How the NBA Built a Data Product Operating Model to Drive Self-Service Analytics

In professional sports, data is a game-changer—and nowhere is that more evident than at the NBA. The league’s data strategy team has embraced a modern approach to data management, transforming raw data into a valuable asset for internal teams, from marketing to finance to product development. At the heart of this transformation is a shift to treating data as a product, paired with technology and governance processes that enable discovery, collaboration, and trust.

At the recent Gartner Data & Analytics Summit in Orlando, I hosted a session with Jeff Cruz, Technical Data Product Manager at the NBA, to explore how the league has built a successful data product operating model — and how Alation helps make that model work.

The NBA’s data product journey

Three years ago, the NBA underwent a large-scale data migration project, moving from one data warehouse to another. As anyone who’s managed such a migration knows, it’s far more than a technical exercise — it requires deep collaboration across business and technical teams to validate data, align on definitions, and ensure the right stakeholders are involved at every stage.

That experience became a turning point for the NBA’s data strategy team. As Cruz explains, “That migration is what led to what we see today, where now we have planning sessions, we have a day in a full cycle where everybody's involved, from the end users to all the forward technical stakeholders — just bouncing feedback off of each other.”

This collaborative process ultimately evolved into a formal data product operating model, where every analytical asset — from dashboards to machine learning models — is treated as a product with clear ownership, defined requirements, and a lifecycle that mirrors software development.

Involving stakeholders in the data product lifecycle

Cruz sees his role as a bridge between business and technical teams, ensuring data products are designed to solve real business problems. Drawing on his background in sales, he emphasizes the importance of asking the right questions upfront: “What are you trying to solve, really? What is the high-level problem? And why do you want this product?”

This rigorous approach to requirements gathering ensures every data product starts with a clear understanding of its purpose, audience, and success metrics — bringing clarity and accountability to the entire development process.

From there, each product moves through a full product lifecycle — planning, design, development, testing, deployment, and ongoing maintenance. Data products are never considered “finished,” but instead evolve through regular feedback and iteration.

Self-service at scale: Enabling discovery and collaboration

A core goal of the NBA’s approach is to empower self-service analytics at scale. Cruz’s team maintains an internal intranet portal, serving as a data product marketplace for the broader executive stakeholder group, where they can browse and access existing products, from Tableau dashboards to custom-built Streamlit applications.

The goal of the platform, Cruz explains, is to make it easier for users to find what they need — and to prevent redundant work. “We want users checking out our products, using our products, giving us feedback on what should be displayed and how.” By providing a central location for discovery, the portal helps break down silos between teams, fostering collaboration instead of duplication. “It’s been a net positive having everything in one place,” Cruz underscores. 

The role of Alation: Search, discovery & governance

As the NBA formalized this product-centric approach, Alation became a key enabler. Cruz joined the NBA at the same time Alation was onboarded, allowing him to build governance and discovery directly into the product lifecycle.

With Alation, the NBA can:

• Centralize discovery across all data products and assets, so users can find what already exists before requesting something new.

• Standardize definitions by centralizing several glossaries into a unified data glossary within Alation. This ensures teams use consistent definitions, no matter who builds the product.

• Track lineage and ownership, so every product’s origins and evolution are fully transparent.

Alation has also helped prevent the all-too-common problem of inconsistent metrics across teams. As Cruz explains, “If someone on product is interested in understanding how someone on the marketing team calculates a metric, everybody’s on the same page because there’s one calculation — and that one definition can appear in multiple objects.”

Operating like a software development team

One of the biggest shifts has been applying agile product management principles to data work. The NBA’s data strategy team works in sprints, delivering new products every three weeks, with larger projects taking a bit longer. Every six weeks, they hold a data product showcase, where the team presents its latest releases to the broader analytics community.

This showcase isn’t just about celebrating work — it’s about avoiding redundant development by making everyone aware of what’s already being built. “With our data product showcase, there's less repeatable products,” Cruz says. “So someone in marketing isn’t developing the same product as someone in finance.”

The results: Faster time to value & stronger trust

The results of this approach have been clear and measurable. Products now launch faster and with fewer surprises because planning, testing, and stakeholder input are baked into every phase. “We’re confident in what we present to stakeholders,” Cruz says. “There’s far less back and forth and redevelopment.”

Perhaps even more importantly, the approach has built trust — both in the data itself and in the team delivering it. “I would say trust is the right word to use,” Cruz emphasizes. “We have grown that trust internally across the board. So when someone from my team speaks, it's like, okay, they know what they're talking about.”

Advice for other data leaders

For organizations considering a similar approach, Cruz’s advice is simple: treat your data consumers like customers. Understand their goals, involve them in the process, and prioritize discoverability and governance from day one. And to make that possible, Cruz underscores the importance of a platform like Alation.

“Alation has helped with search and discovery and just making things more readily available for everyone,” Cruz says. It’s also kept the NBA from reinventing the wheel, ensuring teams can build on each other’s work instead of duplicating efforts.

With Alation as a trusted foundation, the NBA has transformed data from a behind-the-scenes asset into a frontline tool for driving smarter decisions, stronger fan experiences, and ultimately, better business outcomes.

Curious to hear more about how the NBA innovates with data? Listen to this interview with Mike James, SVP and Head of Data Strategy & Analytics, to learn how the teams share wins and scale best practices across the organization.

To learn more about how Alation helps organizations build successful data products, book a demo with us today.