Navigating the rising data cloud costs maze

The rise of cloud computing and various Software-as-a-Service (SaaS) applications have increased cloud data costs. As more businesses move their operations to the cloud, the amount of data being stored and processed in the cloud has grown exponentially. This has led to a surge in cloud data storage and processing services demand.

The ongoing conflict and the COVID-19 pandemic have seriously disrupted the supply chain for key components of cloud computing, such as neon and palladium. As a result of these supply chain problems, there are fewer servers available in the cloud, which has resulted in costs rapidly increasing as opposed to the previous long-term dropping trend. Also, this has impacted Europe’s capacity expansion prospects, thereby restricting the development of new data centres and hyperscalers necessary to support the increase of cloud use. Overall, this has increased the price of cloud data.

Cloud prices first seem to be affordable, but as organisations grow, they may lose track of their cloud utilisation and the accompanying expenditures. Tracking, analyzing and optimizing the expenditure is difficult in this situation, therefore necessitating a re-architecture of the entire infrastructure – a time-consuming endeavour to avoid data trap. Further, businesses often shift their focus to transferring data from one source to many destinations when they require a different set of solutions, resulting in an intricate web of point-to-point integrations.

This then leads to several challenges:

1. Inefficient data management, redundant processing, high maintenance costs, and poor data quality leads to missed opportunities and a lack of access to strategic insights.
2. It is difficult to plan when there are frequent cost surprises associated with black box tools, making it difficult to budget effectively. This limits the ability to predict the ROI of data initiatives.
3. Since the data is not centralised, the time-to-value for data-driven initiatives gets impacted by the inability to access complete and accurate data leading to poor outcomes, missed opportunities, and increased risks.

The best practices to manage data cloud costs

As per a report by the FinOps foundation namely, ‘The State of FinOps 2023’, 49% of organizations globally shifted to monthly cloud cost forecasting as compared to 27% of organizations in 2022. This trend indicates a growing number of organizations are paying attention to effectively managing costs from the outset.

We will recommend the following best practices to help organizations embark on this journey.

1. Centralize data management: By centralizing all data sources and processing pipelines in one place, businesses can improve data governance, increase observability, and reduce the complexity of managing multiple integrations, pipelines, and infrastructures.
2. Shunning Black-box tools: Black-box tools are difficult to monitor and predict. For instance, many ingestion tools have hidden costs. In addition to charging for the number of rows or events, they unknowingly run the data de-duplication process using the data warehouse computing resources, which makes it difficult to predict the total cost of ownership. By staying away from black-box tools, they can predict costs better and avoid surprises.
3. Enhance data security and governance: Once data is unified, it is easy to centralize control and monitor data access and usage. This helps businesses to protect themselves from data breaches and comply with regulatory requirements. Besides providing varying levels of access, this can also prevent unintentional data manipulation and misappropriation of infrastructure resources.
4. Adopting FinOps to optimize costs: FinOps advocates KPI-driven decision-making of cloud costs. To optimize costs that can track business growth, segmenting costs and analyzing and monitoring their behaviour with other variables are necessary steps.

Businesses that wish to profit from cloud computing while still keeping an eye on their expenditures must manage the costs of cloud data. This will help improve decision-making, allocate budgets more effectively, and achieve long-term financial sustainability by proactively tracking and optimising cloud data usage and costs.

(The author is Supratik Shankar and Kauts Shukla-  Co-Founders, DView.io, and the views expressed in this article are his own)