By Dale Partridge, Executive Director, DHS Division
Cost forecasting plays a key role in optimizing cloud costs and supports better informed decision-making. This practice enables organizations to better predict and manage future costs effectively, while promoting a culture of financial accountability and efficiency.
Dale Partridge
The biggest challenge to cloud cost forecasting lies in its consumption-based and variable nature. Forecasting involves predicting or estimating future events or trends. Forecasting accurately requires gathering the right historical data to improve the likelihood of a correct prediction, and thus of obtaining the expected goal.
Importantly, this process of comparing cloud spend forecasts to actuals, and adjusting the model accordingly, is the key to continuous improvement. Each new data set adds to the historical data, where variances against actual results are used to make decisions, highlighting areas in which to apply changes in the never-ending pursuit of continuous improvement.
Cloud spend forecasting requires upfront considerations, planning, and decisions about:
- What data to use;
- How to normalize the data across the cloud service providers; and
- The timeframe of historical data available use.
The FinOps Foundation states “accurate financial forecasting depends on an organization’s other FinOps capabilities also being robust to provide accurate data as input” (Foundation, 2024). As an organization takes on forecasting cloud spend, it should keep in mind the FinOps framework, applying the maturity model “Crawl, Walk, Run” to manage expectations and minimize frustrations.
Although cloud service providers deliver lots of data, data alone is not the silver bullet. The voluminous amount of data available becomes a challenge as each cloud service provider has its own nomenclature, scaling options, and other differences which bring increased challenges in forecasting cloud spend.
Maturity Assessment
Crawl
- In organizations with simple implementation models or with limited cloud spend, a limited variety of cloud cost data sources and tools are used for forecasting by stakeholders across the organization
- Forecast models are created manually and/or on an ad-hoc basis
- Forecasts are primarily based on historical spending, as workloads tend to be more stable or simple
- Forecasting variance analysis is done manually
- Limited/aggregate forecasting visibility (only by business unit or cost center)
- Engineering/Operations teams are not involved with the creation of cloud cost forecasts or tracking of discrepancies from forecasted spend
Walk
- Forecast costs are tracked against actual usage and used to establish budgets
- Forecast is inclusive of cloud rate optimization and commitment-based discounts
- Forecast models are rolling and trend-based
- Forecast updates are done on a regular cadence but not automated
- Stakeholder teams (Product, Leadership, Engineering, Finance) have access to cloud cost forecasting data
- Cloud cost forecast data is used to supplement back-end accounting system data
- Regular review cadence is established by the FinOps team of forecast thresholds and trends
Run
- Global policy developed for applying allocation metadata to prevent unallocated cost
- Forecast tracked and updated against discount-adjusted, amortized cloud usage
- Forecast models are a combination of rolling, trend-based, and driver-based
- Forecast is inclusive of usage optimization opportunities
- Forecasts aligned to the organization’s allocation constructs being used across the organization for reporting cloud costs
- Granular forecasting visibility (by business unit, cost center, team, product, service, etc...) in the context of organizational KPIs
- Stakeholder teams (Executives, Engineering, Finance) have real-time visibility into a single source of truth for how cloud usage is impacting forecast trends and budgets
- Integration and automated data flow between cloud cost forecast data and back-end accounting systems used for broader organizational reporting
(Foundation, 2024)
Forecasting cloud spend is challenging, requiring continued iterations and improvement to reduce variances supporting cloud cost optimization.
The FinOps framework aligns with the ITIL4 concept, where continuous improvement applies throughout the cloud spend forecast process. Each iteration will provide insight into areas of improvement, support decision-making, and identify focus areas to zero in on the accuracy of the intended outcomes.
Forecasting cloud spend plays a crucial role in helping public sector and federal customers effectively manage their budgets and resources. By accurately predicting costs, these agencies can plan their budgets more efficiently, allocate funds to various projects, and avoid overspending. Additionally, forecasting cloud spend enables government entities to ensure compliance with regulations such as FISMA, by monitoring and controlling costs associated with cloud services. This proactive approach to cost management allows public sector organizations to optimize resource usage, identify cost-saving opportunities, and align their cloud strategies with budgetary constraints and mission requirements.
Forecasting cloud spend provides transparency into the costs of different cloud services, empowering federal customers to track expenditures, allocate resources effectively, and optimize their cloud environments for maximum efficiency and value.
The cloud spend forecast process and journey not only supports future predictions, it also develops an environment and capability to make meaningful decisions and actions in the present. The process brings additional clarity to the organization and teams with increased collaboration, allowing them to take impactful actions in the present, as well as helping drive the FinOps culture within the organization.
For more information about ManTech Cloud FinOps, email Dale Partridge: [email protected]