Features of FinOps
The FinOps LifeCycle
The course is part of this learning path
As spending on the public cloud is increasing globally, companies are looking for ways to reduce cost and increase efficiency. Financial Operations, or FinOps, is similar to DevOps, which enables companies to accelerate technology delivery. FinOps is a new operating model that maximizes the value of an organization's cloud investment.
In this course, you are going to learn about FinOps Principles and how to build FinOps Teams, as well as the three phases of the FinOps Lifecycle. Specifically, you will learn how to apply FinOps processes and practices to reduce rates and avoid unnecessary cloud costs.
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- Understand what makes the cloud so powerful and why it is changing how businesses operate
- Understand what makes cloud challenging from a technology, management, and financial perspective
- Learn about the six FinOps Principles and how to build successful FinOps Teams
- Learn about FinOps capabilities and how to build a common language within your organization
- Learn about the anatomy of a cloud bill and how to take advantage of the Basic Cloud Equation
- Learn about the three phases of the FinOps Lifecycle and how to build successful processes and practices to reduce rates and avoid cost
This course is for engineers, operations, and Finance people looking to understand how to improve efficiency and reduce cost in the cloud.
to get the most out of this course, you should have a foundational understanding of cloud concepts, specifically how compute and storage are provisioned and billed in the cloud. Some familiarity with rate reduction and cost avoidance methods in the cloud would also be helpful but are not essential.
What makes the public cloud so powerful is the ability to run short experiments involving a large number of compute resources, something that is too expensive and takes too much time to procure in the data center.
Specifically, there are so-called non-deterministic polynomial-time problems where known algorithms exist that can find good solutions, however mathematically no best solution can be guaranteed. Examples for these types of problems are Sudoku, a number puzzle, protein folding, the traveling salesman problem, and the knapsack problem.
Software engineers can implement known approximation algorithms in the cloud to see if they will produce viable solutions to real-world problems. Once a proof of concept has been found, the cloud solution can be optimized to make it commercially viable. This will result in a competitive advantage for your business and is the main reason why we don't want to reduce the velocity of innovation by building guardrails instead of gatekeepers in the cloud.
Gartner forecasts that the worldwide public cloud services market will grow to $364 billion USD in 2022, while Forrester estimates the public cloud market will grow to $411 billion USD by 2022. The cloud is changing our businesses. Every company is becoming a technology company in addition to their core industry. Think of Nike, Nationwide Insurance, and Walmart.
But the cloud has many challenges. We are going to look at these challenges from a technology, management, and financial perspective. When we look back to the data center there was a lot of waste because workloads were statically provisioned. Engineers set up buffers to accommodate changes in demand, leadership set up buffers on top of these to accommodate growth, and executives set up additional buffers to mitigate risk. All of this waste was unavoidable due to the lack of elasticity in the data center.
However the cloud is a managed service where someone else is applying software patches and carrying a pager, and using the same buffering strategies will make it more expensive compared to the data center. Forbes says executives estimate that at least 30 percent of their cloud spending is wasted. Unfortunately, cloud technology is still in its early stages and there is generally no native functionality where idle workloads are being suspended to avoid incurring cost.
When we look at the cloud from a management perspective we see that even medium-size businesses may have millions of rows in their cloud bill. And cloud vendors maintain millions of stock-keeping units or SKUs across hundreds of services they offer. For example, Amazon Web Services Elastic Compute Cloud has over 4 million SKUs. Traditional spreadsheet methods cannot handle this volume, making it necessary that customers use specialized tools to manage things like security, compliance, deployment, cost, and waste at cloud scale.
When we look at the third-party vendor market for cloud, there are many companies offering tools, sometimes with overlapping functionality. While we see some consolidation through acquisitions, there is currently not one tool that does everything we need. This makes it necessary for companies to use multiple tools to manage different aspects of the cloud.
And when we look at the cloud from a Finance perspective we see that the centralized approval model from the data center shifted to a decentralized model where engineers are making purchasing decisions. Because software engineers typically don't have a background in Finance, we need new methods to hold them accountable for the cost of their workloads.
Financial Operations or FinOps aims to hold engineers responsible for costs similar to DevOps, where engineers became responsible for operationalizing their workloads. FinOps is primarily a culture shift where cloud cost is moved to the forefront of everyone's thinking. And this is what we will focus on in our lecture.
To summarize the challenges of Cloud FinOps: The cloud is a game-changing technology, where companies are becoming technology companies in addition to their core business. Elasticity replaces fixed spend models with variable cloud consumption. Ownership is decentralized as it shifts from a central organization to engineers. And the cloud is inefficient, as few cloud providers offer native tools to turn off workloads when they are not in use.
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.