Business Analysis · Strategy · Decisions

Your business has the data. I help you make sense of it.

I'm an independent business analyst and strategist working with founders and growing businesses — turning scattered data into a clear read on what's actually happening, and what to do next.

3+ years · Business Analytics · Strategy · Remote / International

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You already have the data. The challenge is knowing what it means.

Sales are growing — but is the business actually more profitable?

Customers are increasing — but which ones are worth keeping?

Marketing spend is going up — but which channels are actually paying off?

Reports show what happened. They don't always show what to do next.

That's where this comes in.

You probably don't need another dashboard.

You need someone who can help you understand what the numbers mean.

Dashboards are part of the work — but they're not the point. A dashboard tells you what happened. My job is telling you what it means, and what to do next.

For businesses that:

Not a fit: if you need someone to build reports from a pre-written specification — this is a thinking and strategy partnership, not report production on demand.

How I can help

  1. 01Business Health CheckA one-time analysis of the metrics that actually drive your business.
  2. 02Decision AnalysisYou have a specific question. I use your data to answer it.
  3. 03Executive DashboardA management dashboard that turns your existing data into a view you can use.
  4. 04Monthly Strategy & AnalyticsAn ongoing, external analytics and strategy partner — without the full-time hire.
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Selected work

Decision AnalysisDraft

Finding the Right Price, and the Right Market

The problem

A health-focused business had room to grow — but the real question wasn't just how to get more customers, it was which customers, which markets, and at what price.

The question

Where should this business compete, who should it target, and at what price, to grow sustainably?

What I did

Researched competitor pricing and positioning, then built a pricing strategy calibrated to attract commercially solid customers — not just volume — while reading the total addressable market for underserved, high-potential niches.

What we found

Pricing, market selection, and customer quality weren't separate decisions — they were the same decision seen from different angles.

Read the full case study →

Decision AnalysisDraft

Should You Trust the AI?

The problem

A compliance software company built an AI system that reviewed cases and issued a verdict — and needed to know if it was ready to decide on its own.

The question

How often does the AI's verdict actually match a human reviewer's — and is that even the right question to be asking?

What I did

Matched every AI verdict to its corresponding human decision, filtering for timing and completeness before drawing any conclusion.

What we found

The AI and human reviewers agreed almost every time on approvals — but on declines, agreement was barely better than a coin flip.

Agreement on approvalsAgreement on declines
Relative comparison, not exact figures — approvals agreed almost every time; declines were close to a coin flip.
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Monthly Strategy & AnalyticsDraft

Your Customer Went Quiet, and Nobody Noticed

The problem

A software company tracked customer health mostly through support tickets — assuming no complaints meant no problem.

The question

Were the noisy, ticket-heavy accounts the same ones actually at risk — or a different group entirely?

What I did

Built an independent view of account health from usage data, then compared it against the ticket-based view, account by account.

What we found

Several accounts were quietly declining in usage with no ticket ever filed — a blind spot a ticket-only view would have missed entirely.

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Business Health CheckDraft

If You Can't Measure Usage, You're Under-Billing

The problem

A software company sold two metered features with no record of who was actually using them — no basis for billing, no audit trail.

The question

Was an in-progress quick fix good enough, or did the business need something more durable?

What I did

Reviewed the proposed fix against the real requirement, then designed a schema that computed billing eligibility at the moment each record was written.

What we found

The quick fix would have restored visibility but silently failed the one use case it was actually needed for: accurate billing.

Read the full case study →
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Experience

Experience across complex businesses.

Three-plus years working inside businesses where the data existed but the decision didn't — translating it into something leadership could actually act on.

3+ years · Business Analytics MS (UT Dallas) · Remote / International

Industries & domains

  • Compliance & regulatory technology
  • Financial services
  • Digital health
  • Nonprofit & social-impact operations
  • Marketing & customer acquisition

Kinds of problems I've worked on

  • Trust & automation decisions
  • Customer retention signals
  • Revenue & billing integrity
  • Reporting & KPI design
  • Segmentation & acquisition strategy

What you get

Every engagement produces the same shape of output.

01

Analysis

What the data says.

02

Insight

What actually matters.

03

Recommendation

What you should do.

04

Next step

What to measure or change.

Have a business question you can't answer with the data you already have?

No pitch, no obligation — just a conversation about what you're trying to figure out, and whether the data you already have can answer it.

Book a 30-minute discovery call →