About

I've spent two decades straddling the line between business and data, from the early days of running analytics on the side of a customer service role, to building enterprise BI programs from the ground up, to now leading data governance efforts for organizations that have never had them before. I've worked across industries — e-commerce, healthcare, financial services, LIMS software — and the problems are always the same. Not technically. Humanly.

Three of the companies I've worked for were acquired. I wasn't brought in to polish things up before the exit — I was there early, when the processes were still being invented. I've seen what data infrastructure looks like when a company is figuring things out, when it's scaling faster than its systems can keep up, and when it's being scrutinized by people with very specific questions and a lot of money riding on the answers. I've been in all of those rooms — at a scrappy startup and at a global enterprise — and the core problems travel remarkably well between them.

Peanut Butter Intelligence grew out of a simple frustration: most of the content in the BI space is written by vendors, consultants, or academics — people who are one or two steps removed from the actual work. They may address the technical challenges, but they don't give voice to the human dynamic. I wanted to write for the person doing the work. The embedded analyst. The IT professional who knows the pipeline cold but can't get a seat at the table. The accidental data person who discovered SQL on a random Tuesday and never looked back.

My primary Data stack is Snowflake and Power BI, with Matillion or DBT in the middle, though I've used lots of other platforms for a time. I've got a healthy respect for what Data Governance actually costs when you skip it. I've chaired steering committees, rebuilt trust in data after it collapsed publicly, and watched more than one carefully-built report collect digital dust because nobody thought to ask what decision it needed to support.

That's what this is about. Not the tools. The thinking behind them.

If you're trying to do real work with data inside a real organization — with its politics, its competing priorities, and its twelve different definitions of "revenue" — you're in the right place.

Business Intelligence That Sticks