Becoming Data Ready For AI: How Confluent Empowers Partners With Real-Time Streaming

As artificial intelligence becomes more deeply embedded in business strategy, organizations are realizing that AI is only as powerful as the data behind it. Many companies still struggle with siloed, slow-moving data that can’t keep pace with real-time decision-making. To truly unlock AI’s potential, businesses need to be “data ready,” with continuous, reliable access to streaming data across their entire organization. That’s where real-time data streaming comes in. Confluent is helping partners modernize how data moves, enabling scalable, secure streaming that powers AI, analytics and next-generation applications. Adam Tybor, field CTO at Confluent, spoke with CRNtv’s Jon Alba about what it means to be data-ready for AI, how Confluent empowers partners through its platform and OEM program and why streaming data is foundational to modern AI strategies.

Jon: For those who might not live and breathe data streaming every single day of their lives, how do you define what data streaming is and what makes it so critical in today's AI driven world?

Adam: When I talk to many of my friends and family about data streaming, the first thing that comes to mind is Netflix video streaming. While Netflix video streaming certainly demonstrates the potential of moving large volumes of data in near real time, what we mean by data streaming is the continuous movement of events happening across an organization and throughout the world.

Unlike traditional applications that typically take snapshots of data at specific points in time and work on those snapshots, data streaming enables all this information to move across complex ecosystems and around the globe in near real time. This allows us to process and react to data much faster, which is the core purpose of a data streaming foundation.

Jon: When you look at the much broader market, what macro trends have really actually been driving interest and investment in data streaming over the last few years, and what major technology advancements are actually shaping the landscape today?

Adam: I think it’s more than just one factor. Right? The obvious answer is AI, of course. If we look at where the hype cycle is and where organizations are investing, it's all about AI. When you peel that back and start figuring out how to get the most productive results from AI, it quickly becomes a data conversation.

I’ve never had an AI discussion with a company that didn’t evolve into, Is our data ready, available, and high-quality enough to feed into AI and get the best results? But if we step back beyond AI, it’s really about practicing good data hygiene. We’ve seen organizations graduate from digital and cloud transformations, and now begin to treat data as a first-class citizen.

It’s not just about siloed data inside applications, it’s about making data available as products, just like applications are available as products throughout organizations. We’ve also seen increased complexity in architectures, with so many different cloud, software, and platform solutions out there. Streaming can actually help simplify your architecture.

Streaming can handle almost any type of workload. Whether it’s batch-based or real-time, we often say at Confluent that traditional batch workloads are just a special form of streaming. You can always turn a stream into a batch, but you can’t always turn a batch into a stream.

As companies continue to evolve and strive to do more with fewer resources, and as the window of time between a piece of data occurring and a business needing to act on it shrinks, streaming tends to be the inevitable choice. We’re seeing more organizations move in that direction.

Jon: How has the rise of AI changed organizational expectations around streaming, and what types of challenges is it actually helping teams overcome?

Adam: I think one of the big differences we've seen, we'll call it the “ChatGPT Moment” a couple of years ago, is this difference of these very purpose-built, traditional machine learning models that were great at a very specific task to these very generalized foundational models.

And what that's really done is, these large foundational models have now democratized the availability of, I don't need a lot of data scientists anymore to go out and build me a model. I can just make a simple API call to go consume a model.

However, what that's really changed is, the differentiating factor to all of that is the data and the context I can bring to that model. That general-purpose model, while it has a lot of general data, it doesn't have my context to solve my problems.

So, all models degrade very, very quickly when they're fed with stale, low-quality and inconsistent data. And so we've really seen this trend of organizations saying, I need to get my head around the data and understand what data I have and how I can pull that together, and how I can consistently feed that into a model, and then how I can observe the results that I'm getting back and making sure that it's accurate.

For more information on Confluent’s OEM Program, you can visit its website.