Case studies in operationalized data.
A mix of real engagements and representative scenarios — each one a pipeline made reliable, a metric made trustworthy, or a model put to work. Names abstracted; outcomes are the point.
Cutting warehouse spend 43% without losing a single dashboard
A Series-C fintech's Snowflake bill was doubling year over year. We profiled query patterns, killed redundant full refreshes, introduced incremental models, and right-sized warehouses by workload — all while keeping every downstream report intact.
Self-serve analytics for 200 people
A semantic layer and metric framework that took analytics from a ticket queue to a tool the whole company opens daily.
3× adoptionInventory forecasting that cut stockouts
A pragmatic demand model, monitored and retrained on schedule, that kept shelves stocked without ballooning inventory.
−61% stockoutsUnifying metrics across 6 business units
One governed definition layer ended the "whose number is right?" debate in exec meetings.
1 source of truthFrom midnight notebook to managed pipeline
A founder's nightly manual export became an orchestrated, tested, alerting DAG nobody has to babysit.
0 manual runsAn executive scorecard leadership trusts
A single weekly board view, fully reconciled to finance, replaced five conflicting team dashboards.
5 → 1 dashboardsA 90-day reliability turnaround
Data contracts, freshness SLAs, and on-call runbooks took pipeline incidents from weekly to rare.
99.5% on-timeHave data that should be doing more?
Tell me about the pipeline that breaks, the metric nobody trusts, or the analysis stuck in a notebook. Let's operationalize it.