Why I Built Clarivant

Someone in the C-suite asks a simple question:

"What's our customer lifetime value by region?"

Silence.

"How much did we spend on marketing last quarter compared to forecast?"

Someone mumbles something about "pulling that together" and "getting back to you." Three weeks later, an Excel file appears. It's wrong. Nobody trusts it. Decisions get delayed.

I've watched this happen at every mid-market company I've talked to. And it's not because they lack data — they're drowning in it. The gap is between having data and using it to make decisions.

That gap is why Clarivant exists.

Why the gap persists

Big companies solve this by writing large checks. They hire a name-brand consulting firm, buy the enterprise stack, build a 10-person data team, and wait 18 months for results.

Mid-market companies can't do that — and shouldn't have to.

They're too big for Excel and Power BI. Too small for enterprise budgets. Too lean to wait a year and a half. So they limp along with spreadsheets, manual reports, and decisions made on gut feel.

But the conventional wisdom — that enterprise-grade analytics requires enterprise-level spend — is wrong. The frameworks that power billion-dollar operations aren't inherently expensive to run. They're expensive because of how they're typically delivered: layers of project management, junior consultants learning on your dime, and discovery phases that produce nothing but slide decks.

Strip all that away, and what's left is a pattern. A very repeatable one.

My own path to this

I discovered data analytics the way most people do: out of desperation.

Early in my career, someone handed me an Excel file with 60,000+ rows. The file wouldn't open — Excel at the time couldn't handle it. That moment forced me into Access, then SQL, then automation. What started as a workaround became a revelation: if you knew pivot tables and conditional formatting, you were in the top 1% of any company's Excel users. If you could automate it with macros, you became indispensable.

That curiosity became a pattern. At P&G, when Excel and Access weren't enough, I learned R, KNIME, and Hadoop. At eBay, when I needed to process millions of transactions across 15 countries, I learned machine learning at scale. The progression wasn't planned — it was driven by increasingly complex problems.

And here's what I realized along the way: the tools matter far less than knowing which problem you're actually solving.

Every mid-market executive I spoke to said some version of the same thing: "We need what you built at those companies, but we can't afford the enterprise price tag." And they were right — they couldn't afford the delivery model. But they could absolutely afford the solution.

What Clarivant stands for

Clarivant exists to close that gap — to bring the frameworks that powered Fortune 100 analytics to companies that need them most, without the overhead that made them inaccessible.

That means senior expertise from day one, not a partner who sells and a junior who delivers. It means outcomes tied to business metrics — margin, cost, churn, revenue — not vanity dashboards. It means working fast enough that you see results in months, not fiscal years.

I'm not building a consultancy that scales by adding headcount. I'm building one that scales by making the playbook tighter, the tooling sharper, and the time-to-value shorter.

There's a version of this work where the consultant becomes a dependency — where the client needs you forever because you built something only you understand. That's not what I'm interested in. Every system I build, you own. Every pipeline is documented, tested, version-controlled. When I leave, your team runs it.

The point of view

Most analytics projects fail not because the technology is wrong, but because nobody asked the right question at the start. The right question isn't "what tool should we buy?" — it's "what decision are we trying to make, and what's blocking us from making it with confidence?"

Start there, and the architecture follows. Skip it, and you end up with a warehouse full of tables nobody trusts and dashboards nobody opens.

That's the lens I bring to every engagement. Not "here's our methodology" but "what's actually broken, and what's the fastest path to you trusting your own numbers?"