Why FinOps Needs DataOps Observability

In this special guest feature, Chris Santiago, Vice President/Solutions Engineering at Unravel Data, discusses managing cloud spend through the three phases of the FinOps lifecycle. Chris is a seasoned data executive with over 15 years of experience. Currently, he is Vice President of Solutions Engineering at Unravel Data, where he helps companies maximize their data application and infrastructure investments.

Data cloud costs are skyrocketing out of hand. The modern data stack has enabled great innovation in many areas, but has come at a tremendous cost, both literally and figuratively, for many organizations. With businesses ambushed by monthly bills and limited visibility into where all the money is going, capacity and budget projections are reduced to a guessing game. It’s no wonder you find your cloud migration project hitting a wall with your annual budget being depleted in the first few months.

As cloud costs continue to rise and there is no clear end in sight, businesses cannot continue to take a “pay with your ears” approach to cloud spending. In fact, Gartner estimates that 60% of his public cloud customers will experience cost overruns in the next year, and KPMG estimates that two-thirds of organizations have yet to see sufficient his ROI from their cloud transformation. I discovered that I didn’t get it. To combat skyrocketing cloud costs, more and more businesses are turning to FinOps practices as a means to control cloud spending and secure any funding. teeth Spending is done in such a way as to maximize revenue.

FinOps-style cost management

Managing costs consists of three iterative phases across the FinOps lifecycle, each offering unique insights and opportunities for cost rationalization.

Observability: Before organizations can tackle costs, they need fine-grained visibility. Observability provides a holistic view of your applications, workloads, users, data, and resources. This phase uncovers trends and patterns that reveal how the cost of cloud resources changes over time, enabling next steps in the process.
optimization. If the observability phase knows what is happening and why, the optimization phase can use that information to eliminate waste, remove inefficiencies, and reduce costs without sacrificing SLAs. It’s about identifying where you can take advantage of different cloud computing configurations.
governance. During this phase, the team moves from passive problem solving to proactive problem prevention. Having guardrails in place allows for continuous iterative improvement and empowers employees to make better choices.

Each phase builds on the former, creating a virtuous cycle of continuous improvement and empowerment to help individual team members make better decisions about cloud usage while meeting SLAs, regardless of expertise. produces In essence, it shifts budgets to the left and extracts accountability for controlling costs forward.

FinOps, done right, can help organizations navigate the complexities of the cloud and balance cost and performance efficiencies. However, none of them provide the level of detail necessary for organizations to make sound data-driven decisions about cloud usage, determine optimization opportunities, and ultimately realize ROI from their data stack. Knowing how much money is being spent is not enough. investment. For FinOps to be truly effective, teams need multi-dimensional and multi-dimensional insight into what’s happening within and across data apps and pipelines.

That’s where AI-powered DataOps observability comes in.

AI-Driven Analytics Brings Observability to 11

DataOps observability captures detailed and detailed performance and cost details from various systems in the data stack, and associates everything with a meaningful “workload aware” context. By applying AI to these fine-grained details, data teams can track, visualize, and ultimately allocate resources according to actual usage requirements and perceived need. FinOps teams can accurately forecast capacity requirements, implement automated governance guardrails to control costs, and even initiate remedial actions automatically.

AI-driven observability provides deeper, more meaningful context throughout the FinOps lifecycle.

Observability: A layered view of the data stack allows teams to track actual spending and budgets. This allows you to proactively know in real-time whether workloads, users, or projects are on track, at risk, or declining. red. They can identify high spenders, whether it’s projects, teams, users, or applications. This detailed information identifies the number and size of resources required to actually run a job, what has been allocated, and how long an optimized job actually takes to run, so data teams can , you can determine if running on Spot Instances will reduce your costs. Efficient autoscaling.
optimization. Workload-aware optimization enables teams to not only reduce deployed but unused resources, but also identify code inefficiencies and gracefully size over-provisioned clusters. Not only can you target cost-saving opportunities. AI-driven observability not only tells us what to fix, but how to fix it.
governance. Reducing costs passively is good, but actively managing costs is better. AI-powered automation not only turns optimization into insight, it turns analysis into impact, empowering employees to make better choices and, in some cases, solving problems. Without cost governance, managing costs becomes like herding cats and is only less effective.

Enabling self-service across your organization frees up experienced team members to spend less time troubleshooting and more time innovating. It’s that power that frees data teams from the equivalent of coupon clipping to do what they do best.

FinOps isn’t just about saving money, it’s about making money. And that’s what AI-driven observability empowers the enterprise.