I'm Jonathan Doreau — the person behind Aesop Analytics. I've spent my career taking fragmented, distrusted data environments and turning them into reporting organizations actually rely on.
I started in finance — regulated reporting, risk dashboards, and the discipline that comes from environments where a wrong number has real consequences.
Healthcare and research followed — including an NIH-funded research program where data credibility mattered as much as the work itself, and reporting had to serve both leadership and external funders. That's where governance stopped being abstract: the unglamorous work that makes everything downstream trustworthy.
Real estate and nonprofit came next, and what struck me most was how similar the data problems were across industries that seemed completely different. Fragmented sources, undefined metrics, reports that couldn't be reconciled, a data team spending most of its time answering one-off questions instead of building anything lasting. The patterns repeat.
Aesop Analytics is the distillation of those fifteen years — bringing enterprise-quality thinking and execution to organizations that don't need a large team to get there.
I'm genuinely tool-agnostic at the strategy layer. At the build layer, I work most fluently in Power BI, Tableau, SQL, and Snowflake — the tools that cover the majority of what growing companies actually need.
Technology decisions made before the strategic questions are answered tend to create expensive, underused infrastructure. The question "what do you need your data to do?" has to precede "what platform should we use?"
The real deliverable of analytics work isn't a dashboard — it's a data environment that people trust enough to use when they make decisions. Everything technical is in service of that.
I take fewer engagements and do them better. The work I take is work I believe I can genuinely add value to. If that's not the case after an initial conversation, I'll say so — and point somewhere better.
The same standard applies to the field studies on this site and to client work: steelman the counter-argument, be clear about what the data can and can't say, and don't let a preferred conclusion drive the analysis.
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