December 27, 2023 6:23 AM
Presented by SQream
The Challenges of AI Makes Your Organisation’s Data Analytics Transformation Crucial
The challenges of AI compound as it hurtles forward: demands of data preparation, large data sets and data quality, the time sink of long-running queries, batch processes and more. In this VB Spotlight, William Benton, principal product architect at NVIDIA, and others explain how your org can uncomplicate the complicated today. Check out the on-demand event now!
The soaring transformative power of AI is hamstrung by a very earthbound challenge: not just the complexity of analytics processes, but the endless time it takes to get from running a query to accessing the insight you’re after.
“Everyone’s worked with dashboards that have a bit of latency built in,” says Deborah Leff, chief revenue officer at SQream. “But you get to some really complex processes where now you’re waiting hours, sometimes days or weeks for something to finish and get to a specific piece of insight.”
In this recent VB Spotlight event, Leff was joined by William Benton, principal product architect at NVIDIA, and data scientist and journalist Tianhui “Michael” Li, to talk about the ways organizations of any size can overcome the common obstacles to leveraging the power of enterprise-level data analytics — and why an investment in today’s powerful GPUs is crucial to enhance the speed, efficiency and capabilities of analytics processes, and will lead to a paradigm shift in how businesses approach data-driven decision-making.
The acceleration of enterprise analytics
While there’s a tremendous amount of excitement around generative AI, and it’s already having a powerful impact on organizations, enterprise-level analytics have not evolved nearly as much over the same time frame.
“A lot of people are still coming at analytics problems with the same architectures,” Benton says. “Databases have had a lot of incremental improvements, but we haven’t seen this revolutionary improvement that impacts everyday practitioners, analysts and data scientists to the same extent that we see with some of these perceptual problems in AI, or at least they haven’t captured the popular imagination in the same way.”
Part of the challenge is that incredible time sink, Leff says, and solutions to those issues have been prohibitive to this point.
Adding more hardware and compute resources in the cloud is expensive and adds complexity, she says. A combination of brains (the CPU) and brawn (GPUs) is what’s required.
“The GPU you can buy today would have been unbelievable from a supercomputing perspective 10 or 20 years ago,” Benton says. “If you think about supercomputers, they’re used for climate modeling, physical simulations — big science problems. Not everyone has big science problems. But that same massive amount of compute capacity can be made available for other use cases.”
Instead of just tuning queries to shave off a few minutes, organizations can slash the time the entire analytics process takes, start to finish, super-powering the speed of the network,
