How to Address Cloud Forecasting

How to Address Cloud Forecasting
Overview
Difficulty
Intermediate
Duration
1h 14m
Students
133
Ratings
5/5
starstarstarstarstar
Description

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.

Learning Objectives

  • 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

Intended Audience

  • 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.

Prerequisites

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.

Transcript

To get started, let's look at why cloud forecasting is so important. When we examine how businesses spend their revenue, on average about half goes toward headcount and another third toward offices and general operations, like marketing, sales, and administrative expenses. The last-mentioned includes things like cafeteria, facilities, landscaping, and physical security.

This leaves about one sixth of the revenue to the cloud as well as research and design, things like  new technologies and capabilities. This means that over forecasting cloud spend results in lost opportunity on the R&D side, and under forecasting requires us to put R&D projects that are currently in progress on hold. Both situations are undesirable for the business. The goal is to forecast cloud spend as accurately as possible with the available data and tools.

What makes cloud forecasting challenging is that cloud spend is highly variable, meaning engineers can start cloud workloads at any time without having to go through a procurement process, which is inherently difficult to predict.

Additionally the tools cloud providers offer around billing and reporting are not aligned with how leadership and finance think about cost. The basic cloud equation is Usage times Rate equals Cost, meaning billing from cloud providers is metered usage over time. However the business views spending in the form of business units and cost centers. This requires that we attribute cloud workloads back to the business owners either using account or project structures, or tagging.

 

To summarize How to address Cloud Forecasting, forecasting over or under actual spend takes away from potential innovation opportunities, making cloud forecasting the single most important thing we can do to help with innovation and customer growth. The highly variable spend model of the cloud makes forecasting challenging and unfortunately there is no one forecasting method that fits all situations.

About the Author
Students
2636
Courses
3

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.