SanjMo’s Sanjeev Mohan talks with theCUBE’s Dave Vellante about Cloudera’s positioning in AI.

“Cloudera has a full stack, which includes not just their maturity in the data space, which they’ve had for many years now, but also in the AI,” Mohan said during the event. “They have this whole AI in a box. You can run AI training modules, inferencing on the edge [and] on-premises in the cloud. So it’s a very well-rounded stack with all the pieces.”

During the event, experts discussed Cloudera’s goal to position itself at the intersection of AI, hybrid cloud and modern data architecture. TheCUBE’s coverage featured interviews with industry professionals about AI-ready data architecture, product innovation and open-source leadership. (* Disclosure below.)

Here are three key insights you may have missed from theCUBE’s coverage:

Insight #1: There’s a shift toward AI-ready data architecture.

The enterprise data landscape is entering a new phase as AI moves from experimentation to large-scale deployment. This shift demands AI-ready data architecture that span cloud, on-prem and edge environments, according to Mohan. It marks a turning point and  highlights a comeback for vendors that once lagged in cloud adoption. Cloudera combining its long-standing strengths with new capabilities to become more competitive.

SanjMo’s Sanjeev Mohan talks with theCUBE about Cloudera’s acquisitions to strengthen governance and portability, and customer use cases proving AI’s billion-dollar ROI.

“Cloudera is finding its legs,” Mohan told theCUBE. “It has taken them a long time … [but] I think the market has now come to Cloudera.”

AI inferencing at the edge is emerging as a major growth area, according to theCUBE Research’s Dave Vellante. He pointed to Cloudera’s broad deployment flexibility — spanning public cloud, multicloud, hybrid on-premises and edge environments — as a key differentiator.

“It can be ported anywhere … because of that portability capability,” Vellante said. “So I like the story … as you say, the markets come to them now.”

Several stories shared during Cloudera EVOLVE25 underscored the company’s growing differentiation. One standout example came from pharmaceutical giant AbbVie Inc., where AI is helping shorten decade-long drug development timelines and reduce weeks of manual documentation to days, according to Mohan.

“AI is helping them bring the workload in-house, save on time and cost [and] produce these documents faster,” Mohan said. “If they can reduce the time for a new drug discovery … we’re talking about billion-dollar ROI just for this one use case, document generation.”

Here’s the complete video interview with Sanjeev Mohan and Dave Vellante:

Insight #2: A strong data strategy helps drive AI success.

As enterprises move from proving AI’s potential to realizing its impact, the conversation is shifting toward execution. For Cloudera, that means emphasizing being intentional about the data strategy that underpins AI.

The priority is understanding where data lives and how to use it effectively. Cloudera’s goal is to simplify complex open-source capabilities into a AI-ready data architecture, helping organizations turn their data story into real business value, according to Jason Mills (pictured), senior vice president of solutions engineering at Cloudera.

“In that sense, customers are looking to us to help them define their data strategy,” Mills told theCUBE. “You don’t have an AI strategy without a data strategy. So the first point is defining that.”

Here’s theCUBE’s complete interview with Jason Mills:

Building on that foundation, the next challenge is designing data architectures that can support AI at scale. Many of Cloudera’s global enterprise customers keep data within specific regions — such as Asia-Pacific or the Americas — to meet data residency rules and minimize latency, according to Manasi Vartak, chief AI architect at Cloudera.

Cloudera’s Manasi Vartak talks with theCUBE about the growing impact of agentic and generative AI.

“What that means is if you want to build models or serve models for those people, the models need to be located where the data is … so, that’s what we’re doing,” Vartak told theCUBE. “Our whole philosophy is you can have a central control tower for your AI where you might have different kinds of fraud models, but if you have a fraud model for Europe [and] run it within Europe, whether that’s on-prem or in the cloud.”

Enterprise data is already distributed across regions for customer and regulatory reasons. By bringing models to where the data resides, organizations can stay within regulations while delivering a better user experience, according to Vartak.

“If you start shipping data across the world to get an [large language model] prediction, you’ve lost the game,” Vartak said.

Here’s theCUBE’s complete interview with Manasi Vartak:

Insight #3: Converged platforms simplify enterprise AI adoption.

As AI expands across hybrid and multicloud environments, enterprises are looking for ways to reduce complexity. For IBM Corp., scalable AI hinges on a trusted data architecture, according to Marcela Vairo, vice president of data and AI, Americas, at IBM.

IBM’s Marcela Vairo talks with theCUBE about how strong data architecture enables enterprises to scale AI effectively across cloud environments.

“We are helping customers to be successful as AI-driven companies,” Vairo told theCUBE. “What does it mean? It means preparing their data, getting their data architecture-ready, because there is no AI without data. This hasn’t changed. We are helping them to select the best use cases and to think about the return on investment and how to grow and scale AI to transform the way they operate.”

A few years ago, most customers were only experimenting with AI and generative AI use cases, according to Vairo. Now, as they move into production, they’re realizing that without accurate, trusted and well-governed data, an AI-ready data architecture is essential for scaling AI.

“I think this concept of data and AI coming up together, it’s clear not only to the IT guys. I think this is a major change, but the line of business is also realizing that they need to figure out that data. They need to trust their data in able to scale with AI,” Vairo said. “I think it’s a much more mature conversation nowadays.”

Here’s theCUBE’s complete interview with Marcela Vairo:

That focus also extends to how enterprises interact with their data. Cloudera has a vision for unified, converged platforms that make AI accessible through a single interface, according to Sergio Gago, chief technology officer of Cloudera.

“My vision and Cloudera’s vision is that as a data practitioner, you should not need to even know or care what is the environment that you’re working with,” Gago told theCUBE. “Instead of the traditional ingest, prepare, curate [and] distribute data, just a single interface that makes it easy to get insights from the data. Because at the end of the day, that is what we’re trying to do: Get value from insights.”

Gago outlined three eras of data: governance, convenience and now convergence. After governance-focused platforms and the cloud’s era of convenience, the next phase merges data and compute.

“We’re bringing that cloud experience into the data center and vice versa,” he said. “You can run your pipelines, you can store your data, you can manage your models, you can do everything with the same governance and controls that you had in the first era, but also with the simplicity you had in the second era. We call that the era of convergence.”

Here’s theCUBE’s complete interview with Sergio Gago:

To watch more of theCUBE’s coverage of Cloudera EVOLVE25, here’s our complete event video playlist:

(* Disclosure: TheCUBE is a paid media partner for Cloudera EVOLVE25. Neither Cloudera, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE