Turning 1.1 million body scans into a platform where anyone at Nike could query population data and get answers that shaped what they made. From research scientists to marketing directors.
Nike's sizing system had been built around a small set of idealized mannequin measurements, the same approach the industry had used for decades. The result: products that fit a narrow slice of the population well, and everyone else poorly. High return rates. Inventory inefficiencies. A growing gap between Nike's stated commitment to serving every athlete and what the products actually delivered.
The data existed. Nike had 1.1 million body scans sitting in fragmented archives, external datasets, and portable scanner campaigns. But it was inaccessible, inconsistently formatted, and disconnected from the design and product teams who needed it. The goal: turn body data from a research asset into infrastructure that every team at Nike could use to make better products.
Key insight: The goal was never to build a better sizing chart. The goal was to make body data a permanent capability, infrastructure that any team could build on rather than a project that any team could defund. That distinction is what determined every architectural and organizational decision along the way.
Rebuilt extended size grade rules from body scan data, not scaled-up base sizes. First time Nike's plus-size products were graded against the actual geometry of the bodies that wear them.
Developed size systems for kids' products grounded in actual child body proportions, removing binary gender assumptions from categories where they didn't reflect how kids' bodies are shaped.
Reduced thousands of historical MLB uniform patterns to a 32-size matrix derived from athlete body scan data. Less manufacturing complexity, better fit across the league.
Built CAD fit tools that gave designers direct access to population body data during the creation process, replacing subjective fit sessions with geometry validated against the scan database.
After five years, body data was no longer a research initiative at Nike. It was a permanent part of how the company designed, sized, and validated products. Design teams adopted new creation methods. Return rates on high-difficulty silhouettes improved. The BodyHub platform became a shared resource across multiple business units. The work established a new standard for what inclusive sizing can mean at global scale.
The Nike work is the fullest expression of a principle that runs through everything since SOLS: physical products are data problems. The question was never "how do we make better orthotics" or "how do we make better leggings." It was always: how do we build the data infrastructure that makes better products inevitable?
At Nike, that meant building something that would outlast any individual initiative, a platform that didn't require ongoing advocacy to justify its existence because the teams who used it couldn't imagine working without it. That's the definition of infrastructure that worked.
I help teams build body data infrastructure that becomes part of how products are made — not a project that ends when the budget cycle turns.