Shadow waste keeps growing in modern data platforms because FinOps is treated as cost-cutting instead of efficiency improvement. This session shows how moving from visibility to action delivered measurable results in two enterprise data environments.
We present anonymized case studies where FinOps principles were applied directly to workload design and operating models. In a Redshift environment, we reduced total platform cost by 30% while simultaneously increasing usable capacity and throughput by 30%. In a Databricks environment, we lowered spend by 25% by redesigning how workloads were built, scheduled, owned, and governed.
The key wasn’t isolated optimizations—it was anchoring decisions on unit economics: cost per query, per pipeline run, per insight. We established clear ownership through allocation and tagging, then focused on the true cost drivers: ingestion throughput, operational skew, concurrent BI workloads, and Lakehouse interoperability using Iceberg tables.
The result? Teams could scale usage without scaling waste.
You’ll leave with a repeatable framework for applying FinOps to data platforms—one that improves both efficiency and business value delivery.