The analyst who always knew where the forecast came from

What tipped them over

Camille's work lives in a lot of places. A short-horizon demand-sensing model in Python, a stack of promo-lift queries in Snowflake, a spreadsheet of shipment-versus-consumption overrides, dbt models she inherited from someone who left, and the parameter tables that supply planners actually read from. She knew all of it cold. The problem was that nobody else could see the shape of it, and when she was pulling in a new POS feed she could feel pieces of the estate quietly slipping out from under her.

The breaking point was ordinary. A supply planner asked why a category forecast had moved eight points week over week, and Camille knew the answer but spent an afternoon reconstructing it from memory and file timestamps. She wanted a place where the map already existed.

The first problem they solved

She didn't file a request or wait for a budget line. She started the Explorer trial on a Tuesday, no credit card, and pointed it at her local folders and her cloud warehouse. Because MyDataWork only reads metadata, names, types, paths, relationships, never the actual values, she wasn't exposing a single row of shipment or POS data to do it. Within an afternoon her models, queries, and files were cataloged in one view.

The part that fit her flow was that cataloging wasn't the point, it was the setup. She framed each forecast as a use case with its business value and the stakeholders attached, then mapped lineage so the promo-lift query, the demand-sensing model, and the downstream safety-stock parameters were visibly connected. It slotted into how she already worked instead of asking her to work differently.

The next time a forecast shifted, the afternoon of reconstruction didn't happen. Camille opened the lineage and showed exactly which inputs and assumptions drove the move and who owned each one. The planner stopped second-guessing the change and started acting on it. Quietly, she'd become the person who knew where everything was, and could prove it.

"When the forecast moved, I could point straight at what drove it and who owned it. Planners stopped second-guessing me and started trusting the change."

Scaling from here

Her manager noticed that Camille could answer lineage questions in seconds while the rest of the team was still hunting through folders. Rather than let it stay a one-person habit, he standardized the team onto a Team plan. Camille's work stayed private by default, and leadership saw only the aggregate "Architecture & Value" rollup, no peering into anyone's half-finished models. The bottom-up trial became the team standard without a single top-down mandate.

Toward agentic & generative AI

Camille uses the in-app AI to draft the business value of each forecasting use case and to translate her models into plain language for brand and marketing partners who don't read Python. It turns "here's what this promo model assumes" into something a non-technical partner can actually react to.

She's also eyeing Agent Access as the safe on-ramp to an approved forecasting copilot. Because it's read-only metadata context, an agent can understand the shape of her estate, what feeds what, what each asset is for, without ever touching raw shipment or POS values. Getting AI-ready, without handing over the data itself.