Why I Built Clarivant

After 15 years building data systems at companies like P&G and eBay, I'm officially launching Clarivant. And no, this isn't another "ex-Big Tech consultant hangs out a shingle" story. This one's different.

Let me explain.

The Problem I Keep Seeing

Every mid-market company I've talked to has the same movie playing out:

They hit $100M in revenue. Maybe $500M. They're crushing it in their market. Business is good.

Then 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?"

More silence.

"Which products actually drive margin vs just revenue?"

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.

Sound familiar?

Here's the Thing

The problem isn't the data. Companies have TONS of data.

The problem is the gap between "we have data" and "we can actually use it to make decisions."

Big companies solve this by throwing money at it:

• They hire big consulting firms for $500K+
• They buy Snowflake Enterprise
• They build 10-person data teams
• They wait 18 months for results

Mid-market companies? They're stuck.

Too big for Excel and PowerBI. Too small for enterprise solutions. Can't afford the big firm price tag. Can't wait 18 months for results.

So they limp along with spreadsheets, manual reports, and decisions made on gut feel instead of data.

I've watched this happen dozens of times. And honestly? It drives me crazy.

Because these companies CAN have enterprise-grade analytics. I've built those systems. I know what works.

My Excel Hell Moment

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

Back when I was working at Future Electronics in Montreal, someone handed me an Excel file with 60,000+ rows.

The file wouldn't even open. Excel at the time couldn't handle that many rows.

I panicked. Then I got curious.

That moment forced me to learn Access. Then SQL. Then macros and automation. What started as a workaround became a revelation:

If you knew how to use pivot tables and conditional formatting, you were in the top 1% of any company's Excel users.

And if you could automate that 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, I learned machine learning at scale—building pipelines that handled 50M+ users across 15 countries.

The progression wasn't planned—it was driven by solving increasingly complex problems.

And here's what I realized along the way:

The tools matter way less than knowing which problem you're actually solving.

The Mid-Market Gap

I spent 10 years at P&G and eBay building systems that powered billion-dollar operations:

• Walmart analytics that moved millions in spending decisions
• Multi-country marketplace infrastructure serving 50+ million users
• Predictive models that optimized supply chains and customer targeting

Every time I talked to mid-market companies about their data challenges, I heard the same thing:

"We need what you built at [P&G/eBay], but we can't afford the enterprise price tag."

And they're right! They can't afford:

• $500K+ consulting engagements that take 18 months
• Junior consultants "learning on their dime"
• BI tools that cost more than their entire IT budget
• Enterprise platforms that require a 5-person team to maintain

But here's the thing that frustrated me:

They don't NEED to spend that much.

The frameworks are the same whether you're P&G or a $200M e-commerce company.

The difference is execution speed and focus.

That's Why Clarivant Exists

Clarivant takes the frameworks that powered Fortune 100 operations and makes them accessible to companies that need them most.

No junior consultants figuring it out on your dime.

No 18-month discovery phases that produce nothing but PowerPoints.

No surprise scope creep that doubles your budget.

Just proven playbooks, senior expertise, and measurable ROI.

Our first client (a B2B SaaS company) saw 606% Year 1 ROI from a modern data stack migration.

Another reduced supply chain costs by 18%.

Another improved customer targeting by 23%.

These aren't "data projects." They're business outcomes.

Who This Is For

If you're a CFO or COO at a mid-market company ($100M-$1B+ revenue), and you're:

• Drowning in spreadsheets and manual reports
• Frustrated that you can't get straight answers from your data
• Tired of BI tools that promise "self-service" but still need a team
• Burned by consultants who sold with partners, delivered with juniors
• Wondering why you can't have what the Fortune 500 has

That's exactly who this is for.

The Pattern I Keep Seeing

Here's what a successful engagement looks like:

Month 1: Discovery + Quick Wins

• Identify data sources, pain points, key stakeholders
• Deliver 1-2 automated reports that save 10+ hours/week
• Build stakeholder confidence

Month 2-3: Infrastructure Build

• Modern data warehouse deployed
• Core data pipelines automated
• Semantic layer established

Month 4-6: Scale & Optimize

• Full analytics suite operational
• Self-service analytics for key teams
• Continuous improvement based on usage

Result: ROI in 3-6 months, not 18 months.

For a B2B SaaS client: 3-month implementation, 606% Year 1 ROI.

For a supply chain client: 4-month implementation, 18% cost reduction.

Same playbook. Different industry. Consistent results.

What Makes This Different

I'm not going to sugarcoat it: there are a lot of analytics consultants out there.

Here's what makes Clarivant different:

1. Senior expertise from day one

• You work directly with someone who's built these systems before
• At scale. For global companies. Successfully.
• No handoff to juniors after the sale

2. Proven frameworks

• Not "best practices" from white papers
• Methods actually used to move millions in business value
• Adapted for mid-market speed and budget

3. Measurable outcomes

• Every engagement ties to business metrics (margin, cost, churn, revenue)
• ROI in months, not years
• No vanity metrics, no fluffy dashboards nobody uses

4. Speed and accountability

• Faster delivery than big firms
• More strategic than a tool
• More reliable than hiring and hoping

How We Work

Every engagement follows the same proven process:

Phase 1: Discovery (Week 1-2)

• Map your current state: where data lives, who uses it, what breaks
• Identify the 2-3 highest-ROI opportunities
• Define success metrics tied to business outcomes

Phase 2: Design (Week 2-4)

• Architect the solution using modern stack best practices
• Validate technical approach with your team
• Lock scope, timeline, and deliverables

Phase 3: Build (Week 4-10)

• Implement infrastructure (Snowflake, dbt, Fivetran)
• Build semantic layer and analytics models
• Quality gates at every milestone

Phase 4: Handoff (Week 10-12)

• Training and documentation
• Knowledge transfer to your team
• Optional: ongoing advisory retainer

No surprises. No scope creep. Clear milestones with go/no-go decisions.

The Frameworks That Power This

These aren't "best practices" from white papers. These are the actual playbooks I used at P&G and eBay:

Modern Data Stack Implementation

• Snowflake for storage + compute
• dbt for transformation logic
• Fivetran for automated pipelines
• BI layer (Looker, Tableau, or custom)

The Four-Layer Architecture

• Raw data → Staging → Intermediate → Marts
• Every transform documented and tested
• Version controlled, peer reviewed
• One source of truth for every metric

Quality Gates at Every Step

• Data quality tests built into pipelines
• Automated alerts on anomalies
• Schema changes require approval
• Monthly health checks

This is the same infrastructure that powered $500M+ operations.

Adapted for mid-market speed and budget.

What You Actually Get

Not PowerPoints. Not promises. Actual infrastructure:

• Modern data warehouse (Snowflake) with automated pipelines
• Transformation layer (dbt)—documented, tested, version controlled
• Semantic layer—one source of truth for every business metric
• Analytics suite—self-service dashboards and reports
• Documentation—technical architecture, data dictionary, runbooks
• Training—your team can maintain and extend the system

You own everything. No vendor lock-in. No ongoing dependency on me.

(Though most clients keep me on retainer for optimization and new use cases.)

A Personal Note

Look, I've spent 15 years learning to turn data chaos into clarity. I've lived through the Excel hell, the failed BI implementations, the "why doesn't this number match that report?" meetings.

I built Clarivant because I'm tired of watching mid-market companies struggle with problems I know how to solve.

If you're reading this and thinking "this guy gets it"—you're right. I do.

Because I've been in your shoes.

What's Next

Clarivant is officially open for business as of today.

If you want to chat about your data challenges, I'm here.

If you know a CFO who's drowning in Excel hell, send them my way.

If you're curious about how we'd approach your specific situation, let's talk.

No hard sell. No pressure. Just a conversation about whether we're a fit.

Book a discovery call or reach out on LinkedIn.

Welcome to Clarivant. Let's turn your data chaos into clarity.

— Arturo