Cost optimization is never a one-and-done exercise. As long as you’re spending money to run data workloads in the cloud, you’ll need to keep an eye on costs. You might have a couple of thousand data engineers spinning up 100,000+ instances every month to run their data jobs. You’re constantly incurring cloud data expenses, up and down the line.
A DataFinOps approach shifts the responsibility for controlling cloud data costs left, onto the people who actually incur the expenses. Each individual is held accountable for their own cloud usage and cost. All those 100,000+ individual spending decisions are informed, data-driven decisions. Basically, get it right early and often.
In DataFinOps, this is done by applying automation and AI to the constant influx of usage/cost data to serve up actionable insights and remediation recommendations about how you’re spending your cloud data budget. These insights and AI recommendations are (automatically) available to every individual who’s incurring cloud data costs—really, anyone. Armed with these insights and AI recommendations, every individual is empowered to “do the right thing” and optimize for cost.
This is true cloud data cost governance. It’s getting ahead of cost issues beforehand rather than after the fact. Three capabilities drive successful DataFinOps operations:
- Automated guardrails
- Alerts and automated “circuit breaker” actions
- Self-service