies without harmonization, we'll codify redundancies rather than eliminate them.

Julie Marlowe from the SEC's Office of Structured Data shared encouraging evidence: their structured datasets saw over 350 million downloads in just the first seven months of 2025—clear proof of market demand for standardized, machine-readable data.

Data Standards Enable Responsible AI

Here's why this matters more than ever: 90% of federal Chief Data Officers are now using AI, according to the Data Foundation’s latest survey. But AI is only as good as its underlying data.

As Julie noted during our panel, "We're seeing people thinking we don't need standards because we have AI now. But AI needs context. It needs semantic standards to work well. You still need standards."

Standards enable data lineage, support model explainability, and provide the semantic context that makes AI useful rather than just impressive. If government data quality is poor, our AI systems will be unreliable—affecting society broadly, since many AI models are trained on government open data.

Practical Steps Forward

Several actionable mechanisms emerged from our discussion:

• Use existing processes creatively: Submit public comments during Information Collection Request (ICR) processes when agencies update forms. Want LEIs on IRS nonprofit 990 forms? Comment during the ICR period.
• Demonstrate value quickly: Don't wait for perfect standards. Show that implementations work affordably and rapidly.
• Leverage collective expertise: Data Coalition members—including XBRL US, FactSet, Bloomberg, DFIN, and others—have deep implementation experience to share.
• Build market pressure: As the EDM Association’s John Bottega emphasized from his federal CDO experience, market forces can drive adoption even when regulation lags.

Why We Lead by Example

The Data Foundation has an LEI because we believe standards are essential for high-quality, accessible data—and because adoption was straightforward and affordable. If a mid-size nonprofit can implement entity identifiers without drama, the perceived barriers are largely misconceptions.

With final FDTA rules anticipated and continued momentum on AI governance, this is an exciting moment for federal data policy. The future belongs to organizations that act now—that implement standards, share lessons learned, and demonstrate what's possible.

At the Data Foundation, we're committed to being that bridge between technical standards and practical policy implementation.

And we have the LEI to prove it: 254900I43CTC59RFW495.

Special thanks to our Data Coalition members who participated in and supported this important conversation, including XBRL US, DFIN, Workiva, FactSet, and many others working to advance better data standards. Thanks also to Michelle Savage for organizing such a substantive panel, and to my fellow panelists—Peter Warms (GLEIF), John Bottega (EDM Council), Campbell Pryde (XBRL US), Julie Marlowe (SEC Office of Structured Data), and Louis Matherne (FASB)—for their insights and collaboration.