Data Governance by Design: Lessons from Fanatics Betting & Gaming

What does it take to build a high-performing, compliant, and scalable data organization—especially in a highly regulated industry like sports betting?

In this blog, we recap key insights from an episode of the Data Radicals podcast featuring Maddy Want, Vice President of Data at Fanatics Betting & Gaming and co-author of the book Precisely: Working with Precision Systems in a World of Data. Drawing from her experience launching Fanatics’ data strategy and building the data team from the ground up, Want shares valuable lessons on data governance, platform design, and why precision and accountability are critical for AI success.

Whether you're a data leader in a regulated industry or just navigating the challenges of scaling a modern data stack, Want’s perspective offers a practical blueprint for doing it right—from day one.

Building a data foundation from the ground up

When Maddy Want joined Fanatics Betting & Gaming, she faced an unusual challenge — and an uncommon opportunity. Because the business was new, the data inventory was essentially zero. That blank slate allowed Want and her team to build a modern, well-governed data platform from the ground up.

“Our data inventory is zero — or was zero for a while there — and so we had the opportunity to really have crystal-clear awareness of what data we have, where it comes from, everything,” Want explains.

This fresh start stood in stark contrast to the typical data leader experience: inheriting a sprawling, undocumented platform with tens of thousands of tables and years of accumulated tech debt. “Being faced with sort of an audit clean-up mission of an old data platform, that’s a totally different type of challenge.”

Rather than playing catch-up, Want’s team could focus their early efforts on getting governance right from day one. That meant hiring engineers and data governance analysts early, establishing naming conventions, setting access controls, and choosing platform partners that could scale with them.

By investing in cataloging, lineage, and classification systems early on, Fanatics created a data foundation that prioritized trust, transparency, and control — and gave the team the clarity they needed to move fast without sacrificing compliance.

Rethinking innovation: Accountability in AI

In today’s AI-driven landscape, Want urges caution around blind automation—especially in high-stakes decisions like lending, pricing, and eligibility.

She cites the example of the poorly launched Apple credit card, where “the algorithm by which a customer or an applicant… was being assigned a credit score was completely AI-driven,” and neither customers nor service representatives could understand or explain the decisions beyond pointing to the algorithm. “That kind of positioning,” Want says, “is going to be the reason why a lot of things fail or go badly.”

Her perspective is clear: The higher the stakes, the greater the need for transparency and accountability.

“There are very few situations where we’d be happy to let a machine fully make a decision autonomously on our behalf… but I do think that's what's needed,” she adds. “And the higher the stakes are on the product, the more critical that is.”

For Want, explainability isn’t optional. It’s foundational to responsible data practices—and essential for ensuring trust in AI systems.

Innovation meets resistance: Driving data change in legacy cultures

One of the most powerful insights Want offers—drawn from her interviews for Precisely—is that building precision systems is rarely a technical challenge alone. It's a cultural one.

“There’s a saying in tech: ‘The tech is the easy part. The people are the hard part,’” she notes. That dynamic is especially visible when data-driven methods challenge long-held beliefs or workflows. Want recounts the multi-year transformation of The New York Times, where experimentation and personalization met resistance from the editorial team.

“Just introducing the idea that there isn’t just one homepage… that there might be n homepages where n is the number of customers — that was very tough,” she explains. “The editorial psychology had been, ‘It’s our job to tell people what they need to know when they don’t know it… That’s never going to be handed over to an algorithm.’”

Even with executive backing, data leaders in these environments must earn trust, demonstrate value, and win support brick by brick. “They got zero credit. They got zero advanced trust. They really had to carve it out.”

Want’s takeaway is instructive: achieving precision in a product or platform starts with empathy for the people it will affect—and a long-term commitment to cultural change.

Precision is power—if it’s accountable

As Maddy Want emphasizes in both her book and her work at Fanatics, precision is a hallmark of modern technology. It enables faster decisions, better personalization, and scalable operations.

But it comes with a caveat: without explainability and governance, precision systems can erode trust just as easily as they build it.

Want puts it best: “We may have had a step function improvement in generation, but we haven't had matching improvements in explainability and in trust development and in controls. And so it's not gonna be that useful until we get there.”

For data governance professionals, the message is clear: Precision isn't just about accuracy. It’s about accountability.

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