Enterprises are facing unprecedented challenges in the wake of a potential economic downturn. As a result, leaders are making difficult decisions about budgets and staffing, focusing on what and who is essential to the success of the organization. With over 108,000 workers laid off in 2023, many are turning to the cloud as a cost-cutting solution.
Navigating Economic Turbulence with Cloud Technology
Cloud computing is one of the top four technology investments that enterprise leaders are pursuing, even in the face of a potential recession. According to a recent survey, about 84% of enterprises plan to increase their public cloud storage and the amount of data they store in the public cloud within the next year. This is because leveraging the cloud can introduce benefits like scalability and flexibility to system infrastructure and reduce IT costs.
However, it’s crucial to use the cloud efficiently while keeping costs in check. Overspending and under-utilization of resources can be a costly liability, especially during an economic downturn. With cloud spend having increased by 20 to 30% in 2022, it’s more important than ever to scrutinize cloud spend and optimize cloud usage to maximize value and reduce costs.
Big Data in the Face of Economic Uncertainty
For engineering teams, cloud computing is critical to implementing IT transformations that further business goals, enhance customer experience, and create new revenue streams. While the cloud can improve the effectiveness of an organization’s processes and have a significant business impact, cost- and labor-cutting measures can create challenges for data engineering teams tasked with ensuring powerful and resilient application performance. Even fully staffed teams may struggle to create, test, and deploy services and applications quickly enough using cloud-native tools like Kubernetes, MapReduce, and Spark.
One solution is to leverage automation to streamline operations, limit errors on repetitive tasks, save time, and improve performance. Automation can help manage continually expanding environments that are increasingly complex and dynamic, allowing big data teams to focus on higher-value tasks. This can lead to lower operating expenses and optimized workloads that operate more efficiently.
Addressing Workload Challenges with Automated Solutions
Engineering teams need to look critically at their cloud program and leverage technologies that optimize resources critical to providing services and products that meet customer expectations. Autonomous solutions can help reduce cloud costs and free up R&D teams to focus on maintaining, updating, and improving applications to satisfy end users.
By effectively reducing costs, optimizing resources, and continuing to create value, teams can help their organizations weather periods of economic uncertainty. With an autonomous workload optimization platform, you can create a more agile enterprise that can maintain momentum during any economic conditions and be prepared to meet future demands.