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5 Key Ingredients to Accurate Cloud Data Budget Forecasting

Hey there! Have you ever found yourself scratching your head over unpredictable cloud data costs? It’s no secret that accurately forecasting cloud data spend can be a real headache. Fluctuating costs make it challenging to plan […]

  • 7 min read

Hey there! Have you ever found yourself scratching your head over unpredictable cloud data costs? It’s no secret that accurately forecasting cloud data spend can be a real headache. Fluctuating costs make it challenging to plan and allocate resources effectively, leaving businesses vulnerable to budget overruns and financial challenges. But don’t worry, we’ve got you covered!

Uncontrolled fluctuations in cloud data spend can hinder growth and profitability, disrupting the smooth sailing of your business operations. That’s why it’s crucial to gain control over your cloud data workload forecasts. By understanding the changes in your cloud data spend and having a clear picture of your billing data, you can make informed decisions that align with your company’s goals.

We’ll discuss practical strategies to improve forecast accuracy, identify data pipelines and analytics workloads that are above or below budget, and enhance accountability across different business units.

So let’s dive right in and discover how you can steer your business towards cost-effective cloud management!

Unanticipated cloud data spend

Last year, over $16 billion was wasted in cloud spend. Data management is the largest and fastest-growing category of cloud spending, representing 39% of the typical cloud bill. Gartner noted that in 2022, 98% of the overall database management system (DBMS) market growth came from cloud-based database platforms. Cloud data costs are often the most difficult to predict due to fluctuating workloads. 82% of 157 data management professionals surveyed by Forrester cited difficulty predicting data-related cloud costs. On top of the fluctuations that are inherent with data workloads, a lack of visibility into cloud data spend makes it challenging to manage budgets effectively.

  • Fluctuating workloads: Data processing and storage costs are driven by the amount of data stored and analyzed. With varying workloads, it becomes challenging to accurately estimate the required data processing and storage costs. This unpredictability can result in budget overruns that affect 60% of infrastructure and operations (I&O) leaders.
  • Unexpected expenses: Streaming data, large amounts of unstructured and semi-structured data, and shared slot pool consumption can quickly drive up cloud data costs. These factors contribute to unforeseen spikes in usage that may catch organizations off guard, leading to unexpected expenses on their cloud bills.
  • Lack of visibility: Without granular visibility into cloud data analytics billing information, businesses have no way to accurately allocate costs down to the job or user level. This makes it difficult for them to track usage patterns and identify areas where budgets will be over- or under-spent, or where performance and cost optimization are needed.

By implementing a FinOps approach, businesses can gain better control over their cloud data spend, optimize their budgets effectively, and avoid unpleasant surprises when it comes time to pay the bill.

Why cloud data costs are so unpredictable

Cloud data costs can be a source of frustration for many businesses due to their unpredictability. Cloud and data platform providers often have complex pricing models that make it challenging to accurately forecast expenses. Here are some key reasons why cloud data analytics costs can be so difficult to predict:

  • Variety of factors affect analytics costs: Cloud and data platform providers offer a range of services and pricing options, making it hard to determine the exact cost of using specific resources. Factors such as compute instance and cluster sizes, regional pricing, and additional features all contribute to the final cloud and data platform bill.
  • Usage patterns impact cost: Fluctuations in usage patterns can significantly affect cloud data costs. Peak demand periods or sudden increases in data volume can result in unexpected expenses. Without proper planning, businesses may find themselves facing higher bills during these periods.
  • Lack of visibility into resource utilization: Inefficient workload management and a lack of visibility into resource utilization can lead to higher expenses. Without the ability to monitor and optimize resource allocation, businesses may end up paying for unused or underutilized resources.
  • Inability to allocate historical spend: A lack of granular visibility into data costs at the job, project, and user level makes it nearly impossible to accurately allocate historical spend or forecast future investments. This makes budgeting and financial planning challenging for businesses that rely on cloud data platforms.
  • Changes in technology or service offerings: Cloud and data platform providers frequently introduce new technologies or adjust their service offerings, which can impact cost structures. Businesses must stay updated on these changes as they may influence their overall cloud expenditure.

Navigating the complexities of cloud data forecasting requires careful analysis and proactive management or resource consumption fluctuations and cost unpredictability. By understanding usage patterns, optimizing capacity utilization, and staying informed about changes from cloud and data platform providers, businesses can gain better control over their cloud data costs.

5 key ingredients of an accurate cloud data cost forecast

To ensure an accurate cloud data cost forecast, several key ingredients must be considered. These include:

  1. Comprehensive understanding of historical usage patterns and trends: Analyzing past usage data provides valuable insights into resource consumption and enables more accurate predictions of future spending.
  2. Granular visibility into data resource usage: It is essential to have detailed visibility into the utilization of resources down to the job and user level. This level of granularity enables a more precise estimation of costs associated with specific tasks or individuals.
  3. Analysis of current platform configurations and workload requirements: Evaluating the existing data platform settings, data access patterns, and workload demands help predict growth rates and changes in cloud data spend.
  4. Consideration of external factors: External factors such as market conditions or regulatory changes can significantly impact cloud data processing costs. Incorporating these variables into the forecasting model ensures a more realistic projection.
  5. Utilization of advanced forecasting techniques and algorithms: Leveraging advanced techniques and algorithms enhances the accuracy of predictions by accounting for various factors simultaneously, resulting in more reliable forecasts.

        By incorporating these key ingredients into your cloud data forecasting strategy, you can gain better control over your forecast and achieve higher accuracy in predicting future expenses. With a comprehensive understanding of historical patterns, granular visibility into resource usage, analysis of configurations and workload requirements, consideration of external influences, and advanced forecasting techniques, you can make informed decisions to increase the accuracy of your cloud data spend forecasts.

        Remember that accurate cloud data cost forecasting is crucial for effective financial planning within your organization’s cloud environment.

        Explore different methods and tools for accurate cloud data forecasting

        Statistical modeling techniques like time series analysis can be used to predict future trends based on patterns in historical data. These predictive models help improve forecast accuracy by identifying recurring patterns and extrapolating them into the future.

        Machine learning algorithms offer another powerful tool for cloud data forecasting. By analyzing vast amounts of information, these algorithms can generate accurate forecasts by identifying complex relationships and trends within the data. This enables organizations to make informed estimates of their cloud data processing patterns to help anticipate cyclical usage and growth.

        Cloud management platforms provide built-in forecasting capabilities. However, it is important to note that forecasts are only as good as the underlying data. Without granular visibility into the jobs, projects, and users utilizing the cloud resources, forecast models cannot take key drivers into account. To ensure accurate predictions, it is crucial to distinguish one-time or end-of-period data processing from ongoing processing related to growing customer and end-user activity.

        How purpose-built AI improves cloud data cost forecasting

        Purpose-built AI is a game-changer. By leveraging advanced algorithms and machine learning capabilities, businesses can unlock the full potential of their cloud data cost management. 

        Here’s how purpose-built AI improves cloud data cost forecasting:

        • Identifying hidden cost drivers: Purpose-built AI has the ability to analyze vast amounts of data and identify subtle factors that contribute to increased costs. It goes beyond surface-level analysis and uncovers underlying patterns, enabling businesses to accurately anticipate cloud analytics resource needs.
        • Continuous learning for improved accuracy: Machine learning models are continuously trained on past performance, enabling them to learn from historical data and improve accuracy over time. This means that as more data becomes available, the forecasts become increasingly reliable.
        • Proactive decision-making with predictive analytics: Predictive analytics powered by purpose-built AI enable businesses to make proactive decisions regarding their cloud expenditure. By analyzing trends and patterns, organizations can anticipate future costs and take necessary steps to avoid unnecessary expenses or mitigate risks associated with fluctuating costs.
        • AI-driven recommendations for enhanced cost efficiency: Purpose-built AI provides valuable recommendations based on its analysis of cloud data cost patterns. These recommendations help businesses improve their overall cost efficiency, ensuring that resources are allocated optimally to accelerate cloud data platform usage.

        Using Unravel to Improve Cloud Data Cost Forecast Accuracy to within ±10%

        These different approaches help improve the accuracy of your cloud data cost forecasts. But how can you take it a step further? That’s where Unravel comes in.

        Unravel is a purpose-built AI platform that can revolutionize the accuracy of your cloud data cost forecasts. Unravel provides real-time insights into your data usage patterns, identifies budget trends, and predicts future costs with remarkable accuracy. With its intuitive interface and easy-to-use features, Unravel empowers you to make informed decisions about resource allocation, budget planning, and overall cost management in the cloud.

        Ready to take control of your cloud data costs? Start using Unravel today and unlock the full potential of accurate cloud data cost forecasting.


        Q: How does Unravel improve cloud data cost forecasting?

        A: Unravel leverages advanced machine learning algorithms to analyze historical usage patterns and identify trends in your cloud data costs. By understanding these patterns, it can accurately predict future costs and provide actionable insights for optimizing resource allocation.

        Q: Can I integrate Unravel with my existing cloud data platform?

        A: Yes! Unravel seamlessly integrates with popular cloud platforms such as AWS, Azure, and Google Cloud Platform. It supports both on-premises and hybrid environments as well.

        Q: Is Unravel suitable for businesses of all sizes?

        A: Absolutely! Whether you’re a small startup or a large enterprise, Unravel caters to businesses of all sizes with large data estates looking to leverage data to maximize business value. Unravel’s scalable architecture ensures that it can handle the needs of organizations with 10,000s of data platform jobs, such as DBS Bank.

        Q: How long does it take to see results with Unravel?

        A: You’ll start seeing immediate benefits once you integrate Unravel into your infrastructure. Its real-time insights and actionable recommendations allow you to make informed decisions right from the get-go.

        Q: Can Unravel help with other aspects of cloud data management?

        A: Yes, Unravel offers a comprehensive suite of features for end-to-end cloud data performance management, FinOps for your data cloud, intelligent data quality, and forecast accuracy. From performance optimization to cost governance, Unravel provides a holistic solution for your cloud analytics needs.