Big data gets smart as AI powers the next ‘step function’ in industrial productivity
Big data has grown up. Far from being an outdated concept, the wunderkind of the 2010s has matured to become the driver of digital insights for a new generation of business.
But it didn’t do it alone. Mapping big data’s move from static lakes to actionable insights are semantic layers that simplify digital transformation by providing businesses with a logical view of their data resources.
“It’s very difficult to perform heart surgery on a patient while he’s running the Boston marathon, and that’s the analogy I would give you for trying to digitize your environment without this semantic layer that allows you to first create a logical layer, put this information in a logical mapping so that you can gradually move data to the appropriate place,” said Chris Lynch (pictured), executive chairman and chief executive officer of AtScale Inc.
Lynch spoke with John Furrier, host of theCUBE, SiliconANGLE Media’s livestreaming studio, for a digital CUBE Conversation on the relevance of big data in the digital era and how AtScale is at the center of the evolution of business intelligence.
Easy digital evolution versus stressful revolution
Rather than the upheaval caused by drastic change during today’s digital revolution, transformation can be a smooth, seamless evolution from one stage to the next, according to Lynch. The key is technology, such as AtScale’s Universal Semantic Layer, that reduces the risks from companies digitizing data and moving it to cloud while simultaneously using it.
“There’s is a demand for big data, and big data means being able to access all your data into one logical form, not pockets of data that are in the cloud, that are behind the firewall, that are constrained by vendor lock-in, but open access to all the data to make the best decisions,” Lynch said.
The semantic layer is important because it separates the source from the actual presentation of the data, allowing data to stay where it is while providing one logical view. This was important for legacy data workloads but becomes even more so with hybrid and multi-vendor cloud models because of the ability to having one source of truth, consistency, consistent access, secure access and actual insights to all, according to Lynch. AtScale “delivers this with no code, and we allow you to turbocharge the stacks of Microsoft Azure and Amazon Redshift and Google BigQuery while being able to use the data that you’ve created in your enterprise,” he explained.
Take the AI way or the highway
The insights provided by artificial intelligence and machine learning infused applications are going to bring about “the next step function in industrial productivity,” Lynch predicted. He compared the impact of the internet to the impact of AI, stating that the knock-on effects of connectivity “pales in comparison to what the application of AI to all walks of life is going to do … every vertical function in the marketplace is going to be impacted by AI.”
In fact, companies that don’t have an AI strategy within the next two years “will be shorted on Wall Street,” according to Lynch.
AtScale’s focus on the democratization of AI and machine learning models will position the company “right in the middle” of the upcoming mass adoption of AI, according to Lynch. Taking a page out of Snowflake Inc.’s book, AtScale is delivering integrated solutions that map small and medium enterprise data with public markets data and models, integrating it into the business’s favorite software-as-a-service applications, such as QuickBooks or Salesforce.
“We’re going to turbocharge them with big data and machine learning in a way that’s going to enhance their operations without increase to complexity,” Lynch stated. “It’s about delivering analytics in a way that customers can absorb, big customers and small customers alike.”
Here’s the complete video interview, one of many CUBE Conversations from SiliconANGLE and theCUBE:
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