FinOps for Data Audit Checklist (8 Cost Levers)

Your cloud data bill grew last year. Can anyone explain which workloads drove it?

This conversation happens at almost every scaleup I work with. Snowflake, BigQuery, Databricks - the bill compounds quietly until finance flags it. Then everyone scrambles for a “cost optimization initiative.”

Skip the initiative. Run an audit. Eight places to look:

• Compute scheduling. Are warehouses running 24/7 when nobody’s querying overnight? Auto-suspend after 1 minute of idle.
• Storage tiering. Cold data on hot storage is wasted spend. Move historical partitions to cheaper tiers.
• Query optimization. Rank queries by actual cost, then inspect the expensive recurring patterns.
• Unused tables. Run a lineage scan. Tables with zero reads in 90 days are candidates for deletion.
• Duplicate pipelines. Two teams built the same customer rollup independently. You’re paying twice.
• Dev and test environments. Almost always over-provisioned and never spun down. Quick win.
• Reserved capacity. If usage is predictable, compare the provider’s current commitment price with on-demand before buying.
• Data retention policies. You’re keeping 7 years of event-level logs because nobody set a policy.

Compute scheduling and storage tiering are often the first places worth checking because the usage evidence is visible and the changes are reversible.

The trap: chasing a warehouse migration or platform rebuild before doing this audit. The obvious waste in the current platform is cheaper to inspect than a migration whose business case hasn’t been established.

Audit first. Architect second.

When was the last time someone audited your data infrastructure costs?