Trusted data for faster decisions.
When reporting is slow and data is fragmented, executive decisions suffer.
I help leadership teams replace Excel-driven analytics with systems they can trust — without hiring a full-time analytics executive.
No pitch. I’ll identify where analytics is slowing decisions — and whether it’s worth fixing.
The problem isn’t lack of data.
It’s lack of ownership.
Most organizations know their data matters — but the way analytics actually works inside the business creates hidden drag:
Month-end reporting depends on manual reconciliation across systems and custodians
Leadership debates numbers instead of decisions because no one trusts the source
Regulatory and operational reporting relies on one or two people who can't take a vacation
AI initiatives stall because the underlying data isn't clean, governed, or documented
Over time, this slows execution, increases risk, and makes it harder to scale with confidence.
Who this work is designed for
You’re likely a fit if:
Your organization already has data, but reporting is slow or untrusted
Decisions are high-stakes and mistakes are expensive
Analytics exists, but no one owns it end-to-end
You need senior capability without adding permanent headcount
The same disciplines that work at Fortune 500 firms apply at smaller scale — often with faster results and less bureaucracy.
How I help
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Manual reporting is replaced with repeatable systems that deliver accurate numbers on time — without constant intervention.
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Dashboards are designed around how leadership actually makes decisions, not vanity metrics or tool defaults.
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Before AI is introduced, data quality, governance, and pipelines are put in place so initiatives don’t stall or backfire.
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I act as a senior owner of analytics — setting direction, building systems, and reducing dependency on ad-hoc effort.
Selected case work
Reporting Modernization for a National Life Insurer
Replaced 40+ recurring Excel-driven reports with automated pipelines and dashboards, reducing manual reporting effort by 90%.
Asset Forecasting for a Fortune 500 Wealth Management Firm
Built Monte Carlo simulation models in Python to help executives evaluate asset growth scenarios affecting hundreds of billions in client assets.
Data Infrastructure for a Financial Services Firm
Built automated data pipelines and reporting to surface data issues, improve governance, and give leadership timely visibility into emerging risk.
Why this works
Most analytics initiatives fail for predictable reasons: tools are implemented without ownership, dashboards are built without decision context, and AI is introduced before the data is ready.
My approach is deliberately practical. I focus on building systems that leadership can rely on — not demos, not hype, and not fragile solutions that depend on heroics to survive.
A simple next step
If this sounds familiar, the next step is a short diagnostic conversation. In 30 minutes, I'll clarify where analytics is breaking down, identify what's slowing decisions, and determine whether a focused engagement makes sense.
There's no obligation and no sales pitch — just clarity.