On this episode of the Futurum Tech Webcast – Interview Series, I am joined by Quantum Inc’s CEO, Jamie Lerner for a conversation on how Quantum is solving the challenges of data management for today’s business.
Our discussion covers:
- The current macro trends in the data management and storage markets
- Quantum’s current portfolio and how it solves customers’ challenges in ways that other vendors cannot, and which products are driving the most interest
- Where the data management and data storage trends are going in the next 5 to 10 years
- How the rise of AI is changing customer requirements for managing data
- What is missing from other storage and file systems in the market and why Quantum is launching their new file and object system
Learn more about Myriad Software-Defined All-Flash Storage Platform for the Enterprise on the Quantum website.
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Transcript:
Daniel Newman: Hey, everyone, welcome back to another episode of the Futurum Tech Podcast. I’m Daniel Newman, your host, CEO of The Futurum Group, excited for this interview series.
We have a new guest today. We have Jamie Lerner, CEO of Quantum, joining the show for the first time. Very excited to have a conversation with Jamie. So without further ado, I’m going to welcome him into the show. Jamie, thanks for joining me today. How are you doing?
Jamie Lerner: I’m doing great. Thanks for having me, and I look forward to the conversation.
Daniel Newman: Yeah, it’s great to chat to you. I always know when someone understands the uniform of technology, fellow vest guy, that we are bound to be having a great conversation. But look, in the era of rapid digital transformation, the proliferation of data at scale, the business you’re in is of critical importance, and people are looking across all types of different storage data management systems, and of course, all the compute and network required. So we’re going to get into a lot of that with you today.
Before we do that, though, I would love to just take a moment and quickly have you introduce yourself to the audience. Give us just a little bit of your background, and how you landed as CEO at Quantum.
Jamie Lerner: Yeah, well, I’m Jamie Lerner, and I’ve been the CEO of Quantum for the last five years. I’ve spent the majority of my career in infrastructure, whether it’s networking infrastructure, data center infrastructure, data center monitoring, provisioning, but really, all things data center infrastructure.
I’ve spent the last five years driving a transformation at Quantum, from a group of enterprise storage products, into an end to end portfolio, completely focused in and around managing unstructured data.
Daniel Newman: For anyone out there that’s not familiar, unstructured data is the stuff that, it’s not rows and tables and columns. By the way, it’s what we are all creating at exponential scale, like right now.
Jamie Lerner: Right, creating it right now.
Daniel Newman: It’s the whole bunch of unstructured data.
Jamie Lerner: Right.
Daniel Newman: And in our lives, and in our worlds, that’s the way we will see AI scale, in great capacity, for our utilization. It’s when we can not just take those columns and tables and databases that everybody has done a pretty good job of figuring out how to manage, but then, get all the rest of this data that we’re continuously creating in the real world, at the edge, in the cloud, and of course, on-prem, so let’s talk all about that.
If you follow my show at all, one of the things you’ll know about me is that I do love talking about the macro environment, spend a lot of time talking to the Wall Street Journal, and to CNBC, and trying to tying the threads together of technology and innovation, and stuff that we do, but also, in how it affects the world, the globe of the economy.
I’d love to get your broad takes, Jamie, just what are you seeing as the big macro trends in the market, and the things that have really got you thinking about how you want to continue to build Quantum?
Jamie Lerner: Yeah, when I came to Quantum five years ago, quite honestly, I didn’t know what to do. We had a couple products, but I wanted to build a strategy that was very forward-looking, that I knew for us to be relevant as a company, we had to do something very bold, very ambitious, and something that was years ahead of the market.
To figure that out, I just immersed myself with our customers. And at the time, a lot of our customers were television and movie and sports production people. I realized one thing very quickly, that companies have basically two buckets, if you will, of data.
One, like you mentioned, structure, rows, columns, very mature tools for searching, querying, analyzing, graphing, very mature technology. But the other area of storage is actually much bigger, growing much faster, and much less defined. That’s just unstructured stuff.
Now, in the movie world, a company like a Disney or a Warner Brothers has over a hundred years of video clips, sound, music, and to have several billion of these is not abnormal. They were coming to us, saying, this data’s growing. “It’s very expensive to keep, but we can’t throw it out. The enterprise companies can throw out their data. We can’t. We’re not going to throw out the original Mickey Mouse videos and films. We’re not throwing it out, ever. So we need to keep it forever, but we don’t know how to manage it. It’s hard to search, it’s hard to know what’s happening in the video. We need analytics tools.”
And what I started to learn is what was happening five and 10 years ago at movie and television companies is now what’s happening at every company. Every company has video surveillance data, has imagery data, has log files, has growing bodies of unstructured data that they need to catalog, analyze, and now, like movie companies, almost every enterprise is saying, “I’m not going to throw out anything, either.” And this really is what created, what I think is the biggest trend right now, is end-to-end unstructured data management, because the structured data never moves. It stays and lives its whole life in an Oracle database.
Unstructured data, clips or videos, are moving around. They may go to the cloud, they may go into an analytics system, they may be archived. So there’s movement, they’re kept forever. Most of the work we’re now doing around AI and analytics is not pointed at structured data. It’s pointed at support calls, voice, the spoken word, the written word, video, photography, that’s where AI is focused. And nobody is going to be able to do AI properly without the infrastructure, which ends up being an end-to-end infrastructure for unstructured data.
Daniel Newman: Yeah, I think you’ve hit on a lot of things there. And in a world from a macro perspective that’s in a challenged environment, high interest rates, you’ve got challenging inflation, AI has sort of been this really interesting digital transformation and business accelerator, that’s almost offset.
What you’re mentioning is something I’m going to want to come back to in a few minutes with you. But what you’re mentioning is effectively this, companies realizing efficiency and productivity that exists inside of their data is massive, but it’s got to be all of their data. It can’t be fragments of their data.
Jamie Lerner: Well, I mean, I think you’re hitting on something really important. Your AI will be no different than anyone’s, unless you’ve kept your data. The thing that makes one company’s use of AI valuable, unique, and special is because that AI was built off of their very special data.
For example, if you’re using AI to support your customers, if you have not kept 10, 20, 30 years of support history of recorded calls, written calls, if you don’t have that special thing, and you’re using generic internet data, yours is no different. So basically, the companies that have archived their unstructured data and kept it, will have a distinct advantage over the people who’ve thrown theirs away.
Daniel Newman: Yeah, you got it.
Jamie Lerner: An archival strategy, and the companies that invested in archival, are going to have much higher fidelity AI. And I think a lot of people are now saying, “I need a strategy to store this, what is kind of an obscene amount of data. Because of what we’re going to be able to harvest from it in the future. I need to store it, I need to store it economically.”
We’ve spent about five years working with the largest cloud companies to say, “How do they store tens of exabytes of data very economically?” Part of the end-to-end unstructured part of it is very expensive, very fast storage, but part of it is very large, very inexpensive, 100-year archives. And you need to have both ends of that spectrum, to really achieve end-to-end architecture.
Daniel Newman: Yeah, which is interesting too, is because you can have that low cost tier, but it still has to be available, searchable, retrievable in a somewhat timely manner. Or at some point, it gets to be, it’s like filling up a big storage facility, right?
Jamie Lerner: You don’t have to.
Daniel Newman: And you can’t get to the stuff in the back, because you need trucks and forklifts and dump trucks. So that’s the physical example of it. But the complexity right now is that most of us don’t always know that data.
Something else you kind of said, and I’m bouncing around a bit, but I think this is good, let the conversation roll. With the whole advent of these large language models, we sort of thought that these companies were going to get a huge advantage, but then, what we immediately saw with ChatGPT and Llama, and with Bard, and all these others, is that it actually just leveled the playing field for everybody.
So when it comes to that widely available public data, every company’s now on the same playing field. You can search it, you can scan it, you can have it create quick queries for you, but then immediately, you saw, where did companies start to rise up? It’s the rise of the foundational models, the rise of the microphone.
Jamie Lerner: I have my own special data that, when analyzed, will give results that you cannot get off the general internet. And that’s what’s so important.
Daniel Newman: Well, you have to.
Jamie Lerner: The other thing that’s happening is, when you keep your data, and you have millions, hundreds of millions, billions of files, traditional storage systems have a major problem. Most of them store a file name.
Now, if your file name is a thousand hours of video surveillance, you don’t know who’s in that video, what’s happening in that video. Did something happen? Was there a slip and fall? Did someone get injured? Was something stolen? When did that happen? Who did it? You don’t know any of that. So what we’ve been doing is changing the concept of data services. Data services used to be compression and D-Dupe. Oh, great.
But now, the data services we’re adding are analytic algorithms that say, “Okay, you have a billion files. Well, what are they? Are they X-ray images? Are they MRI images? Are they video surveillance? Are they log files? What are they?” Then, once we know what they are, okay, it’s an X-ray image. Well, X-ray of what is it a man? Is it a woman? Is it a child? Is it a broken bone, is it osteoporosis, is it bone cancer? And we can begin to fill out a metadata library that says, “You have a file. This file is an X-ray. It is osteoporosis of a woman above 80 years old.” Now you can say, “Not only do we have billions of files, but we know what they’re of.” So you can go, “I want to analyze all osteoporosis patients that are under 50 years old, in this demographic of the world.” And you can begin to slice your data, so that you give the AI engines the subset of data that’s most relevant, but a storage system doesn’t do that.
Daniel Newman: Nope.
Jamie Lerner: And we started adding metadata. The old way you did this, like the way sports teams do it is they have loggers. “Oh, it’s a fastball, it’s a curveball.” They’re typing in every play in the NFL and MLB formulary.
Daniel Newman: It’s like early ML store object recognition, almost in, they’re tagging it. “It’s a cat, it’s a dog.”
Jamie Lerner: How would you do tagging? How do you do hand tagging on a billion files, on the entire archives of Disney or Showtime or HBO? It’s like, it’s impossible.
Daniel Newman: You could solve all the gaps in labor. That just becomes…
Jamie Lerner: Right.
Daniel Newman: I’m being–
Jamie Lerner: And that is, it just doesn’t work. So now that we’ve added this, our systems now automatically populate the metadata tagging. That’s why we’ve layered on a cataloging engine as part of our architecture, and that it doesn’t just move files around. It knows what’s in those files.
And we’ve introduced this concept of data enrichment, that we don’t just store your data, we actually begin to enrich it, to say, “Yes, it’s a file, but we’re adding the metadata tags about it. It’s got these actors in it, and this is what the actors are saying. And by the way, if you want us to translate what they’re saying into Mandarin, we can do that. And if you want us to re-pixelate their mouth that they’re no longer speaking English, that they’re speaking Mandarin, and we’re re-pixelating their teeth and their facial expressions into that language,” that becomes data enrichment that isn’t outside the storage system, but it’s in the very DNA of the architecture.
That’s why what we’re doing is so different. What’s happening is, the people are coming to us with these tremendous unstructured data problems, and realizing that we’ve probably got the only architecture designed to handle these very large and complicated customers that have lots of unstructured data.
Daniel Newman: When you were talking about osteoporosis, I did want to know if you had a solution for the follicularly challenged, that you could come up with. I still wonder, to this day, why they can’t solve this for me. And anyway, I digress.
I want to get into something a little bit more serious. One of the biggest challenges that the storage industry has, and Quantum has not been able to escape this, and I think it’s always great to address the challenges head on. And that’s, we have this old to new continuum, meaning that there was a time when storage looked a certain way, and storage companies did things a certain way, and they were sort of seen as innovative. And then, storage shifted, and you saw, you mentioned the rise of the hyperscalers, and you talked about some.
There’s new entrants, and what you said earlier, you said, “We’re one of, or we’re the only to kind of,” and I agree. I have the benefit of talking to many of your peers, and I would say what you’re talking about is not what they’re talking about being able to do. There’s a few, and honestly, the ones that are talking about it, aren’t the ones that you’d be thinking about. They’re not storage companies, they’re these new data companies.
Jamie Lerner: Right, right.
Daniel Newman: But I want you to talk about that evolution, because Quantum was narrowly seen as one thing. It had gone public, it went private, it’s back public, and you’ve come back as a different, I think, cooler, more innovative company.
Jamie Lerner: Right.
Daniel Newman: But sometimes, it takes a while-
Jamie Lerner: Yeah, we’re a 45-year-old company.
Daniel Newman: It takes a while for people to see it.
Jamie Lerner: Right.
Daniel Newman: Talk about that evolution, and how you’re kind of getting the world to appreciate that you are very different from that tape or M&E company of the past.
Jamie Lerner: Yeah, yeah. I mean, Quantum started its life in 1980 as a disc drive company, and then, as a disc drive industry consolidated, it made a very difficult transformation from a component supplier, a disc drive is a component in a storage system, to becoming an enterprise provider, getting into backup systems, tape systems, disc-based systems, high speed storage with our StorNext product. And it made a transformation from disc to tape, to tape into enterprise systems.
When I came, I felt it was time for another transformation, keeping some of our assets, removing some of our assets, but really transforming ourselves into an an end-to-end company. There’s not a lot of companies that are 45 years old out of the Silicon Valley, and the ones that are still here have transformed their missions drastically over those years. I think we’ve done a good job at keeping our linkages to our legacy. I think we’re very trusted as a vendor. I think we’ve been around a long time. I think we have a lot of the same employees, and a lot of the same customers, back from the ’80s.
I have employees celebrate their fortieth anniversary here all the time, and it’s pretty amazing to watch. And we have customers that come in and say, “We’ve been a customer for 30 to 40 years, as well.” And that legacy of trust is really important to me, in the culture of our company. But that trust is based on, that our customers know we’re looking five and 10 years into the future, and building the solutions for them. While we have a lot of legacy and tape, we’re not a tape vendor.
If you look at our newest products, and almost everything we’re doing at Quantum is all Flash. Even our archive and backup products are all Flash. Part of our end-to-end architecture is, we’re end-to-end all Flash, as well. So we are going all Flash, but we’re doing something that no one else and not many other all-Flash vendors do is, we’re tiered Flash. While we’re Flash across everything we do, you can always tier to disc, to low cost tape, to the cloud, to multiple cloud vendors, even tier to third party vendors. Tiering has always been a part of what we do, but we know that a modern architecture is an end-to-end all Flash.
Daniel Newman: It sounds like you’ve made a ton of progress, and I was hearing bits of it in your last answer, but I’m going to ask you a little bit more directly, Jamie, is how do you tell your unique, “We are different story?”
When you’re sitting down, and you’ve got those decision makers that are in this transformation moment, whether it’s to come over as net new, or they’re coming to you Jamie, and saying, “Look, we’ve got a lot of options right now. There’s a lot of storage file systems in the market.” What are the three things, maybe, and you can do four, too? This isn’t a trick question. What are the couple of things that you kind of say, “Here’s the reason to come, or here’s the reason to stay, despite all the options?”
Jamie Lerner: Yeah, I think customers choose Quantum for three reasons. Architecture, experience, and trust. Now, our experience, we have more experience with unstructured data than anyone else. We have the longest legacy with television, movies, sports, and those were, before enterprises got there, they were the largest custodians of unstructured data in the world. They just had the most video, the most photographs, the most sound clips. They just had the most unstructured data, while banks and the rest of the world really lived in the world of Oracle databases, structured data. Maybe if they played in unstructured, it was Word documents, that I would still consider to be quite structured.
But it was us with those big movie companies, also the big fashion companies, with just billions of photographs. Us working with NASA and space exploration, having, archiving the largest repositories of photography in the world, we just built more experience with unstructured data for longer than just about anyone else. We’ve translated that into the enterprise with their log files, surveillance data, the kind of data that you’re more likely to see, corporate video training, video support cases. We’ve translated that in the enterprise.
Secondly, because of that experience, we’ve built an architecture that just our peers don’t have. It is an architecture for and built around this experience.
Lastly, we’ve been trusted now for 40 years, whether it’s with storing people’s data on hard drives, on tape systems, on our extremely high-speed StorNext system, and now, into the future, with our Myriad architecture. And I think it’s those three things. It’s our experience, our architecture, and our 45 years of trust, is why customers go with us.
Daniel Newman: I won’t date myself perfectly for the public, for all you people that want to steal my passwords, but I will tell you this, Quantum is older than I am. And that is pretty impressive.
Jamie Lerner: Right.
Daniel Newman: So we have just a couple of minutes left. I really enjoyed it, I appreciate you kind of staying with me. I told you, I like to bounce around a bit, but what you’re doing is really important, Jamie, and the ability to A, help companies navigate into this era of unstructured data, and do it at scale. Like I said, I love what you mentioned, when you talked about the complexities and stuff, with metadata and tagging. The ability to do these things rapidly without a significant human in the loop is going to be critical for companies to really get that competitive differentiated edge, with all of their data.
Now, you mentioned your new product, so this is going to be my one little moment here, where I’m going to take you off the hot seat. I’m going to give you a little bit of a leading question, but talk about Myriad. Because I read that announcement, I said, “This is cool,” and by the way, totally different than the Quantum that I thought about, when I thought about it.
Jamie Lerner: Yeah, I mean, Myriad is a very ambitious storage project. We set out to build the world’s fastest combined file and object system, all-Flash, all NVME over fabric, but completely automated, that there are no nerd knobs and settings. That it sets itself up, it sets its network up, it sets its storage parameters up, it learns how you use it and evolves, but it is designed to just work. It just works. You don’t have to configure it, and administer it. It just works.
But more than that, it just works anywhere. No hardware bindings. So the entire storage architecture is pressed into a container, and that container can run on the cloud, it can run on a Dell server, but if you’re in China, it can run on a Huawei server, in India, it can run on a made in India server. It has no hardware bindings. So it’s built like the cloud. It’s entirely built as a container. It’s scales, just add more containers, and it scales. It balances itself, it spreads itself. You don’t have to administer that.
It comes with all the data services you’d expect. You don’t have to set them up. It does compression, it does de-dupe. It will replicate to other places, other vendors, if you’d like. It has metadata tagging that we discussed, in its very DNA. So the data’s all tagged, as it’s operating. And you can do custom tags, AI-based tagging. It’s very, very simple to administrate, but runs at incredible speeds, and it doesn’t have custom administration. You administer it the way you administer anything in the cloud, just as a container.
So it uses Kubernetes failover, Kubernetes installs, all the ways that you would use Kubernetes to manage a data workload. Our workload is no different. It’s a completely modern way of building a storage system. Everyone who’s seen it and has used it, now we’re in pretty late stage trials, is pretty blown away by what its potential is. We’ll be moving from late stage trials into our first commercial deployments this fall. It’s actually pretty much happening in real time, and it’s a pretty exciting time, but it’s fully part of, most of our competitors build a product like this. We build a product like that.
So this product tiers, it talks to our other products. It moves data up and down the unstructured continuum, from this being our most high-speed platform, but it’s completely connected to a data lake. Whether that lake is on the cloud, whether it’s on very low cost storage, nearline storage, it will move data from these lower cost archival areas, up into the high speed area. And that’s all built into its very DNA. So it’s a very different approach to building this kind of product. It’s based on 25 years of experience working with the biggest custodians of unstructured data. The result is its early days, but right now, it’s one of the more awesome things I’ve seen in my years of building infrastructure. What’s so cool about it, as infrastructure, there is no infrastructure, there’s no hardware. It is just containers, completely unbound. It’s the first time, as an infrastructure builder, I built something that is anti-infrastructure. And it’s pretty cool to see it be deployed.
Daniel Newman: Well, that’s where the world is heading, right? Infrastructure, increasingly, except for your most specific use cases, is getting more and more commoditized.
Jamie Lerner: Yeah.
Daniel Newman: Of course, I’m not talking about big NVIDIA GPUs, people understand, but at some point, it’ll do the same thing. That’s what happens to hardware.
Jamie, I could talk for a long time about this. Let me tell you, I’m excited to see more, hear more. I’m the type of person that loves to look at the way your customers are going to implement and employ the Myriad solution. I look forward to reading about those, and having you back, when you start to get this out in the market at scale. But congratulations on the progress you’ve made. No doubt, it’s a tough industry. It’s a very, very competitive space, and continuing to innovate, differentiate, and being able to drive up margins. And then, of course, trying to do all of that while appeasing your shareholders …
Jamie Lerner: Right.
Daniel Newman: Makes for a lot of long nights, and hopefully also gives you the inspiration to keep driving Quantum’s future success. Jamie, thanks so much for joining the Futurum Tech Podcast.
Jamie Lerner: Yeah, Daniel, thanks for having me. I enjoyed it.
Daniel Newman: All right, everybody. Hit that subscribe button. Join us for all the great episodes here on the Futurum Tech Podcast. In the interview series, we talk to many executives, from CEOs like Jamie Lerner here, to top product and technologists at these companies, and it’s a lot of fun. You’ll enjoy it, but you can’t catch the episodes if you’re not subscribed, and you’re not tuning in. So we’ll see you all soon. Bye-Bye for now.
Author Information
Daniel is the CEO of The Futurum Group. Living his life at the intersection of people and technology, Daniel works with the world’s largest technology brands exploring Digital Transformation and how it is influencing the enterprise.
From the leading edge of AI to global technology policy, Daniel makes the connections between business, people and tech that are required for companies to benefit most from their technology investments. Daniel is a top 5 globally ranked industry analyst and his ideas are regularly cited or shared in television appearances by CNBC, Bloomberg, Wall Street Journal and hundreds of other sites around the world.
A 7x Best-Selling Author including his most recent book “Human/Machine.” Daniel is also a Forbes and MarketWatch (Dow Jones) contributor.
An MBA and Former Graduate Adjunct Faculty, Daniel is an Austin Texas transplant after 40 years in Chicago. His speaking takes him around the world each year as he shares his vision of the role technology will play in our future.
