As businesses grow their spend in the public cloud to accelerate innovation and to build a competitive advantage, predicting cloud growth accurately short- to long-term becomes increasingly important for leadership. Finance and executives need to know available funds several years into the future to build their innovation roadmap.
In this course, you are going to learn about cloud forecasting and how to align forecasting models with the maturity of your FinOps / Cloud Financial Management practice. You will learn about the relevant terms and concepts as well as how to identify ownership and accountability. We will break down the challenge into addressable parts and walk you through solution approaches at each step.
- Understand what cloud forecasting is and why it's important
- Understand what challenges exist in cloud forecasting and how to address them
- Learn about the different ways you can forecast in the cloud
- Learn about what you can do to improve cloud forecasting
- Learn about the role forecasting plays in FinOps
- This course is for FinOps / Cloud Financial Management and Finance people looking to understand how to improve cloud forecasting and how to increase forecast accuracy.
A basic understanding of how the cloud works, including compute and storage services and their pricing models, as well as an understanding of the financial processes around forecasting, budgeting, procurement, and allocations.
The first principle of FinOps is "Teams need to collaborate". I expand on that in my course titled "Cloud Financial Management — Beyond Just Optimization". In this section, we are going to look at which roles are driving what interests when it comes to cloud forecasting.
Let's start at the top with executives. They are the primary sponsors for process improvements around cloud usage. FinOps needs their understanding, buy-in, and support so that improvements can trickle down the organizational hierarchy.
Let's look at the needs of the chief information officer or CIO and chief technology officer or CTO. At their level visibility of overall infrastructure costs consolidated for any given line of business or product is needed. This requires the ability to see costs for each cloud provider along the same dimensions. Here a consolidated or stacked view is needed for cloud cost visibility across multiple cloud providers using universal terminology like compute, storage, or data transfer charges to aggregate different cloud products under the same umbrella terms.
When the goal is to match business priorities for a given line of business or product with specific cloud technologies a breakout or unstacked view specific for each cloud provider is required. Here vendor specific terminology is used for deep dives and troubleshooting.
The chief financial officer or CFO needs to understand how spend for each cloud infrastructure is tracking against forecasts. Additionally, visibility into progress toward spend commitments as part of enterprise discount programs is needed. This requires the ability to easily explain why spending is substantially lower or higher than forecasts, and to also validate and correct forecasts as needed.
At this level, we need to know how much to invest in savings instruments to get reservation discounts versus commitment size and duration across cloud providers. This is challenging as savings instruments characteristics vary widely between cloud providers and between product offerings within a cloud provider. This requires tracking of reservation utilization, coverage of on-demand usage, and remaining commitments.
Lastly, business decisions should be driven by unit economics across cloud providers. Here visibility into key performance indicators or KPIs is needed, such as cost per active customer, cost per widget sold and so forth. These KPIs will differ for each organization and their performance needs to be tracked over time.
The finance team Is the main consumer of forecasts and will drive frequency, granularity, and quality requirements around forecasting. FinOps practitioners collaborate with finance around the requirements for forecasts and determine who makes which contributions during a forecast cycle.
Procurement has established processes that need to be extended to cloud services and prepayment products such as reservations, savings plans, and committed use discounts.
While procurement needs the ability to validate that the business is getting the rates that have been negotiated, accounts payable needs to validate that invoices match actual usage. Here we start with periodic spot checking and relying on more technical staff to assist with the validation. As we progress in our crawl, walk, run maturity some of these processes can be automated and made available as self service.
Legal needs to understand the legal obligations with each cloud vendor resulting from enterprise agreements, private pricing agreements also called rate cards, and the size and duration of spend commitments from savings instruments. A common first step is to start tracking these commitments in spreadsheets and later we may progress to using automation to enable self service.
Cloud operations is a team of engineers that are tasked with day-to-day operations in the cloud across all business units. They are responsible for implementing requirements from the FinOps team or Cloud Center of Excellence, CCoE for short, around governance, efficiency, and security.
Cloud operations needs a consolidated view of operational tasks and cloud governance across cloud implementations. This extends to cloud financial management or FinOps activities such as right-sizing, waste elimination such as zombie assets, adoption of more cloud native technologies such as Spot or preemptible compute and serverless technologies, and the ability to influence architecture decisions to take advantage of best of breed technologies. Many of these activities require executive, product, and engineering buy-in to dedicate time and effort to them.
What is Cloud Analytics? Some organizations have dedicated teams of engineers that are focussing on visibility and reporting in the cloud across business units. They are responsible for building actionable, accurate, consistent, near real-time insights for engineers, leadership, and finance based on requirements from the FinOps team or the CCoE.
FinOps / CCoE.
The FinOps team or Cloud Center of Excellence is the heart of the FinOps practice. Here we gather requirements for FinOps processes and practices, get buy-in from executives, and communicate requirements and deliverables to engineering leaders.
Engineering leaders can be on the vice president, director, or manager level. They communicate FinOps processes and practices to engineers, provide training opportunities, validate that processes are being followed, and reward positive outcomes.
Engineering leadership needs a consolidated view of cloud costs and cost reduction opportunities to balance new feature delivery with technology debt reduction. Here it is vital to get connected with cost impacts of infrastructure decisions as early as possible.
Engineers are the front-line executioners of FinOps processes and practices. Finance relies on them for quality tagging for cost attribution to be accurate. Additionally engineers need to be trained in adopting new technologies such as Spot or preemptible compute and serverless technologies with templatized paved road approaches and automated guardrails that support velocity of innovation without the slow down of gatekeepers.
To summarize Identifying ownership and accountability, collaboration is the engine of the practice of FinOps, enabling continuous improvement and fast decision making. We need to understand who the key stakeholders are, what they contribute, and what requirements they have. This will determine the frequency, granularity, and quality around forecasting.
Dieter Matzion is a member of Intuit’s Technology Finance team supporting the AWS cost optimization program.
Most recently, Dieter was part of Netflix’s AWS capacity team, where he helped develop Netflix’s rhythm and active management of AWS including cluster management and moving workloads to different instance families.
Prior to Netflix, Dieter spent two years at Google working on the Google Cloud offering focused on capacity planning and resource provisioning. At Google he developed demand-planning models and automation tools for capacity management.
Prior to that, Dieter spent seven years at PayPal in different roles ranging from managing databases, network operations, and batch operations, supporting all systems and processes for the corporate functions at a daily volume of $1.2B.
A native of Germany, Dieter has an M.S. in computer science. When not at work, he prioritizes spending time with family and enjoying the outdoors: hiking, camping, horseback riding, and cave exploration.