With rise of AI, new report uncovers widespread data problems for US enterprises due to exorbitant running costs and frequent compromises that see 98% of companies experience project failure
NEW YORK, Aug. 5, 2024 /PRNewswire/ — In its 2024 State of Big Data Analytics Report, SQream sheds light on the growing disconnect for enterprises between the cost of analytic projects and the operational value being realized, highlighting the pressing need to change how companies handle huge volumes of data to reduce ‘bill shock’ and avoid the risk of project failures.
The advent of cloud computing combined with recent advances in generative AI has placed data analytics and powerful business insights in reach for large enterprise organizations. Yet these two tech trends are also responsible for producing massive and ever-increasing volumes of data, causing IT costs to exponentially increase at an unsustainable and unprecedented rate.
To get to the crux of why data analytics are draining enterprise budgets and how to change this, SQream surveyed 300 senior data management professionals from US companies with at least $5M+ annual spend on cloud and infrastructure. Despite the already substantial budgets at play, 98% of these companies still experienced ML project failures in 2023.
Adding more compute power has in the past been the go-to way to yield better AI results. However, SQream’s survey highlights that doing so indefinitely is an untenable approach for modern, data-driven enterprises, with the complexity of queries and volume of projects being compromised due to skyrocketing bills and IT costs.
“This survey underscores the widespread nature of these data management challenges for large enterprises,” said Deborah Leff, Chief Revenue Officer of SQream. “Leaders are increasingly recognizing the transformative power of GPU acceleration. The immense value of an order-of-magnitude performance leap is simply too valuable to be ignored in the race to become AI-driven.”
The State of Big Data Analytics Report includes a range of detailed insights and findings for team leaders, innovation executives and data-oriented professionals, including:
- Most organizations experience analytics “bill shock”: Although billing cycles vary from company to company, when asked how often they experience bill shock, 71% of respondents (more than 2 out of 3 companies) reported they are surprised by the high costs of their cloud analytics bill fairly frequently, with 5% experiencing bill shock monthly, 25% bimonthly and 41% quarterly.
- 41% of companies report high costs as the leading challenge: As with data analytics, the cost-performance of ML projects is key to successful business predictions. However, given that in ML the more experimentation a company conducts, the better the final result – it is no surprise that 41% of companies consider the high costs involved in ML experimentation to be the primary challenge associated with ML and data analytics today.
- 98% of companies experienced ML project failures in 2023: The top contributing factor to project failures in 2023 was insufficient budget (29%), which is consistent with findings throughout the report. In addition to cost concerns, the other top contributing factors to project failures were poor data preparation (19%) and poor data cleansing (19%).
- 3 out of 4 executives are looking to add more GPUs in 2024: 75% of those surveyed said that adding GPU instances to their analytics stack will have the most impact on their data analytics and AI/ML goals in 2024.
- Close to half of the respondents admitted they compromise on the complexity of queries: 48% of the respondents admitted to having compromised on the complexity of queries in an effort to manage and control analytics costs – especially in relation to cloud resources and compute loads. 92% of companies are actively working to “rightsize” cloud spend on analytics.
“To get ahead in the competitive future of AI, enterprises need to ensure that more big data projects reach the finish line. Constant compromising, including on the size of data sets and complexity of queries, is a huge risk factor that corporate leaders need to address in order to effectively deliver on strategic goals,” said Matan Libis, VP Product at SQream.
The State of Big Data Analytics Report is available for download here.
About SQream
SQream specializes in data processing and analytics acceleration, revolutionizing the way organizations approach big data analytics and AI/ML workloads with its unique GPU-patented SQL engine. SQream’s solutions are designed to meet the needs of enterprises grappling with massive or complex datasets, offering unparalleled performance, scalability, and cost-efficiency. Tailored for industries ranging from finance to telecommunications, SQream empowers businesses to unlock actionable insights from their data with unprecedented speed and efficiency.
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SOURCE SQream
