How One CEO Uses GPU Technology To Analyze More Data, Faster

This article series spotlights key business trends identified by the expert members of Forbes Councils. Find out if you qualify for Forbes Technology Council here.

As more people work from home (WFH) than ever before and IoT and 5G continue to grow, businesses are struggling to keep up with exponentially increasing data. The legacy systems many organizations currently have in place simply aren’t able to handle today’s massive workloads. Complex queries can take hours to weeks and often time-out before completion. As a result, business leaders are missing key insights that could give them an advantage over the competition.

Many organizations are looking for new and creative ways to meet the challenge of managing, ingesting and analyzing such massive data stores. One method that’s proving successful is data acceleration technology powered by graphics processing units (GPUs) to prepare and analyze more data, faster, in order to reach business-critical insights that can optimize services, save money and grow revenues.

Forbes Technology Council member Ami Gal is the co-founder and CEO of SQream, a data acceleration platform built on GPU technology that allows organizations to extract critical business insights from exponentially growing data stores. The SQream platform’s accelerated ingestion and analysis reduces query times from days to hours and from hours to minutes for data stores ranging from terabytes to petabytes. Gal said organizations using SQream’s solution consider it their “secret weapon” against competition.

Working with Global 1000 enterprises, Gal said SQream’s platform produces significant tangible results. “One leading global telecom used SQream to enable engineers to implement network analysis, identifying and tracking throughput, drops and anomalies in near real-time. They used this information to reduce dropped calls by 90%,” he said.

“We had another case with a financial services company that was using algorithms to detect and prevent fraudulent activity. They were updating these algorithms every few minutes, and it had to run on a massive data set for over a month in order to be effective. We helped them bring this timeframe down to under an hour, while increasing fraud detection from 89 to 97%.”

As data continues to reach massive proportions for many organizations, it poses a host of new challenges for data analysts, including queries that take too long to run or don’t complete at all. Gal said this creates more problems for business decision-makers; without key insights produced by the right data, leaders may be missing out on growth opportunities they might not even realize they can achieve.

“Organizations don’t have to settle for this,” Gal said. And they don’t have to replace their existing legacy infrastructures to meet today’s challenges. Instead, they can complement existing systems with a solution built on a massively parallel engine that’s faster than CPUs and capable of rapidly and efficiently analyzing data at scale.

Gal sees growing data stores as an organization’s most powerful asset. “Our vision at SQream is to provide organizations with a solution that puts them fully in control, with the ability to drill down to the nuances of their data, analyze raw data sets – be it on-prem, hybrid or on-the-cloud – and get the answers they need when they need them.”

For today’s organizations, being able to quickly analyze the full scope of their data is no longer just nice to have; it’s absolutely essential for business success. Gal is enthusiastic about the potential for better data analysis to build a better future for business: “Massive data is here to stay; it changes the game and we believe organizations should be using this to their advantage.”

For more information, check out Ami Gal’s executive profile here. To learn more about Forbes Technology Council and see if you qualify for membership, click here.

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