Generative artificial intelligence is currently a hot topic. While its consumer applications in smartphones and smart home devices are well-developed, the race to unlock its full potential for businesses is still ongoing.

One key reason is that real-time data is the lifeblood of AI, and many organizations have yet to fully understand and harness its complexities.

“Now we need to be in the moment, and most companies are not prepared for that,” said Lenley Hensarling (pictured), chief product officer of Aerospike Inc., the company behind the Aerospike open-source NoSQL distributed database management system. “They know they have to apply this information to create the context that’s happening right now when they use gen AI or they’ll get hallucinations. So, this is a point where the real-time data is becoming more and more important.”

Hensarling spoke with theCUBE’s John Furrier ahead of the Real-time Data Summit, during an exclusive Conversation from SiliconANGLE Media’s studio in Palo Alto, California. They discussed how enterprises can adapt to the real-time data paradigm by developing infrastructures that support continuous data ingestion and processing. (* Disclosure below.)

The requirements and challenges of harnessing real-time data

The rapid growth in real-time data usage — estimated to be increasing by 35% compared to the overall data growth of 25-30% — underscores the urgency for businesses to adapt, according to Hensarling. However, the transition to real-time data is fraught with infrastructural and operational hurdles. Companies must therefore prioritize the development of new infrastructure and processes to ensure that data is current and contextually relevant.

“Companies don’t just have their own data that they have to deal with, they’re ingesting data from multiple sources,” Hensarling said. “That mashup of external data and internal data becomes important. Being able to supply that in real time so that it’s up to date is becoming key to differentiation and competitiveness. The most important thing companies can do is be able to ingest that data, but also to make that data available to other applications.”

Harnessing real-time data implies the continuous ingestion and processing of transactions and customer information. Companies are beginning to realize the importance of leveraging this data to enhance the decision-making capabilities of both generative and classical machine learning models. This involves understanding the intricacies of data ingestion, ensuring that the data is up-to-the-minute and applying it effectively within AI systems, according to Hensarling.

“It takes this complete ecosystem to build a solution that delivers meaningful results,” he said. “For that to happen, we’ve all got to get together, educate customers jointly and give customers a place that they can come and understand that full ecosystem around real-time data. That’s what we’re trying to do with this Real-time Data Summit.”

The era of single-context data is no more. Today’s requirements mean that companies must build comprehensive infrastructures to support real-time data, integrating various technologies and data models. This includes utilizing streaming solutions, databases and other tools to ensure that data is instantaneously available. The goal is to create a seamless flow of information that can drive immediate and relevant decision-making processes, Hensarling pointed out.

“Now you have multiple technologies and multiple datasets, but it also has to be rationalized and come together some place,” he said. “That means a fairly complex environment working cooperatively.”

Here’s theCUBE’s complete video interview with Lenley Hensarling:

(* Disclosure: Aerospike Inc. sponsored this segment of theCUBE. Neither Aerospike nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE