The Context
Organizations building centralized data platforms on cloud infrastructure face a common problem: analytics capabilities improve, usage increases, and so do costs. Teams adopt the platform for faster reporting and better decisions, but without intentional cost management, spending outpaces value delivered. This becomes a constraint on further investment. The real issue is that cost and capability get treated as separate problems when they’re actually interconnected.
The Challenge It Addresses
Cost visibility disappears. You know total cloud spend but not why it’s high. Is it expensive queries, idle warehouses, or storage bloat? Without granular visibility into who consumes resources and why, intelligent optimization is impossible.
Warehouses get sized wrong. Platforms typically provision for peak capacity to avoid performance problems. A warehouse built for the busiest hour runs all day and idles the rest. Over-provisioning becomes the default because it’s simpler than rightsizing.
Queries are inefficient by default. Analysts prioritize getting answers quickly over writing optimal code. Without visibility into query costs, there’s no incentive to optimize. Best practices aren’t learned or enforced.
Storage accumulates with no governance. Datasets created for specific projects never get deleted. Staging tables persist. Test data multiplies. Backup copies sit alongside production. A year in, you’re storing redundant datasets nobody uses.
Resource consumption is uncontrolled. Without spending limits or monitoring, teams use resources without understanding cost impact. Batch jobs run during peak hours. New workloads get provisioned without cost checks. Governance mechanisms don’t exist.
