About
I’m an enterprise Data, Analytics & AI executive based in New Hampshire. I’ve spent twenty years in this field, long enough to have built things from scratch, inherited things that were broken, and led organizations through modernization efforts that look straightforward on a slide and aren’t.
My background spans SaaS, consumer products, retail, and distribution. The industries vary. The underlying challenge is usually the same: the data exists, the business needs it, and something structural is getting in the way.
What I’ve done
At SharkNinja I own the global Data and AI function across commercial, supply chain, and product. Before that, at Veracode, I ran enterprise data and analytics through a transformation period focused on revenue forecasting, customer intelligence, and governance in a regulated environment. At Bonterra I started the Data Products organization from one person and built it into a team spanning analytics, data science, and platform work. We turned embedded analytics into a product line that generated around three million in incremental ARR.
Across those roles I’ve managed roughly ten million in annual operating budget, led distributed teams across engineering, analytics, and data science, and owned the vendor relationships and contract negotiations that don’t show up on roadmaps but matter a lot in practice.
How I think about this work
A few things I’ve come to believe through direct experience rather than theory:
Most analytics dysfunction traces back to org design, not talent. The question of who controls analytical resources and what they can see from that position determines what work gets done. When the structure is wrong, capable people still can’t fix it.
Governance is what separates AI experiments from AI that scales. Getting a model into production is the easy part. Building the access controls, monitoring, accountability structures, and decision frameworks that let an organization deploy AI confidently across multiple use cases is where the real work is.
Data capability compounds, but only when you build it that way. Organizations that stay behind on data aren’t usually behind on tools. They’re behind because they never built a foundation where each new use case gets cheaper and faster than the last.
I’ve also learned to be skeptical of scaling teams before the structure is ready for it. Larger organizations don’t always mean more output.
Conversations worth having
I take data functions that are treated as cost centers and rebuild them as revenue drivers. The pattern holds across company sizes and industries: establish the foundation, connect the capability to the decisions that move the business, and build it so each new use case gets cheaper and faster than the last.
Most of the problems I write about have the same shape whether the company is a $6B consumer products brand or an early-stage SaaS. If you are working through something in this space, I am interested in the conversation.
Common questions
Why do most data and AI initiatives stall?
The tooling is rarely the problem. A competent team can stand up a modern data stack in a quarter. Initiatives stall on structure: org design that gives analytical control to people who can see only part of the problem, governance that was never built, and AI deployed at the data layer instead of the decision layer. The hard part starts the day after the pipeline works.
What separates AI experiments from AI that scales?
Governance. Getting a model into production is the easy part. The organizations doing well in AI are not the ones with the best models. They are the ones that built the infrastructure to govern AI at scale: access controls, monitoring, accountability for wrong outputs, cost attribution, and a framework that does not need to be rebuilt for every new use case. The model question resolves quickly as the market moves. The governance layer is what determines whether AI scales beyond the team that built it.
How should analytics teams be organized?
Most analytics dysfunction traces back to org design, not talent. Organizing analysts around business pillars creates silos with authority over people who can see more than they can. The fix is not a better coordination process. It is changing who controls analytical resources and what they can see from that position: capacity that can be directed across functional boundaries, a decision cadence with real authority, and an architecture where each new capability costs less than the last.
How do you connect data investment to business outcomes?
Connect every dollar of analytical work to a number the board already watches. A dashboard that does not change behavior is decoration. The difference between a metric and a decision tool is whether anyone does something different on Monday because of it. The goal is a decision system: shared definitions, governed entities, and a clear path from data to the decisions that move revenue.
What industries and company sizes have you worked across?
Twenty years across SaaS, consumer products, retail, nonprofit tech, and distribution: SharkNinja, Veracode, Bonterra, Cole Haan. The problems rhyme regardless of size, from a $6B consumer brand to an early-stage SaaS: data treated as a cost center, AI spend that does not compound, and org structures that create friction before the real work starts.