The work behind data is the bottleneck for AI.
Why the market for helping data workers is enormous, underestimated, and largely unserved — and where MyDataWork fits. A view for investors, partners, and anyone sizing the space.
AI doesn't replace data workers; it makes them more critical. Agents and models only succeed on top of analytical work that's organized, documented, and connected to business value — context that today lives in scattered files, tools, and people's heads. That missing work-context layer is the prerequisite for AI readiness, and it's exactly what MyDataWork captures.
Data scientists alone — 246,000 in the U.S., growing ~34% through 2034 — are the tip of a far larger population: FP&A analysts, demand forecasters, BI developers, analytics engineers, tens of millions globally. They run the business on SQL, Excel, Power BI, and Tableau, and almost none of them have a system for the work itself.
The seam catalogs miss
Data catalogs (Collibra, Alation, Purview) inventory the data infrastructure. We capture the analyst work-context — use cases, value, ownership — they don't. We complement them, not compete.
Value EA/FinOps can't see
EA and cost tools model architecture and spend top-down. We supply real, use-case-level value and utilization from the actual work — evidence those tools lack, and can consume.
A bottom-up wedge
The analyst adopts it to organize and prove their work; leadership sees the value roll-up; it becomes the org's standard for AI readiness. Land with the practitioner, expand to the enterprise.
For investors & partners
We're building the work-context layer for the AI era — the foundation agentic initiatives depend on. If you're exploring this space or think you can help, we'd like to talk.
Want the full argument, sourcing, and figures? Read the MyDataWork position paper (PDF) →
Figures are drawn from public sources (McKinsey, Gartner, IBM, BLS) and industry reporting, compiled to size the opportunity; they are directional, not a forecast.
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