How I help leadership teams make better decisions with data
This work is designed for organizations where data exists, but analytics isn’t owned end-to-end — and decisions are slower than they should be.
When analytics becomes a constraint
I'm typically brought in when:
Month-end reporting depends on spreadsheets, manual reconciliation, or one person who built it all
Different teams argue over whose numbers are "right" because there's no single source of truth
Dashboards exist, but leadership doesn't use them to make actual decisions
Regulatory or operational reporting is fragile and doesn't scale
AI is being discussed, but the data foundation isn't clean enough to support it
Leadership knows analytics matters, but no one owns it
These are not tooling problems. They’re ownership problems.
How the work actually gets done
My engagements are hands-on and project-based. I don’t advise from the sidelines — I build, fix, and own the work until it’s stable.
Typical engagement phases:
Diagnose where reporting and data break down
Design a practical target state tied to decisions
Build or modernize the underlying systems
Transition ownership so the solution lasts
This keeps scope tight, outcomes clear, and progress visible. Engagements typically start with a focused assessment and range from short-term projects to ongoing advisory, depending on what the work requires.
What changes when analytics is owned
Organizations I work with typically see:
Faster, more confident executive decision-making
Reporting that runs without heroics
Fewer debates about data quality
Clear accountability for analytics outcomes
A foundation that supports AI without hype
The goal isn’t more analytics. It’s less friction.
Why my background is relevant
I've spent over a decade leading analytics teams and modernizing enterprise reporting systems at firms managing hundreds of billions in client assets — including Edward Jones, New York Life, and Fisher Investments. I've operated as both a hands-on builder and a people leader, and I've done the work across financial planning, investment analytics, enterprise risk, and AI initiatives.
I'm completing a Master's in Analytics at Georgia Tech, and I stay technical enough to design and implement systems directly — in Python, SQL, Tableau, Snowflake, and Databricks — not just recommend them.
The same person who scopes the project builds it. That's the model.
How engagements are structured
Work is typically structured as focused, project-based engagements rather than open-ended advisory. This keeps costs predictable and ensures the work leads to tangible outcomes.
If there’s no clear path to value, I’ll say so early.