From the rise of the internet to the explosion of cloud computing, every major technological era has reshaped how we use—and create—data. Now, according to Cloudera Chief Technology Officer Sergio Gago, we’re entering a third phase of big data focused on convergence.
He recently joined The AI Forecast podcast to discuss how the convergence of cloud and on-premises systems is setting the stage for a new generation of private AI—where enterprises can fully control their data, models, and AI life cycles.
Here are the key takeaways from the conversation.
The Convergence of Cloud and On-Prem—and Why It Enables Private AI
Paul: Let’s talk about your vision. What does the third wave of big data mean to you, and why is it so important?
Sergio: We started with the era of control. Many companies had their own data centers that gave them control of their data. Then the cloud came in and we entered what we call the era of convenience. So, you had teams with a credit card that could go into any hyperscaler and start playing with data either for machine learning or for building dashboards. It was so easy that it brought shadow IT into many enterprises, which made controlling cost, TCO, and data governance growing challenges.
That was the story of cloud and data. Now today, you kick a rock and there are hundreds of engines, databases, and options. We talk about Frankenstein architectures now, where companies have dozens—if not hundreds—of components and are struggling to bring them together. The era of convenience brought this complexity.
Now fast forward with the advent of AI and AI agents and the regulation and compliance requirements for many enterprises and startups alike. To comply, organizations need to bring all the controls of the first era back, especially in large enterprises. All that is forcing companies and individuals to converge and manage both worlds—the data center and the cloud—to have the control and governance of the data center with the convenience of the cloud. That’s why we call the Third Wave, the era of convergence.
Private AI: Full-Lifecycle Control and the Human Advantage
Paul: I wanted to talk to you about the private AI component. With private data, I have a tremendous competitive advantage. How does private AI help me tap into that?
Sergio: Private AI is the ability to control the full life cycle of your AI applications. What models do you use? How do you deploy them? Which ones are approved from a compliance perspective? How do you make sure the model weights stay constant for as long as you need? Then you have data from your company that lives both in the cloud and in the data center. You need to safely bring that data into your model—either for training, fine-tuning, or other techniques like RAG. That’s what makes your model unique to you.
The competitive advantage of most companies today is the data, but also the skills—the human capacity to drive insights. It’s not necessarily the data itself but the experience and domain knowledge that allow you to interpret it. Private AI helps you preserve that advantage by controlling everything from model lifecycle to prompt management, lineage, and benchmarking so you can move from proof of concept to true production workloads.
Build for ROI and Risk—With Agents, Governance, and Culture in the Loop
Paul: When we talk about topics like convergence, we sometimes run the risk of alienating businesspeople who'll see this as more of a CTO-type of discussion, a technical discussion. From your perspective, what does something like convergence do to unlock new use cases or business value that you couldn't get before as a CEO or business leader?
Sergio: I think that the CEO will always want to understand the actual value of a tool, either in terms of ROI or cost reduction, or value improvement for your company. GenAI is just the conveyor belt for all those things.
At the same time, the second angle every CEO has front and center is risk—either from FOMO or from fear of becoming the next company in the headlines due to a massive AI hallucination. Those are the two sides of the scale that CEOs are working with.
GenAI use cases need to start from the business side. Bring in compliance, governance, IT, cybersecurity, and legal from the very beginning so that it doesn’t become an experiment in the garage that then doesn’t go anywhere. Showing value in those terms allows you to then take them to the enterprise.
Catch the full conversation with Sergio Gago on The AI Forecast on Spotify, Apple Podcasts, and YouTube.
