AI’s Impact on Data Management with Nasuni’s Russ Kennedy

This is Techron tv. Hey guys, thanks for the throw. We're here with Russ Kennedy, his Chief Product Officer for Nasuni, and we're talking about hybrid cloud and data management and the volume of data and how all that may finally be changing for the better.

Russ, welcome to show. Thanks, Mike. Good to be here.

I feel like we've been talking about this issue forever in a day, but for the most part, people have been using on-premise and there may be one cloud, and they even hopefully, theoretically saying we're gonna have multi-cloud and hybrid cloud, but for the most part, 90% of their stuff is still in one cloud environment and an on-premise environment, is that getting better or are we finally kind of dealing with this issue of data management that has been holding up this whole shift to hybrid cloud now for a long time? Yeah, it's definitely getting better. Certainly we're seeing that, uh, across a number of different industries and a number of different use cases, uh, where people are looking for, uh, cloud-based solutions because they, they generally don't want to manage, uh, distributed environments where they, you know, they have, uh, workloads and use cases and users accessing data distributed.

It's, it's much harder to manage. So they're looking at ways to consolidate their data in the cloud, uh, and then certainly have choice of which cloud makes sense. You know, they may have one use case and some data in a certain cloud, maybe another use case, another data in another cloud, and they want that all managed sort of collectively.

And that's, that's what we're seeing when we, when we talk to customers, particularly ones that are multinational, that are globally distributed and they have, uh, engineers maybe all over the world or they have manufacturing locations in, in various parts of the world and they wanna bring all this data together. Doing so in the cloud is the best way to do that. How do I overcome the latency issues that always seem to hang us up because, ah, we need to process the data closer to the point where it's being analyzed and consumed.

But, uh, today we seem to have these thing called wide area networks that are getting in the way because, well, the laws of physics have not been changed. So are people getting smarter about this and what's changing? Well, certainly, uh, we talk about the Nasuni architecture, which enables that, uh, access to data where it needs to be.

We have an architecture that consolidates data in the cloud and then allows you to place these edge devices wherever your access needs to be. And we cach data locally there. So when you're accessing, you know, say a particular, uh, file or you're, you're collaborating on a piece of data, the data's sitting there locally next to you, wherever you are.

If you're in an office, if you're in a manufacturing facility, if you're in a a data center, you have access to that data as if it's sitting there next to you. The, the permanent gold master copy is living in the cloud. And we manage that from a data protection perspective and, and, you know, access perspective.

But the hybrid architecture, the nature of na Sunni's architecture is what allows that data to be distributed, to be accessed where it needs to be at a level of performance that you're expecting. 'cause we cach the data locally there so you can get access to it without the latency of cloud, which is, like you say, one of the challenges that people have had. To this point.

Do you think edge computing is forcing this issue finally because we are trying to, uh, push more processing closer to the point where the data is being created and consumed. And as such, once we have that conversation and kind of forces this larger conversation about how do we intelligently manage data in the cloud as well? Sure.

Certainly Edge, edge computing is forcing a lot of this. Uh, but just the, the, uh, economics of being able to consolidate data in a platform that enables it to be used to be protected, to be secured, uh, to be protected from things like ransomware or other cyber attacks, all of that, uh, ease of management, getting the data into a consolidated footprint and then being able to distribute it. Or use cases like you described, edge computing, analytics use cases, AI certainly coming into, into play there.

All of those forces are, are getting organizations to think differently about how they manage data. It used to be there's monolithic devices inside of data centers, and maybe you had a replication device that you protected your data in some other location in case you were hit by a disaster. That's not the way people are thinking these days.

They're thinking consolidation in the cloud and distribution where the data needs to be so it can be processed efficiently. All right. Well, I think we got to four or five minutes before we mentioned the phrase ai, but let's jump in.

Um, is AI also kinda, um, driving this whole conversation about data management? 'cause honestly, I felt like we talked about managing storage all these years, but we never really talked much about managing the data and, you know, we weren't all that good at it, right? To be honest, uh, data management's a bit of a mess.

And now we're kind of looking at all these AI models and everybody's running back to say, uh, well, uh, if we're gonna do this AI thing, we gotta get our arms around data management first. So are, are we having sort of a reckoning here? I think so.

I I definitely think so. And we, we talk to customers all the time and, and you know, one of the things that's very important is you can't have an AI strategy if you don't have a good data strategy. And that involves data management.

That involves understanding what data you have, where it's located, how recent is it, how protected is it, how secure is it, and making sure that you have the right data that you're feeding into these AI models, these LLMs and these other, uh, models. Because first of all, it's so expensive to use those, uh, tools today. I'm sure the, the cost will be coming down over the, over a time period, but right now it's very expensive to use those tools.

So you wanna make sure that you leverage those tools with the right data. And that's all about data management. It's all about understanding the data you have and using the right data for the right need.

Who's taking the lead on this? And I'll ask the question because historically IT folks, man, they process and stored data and they didn't really take a hard look at what data was going where, other than the fact that somebody might have told 'em, you know, we need this in a number of milliseconds. But, uh, the business side usually has a better sense of what data is important and to them and why.

Um, how do we kind of wrap our arms around this thing? Is there a new person emerging as the leader of this conversation? A new team?

'cause it doesn't feel like it was the storage admin, that's for sure. Yeah, I think it is a new, a new individual or new set of individuals that are taking lead in this respect. Uh, the data stewards, the data engineers, the people that are responsible for, for leveraging the data and using the data as efficiently inside the business, they're, they're, they're not IT people.

They don't manage infrastructure, but they understand data and the importance of data and how data is used in these various use cases. So yes, we're certainly seeing an emergence of, of data engineers, data scientists, data stewards, people that are tied to the, uh, effects of data and, and how data is going to be used by the organization. And I, I certainly like, uh, the fact that that's emerging now because I think it gives more people more opportunity to understand how to, to leverage data, how to use data to, to advance the organization and to do more productive things.

And that's what AI's really all about. It's all about automation, it's all about productivity. It's all about, uh, making the organization more efficient.

And certainly these individuals coming up through the ranks and taking on these roles is helping. And we've become victims of our own buzzwords. And I'm asking the question 'cause we have cloud computing on the one hand, we got edge computing on the other side.

There's all this data management in the middle Mm-Hmm. Do we need to just think about this in a more holistic fashion? I, I think so.

I think, uh, certainly, you know, organizations are, are, you know, and, and vendors are, are, are full of buzzwords and they talk about concepts and, and they try to to frame them in certain ways. But I think fundamentally it comes down to the organization's need for data and how they want to use the data. And then looking for the right tools, the right solutions that can help them leverage that data, mine that data, understand that data, have the data, create more data, and that's the whole generative ai uh, solution.

Have the data, create more data so you can be more productive. And I, I do think it's, uh, it's really coming down to the fact that data is, is important. It's very important in this, in this use case, it's certainly very important going forward for organizations to be able to manage it much more efficiently.

So I, I do think it's a, it is a reckoning of some of somewhat or some degree, but I think it's the right direction for us to be going as, as a, as an IT industry. So what's the biggest hurdle that, that you see organizations encountering as, I think everybody's coming to the conclusion, but we have these things called cultures that seem to be getting in the way. Sure.

When you go talk to customers, what do they do the kind of get over this proverbial hump as it were? Well, I think they're, they're educating themselves first and foremost. And that's, that's a good thing.

I think people understanding what the tools can do, what the models can bring, how you know they're used and leveraged and how they can be, you know, uh, uh, transformational if they are used the right way. I think people getting a good sense of, of what the capabilities are is a, is a good start. Uh, that, uh, sort of takes down some of the barriers of, well, you know, I really don't understand it.

Or it's, it's, it's, it's, you know, it's, it's all Greek to me. Having a good understanding of how you can leverage data and how you can use data more efficiently going forward, I think is, is gonna propel organizations to get comfortable with the fact that they have to have a good strategy, a good data strategy, results in a good AI strategy, and they can then take those solutions and those capabilities and advance their business. And I think that's really what, uh, organizations and individuals should be looking at going forward.

How do I get there from where we are? A lot of organizations have invested in lots of different storage systems and data systems and, um, they're everywhere. And, you know, some are objects, some are files, some are block, and you know, how do I get all that into something that feels like I can manage the right thing for the right task?

Well, certainly, uh, you know, it, it is a, a consolidation effort and you have to look at what tools are out there, what vendors are offering, what solutions are, are being brought to bear and how they're, they're able to access data in, in the form or in the, in the location that it currently exists. And that's why, you know, when we talk about Nasuni, we talk about the architecture that enables customers to consolidate their data in the cloud. Once it's there, it's easily accessible from a, a variety of different AI tools or machine learning tools or just analytic tools in general in a format that those tools can understand and those tools can leverage.

And I think that's important for organizations that have a variety of different technologies. They're using them for different business use cases, they're using them for different processing needs, et cetera. But being able to look at a consolidation model where they can get access to that data in the cloud, make it easy for those tools to access that data in a format that makes sense for the tool to leverage the data is I think what what many organizations are going through right now.

And we, we, we kind of help them with that, that journey in understanding that getting the data in the right location, getting it in the right format, and making it easy for the various tools to access that data is a step in the right direction. And you have to go through those steps in order to start to take advantage of the new solutions that are out there. We talk a lot about the volume of data that we're trying to figure out how to manage, but are we underestimating the velocity side of this equation?

'cause it seems like everything that we're doing these days is real time or near real time. Oh yeah. It's not this, you know, batch oriented model that, you know, it's still relevant, but I feel like everybody wants to know what's going on now.

No, you have to, you have to think in a real time mode. You have to, to understand that data, data has importance relative to its, its, its lifecycle and when it's important, it needs to be accessed, it needs to be processed, it needs to be used and leveraged. And you have to, you have to think about it that way.

So when you're designing solutions to take advantage of the data that you have, again, making sure that it's available, making sure, making sure that it's accessible, but doing so in a real time manner. You have the processing capability there, you have the, the network capability and capacity to get the data to where it needs to be quickly so it can be used. That's an important mindset for people to, to think about as they go through.

You guys have a number of customers, what do you see the ones who are doing right actually, what do they know that everybody else hasn't quite figured out yet? Well, they know the value of the data that they have. And that's, that's the important thing.

I think many of the sort of forward looking customers that we en engage with all the time understand that their data has value, understanding, understand where that data is and how it can be used and how it can be leveraged. They're thinking about applications that allow them to leverage the resources within the organization, the, the people, wherever they may be in whatever location they be. And so they can collaborate, they can work together, they can solve problems together, but it all comes down to the data and the value of the data that they have.

And, and most of those forward looking organizations have gone through that process and really have the ability to make sure that they can leverage that data and understand it and use it effectively. What's that one thing you see customers doing that makes you shake your head and go, folks where you gotta be better than this? Well, uh, in a lot of cases, if they just punt the decision down the road or they just, you know, they just stay with the status quo, they're probably gonna get further and further behind.

Uh, change is, is always difficult for organizations. It's always hard for people to get their arms around how I need to change and how I can, uh, leverage change more efficiently. Uh, but once they do sort of get over that hurdle, if you will, and start to think about the possibilities and the capabilities that, uh, a new data strategy and, and new data solutions can enable for them, and then they start, the light bulbs start to go off and they start to think about, okay, I can really take advantage of this.

Let's start, start, let's start down this journey. All right folks. Well, you heard it here.

I think we all know data has gravity, but if you don't do anything to manage it, it will suck you down that black hole before you know it. Hey Russ, thanks for being on the show. Thank you, Mike.

Good to be with you. All right. And back to you guys in the studio.