With so many companies working with teams spread across the globe, real-time collaboration has become one of the greatest challenges of the 21st century.
In this episode of UpTech Report, I speak with Patrick Harr, CEO of Panzura, a company that’s using AI and machine learning to analyze data off of multi-cloud data management solutions and help global teams work in real time.
In addition to discussing some of the massive technological challenges modern companies face in managing their data on the cloud, Patrick talks about the core values he’s learned from flipping burgers and delivering pizzas, lessons that can be scaled to enterprise systems.
Data is the new oil
In a world full of data, how can a business take advantage of that? The global economy presents the challenge of how to deal with data as more firms have distributed teams working out of state simultaneously.
“At its core, the job that we’re hired to do is help these companies collaborate in real-time—make these distributed teams much more productive. And in turn, our customers see huge productivity gains, they lower their cost, and by the way, they are natively integrated into the cloud.”, says Patrick.
The idea of a global file system is another important part of what Panzurra works with, as they have thousands of hundreds of data to share with their clients.
Patrick Harr is with more than two decades of industry experience, joins the company after holding positions as vice president and general manager at Hewlett-Packard Enterprise (HPE), vice president at VMWare as well as a go-to-market leader and CEO of multiple start-ups, including cloud storage pioneer Nirvanix which he founded, Preventsys which was acquired by McAfee and storage networking leader Sanera which was acquired by McDATA/Brocade.show more
While at HPE, Harr scaled America’s cloud business 19X leveraging key GTM partners and generated over $1.5B in revenue in five years. He has extensive startup and Fortune 500 vendor experience across cloud, storage, security, and networking. Harr received his MBA from the University of Maryland and a BA from Tulane University in Political Economy and Russian. He has four kids and lives with his wife and family in Los Gatos, CA.show less
DISCLAIMER: Below is an AI generated transcript. There could be a few typos but it should be at least 90% accurate. Watch video or listen to the podcast for the full experience!
Alexander Ferguson 0:00
With so many companies working with teams spread across the globe, real time collaboration has become one of the greatest challenges of the 21st century. In this episode of UpTech Report, I speak with Patrick Harr, CEO of Panzura, a company that’s using AI machine learning to analyze data, offer multi cloud data management solutions, and help global teams work in real time. In addition to discussing some of the massive technological challenges modern companies face in managing their data on the cloud. Patrick talks about the core values he’s learned from flipping burgers and delivering pizzas, lessons that can be scaled to enterprise systems. Thank you so much for joining and Patrick excited to learn more about Panzura and how it started, where you guys are headed, and also you how are you innovating and continuing to learn and grow? To start us off? What year did did penzer Start? When did you join?
Patrick Harr 0:52
So company started a little over 10 years ago in 2008. And I joined about three and a half years ago.
Alexander Ferguson 0:58
Gotcha. Where are you guys located? Headquarters wise,
Patrick Harr 1:01
we’re in Campbell, just outside of San Jose, California.
Alexander Ferguson 1:06
Nice now bootstrapped or VC funded.
Patrick Harr 1:09
So VC funded, raised a little north of about $90 million over that period of time.
Alexander Ferguson 1:15
Got it? And how big is the team now?
Patrick Harr 1:18
So we have about 225 employees. We are worldwide in nature. We’ve got folks in developers really across the world as well as salespeople. What’s the point? Here?
Alexander Ferguson 1:30
Gotcha. The headquarters being there? Yeah. What’s the core industry that you’re focused on serving right now?
Patrick Harr 1:36
There’s two core industries for us. They’re both very we’re, you know, related. It’s all about data. For us. On the one side, we have a global file system, which really helps customers, quote, move into the cloud. And second, take advantage of the cloud, in terms of being able to interact with file based data store that data. It’s not a typical that we see about 70 70% cost reduction versus our traditional competitors, like NetApp, Isilon and other on prem file systems. The second market that we play in is really the growth market for us is machine learning analytics, a lot of exciting things going on and big data, quote, big data, you probably heard statements, data is a new oil. So really, for us, it’s about how do you analyze that data and get patterns to that data for our customers?
Alexander Ferguson 2:25
It really is I keep hearing everyone says I have all this data should do something with it. So but trying to do something with it. That’s a whole nother thing. And that’s your growth area right now, you said
Patrick Harr 2:34
yet both are growing very well. So on the core, what we call our global file system, it’s what’s called Panzer freedom. It’s really built around this notion. If you ever read the book, Chris Christensen’s book, is he really focused on why do customers buy? Or What job do they hire you for? At a core, I really like what’s going on and macro trend basis, you have a global collaborative economy. And what I mean by that, you can no longer work in isolation. If you’re just here in San Jose, you can’t really tap into talents that may be in Boston, or in Mumbai, China, etc. So that’s one second, I typically have a globally distributed business. So I have offices in Europe, I have offices in the Middle East, I have offices in APJ, and also have offices in the US. That presents a challenge when it deals with data. And more more firms, these distributed teams are working on this data simultaneously. So whether I’m a gamer, I’m building a game, whether I’m an engineer, designing a bridge, or an engineer actually building chips. We have a lot of chip companies and tell via one of those here in the valley. Or if I’m a medical industry, right, I have distributed from a health care hospital side have multiple offices around particular locations. Finally, it could even be in financial services. And I’ve got offices all over the world. At its core, how do I help teams collaborate in real time together. So I can either get game out on time on budget, if I’m a software developer, or get a package, software, package design and get that out to market. At its core, we’re what the job that we’re hired to do is help these companies collaborate in real time, make these, these distributed teams much more productive. And in turn, our customers see huge productivity gains. They lower their costs, and oh, by the way, they’re natively integrated into the cloud. That’s really a side benefit, but it’s not the core benefit for why they hire us. The second aspect for why they hire us is as we brought a significant amount of data. We currently manage about 100 petabytes of data in the cloud. And that’s distributed across AWS, Azure, Google as well as private clouds. That data set dark before binning. And you’ve really didn’t know where that data sat, what type of data was what was inside that data. So we introduced a new service called vision.ai. And vision nadai as a ground up new platform designed At its core, tactically, it’s a hyper scale platform, meaning it scales. The bigger the data, the bigger the number of users. It’ll grow with them to millions and millions of users and petabytes and petabytes of data. But at its core was how do we look inside that day to give more insight, more analytics, more pattern matching, so I can now turn that quote data into oil, and really drive a lot of value and insight for that customer. There’s a secondary benefit to particularly with compliance GDPR other regulations where data patriot act as an example, you have to really control where data sits.
So as I was saying, this is no longer world about just innovations, but integration, it to be able to co opt all the great ideas in this open community fashion. And inside vision, we harness the power of that open source world and for analytics. First, we at its core, we use Kubernetes, and containers for mass scale out design. We did add a quote proprietary layer here, which is our own unique IP, which is called the vision block object service. And all this means in the analytics world, you want to be able to keep your data, huge datasets and online and always available, always fast, never tear it off. Much like Gmail, you never want to delete it. In the analytics world, you never want to delete your data. But all the other platforms are challenged with because of cost, because they tactically run in what’s called Block service. If you look inside of Amazon example, EBS is very expensive versus object storage, Cloud Storage. So we we use the power of kind of the cost effectiveness of object store, but that that performance of block, but that’s at the core, we marry that with Kubernetes, and Cassandra, all the great things we that’s where we store our store metadata. We then put on top of that orchestration layer again inside of Kubernetes. And then on top of that the open elk environment, which is Elasticsearch, LogStash, Kibana, and in there there 1000s. And 1000s of developers have added insights and analytics for almost everything, you can imagine that data connectors from networks, to computers to IoT devices, the net of this, now we effectively have the best place to run Elasticsearch on top of this platform and a much more cost effective manner. You’ll then see Spark is another huge open source analytics database that will run on this. So at its core, what we’re really good at doing is stitching this stuff together, running on that proprietary layer, so you get 10x disruptive economics, but still maintain huge performance gain and advantages. And then put an open marketplace on top of that. So through one click, I could deploy any of these analytics modules. And so we’re while open source is great, the clouds great. You also want to follow the cloud kind of methodology where you make it dead simple, stupid, and don’t have to do any optics. And so some of the challenges in open source is pretty complex, you have to have a propeller head, nothing wrong with that. A lot of business don’t want to operate that way. And so we’ve been able to marry all the greatness integration of open source together with the cloud with our own proprietary area. And to really drill I would say deliver something that’s truly unique and innovative into the marketplace.
Alexander Ferguson 8:16
There’s there’s always growth and learnings from where you continue to to move forward. Anything that you can share that other businesses entrepreneurs can can learn from, and hopefully maybe avoid having to go through a similar hurdle.
Patrick Harr 8:29
Yeah. I think we’re always continually learning. I probably shouldn’t say this, but I crossed over the Big Five Oh, and I continue to learn every day. You know, I grew up pretty poor. I worked every job you could possibly imagine I’ve ever I started at Taco Bell worked at Fuddruckers flipping hamburgers through bags, a united deliver pizzas, did many, many odds and end jobs worked my way through college. But at its core, what I learned back then and continues to be present today is you got to learn, you got to work really hard. Listen to people really identify what they’re looking for to get ahead. And in turn, you got to be able to put that grit, right. I always hire people. And that’s always why I talk to each and every one we bring into the company. Do they have the passion, the persistence and really the planning to be successful? And as I looked at today, what have I learned and some of the things that we’ve gone through? One key aspect that I will tell you, when I joined the company, we’re an appliance model. Not only were you an appliance model, we were appliance and perpetual licensing and for those of you don’t, that’s really capex licensing the cloud is more optics focus, right and subscription focus. And finally, it’s also software focus. It’s not necessarily hardware. So I would tell you one of the big learnings and that I’ve gone through and been very successful in doing it, but there was it was a hard process we first moved from an appliance model to software. We now represent over 90% of our business is pure software. The second piece is we shifted from a CapEx model to subscription model. Each one of those changes is not easy. undergo a change with with customers, you undergo a change in your financials. And in fact, you’ll you’re heard this notion of these trowel disillusionment as you move from capex where you take in all the revenue upfront to optics, where you’re taking revenue, basically monthly or on a yearly basis. So a lot of hard work a lot of learnings through that process. But in perhaps I should write a few blogs on this because there are lessons learned. Sometimes you get some scars on your back. But it’s actually great to go through that model. Because we’re really aligned back to that core of why do customers hire hire us? And why? How do they buy us? Now we’re aligned very much with quote, the cloud centric economy, where you buy in a subscription basis, you’re basically able to deploy on demand, whether that be in the cloud itself on the edge. And again, it’s much more self service. So
Alexander Ferguson 11:04
going forward, looking forward, where do you see in five years, the business.
Patrick Harr 11:10
So, you know, I came into this business, you know, every time you, you take on a new job, large or small, you have your personal opportunity cost. So you really want to look at its core, what are macro trends are things that you have are passionate about, you believe in. And so for me coming into pads are really number one, I felt that we would continue to have this huge growth of unstructured data. If you divide data into two types, unstructured data and structured data. Structured data is really database tier data. So meaning it’s structured, because I can put it in a tab or a column, or a row inside of a database, unstructured data is video, it’s IoT data, it’s data, you can’t necessarily easily form into those database rows. And that world 90% In between depend on who you’re listening to 80 to 90% of all data now is unstructured that so I felt coming in that we had this huge growth of unstructured data. Second, that we would continue to see the accelerant to two clouds. And I do say clouds because I felt just as in the past where you had best of breed versus single stack. And I’ve been around the block a few times. Now, back in the 90s, it was all about the Microsoft stack. And then it was the VMware stack. And today it’s about AWS stack, great, great company. But I did feel that we would move into not just a single cloud, but multiple clouds, right, and customers would want to take about the advantage of the best of breed approach. So that was a second dimension, I felt opportunity costs, that would be a great opportunity. Third, I did feel that the more and more analytics, more insight that we could bring to bear or you could bring to bear into unstructured data, it would really offer a tremendous opportunity for any company to take advantage of that and offer to its customers. So with that as a backdrop, we really focus on executing, number one kind of getting coming in the company. How do we deliver the number one global cloud file system that really helped companies bring all this data into cloud based environments help those companies collaborate on that, that data in real time. Do all the let’s call it the boring things in it, do automated backup, automated Dr. Business Continuity, all that good stuff, and oh, by the way, help them reduce their cost by 70% in managing this huge growth of unstructured data. So outside of his core, focus very much on that delivering the highest quality solution be the best maniacal support company that delivered that high net promoter score. So we accomplish that, right. And second, we also deliver vision.ai, which gives us really just tremendous insight into this data.
Alexander Ferguson 13:59
I imagine in order to achieve this vision, though, and ride the wave of this industry. There’s there’s also a few hurdles, you’re going to have to figure out overcome. What do you see as one that you’re right now that’s on your scope?
Patrick Harr 14:12
Yeah, I think as a small company, you’re always fighting every day to drive value for your customers. And so I’d say this just number one, overall, you have to continue to maniacally listen to customers and innovate with your customer as fast as possible. And one of the key challenges you have particularly in this valley is out innovating others, right. And so you have to continue to do that. And perhaps I’ll come back to what I said before. That means you have to be able to tap into the best talent not just here but worldwide. I think those that are able to tap into the best development talent because at its core, you’re as good as your engineers are and how you’re matchI meeting those requirements for your customer and listening to your customer. And so that is a challenge particularly again, living in the here in the valley, Google Apple, Facebook pay very, very big dollars for, for the best talent. And so you know, certainly a challenge, but also an opportunity for us is to tap into that bass, that best talent worldwide in a global nature to be able to deliver on behalf of our customers. Second, I always like to say there’s, for every great idea there’s a mother behind 10 other great ideas, right? That are simultaneously working on that. So comes back to that innovation. You have to get out of bed every day to work as hard as possible on behalf of your customers. And I know there’s a both related, but those who get complacent die. That’s this, this valley is littered with great technologies that perhaps didn’t necessarily and continue to the innovation curve and continue to grow.
Alexander Ferguson 15:46
How do you personally innovate yourself? What are you looking to? What are you reading? What are you looking at to keep yourself fresh?
Patrick Harr 15:54
These I mentioned like, I’m kind of a big fan of Chris Christensen and some of his books was at a CEO Summit coastal Ventures is one of our investors, and they invest in some of the coolest technology across very, very different, broad spectrum. And that the one book was perfectly Why do customers hire McDonald’s for for a milkshake? Client? heck would you even talk talk about a job of a milkshake. But it’s amazing when you read some of these different analysis analogies, that the highlights you can get out or the learnings you can get out it. So one, I really focus on a lot of blogs, a lot of books. But interestingly enough, and in process, it also gives part of the edge. I’ve got four kids from ice, just out of college till Elementary School, and just continually seeing all the technology and the interaction points that they’re that they’re utilizing, really keeps abreast of some of the key trends that you see. I mean, some of the things that my younger kids do. And technology is pretty incredible. They’re just very immersed. So I think also seeing keeping abreast there. And finally, coach basketball. And if you ever, if you ever want to learn management, one on one, teach or teach third and fourth graders how to play basketball I’ve never played you just can’t assume anything. And Ellie highlight that because we really get more of the soft aspects for how to coach and work with people to help them grow.
Alexander Ferguson 17:31
Where can people go to learn more, and what would you recommend is kind of a first good step for them to take.
Patrick Harr 17:37
Yeah, first great stuff you want to get insight into Panzuradivision.ai is actually take a test drive on vision.ai The AI xe io n.ai You can set up your own account, you can start get a little insight search your data, get some and you know do the analytic aspects that I talked about experienced with machine learning, get a greater vision on your data, vision on your data. Then second, I’m gonna Panzer calm and you can experience our one global file system
Alexander Ferguson 18:07
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