What IT Leaders Still Get Wrong About Data in the AI Era with NetApp’s Ray LaMarca

If AI feels like the right move but you’re still not seeing results, this episode reframes where the real problem is.

In this conversation, Tonya sits down with Ray LaMarca, Director of Solutions Engineering at NetApp, to unpack why most AI initiatives stall out before they ever deliver value. It’s not the tools. It’s the foundation underneath them.

They break down what’s actually going wrong behind the scenes, from siloed data and security risks to misalignment between business and IT. Ray shares what he’s seeing across enterprise teams, where companies are overspending, and why ROI is taking longer than expected.

This episode is a grounded look at what it really takes to turn AI from an experiment into a strategic advantage and why it all starts with your data.

In this episode, they explore:

• Why most AI projects fail before they ever reach production and what’s really causing it
• The hidden breakdown between business and IT and how it impacts results
• Why data security is still the number one concern for IT leaders in the AI era
• How siloed data systems create inefficiencies, risk, and missed opportunities
• What actually happens when your data isn’t clean, complete, or protected
• Why massive investments in GPUs and cloud infrastructure aren’t delivering ROI
• The real reason companies are slow to see value from AI initiatives
• How supply chain, timing, and misaligned investments derail outcomes
• Why asking better questions is the fastest way to avoid failure
• What it means for IT to shift from cost center to revenue driver
• How partnerships, not just products, determine success in modern IT strategy
• Real world examples of companies using AI and data to drive efficiency and innovation
• What IT leaders should prioritize over the next 12 to 18 months to stay competitive

Important Links:

https://app.technologymatch.com/solutions/netapp-ai-data-engine

https://app.technologymatch.com/solutions/netapp-afx

Transcript

SPEAKER_01 0:01

Welcome to Between Fires and Futures, a podcast about the real work of tech leadership, managing today's chaos while building tomorrow's business. I'm Tanya Chirrell, a three-time founder with two successful exits, and the founder and CEO of TechnologyMatch.com. Each week, in this podcast, I talk with the leaders doing the real work. Solving for now, building for what's next, and leading through pressure, not perfection. This is the podcast for tech leaders fighting fires today and daring to build the future anyway. Welcome back to Between Fires and Futures. I'm your host, Tanya Tyrrell. Everybody's talking about AI right now, but most IT leaders aren't struggling with AI itself. They're struggling with everything underneath it: their data, their infrastructure, their ability to actually turn all of this into something that works. And that's where things start to break. Today I'm joined by Ray LaMarca, Director of Solutions Engineering for North Central Enterprise Region at NetApp. Ray spent over two decades working directly with enterprise IT leaders, helping them navigate everything from data management and hybrid cloud to AI and security. So he's on the front lines of how companies are thinking about their data, where they're investing, and where things are falling apart. In this episode, we're getting into what IT leaders are getting wrong about data in the AI era, where companies are wasting money and what actually needs to change to make AI deliver real business value. Ray, I'm I've really been looking forward to this conversation. Welcome to the show.

SPEAKER_00 1:32

Thanks, Tony. I appreciate talking to you and having your listeners hear what NetApp thinks about today's AI troubles and where we think it's going in the future.

SPEAKER_01 1:41

Yeah, great. Well, let's get into it. So everyone, um everyone's talking about AI everything right now, right? But I think most IT leaders that we're talking to day in and day out are really struggling with AI, struggling with everything underneath it. So what are you seeing right now?

SPEAKER_00 1:58

Yeah, it's interesting. We're a data management company, and you don't think of us first when you think of AI, but it's what everybody's struggling with today because there's just so much about it. I it was funny, I saw a commercial the other day, and literally it was just somebody saying AI, AI, AI over and over again. And it just made me laugh thinking what is in the forefront of everybody's minds is where's their data? Where's it coming from, and where's it going to? But just five years ago when AI was starting, nobody thought of data. So it's an interesting dynamic and curve over the last five years of what people are thinking about today.

SPEAKER_01 2:36

Yeah. Yeah. And we're seeing what, something like 70 to 80, maybe even 85% of AI projects never even make it into production. And most of those failures are tied to data readiness, right?

SPEAKER_00 2:49

It is, right? And it's amazing the investments companies, small, medium, and large, are making, and all kinds of companies, not just large companies, it's healthcare and manufacturing and everyone, they're all starting these AI projects. And you're exactly right. They're making these investments from a cloud perspective, infrastructure perspective, people perspective, and they don't get off the ground. And it's failure of I didn't have an end goal, and failure of I didn't know where the data was coming from, or you know, I just didn't invest right in the application layer that makes those projects fail. And it's interesting to think of where today's market's going and needs are going and the crunch around money is going, that you see that kind of failure rate. But it has upticked over the last few years. More projects are getting off the ground.

SPEAKER_01 3:43

Yeah. So where do you see things breaking first? Is it infrastructure or people or data? Where's the initial breakdown happening?

SPEAKER_00 3:51

For NetApp and for myself, right, as a 20-year person here, seeing the curve of IT structure, it's really everybody was always focused on the edge and the devices. And obviously, AI started in self-driving cars, right? That was the thing everybody talked about as where that was gonna be the first AI. And I think the struggle really was around, I didn't know what I was gonna do with that data on the other end when I got it all, or I ran it through an AI model and produced something. Was how do you monetize that to make more money for your company or improves a product better or improves somebody's life better if you're in the healthcare space? I think everything was so focused on I just want to throw and run down this AI path that nobody had the vision of where it's going. And I think it's taken a while for companies to get there from an overall perspective.

SPEAKER_01 4:47

Yeah. So when you're sitting across from enterprise IT leaders today, what's actually keeping them up at night? What are they what are they worried about?

SPEAKER_00 4:55

It's still about data security. I as NetApp, our focus is has been from when we started the company 35 years ago was protecting customers' data and allowing them to recover from that data perspective. And I think as we've gotten into this AI world today, you're gathering a lot of data at the edge, whether you're gathering, like I said, from a self-driving car, you're an insurance company and you're gathering claim data. If you're a trading firm and you're doing all of the trades and how all of that matters, the data security, you need that data to be clean, you need that data to be protected and to make sure nothing happens to it, right? And everybody hears the term ransomware and things like that from a data security perspective, or zero trust is another popular term that's out there for C CIOs and C SOs and stuff that are out there looking at it. But if something happens to that data during the pipeline, you could have days, weeks, months, or years worth of practice just lost because something got corrupted, something got lost. An interesting one that I was talking to somebody about the other day was a hospital down in Cincinnati, and they do a lot of research on their patients. It's just pathology work, imaging, looking for triggers to help get in front of infections and God forbid, something along the lines all the way down to something like cancer. But if you have 10% of your data corrupted or lost, then potentially that entire model has to be thrown away and you have to start all over again. And that could be a lot of money invested in people's time, energy, effort, money on infrastructure such as myself. It could be all lost if you're not protecting it. So I see more and more to your original question, more and more things keeping people up at night. Is that data security? Because I can't produce something at the end that again, in this case, in the example, makes patient life better if I lose something early on or in the middle of the training and modeling that we're doing.

SPEAKER_01 7:06

Right. Yeah, and it makes sense. And with AI, I mean, there's more exposure, right? I mean, there definitely is.

SPEAKER_00 7:15

Sorry? No, go ahead. You said, yeah, I go ahead to what you were asking. Sorry.

SPEAKER_01 7:20

No, I was I was just kind of following on to that question that you know with all that is moving and how quickly it's moving with AI, that is even ramping up the risk on data security.

SPEAKER_00 7:33

It it absolutely is. And as as I mentioned, there's a lot of, I mean, if you think about Nvidia and you think about NetApp and you think about the large hyperscalers of Google and AWS and Azure, people are spending a lot of money on that hardware, on that virtual hardware to rent it from one of the hyperscalers. And if you lose the underpinning of data underneath the underneath everything, again, it's wasted dollars that that does matter to those business owners at the end. I have a funny story of a colleague who had a niece that was in, she was a data scientist. And as a storage company, as we are, a data management company, he asked her, How important is the data to you? Now she's a data scientist, but doesn't buy the storage, doesn't buy the servers, doesn't even buy the GPUs or rents them. She's a data scientist at the edge. And he walked through that story, he's like, Well, you've just did a month's worth of research on what you're working on from a project perspective. He's like, What if after that month somebody hit delete and deleted all of your data? She's like, Oh wow. Yeah, the data does matter in the end, right? I can't do something on the end. So, like I said, when what keeps IT leaders up at night still is their data security, and AI has to run on data.

SPEAKER_01 8:56

Yeah. Yeah. So I mean, NetApp isn't the first company people think of when they think about AI. So where do you actually show up in that conversation?

SPEAKER_00 9:06

Five years ago, we never showed up. It was not our doing it was because it was being run out at the business level, and infrastructure was just told, hey, I need this, I need that to be able to run things, and they weren't part of it. I'd still say we're second or third in line from an overall thought process perspective, from a storage play. But I think we're moving up the stack now because I think people are realizing how important their data is. NetUpp's done a very good job over the last year, year and a half of getting our marketing out there better to so people understand that data is important. And even if you see commercials with an with an IBM or other AI conversations that are out there, they actually mention data in the commercials, right? So I think just organically over time, people are starting to realize that the data is important and it has to be part of the process that I do when I'm evaluating how how am I going to run this AI project within the walls of my company? Yeah. But definitely changed over the last, like I said, 12 to 18 months, definitely more in the forefront than before.

SPEAKER_01 10:19

Yeah, absolutely. So, yeah, what you're saying is that it really does all come down to the data. And so, what are you seeing in terms of like what are most companies getting wrong about their data today?

SPEAKER_00 10:32

I think it's about still in those silos that are out there. I think it's a great conversation to have people understand that from edge to core to cloud, you're using your data in different formats all along those ways, but people are still sometimes looking at things as a silo. Well, I have I'm just collecting data out in my manufacturing firms or inside of a car, inside of a tractor, or at the trading database level, something along those lines, or where I do the medical imaging and I actually store the X-rays. You're if you're just solving for that, then maybe you're going to buy or entrain people on just that part of the process. But then you're like, I'm going to move that data, and now I'm going to create some databases against it, and I have a different skill set, and now it's moving on to a different platform and a different storage platform and all of that. Now I have to have another skill set of people, and they're only looking at it in their myopic world. And then you start throwing in the cloud providers, as I mentioned earlier, that an AWS, you have a certain way of managing applications and storage, and with Azure's the same way. And when it's GCP, so I really see that still being the wrong way that people are looking at it. Fortunately for us, we're business partners and alliance partners with a lot of those people in that I've talked about there, that we have we can provide the data management piece throughout all of it, where if we can get involved in those conversations from a NetApp perspective, I can show people how the skills you learn at the edge where the data is created, core where it's protected and kept, and in the cloud where you're doing the modeling with one data path and one data management platform, I reduce the complexity. And that's what we're trying to do here at NetApp is reduce that data management complexity. But I see that being the number one thing that's still being done wrong because people are just looking at it still sometimes in silos.

SPEAKER_01 12:40

Yeah. Yeah, that makes sense. So can you talk a little bit about how you reduce that data management complexity? Like how does NetApp approach that?

SPEAKER_00 12:50

Yeah, I appreciate the question. So, from a NetApp perspective, we have always been about alliance partners. And I keep mentioning the cloud providers over and over. We're an OEM hardware and software provider for on-prem hardware and software. When people think of NetApp, they're like, well, this is something people buy physical devices. But we've been partnered with the hyperscalers for well over 10 years, providing first-party products in the hyperscalers. They have names like FSXN with AWS and ANF, which is Azure NetApp Files with Azure, and GCNV with Google, where we're actually showing up at their conferences. We have some of the largest booths and some of the most trafficked environments because what we've decided to do is that we don't want to compete with them, we want to partner with them. And they're using our software at in the cloud so we can actually move a database or a portion of a database into the cloud, let them rent GPUs, let them go ahead and do virtualization and training against it, and then move the data back. And as I mentioned, you don't need other people to manage that. The same people that manage your NetApp on-prem can actually manage your cloud for you as well. So reducing costs, reducing complexity, because you're not having to buy all these different tools and different management interfaces and planes that you have to learn how to do that just takes time. And then there's more complexity in all that. So I feel the best thing that NetApp does is we reduce the complexity in a data management plane from edge to core to cloud for our customers so that they're used to it the exact same way. And even in the cloud providers, you can start with the data managing in one hyperscaler and literally move it to another and manage it the exact same way and expect it to work and function the exact same way. So it really reduces that overall cost and complexity. That's what we're about from a data management company. And that's why we really feel if you look at our marketing taglines, that we are the intelligent data infrastructure company, because we want your data to be smart when you use it and where you use it.

SPEAKER_01 15:03

Yeah. Yeah. So when the data isn't smart, if it's not clean or protected or complete, what actually breaks down on the business side? Can you walk us through that?

SPEAKER_00 15:15

Yeah, I mean, when it's not clean or it does break down, it's really where it's not usable. If you in the AI world, you want that clean data where you don't want things like if you're looking at farm data or farm equipment data, you don't want something in there that's coming from a different device or a different tractor or something that's along those lines where it can really break down what that model should look like. Or like I said earlier about the word of ransomware and corruption that's in there, when something happens to a data path or a data in from a data infrastructure perspective, it can really mess up the end results, right? If you look at, if you just look at something from an audit perspective or a math perspective, if you think about things like model trends or number trends or sales trends, I am a sales company, right? If you have something like an anomaly that's in there, whether it's something of a spike or data is deleted, that end result's incorrect. Your pattern may be wrong. I look at it for one of our companies here in the where I'm from is Chicago. We have a large insurance company that does a lot of claims for their customers. And if people are making claims against a certain weather pattern in a certain area, that's how that company can end up doing their pricing for their end customers in the end. But if all of a sudden a different geography is thrown into the middle of all that incorrectly, well, your numbers are all wrong and the baseline you have is wrong. Maybe it's a heavy storm area or something along those lines. If you just the data's not clean, can really produce erroneous answers on the end. To companies at the end, it's money, right? It's right. What do I set my rates for my customers? What do I do chargeback from a part perspective for broken tractors or anything along those lines of like trading firms, especially where they're trying to make predictive trades ahead? Anything like unclean data in the middle of it can just throw the end model all off.

SPEAKER_01 17:19

Yeah, yeah, I see that. And even the examples that you gave are really eye-opening. Do you think that the leaders that you talk to realize the downstream impact?

SPEAKER_00 17:31

I think they are, and I think they do now, right? I think maybe it's happened to them. You mentioned earlier, like a lot of AI projects don't get off the ground. I think that's part of why they weren't, because they weren't really thinking what would happen if I didn't have clean data, if I didn't really understand the last time it was touched, who it was touched by, what what tags are on this data to make sure that it all lines up. So I'm looking at stuff that matters together. So I definitely think people are getting smarter and more intelligent around their data from an overall perspective, but it's taken us all time. And even for us as a company from that is a data management company that NetApp is, I think it's also taken us time to learn where our value is in that process for everybody. What are our investments to make our data more intelligent? And how do we better partner with a hyperscaler or a alliance partner that can help when we combine ourselves together, we can actually make it even more intelligent. And I mentioned NVIDIA earlier, right? We they've we've been a long-standing partner of theirs. There's a lot of things that we are doing that we co-engineer together that we want to produce because they do some things well around the way they use their GPUs, and we do some things well around the intelligent data side that just makes it that much better for our end customers. So it really is about partnering together. And I think customers are seeing that when they talk to when they talk to OEMs such as ourselves or NVIDIA or Azure or VMware or Red Hat or whoever it happens to be. Some of their top questions in our R is who are your alliance partners and how do you partner with them? Because they want to see how they can get the best of both worlds.

SPEAKER_01 19:17

Yeah. Yeah, that makes sense. So where do you see companies investing heavily right now, but are not actually getting the return they expected? Because I feel like there's a lot of this going on.

SPEAKER_00 19:30

It still is in the right with the way supply chains are in the industry today, and everybody in IT, IT hears about I think supply chains every day or every other day. Yeah. Right. I think it's making sure that the investments line up. Right. So when I what I mean by that is it's math, it's all math and lining that investment up. So that's where a lot of people are still questioning. And if I'm if I'm buying a product or, as I said, renting a product from a cloud provider, can I also get the data there in time to match my investment on a GPU or on a virtual machine or on a SaaS offering from a customer? Or same thing with an on-prem investment. If I'm buying servers and storage and switches and the software applications, can everything be lined up and arrive at the same time so I can use it to make sure that my investment that I'm making, because it is a solution to the end customers that they're trying to put together. So I think that that's one of the biggest things right now, is and it's very difficult for customers, is to make sure everything lines up that way.

SPEAKER_01 20:41

Yeah. Yeah. Yeah. I mean, we're seeing massive spend on GPUs and cloud AI infrastructure. And we're also just hearing a lot of pain around like not getting the value, not realizing the value. There's lots of investments in the AI space right now, but a lot of companies are really slow to realize the value of those investments. So I was just curious like what you're seeing there.

SPEAKER_00 21:10

Yeah, it's the number one problem out there. The supply chain problem has caused major issues for companies around investments. Do I invest today? Can I wait six months? Like I said, can I rent something for a certain amount of time through the cloud providers? And, you know, more and more sales reps and more and more value-added resellers that combine the OEM products together. It is all about our ROI today. What is my return on investment? Companies are asking us very explicit, detailed discussions and detailed information that they want back from us to say, what's my investment? What am I going to get out of it? When am I going to start to see it? And when can I start to see that return on investment? Many Many customers were, hey, if I see something in that 18 to 24 month range, I can go back to my CFO and say, hey, this is the investment. This is when we're going to see the return on investment. A lot of times they want it less now, like, hey, I would like it in a year. And obviously the supply chain issue is making that much more difficult. Yeah. And I think that the hyperscalers have something to offer in there where you can start projects in the cloud and then determine can they stay out there from a cost perspective, as you mentioned earlier, right? Around rising costs and things like that, where maybe I do have to, once I can acquire the servers, acquire the storage, acquire the switches, acquire the application, or maybe even just the talent to run it, I can at least start it in the cloud and potentially move it back. And companies like us and others that have that capability to burst up into the cloud and burst out can really differentiate ourselves in the industry long term. But it all goes back to what's the ROI, what's the TCO, and how quickly can I recover my investment?

SPEAKER_01 23:09

Yeah, it's good. Good stuff. What's a mistake that you're seeing over and over again where you can almost tell from the get-go this is not going to work?

SPEAKER_00 23:17

The biggest mistake I could tell is either the business unit and IT are not talking, right? It's just a hey, I and again, I'll say in the storage terms, in the storage world, hey, I need 100 terabytes of disk, right? I need 100 terabytes of space. And typically IT would say, oh, here you go, and here's your 100 terabytes. But it's not what's the backup and recovery? What's the data protection needs? Are you going to move any of that data to the cloud? Like the conversations that we want to come in and have, there are still companies that are out there. And when I see that happening right away, I start to try to help, or my team as a manager here, we start to try to ask those questions of generally the IT folks. Because like I said, more as I said at the beginning, more often than not, a lot of this these AI projects start out at the edge business and then they bring IT in. And when I see it, when I see it failing or know it can tell it's going to fail, is when we don't understand requirements, we don't understand business requirements, we don't understand the real pain around what's happening. And all they're looking for again in the storage world is well, I need this many GPUs and I need 100 terabytes of disk. And okay, that's all they're asking for, and we're gonna give that to them and walk away. Yeah, that's where I know it's not gonna work.

SPEAKER_01 24:40

Instead of digging deeper and really getting at the needs. Yeah. I mean, it's surprising because you know, it doesn't matter whether we're talking about data or AI or anything else. This keeps coming up over and over again, this like lack of alignment between the business, between business and IT. And so what's your advice to leaders out there about how to approach this?

SPEAKER_00 25:04

Ask a lot of questions. That's my advice, right? It's yeah, same I give to my team is right, is you know, you teach your kids when they're young, there's no dumb question, right? If you don't know something, ask. And like I said, I teach my team this, even if you think you know the answer, ask the question, right? When you assume that's right. You know, we all learned that one. We all learned that one when we were young. So for IT people, I ask the question and say, can you find this out for me? Like, I even try to help that way and stuff. I would just keep asking the questions because somewhere along the line, there's going to be that aha moment where, wow, I didn't think of that. And even at the business side, when you're asking the questions, they're like, well, I never thought of that, right? And I go back to that story about the data scientists who never thought of the data. I always go back to that story going, it was just that aha moment for her when it was like, well, the data does matter, right? And it for me, it goes back to the fundamentals of what NetApp does very well is the data security of it all. What happens if somebody hits delete? What happens if there's a phishing or ransomware attack? What happens if I can't recover quickly? And what's the lost productivity? What's the lost dollars? Like the business units sometimes don't think of that stuff because they're not IT folks. They're not. That's not their job and stuff. And the same thing, the IT folks aren't thinking sometimes of the business aspect. What's the dollars and cents and the investment and the ROI, where that has to mesh together. And hopefully, as an OEM and a leader in data management and a leader in AI, we can bring that to our customers. That's what we're looking to do.

SPEAKER_01 26:55

Yeah. No, this makes complete sense. And, you know, I think, especially in this age of AI, we're all moving so fast. We're all moving, like we're running so fast towards accomplishing these initiatives that sometimes we just have to slow down to speed up and really identify. Like, don't take things at face value. Pause, ask the questions, make sure that we're on the same page and have the same understanding about what's actually needed here. So that's great advice. Thank you for that.

SPEAKER_00 27:27

Yeah, I think we we try to do it more today here at NetUpp, and we try to understand, we try to understand the customer's current state, where they are today, make sure we have a good ground level understanding of what they have and more importantly, what they don't have. What are their pains? What are they looking to solve? Where is the struggle? And that's around the data, around anything along those lines. And then as long as we understand that and we can understand their future state and potentially put in things like what NetApp does very well around data security and hybrid cloud and our partnerships and reference architectures like with an NVIDIA and others. So we can get sticky, but then we have to tie it to the business value, right? That has to be that full circle aha moment for everybody from current state to pain to future state to business value. If you're not solving all four of understanding and solving for that at the end, it is business value. You're not going to get the sale. You're not going to get the win, and you're not going to get the internal win either within your own company.

SPEAKER_01 28:30

That's right. This is great. So can you, I'd love for you to share a real world example of a company that's doing this well, like someone who's actually turning their data into a real advantage. I think there's so much to be learned from that.

SPEAKER_00 28:43

Yeah, I have a few. It's uh being here from the Midwest, right? We have a lot of healthcare manufacturing companies and some of them that are on the bleeding edge of that, right? I mean, some of them, like a John Deere, is one of the most innovative AI companies in the world. Nobody would think of a tractor company with that. I work with a trucking company in northern Wisconsin that, uh as an example, they have all sensors on their trucks. And for them, it's all about shipping and costs and keeping their trucks on the road. So their AI is actually looking for anomalies, tire pressure, oil pressure. They want to pull their trucks off the road before they break down so they can do quick maintenance on them and get them back out there, right? And I mentioned, I mentioned John Deere, right? If you've ever read them online and follow them on LinkedIn, like I said, they're one of the most innovative AI companies that are out there. Things like taking truck tractor data back, understanding weather patterns, understanding soil patterns to actually help farmers plant crops better, or getting parts out to their distribution vendors faster. So, hey, this is a most common part that breaks down. This is the local distributor for parts. We want to make sure there's more of those in this area because a lot of this tractor happens to be out there. Or, you know, a hospital clinic in Cleveland that looks at radiology images to try to understand patterns of what's going on for their patients so they can bring, you know, work with drug companies and work with their patients to bring solutions to them faster and cures to them faster and catch something before God forbid something happens. So here in the Midwest, it's great to work with all kinds of different companies that some you would never think of in AI, like a trucking distributor or a tracker company, or the ones that you do think are doing AI, like healthcare and financial, everybody's using data differently, but in the end, they're doing something that whether patients, farmers, truckers, heart distributors, right, they're using this data to make things faster and more available for their end customers in the end. And it's great to see how that's working across the board.

SPEAKER_01 31:03

Yeah, that's really cool. I didn't know that about John Deere. So thank you for sharing that.

SPEAKER_00 31:08

Yeah, but definitely follow them on LinkedIn if you're not.

SPEAKER_01 31:10

Yeah, I know.

SPEAKER_00 31:11

So they're one of the most innovative AI companies out there.

SPEAKER_01 31:14

That's really cool. I did not know that. I'll definitely be following them. So I know you've been in this space over 20 years, so you've probably seen a lot of shifts. And given everything we've just talked about, what's fundamentally changed about how IT leaders need to think today?

SPEAKER_00 31:32

It's been interesting, run here at NetApp, and people have asked me, hey, you know, you're at an OEM for 20 years. That's more of an anomaly than not in the industry and everything. Here at NetUpp, I've seen the virtualization strategy with VMware and others, right, in the late 2000s to the beginning of the cloud strategy to the hybrid cloud strategy where companies are using multiple hyperscalers and now to this AI world that's out there. And you know, what's fundamentally changed is right, there was the term shadow IT. Is that about maybe 10 years ago that was out there from an overall perspective where central IT kind of was letting people do their own thing? And it's fundamentally, I think, coming back to central IT has to protect everything, right? And maybe even 10, 15 years ago, IT was a cost center, right? Inside of the world. A lot of companies are looking at how can I make money from IT, right? And what can we do? And I go back to that ROI and that TCO and all the great acronyms that are out there in the world of IT today.

SPEAKER_01 32:41

But what's fundamentally changed is more three-letter acronyms than any other space.

SPEAKER_00 32:47

Oh, we definitely do. We definitely do. So, but I think that's what's fundamentally changed. Like in the last 10 years ago, it was Shadow IT, and how do we work with them as an OEM to help central IT out? And how do we not make it so much of a cost center? I really fundamentally think it's now core IT has to be a business partner with their business units to make things better. And also, how can we make money with what we're doing in IT? And I think that's the fundamental change that I've seen more and more. And honestly, I think AI has driven that because there is a cost to AI, but we're looking to monetize it at the other end.

SPEAKER_01 33:27

Yeah. Yeah. It's been interesting. It's interesting to watch because yes, I would say pre-COVID, it was really seen, IT was seen as a cost center. During COVID, I feel like people really got, you know, executive leadership really got that IT could be the strategic driver. Although I'm still shocked that sometimes, you know, I get guests on the podcast who still talk about IT as a cost center, which is shocking to me. I feel like we're at least six years past that, or we should be. Um should be. But now evolving to revenue generation is really cool. It's really cool to witness.

SPEAKER_00 34:06

It is. It's been, like I said, it's been interesting. I've been managing in this space for since 2011 and been working here at NetF since 2005, and to watch how these companies have some of them have changed through acquisitions and different people, and even people that I work with that were core storage people back in that are now IT leaders and watching how people change. But I think fundamentally the biggest change is what you said, right? IT was a cost center, right? And how do we keep our costs down and all of that stuff versus now really being can it be a money revenue generating part of the business and the investments that we make in IT, what is it spit out on the other end? And that's why I said earlier, if we don't understand the business value of what NetApp brings to those customers, we're missing the opportunity to sell the value that we bring.

SPEAKER_01 34:58

Yeah. Yeah, absolutely. So if you're an advising and IT leader right now, and not necessarily from a product standpoint, but just from experience, what would you tell them to focus on over the next 12 to 18 months? Because it feels like, and correct me if I'm wrong, but to me, it feels like we're at this critical inflection point with you know where we've been on the AI journey and where it's headed. So what would you tell them to focus on over the next 12 to 18 months?

SPEAKER_00 35:26

I think it's business value. I think you I think ever anybody that's still thinking, as you said, some people still thinking in the stone ages of IT cost center, is what business value, it is, what business value can you get out of your IT investments? And that's not just storage, that's the GPUs and the virtualization products and the hyperscalers and everything. If you're an IT leader right now, I think you have to holistically look at your environment and saying, where can I get more out of something? Right? Is do I have the right products that align together? Do I have the right OEMs that are helping me out? Do I have the right value-added resellers out there helping me move my company forward and advancing in the IT world? And there's just so much thrown at you. You need those trusted advisors. And I do I have really people partnering with me from a product perspective that's looking at it to help me as a business, or are they just worried about their bottom line? And and I hope people see that when they partner with NetApp, we're looking for at to help customers with their business value and how can we enhance that? And that's what I would tell IT leaders that you want to figure out who's the best technology business partner for me, not who is just saving a buck or two along the lines. That's where I would tell IT leaders to look today.

SPEAKER_01 36:51

Yeah, that's so helpful. And you know, I we are we're moving into that world and like the partnerships that we have really drive the success or failures of businesses, I think. You know, that's true, certainly true in my world, but I feel like it's more and more true for everyone. No, this has been such a great conversation. And, you know, I think for me, it was really great to understand where NetApp fits in this picture, in this landscape today. And what really stood out to me in this conversation is, you know, we're hearing all of this about, I mean, AI is the buzzword of the moment, but really it's about the data. Like if the foundation isn't right, if the data isn't clean and protected and actually usable, everything that's built on top of that is it's gonna break. It's inevitable. And so the real work is really in the data. And my other takeaway is find the right partner who can drive the business forward. So thank you so much.

SPEAKER_00 37:56

I I appreciate the time, Tanya, and talking to you and great conversation too, right? It just reinforces for me, I feel like I'm at the right place to help businesses grow what they want to do. And and I appreciate the time. I hope your listeners enjoyed the conversation.

SPEAKER_01 38:09

Yeah, I'm sure it's gonna be really, you know, really useful for our audience. So if you want to go deeper on what Ray and the NetApp team are doing, you could head to technology match.com and enter the keyword NetApp. We've curated solutions specifically around data and AI and everything that Ray shared here today, you can find there. So there are resources there to help you explore this further. And Ray, thank you so much. We'll link everything in the show notes, including your LinkedIn, Ray. Thank you so much for being here and sharing.

SPEAKER_00 38:37

Thank you, Tony. It was great talking to you.

SPEAKER_01 38:40

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