In today’s competitive landscape, data-driven companies consistently outperform their peers. McKinsey says that D&A (Data and Analytics) can provide EBITDA (earnings before interest, taxes, depreciation, and amortization) increases of up to 25% [1]. Forrester research found that organizations using D&A are three times more likely to achieve double-digit growth [2]. Central to this transformation is cloud computing for processing massive and diverse datasets to perform advanced analytics in a flexible, scalable, and cost-effective manner.
Enabling enterprises to be data driven and agile comes at a price and understanding data processing costs on cloud can be complex as it is driven by a host of diverse and dynamic parameters. For most organizations, the three key cost categories that affect resource utilization in cloud computing are:
In this regard, a study from Vertice, a cloud spend performance management firm shows the various categories of cloud costs [3].
So, what are the stakes involved in managing cloud costs? As data volumes and analytics demands rise, the cloud adoption will also grow. In addition, the data processing costs on cloud are increasing because of the increase in AI (Artificial Intelligence) and analytics workloads. All these developments result in increased complexity, effort, and costs in managing the cloud platforms. To further aggravate the matter, increases in cloud computing costs are difficult to forecast and therefore difficult to budget. More than half the organizations are spending at least 20% more on cloud computing than forecasted [4].
Being data driven offers undeniable advantages, including accelerating pace of innovation, improving operational efficiency, and greater adaptability. However poorly managed cloud expenses related to data processing can lead to significant financial and operational challenges. But currently most efforts to control costs involve rationing the resource usage hindering the speed of analytics deployment or refactoring the workloads resulting in more FTE (Full Time Employee) expenses.
These approaches are found to result in inefficient cloud resource management—either through over-provisioning or under-utilization of compute resources. Basically, over-provisioning results in paying for unused resources, bloating costs without adding value, while under-provisioning risks degrading application performance, negatively impacting user experience and productivity.
Overall, the stakes of cloud cost mismanagement are too high to ignore. The strategy to drive measurable business value from cloud lies in managing cost proactively using the right tools, policies, and processes. The response should be based on data processing monitoring mechanisms to manage under-provisioning and over-provisioning with a high degree of automation to reduce FTE costs. These measures can increase the odds of reducing the compute and cloud costs without compromising on the analytics usage
In conclusion, data is a cornerstone of modern business operations, enabling innovation, and driving business outcomes. However, as organisations increasingly adopt data driven practices, managing and understanding costs associated with data processing is a critical challenge. Gaining clarity on key cost drivers and understanding usage patterns holds the key towards financial efficiency and improved business performance.
