New Survey Reveals Shift in Data Processing Strategies: Data Leaders Turn to GPUs to Tame the Data Deluge

Insights from a groundbreaking report reveal why 3 out of 4 enterprise executives surveyed are turning to GPUs in 2024

Our 2024 State of Big Data Analytics Report shines a spotlight on a seismic shift in how data projects are being handled across US enterprises. We’re witnessing a perfect storm of runaway cloud costs, the limitations of CPU-based systems, and the urgent need to enhance the cost-performance ratio of AI initiatives.

The rise of generative AI (GenAI) has ignited a data-driven revolution. Businesses are racing to leverage AI’s potential to generate massive datasets and complex algorithms. However, this data explosion is a double-edged sword. The sheer volume of information threatens to overwhelm traditional data processing methods.

Our research, based on insights from 300 senior data management professionals at US companies investing at least $5M annually in cloud and infrastructure, unveils a critical trend: a mass migration towards Graphics Processing Units (GPUs). GPUs are emerging as a potent solution to tame the data deluge unleashed by GenAI and regain control of spiraling costs.

We’ve uncovered a growing disconnect between the cost of analytic projects and their business value. While cloud computing and GenAI have democratized powerful insights, they’ve also created a data deluge that’s sending IT costs skyrocketing. Despite significant investments, a staggering 98% of surveyed companies experienced machine learning project failures in 2023.

For years, scaling up CPU infrastructure was the de facto response to performance bottlenecks. However, a paradigm shift is underway. Executives are increasingly aware of the diminishing returns and high failure rates associated with this approach. Escalating costs force companies to make agonizing choices: prioritize query complexity, data volume, or project scope. CPU-based processing often introduces bottlenecks and latency, stifling innovation. Our research indicates that executives view this traditional approach as unsustainable in the face of the GenAI era and its massive datasets.

Our findings underscore the widespread nature of these data management challenges. Leaders are recognizing the transformative power of GPU acceleration. The promise of an order-of-magnitude performance leap is too compelling to ignore in the race to AI-driven dominance.

Key findings from our State of Big Data Analytics Report include:

• The “Bill Shock” Epidemic: A significant portion (71%) of respondents experience unexpected surges in their cloud analytics bills.
• High Costs Hinder Progress: Excessive costs associated with machine learning experimentation are the primary obstacle for 41% of companies when it comes to successful data analytics and AI projects.
• Project Failure Breakdown: Insufficient budget (29%) was the leading cause of 2023 project failures, mirroring the report’s overall cost concerns. Poor data preparation (19%) and cleansing (19%) also contributed significantly.
• The CPU Myth: While 78% of companies believe adding more general computing power (CPU) will be the most significant impact on their 2024 data analytics and AI/ML goals, adding GPU instances ranks a close second (75%).
• GPU Acceleration for Existing Pipelines: Our report highlights that GPU processing can significantly accelerate existing data analytics pipelines without requiring a complete overhaul. This allows businesses to unlock the power of AI and address the data deluge without disrupting current workflows.
• Rightsizing the Cloud Challenge: Recognizing the cost burdens, a staggering 92% of companies are actively working to optimize their cloud spending on analytics. Nearly half (48%) admit to compromising on query complexity and project volume to manage these costs.

Download the State of Big Data Analytics Report and discover how GPUs can help your organization overcome the data deluge and unlock the power of AI.