The Ravit Show

Another great conversation from Qlik Connect 2026. I sat down with Christopher Powell, and this one was all about customers. Not in a generic way, but what it actually means when you see real use cases in action.

What stood out was how much focus Qlik is putting on customer stories. When you hear how teams are actually using data in their day to day work, it just clicks. It is not theory anymore. It becomes something you can relate to and apply.

We talked about examples across regions, including teams in places like Japan solving very specific problems, and even sports organizations using data to compete with limited budgets. Those stories make everything feel a lot more real.

He also walked through some of the key announcements.

The Data Impact Awards stood out. Six customers from around the world being recognized not just for using data, but for actually driving measurable impact.

Then the push around agentic AI. You can see where things are going. Less about static insights, more about systems that actually help move things forward.

The ServiceNow partnership was another big one. Bringing trusted data into a system of action instead of keeping it separate. That shift is important.

And there were updates on the engineering side as well, which felt like a direct response to what customers have been asking for.

Looking ahead, there is a lot coming. More agentic AI releases through the year, built by teams across Sweden, India, the US, and Canada. And a continued focus on sharing more real customer stories.

Overall, this conversation made one thing clear.

Technology matters, but what really brings it to life is how customers are using it.

#data #ai #qlikconnect #qlik #daredevil #api #trust #dataquality #agentic #agents #theravitshow

I got a chance to sit with Charlie Farah at Qlik Connect on The Ravit Show and this was one of those conversations that makes you pause and rethink how we look at data and analytics today. We often talk about tools, platforms, and the next big thing in AI. But what stood out to me in this conversation was how different regions are evolving at different speeds, especially across APAC versus the US and Europe. The ambition is the same, but the maturity, priorities, and constraints vary a lot more than we usually acknowledge.

We also discussed Qlik’s analytics roadmap, and it is interesting how fast things are moving. It feels like Qlik Answers just launched, and now the conversation is already shifting toward how these capabilities actually get used in real business workflows. Not just dashboards or insights, but decisions.

And that is where I think the biggest gap still exists. Not between data and analytics, but between analytics and actual business value. Many teams are still very good at generating insights, but not as strong at embedding those insights into day to day operations where decisions are made. That last mile is still broken in many organizations.

Another point that stayed with me was how easy it is to overlook the hidden challenges. It is not always about technology. It is about alignment, ownership, and making sure the right people trust and act on the data.

Looking ahead, the next few years will not just be about better AI or faster analytics. It will be about making these systems actually usable and reliable in real environments. Less experimentation, more execution.

What I liked most about Charlie’s perspective is that despite all the changes, the core excitement around analytics has not changed. The opportunity to turn data into something meaningful for the business is still huge.

And we are just getting started.

#data #qlik #ai #qlikconnect #theravitshow

Most teams do not lose because of lack of talent. They lose because of decisions. At Qlik Connect, last week, I had a great conversation with Andreas Hadelöv, Assistant General Manager and Lead Analyst at Malmö Redhawks on The Ravit Show, and what stood out was how disciplined their approach to data is. This is a team competing in Sweden’s top ice hockey league with one of the lowest budgets, so they cannot afford guesswork.

Everything they do is grounded in data, but in a very practical way. They bring together data from wearables, game events, and training into a single view using Qlik, and use it to plan practices, give player feedback, prevent injuries, and shape game-day strategy. No overcomplication, just focusing on where data actually impacts performance.

Where it gets even more interesting is recruitment. They rely on a few trusted metrics like ice time and performance to make hiring decisions. That clarity helps them stay competitive without overspending.

Their approach to AI was also refreshing. They are testing it, but only using it where the data is highly reliable, mainly in scouting. Everything else can wait until the foundation is strong.

The biggest takeaway for me was simple. Start small, stay focused, and align data with your strategy. That is what makes data actually useful.

#data #qlik #ai #qlikconnect #theravitshow

What if the biggest shift in data security is happening where most teams aren’t even looking? I was at the RSA Conference at the NetApp booth, and had a great conversation with Gagan Gulati, SVP/GM of Data Services at NetApp, that really got me thinking.

For years, we’ve focused on visibility like dashboards, alerts, and detection. But the real shift is moving from detecting risk to actually blocking it at the data I/O layer, right where data is accessed. In a world where AI systems are interacting with data at massive scale, this becomes critical. We spoke about how concepts like a Security Knowledge Graph can help govern not just human users but nonhuman identities by understanding relationships between data, systems, and access in real time without slowing things down.

Another important point was around AI training. It is no longer just about protecting data, but about knowing if your data is even ready by scoring it early and preventing risks before they show up in model outputs. And with most enterprise data being unstructured, the storage layer itself is evolving into a place where context and control come together.

This conversation made me realize that security is no longer just another layer in the stack, it is moving closer to the data itself.

Are we ready to rethink where security should actually live?

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#data #ai #security #storage #agents #api #netapp #theravitshow

“Your next employee might not be human… and your security strategy isn’t ready for it.”

At RSAC, I spoke to Tom Gillis, SVP & GM of Infrastructure & Security Group at Cisco on The Ravit Show, and the conversation quickly moved beyond the usual AI hype into something much more real. We talked about agentic AI not just as a tool, but as a system that can act on its own, make decisions, and operate across enterprise data.

That shift is forcing a complete rethink of security, because traditional models were built around humans, not autonomous agents. One thing that stood out was how security teams have always played it safe, often defaulting to “no,” but with agentic AI, that mindset becomes a bottleneck. The real challenge now is enabling this new layer of intelligence without losing control.

We also unpacked what it really means to secure an “agentic workforce.” If every employee starts running multiple AI agents, each acting independently, the attack surface grows overnight. So do we start treating these agents like endpoints? Do they need identities, permissions, and governance just like humans? And if that’s the case, how do SOC teams even deal with the explosion of alerts and signals? What I found interesting is that this is not some distant future problem, it is already showing up, and companies like Cisco are actively working through how to design security systems that can keep up.

This conversation made one thing very clear to me. The AI conversation is no longer about models or capabilities. It is about control, trust, and how we rethink security for a world where humans are no longer the only actors inside the enterprise.

#data #ai #rsac #cisco #theravitshow

Most security conversations at RSAC start with visibility. This one did not. I was at the Commvault booth, which by the way is set up like a full wrestling ring, and I sat down with the José Gomez Field CTO Security to talk about something that feels much more real right now. Control.

Not dashboards. Not alerts. Actual control over who is accessing data in real time. What stood out to me in this conversation was how much AI is changing the risk surface.

It is not just more data. It is more access, more queries, more non human identities touching sensitive systems all the time. And a lot of traditional tools were never designed for this.

One point that stuck with me. Structured data is still one of the hardest things to secure properly.

We assume it is easier because it is organized. But when access patterns explode, especially with AI, it becomes harder to track who should see what at any given moment.

That is where real time access control starts to matter.

Not after the fact. Not in a report. Right when the query happens.

We also talked about something every team struggles with. How do you enforce governance without slowing people down?

Because if security becomes a blocker, people will find a way around it. The interesting shift here is making security part of the flow instead of a checkpoint outside it.

And tying that directly back to resilience. Because the more control you have over access, the faster you can respond and recover when something goes wrong.

Another great conversation from the Commvault booth.

#data #ai #security #rsac #attack #api #commvault #theravitshow

I did not expect to walk into a wrestling ring at RSAC conference. But that is exactly what Commvault built at their booth. And after my conversation there, it made complete sense. I spoke to Michelle Hartley Graff and Michael Fasulo from Commvault right in the middle of that ring, and we got into what this partnership actually means beyond the announcements with Microsoft.

Here is the reality I keep hearing from teams. Detection is not the problem anymore. The real struggle starts after that. You detect something. Then what?

That gap between detection and clean recovery is where most teams slow down. What stood out in this conversation was how tightly Microsoft and Commvault are trying to close that gap. With Microsoft Sentinel in the mix, the day to day operations start to feel more connected. Signals are not sitting in silos anymore.

Then you bring in Security Copilot. Now you are not just seeing alerts, you are actually understanding them faster and deciding what to do next without digging through ten different tools. And the most interesting part for me was this idea of real signal sharing. Not just integrations on paper, but systems actually talking to each other in a way that helps you move faster when it matters.

Because in a real attack, speed is everything. But so is getting back to a clean state you can trust. That is where this partnership is focused

#data #ai #security #rsac #attack #api #commvault #theravitshow

Just wrapped up a conversation with Vidya Shankaran, CISSP from Commvault here at RSAC, and honestly, this one made me pause and rethink a few things. We talk a lot about resilience, threat detection, and now AI data. But what stood out to me is how the conversation is shifting from just “can you recover” to “can you recover clean”.

That’s a big difference.

With Vidya, we went deep into what’s actually broken in traditional recovery models and why “verified clean recovery” is becoming critical. Not just recovering fast, but recovering without bringing the threat back with you.

We also got into the real tradeoff teams are dealing with today. Speed vs accuracy in threat detection. Quick scans vs deeper AI inspection. And the answer is not either or, it is how you combine both in practice.

Another big takeaway for me was around AI data becoming a new attack surface. Most teams are still thinking about structured data, but AI pipelines, embeddings, and unstructured data are now part of the risk layer.

And the blind spots are bigger than most people think. We also touched on something I hear a lot from teams. How do you actually enforce governance without slowing everyone down. There is no perfect answer, but there are better ways to approach it.

If you are thinking about resilience, especially in an AI-first world, this conversation is worth your time.

Let me know what stood out to you.

#data #ai #security #rsac #theravitshow

Building AI for the real world is a very different problem than building AI for text. I sat down with Steve Xie, Founder & CEO of Lightwheel on The Ravit Show, to break down what it actually takes to train systems that operate in the physical world. Steve’s journey from Peking University to Columbia University, and then into leadership roles at Cruise, NVIDIA, and NIO, gives him a unique lens into where today’s AI systems struggle when they leave controlled environments and face the real world.

One of the biggest takeaways from this conversation is that the core bottleneck in AI is no longer models, it is data. While large language models benefited from massive, passive data sources, robotics has no equivalent. There is no scalable way to collect real-world interaction data, no reliable evaluation layer, and very little infrastructure to continuously improve systems once deployed.

This is where simulation becomes critical. In autonomous driving, simulation is helpful. In robotics, it is foundational. You cannot run thousands of parallel experiments in the real world, and you cannot reset physical environments at will. Simulation is what makes learning, testing, and iteration possible at scale. But not everything that looks like simulation actually works. As Steve explains, true simulation needs to be physically accurate, reproducible, and capable of generating actionable feedback. Without that, it cannot train real systems.

What makes Lightwheel interesting is their approach to solving this problem. Instead of starting with data collection, they start with evaluation. They identify where models fail, generate targeted data to fix those failures, and create a continuous feedback loop. It is a shift from a passive data pipeline to an active data engine built for physical AI.

They are already working with teams like DeepMind, ByteDance, and Alibaba, building infrastructure that sits beneath both robotics companies and AI labs.

The bigger idea is simple. You cannot scrape your way to physical intelligence. You have to generate, test, and refine data in closed loops.

#data #ai #robot #nvidiagtc #lightwheel #api #training #behaviour #theravitshow

Just walked through the Lightwheel booth at #NVIDIAGTC and this is one of those moments where you realize physical AI is moving much faster than most people think. I got a booth tour by Jonathan Stephens, Chief Evangelist at Lightwheel, and what stood out immediately is how deep their approach goes. This is not just simulation for the sake of simulation. This is about building real-world intelligence that actually works outside the lab.

Lightwheel is solving a problem most teams underestimate. You cannot scale robotics or physical AI without massive amounts of high-quality, physics-accurate data. Not synthetic guesswork. Real-world grounded data. And they are doing this at a completely different scale.

We are talking about hundreds of thousands of hours of simulation data, not a few thousand.

What I found most interesting is how they break this down into three layers:

First, the world itself. They are literally measuring real-world physics. Using robotic systems to capture forces, movements, and interactions. Then rebuilding those environments in simulation so models learn from something that actually reflects reality.

Second, behavior. This is where it gets powerful. Their Auto Data Gen layer uses LLMs to break complex robotic tasks into smaller actions. So instead of manually guiding every step, you can scale learning in a much more automated way.

Third, evaluation. Most teams stop at training. Lightwheel goes further. They are benchmarking performance with tools like Isaac Lab Arena and pushing models through real-world scenarios like cleaning a kitchen or navigating a grocery setup.

This full stack approach is what makes them stand out.

Also worth noting the ecosystem they are already working with. Stanford, MIT, Nvidia, Figure, ByteDance. That tells you where this space is heading.

If you are thinking about robotics, embodied AI, or world models, this is a company to watch closely.

Physical AI is no longer theoretical. It is becoming operational. And Lightwheel is quietly building the infrastructure behind it.

#data #ai #robot #nvidiagtc #lightwheel #api #training #behaviour #theravitshow