20VC: Is Big Data Still A Thing with Matt Turck, Managing Director at FirstMark Capital

Matt Turck is Managing Director of FirstMark Capital where he invests across a broad range of early-stage enterprise and consumer startups. Prior to FirstMark, he was a Managing Director at Bloomberg Ventures, the investment and incubation arm of Bloomberg LP. Previously, Matt was the co-founder of TripleHop Technologies, a venture-backed enterprise search software startup that was acquired by Oracle. Matt organizes two large monthly events, Data Driven NYC (focuses on Big Data and AI) and Hardwired NYC(focuses on IOT, AR/VR, drones). At Firstmark, Matt has made investments in the likes of Sketchfab, Sense 360 and the much loved X.ai with Amy Ingram as your personal secretary. In Today’s Episode You Will Learn: 1.) How did Matt make his way into the world of VC? 2.) What does big data really mean? With the cool kids in the data world moving on to obsessing over AI, is big data still a ‘thing’ in 2016? 3.) Why is now the time for big data? What has enabled big data to have sudden mass utility across a variety of applications? 4.) How does Matt view the integration of big data and AI? Is AI helping big data deliver it’s promise? 5.) How can we combat the incumbency advantage of large companies owning the majority of datasets? How can startups access similar datasets? Items Mentioned In Today’s Episode: Matt’s Fave Blog: AVC, Chris Dixon, Brad Feld, Wait But Why Matt’s Most Recent Investment: Hyperscience As always you can follow The Twenty Minute VC, Harry and Matt on Twitter here! If you would like to see a more colourful side to Harry with many a mojito session, you can follow him on Instagram here! The Twenty Minute VC is brought to you by Leesa, the Warby Parker or TOMS shoes of the mattress industry. Lees have done away with the terrible mattress showroom buying experience by creating a luxury premium foam mattress that is order completely online and ships for free to your doorstep. The 10 inch mattress comes in all sizes and is engineered with 3 unique foam layers for a universal, adaptive feel, including 2 inches of memory foam and 2 inches of a really cool latex foam called Avena, design to keep you cool. All Leesa mattresses are 100% US or UK made and for every 10 mattresses they sell, they donate one to a shelter. Go to Leesa.com/VC and enter the promo code VC75 to get $75 off!

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Harry Stebbings?

  This is the 20 Minute VC with your host Harry Stebbings, which as you know you can find me on Snapchat at H Stebbings with two Bs. Now, I'm super excited for our guest today as I've been a massive fan of his blog for a long time now. So joining me on the show, we have Matt Turk, Managing Director at First Mark Capital, where he invests across a broad range of early-stage enterprise and consumer startups. And prior to First Mark, he was a Managing Director at Bloomberg Ventures, the investment and incubation arm of Bloomberg LP. Previously, Matt was the co-founder of Triple Hop Technologies, a venture-backed enterprise search software startup that was acquired by Oracle. And Matt organizes two large monthly events, Data-Driven NYC, which focuses on big data and AI, and Hardwired

• 0:44Harry Stebbings?

  NYC, which focuses on IoT, AR, VR, drones, and other emerging technologies. And at First Mark, Matt has made investments in the likes of Sketchfab, Sense360, and the much-loved X.ai with Amy Ingram as your personal AI secretary. However, before we deep dive on data with Matt today, did you know that sleeping directly after learning something new will improve your ability to remember it? So head to your Lisa mattress when this episode is over. And why a Lisa mattress? Well, they're expertly crafted with three distinct layers, one of memory foam and one of latex-like foam, designed to keep you cool. They also completely avoid the showroom experience, offering an all-online experience, meaning they have a lower cost structure reflected in their prices, which are really simple

• 1:29Harry Stebbings?

  and really transparent. And once bought online, the mattress is then shipped to your door if you live in the UK, US, and Canada for free, and you get a 100-night trial to ensure the mattress is perfect for you. So get yours by heading over to leesa.com/vc and enter the promo code VC75 at checkout for an incredible $75 off. And if you haven't already, then do sign up to the Matt Mark Daily by my good friend Nick Frost at Thinker on Twitter. It really is a must for all startup articles and VC material. But now it's time for me to pass over the mic to the main man, Matt Turk, Managing Director at First Mark Capital.

• 2:10Speaker 01

  You have now arrived at your destination.

• 2:13Harry Stebbings?

  Matt, it's fantastic to have you on the 20 Minute VC. A huge thank you to Nicolas Desailly at Algolia for making the introduction. But thank you so much for joining me today.

• 2:22Speaker 02

  Thank you. Thanks very much for having me here.

• 2:24Harry Stebbings?

  Now, I'd love to start off with your story and how you came to be a managing director at First Mark.

• 2:30Speaker 02

  Sure. So I was born and raised in France and originally came to the US to finish school. But I basically fell in love with America in general and New York in particular and never left. A couple of years after I arrived in the US, I became a startup founder right here in New York. I co-founded a software startup that did enterprise search called Triple Hub Technologies. And after five or six years, the company was acquired by Oracle. I joined Oracle as part of the acquisition. I extended tour of duty there. And then I left to help start Bloomberg Ventures, the incubation and investment arm of Bloomberg LP,

• 3:15Speaker 02

  the financial data company, which has now morphed into Bloomberg Beta. I know you had Roy from Bloomberg Beta on a few weeks ago.

• 3:22Harry Stebbings?

  Absolutely.

• 3:23Speaker 02

  Then about three and a half years ago, First Mark came knocking. It was an amazing opportunity to join a young venture firm experiencing tremendous momentum. So I jumped on board and became a partner. Loved my time at First Mark.

• 3:39Harry Stebbings?

  I'd love to hear what were your biggest takeaways from the Oracle journey? A lot of Oracle employees say they have some very memorable moments. Do you have any key takeaways that you apply to your learnings and practices as a VC today?

• 3:51Speaker 02

  Yeah, absolutely. I think it's a terrific experience to be part of a very large technology company so that you can see and experience what a large company is at scale. So what a truly world-class product organization is, what a truly world-class sales organization is. And I thought that was extremely formative. So, you know, of course, as all large companies, there's all set of issues that come with the territory. But I was actually very impressed by the company altogether and the general spirit of competitiveness and innovation

• 4:31Speaker 02

  that those guys have managed to maintain, you know, several decades into the life of the company.

• 4:37Harry Stebbings?

  And today I want to focus on an element of our industry that we're constantly hearing about, maybe now more than ever with the rise of AI. But not many people seem to actually know what it is and much about it. So and that's big data. But before we do dive into this, I do want to say a disclaimer that Matt's research and expertise extends way beyond big data. And you can see this on his blog, which it really is one of my favourites, mattturk.com. But today we're on big data. So let's start then by covering what the buzzword actually means. So what is big data?

• 5:07Speaker 02

  So in terms of what it means, I think there's really two definitions, a narrow one and then perhaps a broader one. Narrow definition is really that there's a growing group of companies that have absolutely massive data sets, whether they are fast-growing digital companies or large internationals. And for those companies, there's a whole arsenal of tools and enabling technologies that enable them to process data faster and cheaper than ever before. And that's really the world of Duke and Spark and all those other frameworks. But beyond that definition, there's a broader definition that to me is almost more interesting, which is what I would call the spirit of big data.

• 5:52Speaker 02

  That's really that as every company is becoming a technology company, there's an ever-increasing amount of data that's available to them that can be analysed. So that's an opportunity to develop a very powerful data-driven culture and be much smarter about one's business. And there's an aspect that I find truly interesting, when you have a combination of data tools and data culture, and that becomes part of the fabric of every modern business, not just the businesses that have those absolutely massive data sets.

• 6:23Harry Stebbings?

  And I have to ask, with the with the cool kids in the data world seemingly obsessing now over AI, is big data still a thing in 2016, do you think?

• 6:32Speaker 02

  Yeah, absolutely. I think that we're actually just entering a truly interesting part of big data. I think everything so far, and it's been years in the making, has been about the

• 6:49Speaker 02

  Early stage of creation of tools, of creation of companies, startups, of experimentation. And we're just hitting the phase now where people are starting to truly deploy these tools in production. At the beginning of the phase where the big data-driven applications are starting to appear. So I think we're just getting into the thick of things.

• 7:15Harry Stebbings?

  I'm intrigued there. I can't stay away from that, big data applications. Where are you most excited for big data applications to be exploited?

• 7:22Speaker 02

  So if you think, if you look at the evolution of the big data landscape, there's really been several phases, and a little bit to the earlier question as well. First few years were really about the creation and deployment of that core infrastructure. So, you know, we have all this data floating around and that we keep getting more and more and more data every day. Where do we store it? And that's really the infrastructure phase that was Hadoop initially and all the latest framework and many of the NoSQL databases. So that was sort of phase one. The phase that almost immediately followed was, okay, we have all this data, we've been able to store it in great repositories. Now, what good does it do us? I mean, how can we analyze it?

• 8:07Speaker 02

  How can we make sense of it? And that was really a whole generation of analytical tools that worked on top of those big data repositories. And then the latest phase, which is really happening now, is the emergence of all those applications. And there's applications all over the place. There's some applications in the enterprise and those are big data in sales, big data in marketing, big data in HR and so on and so forth. And then you have vertical applications. So you have big data in finance, big data in health, big data in agriculture. So it's less about being excited about any specific vertical.

• 8:48Speaker 02

  I mean, you know, you could talk about specific products, but what I find truly exciting as a trend is really that application phase is now becoming a reality. Because for people who have been following the industry for a while, we've all sort of, you know, been waiting for that because, again, that's when the true value ends up being very apparent specifically to the end user, the business user.

• 9:12Harry Stebbings?

  And I I'm intrigued that a lot of people are saying, you know, we we don't have a hundred billion dollar marketing tech company or martech company as some like to reference it. So so do you think big data will allow for this martech unicorn to be created?

• 9:27Speaker 02

  Yeah, I think I think there is a whole generation of big data driven companies doing very interesting things in in in marketing tech. So, you know, martech is is a beautiful but tricky market because on the one hand, there is a tremendous amount of money available for the right technologies. And that's the whole train around the the CMO becoming a larger producer of technology than the than the CIO. So that's a great part. The the more tricky part is that as a result of the above, there is a tremendous amount of companies in that space. So it's very competitive. It's it's hard to break through the so that's that's in terms of general landscape. Now specifically, I think I think marketing is a

• 10:12Speaker 02

  perfect area for big data technology to be applied. The old dream of being able to consolidate all the information that a company gets from multiple channels, whether that's, you know, online, mobile, call center and all sorts of different sources. And and crunching all of this cheaply enough, fast enough, enabling a real, you know, actual campaigns being built on top of the data crunching. All of this is is really becoming a reality. We have a a company in the portfolio called Action IQ that does exactly exactly that. And interestingly,

• 10:54Speaker 02

  what those guys are doing would simply not have been possible to do up until, you know, two or three years ago, when the emergence of frameworks like Spark made possible, when some of them became possible to to be processing, you know, those massive amounts of data. And some think that's pretty close to to real time.

• 11:14Harry Stebbings?

  And and you you mentioned the deployment phase and and us being ready to kind of fully scale into the deployment phase. I'm intrigued then, what are the key drivers that have allowed us to get to this state? What what's allowed us to progress to the deployment phase?

• 11:28Speaker 02

  From a single company's perspective?

• 11:31Harry Stebbings?

  From a single company's perspective and from a dataset building perspective.

• 11:35Speaker 02

  Yes, so it's a, it's a, it's a complex question as obviously it's somewhat case specific. What I'm seeing is that large companies, so there's really two types of companies, right? In the, that, that, that have been adopting those big data technologies. So one is the first group that was really a whole series of sort of internet native companies. And when you look at the history of the data, a lot of the tools were created by, you know, the Googles and the Yahoos and LinkedIn and Facebook. So those internet native companies that had those massive amounts of data that they needed to deal with and basically build their own technology, build technology for their own

• 12:19Speaker 02

  needs and became not only developers but adopters at scale and introduction of those technologies. So that's, that's one of them. What with those guys had, going for them is that as reasonably, sometimes very new companies, they didn't have any legacy infrastructure to, that they needed to deal with. Now, this is a separate group of companies, which is really the, pretty much of the rest of the world. And those are, those are companies that have a legacy infrastructure and, you know, in general have, have an infrastructure that, that works. So for those companies, you know, the, it's, it's been much more of a tricky exercise to adopt big data technologies because of course, you know, presumably the infrastructure

• 13:02Speaker 02

  as of now more or less works and it's not like they are able to rip and replace overnight their legacy infrastructure that may not have all the bells and whistles, but, but works and replace it by all the, you know, most modern data technologies. So for those companies, getting to the deployment phase has been a, a longer exercise. And because it's such a large group of companies, there's been different types of adopters and of course they were the, the very early adopters and those are people that started those, you know, sort of do. Test pilots maybe five, six years ago, uh, you know, when, when there was this whole hype around a Duke, those companies um have been experimenting with the technologies, have been uh working with

• 13:47Speaker 02

  different vendors, uh hired uh all sorts of people internally, sometimes people whose titles did not exist, like, you know, those data scientists, those chief data officers. And those companies are starting to see the rewards, I think, of those early efforts. So they're going from those early pilots to departmental deployments. And now you're starting to see some companies that uh run on those big data infrastructure at, at scale in production enterprise-wide. And then you have a second group of, of companies, which is, uh, you know, in the traditional adoption cycle, probably more the, the early majority, and a lot of those people are still very much um on the fence and uh are looking at the big data landscape and all those uh

• 14:30Speaker 02

  different vendors and uh are, are, are worried that uh the, the whole thing is just too complicated and uh having to work with all these different companies and stitch all the solutions together and make them work is, is too daunting. So for those companies, the, the, the good news is that the ecosystem is very much maturing and a lot of the early companies and early startups are, are starting to become formidable, uh, mu- uh, much more mature uh companies. And, and those people to get to the deployment phase are going to, uh, those, those large companies to get to the deployment phase are going to uh need uh start investing in and, and jump in on, onboarding and trying those products.

• 15:07Harry Stebbings?

  So it's the solutions for those companies to cross the chasm and actually adopt the big data technologies that they haven't so far yet adopted. Is the solution a one-stop shop solution by IBM? Is that a potential?

• 15:20Speaker 02

  Well, I think that's what uh all those companies have been hoping for. Um, and uh I think it will eventually happen, but it's unclear how quickly that will happen. It's actually a very daunting uh proposition when you, when you, when you look at the reality of the big data, because the big data is really uh not one technology, but it's really an assembly line of different technologies. Processes and culture. Just on the technology part, as I mentioned earlier, you have the repositories, you have the analytics, you have the application and the the the concept of of building a one stop shop is is tricky.

• 15:59Speaker 02

  So eventually the industry will presumably consolidate, every industry at some point consolidates. But my sense is that it's not happening anytime soon, or certainly not at scale. I think a lot of the startups in the space actually doing quite well and experiencing a rapid growth of of revenues and and those companies are gonna want to go public or continue as independent companies. It would be extraordinarily expensive for a large vendor to acquire enough companies to offer a one stop shop. So my my sense and my recommendation to large companies that are still a little bit

• 16:39Speaker 02

  on on the sidelines of big data is that now is a a good time to start playing with those technologies a lot more, the ecosystem is mature, there's not gonna be a one a one stop shop anytime soon. You know, it feels like a good and safe time for larger companies, part of the early majority of adopters to be to be deploying big data tools.

• 17:03Harry Stebbings?

  And and how do you view the integration of big data and AI? Is AI helping big data deliver the promise of a couple of years ago we we thought would potentially be sooner?

• 17:13Speaker 02

  Yes, so AI, from my perspective, is is very much a child of big data. So the the fascinating aspect of of of AI is that, you know, a lot of the current resurrection of AI is around deep learning specifically. And and and deep learning, for the most part, was really developed by some, you know, particularly forward thinking researchers, like, you know, Yehuda Khan, Jeff Hinton, that that that group. And it's really only up until recently when those algorithms.

• 17:54Speaker 02

  intersected with big data, so specifically the ability to process very large amounts of data quickly. And also with all the latest developments around processing powers and especially all the great work that Nvidia did around GPUs. That when all of this intersected, then deployment was resurrected and its worth became truly apparent to everyone. So AI is a child of big data. At the same time, AI is really enabling big data to truly deliver its promise. The great thing about big data is that you have plenty of data, but of course it doesn't do you any good.

• 18:34Speaker 02

  Unless you can really, you know, do actual analysis and actual predictions with it. And that's exactly what AI and various forms, machine learning in general, enables. So there's a whole series of big data applications around predicting churn, predicting security intrusions, predicting which one of your employees will possibly quit, all sorts of different things that AI enables. As long as you have the data and as long as you can process it fast enough.

• 19:09Harry Stebbings?

  You absolutely nailed it there, obviously, with as long as you have the data. And your final question before we dive into the quickfire, you're an investor, obviously, in Amy, the brilliant assistant. Absolutely love that. But is there not an incumbency advantage then in this early AI startup space? For startups without the available data sets that your Facebooks, Googles have?

• 19:30Speaker 02

  Yeah, so that's a great question and that's certainly something that I think about a lot. I think from a founder, startup perspective and from an investment perspective, I think one needs to think about applications where you have a chance to build something reasonably.

• 19:51Speaker 02

  Reasonably quickly and something that's not going to be absolutely in collision course with something that's core to the goals of the world. I think there's going to be a whole range of interesting and compelling AI vertical applications and X dot AI that you mentioned is certainly one of them and certainly at the forefront of that trend with a rare opportunity to become a category defining company. But I think one needs to be very careful in how they think about it. So, you know, if you do something like image processing, which is something that deep

• 20:31Speaker 02

  learning is particularly great at, you know, you need to think of a business model and a way of penetrating the market that somehow doesn't put you in a direct collision course with Google. Because, you know, Google already does and will want to do this at scale because it's absolutely part of their core mission.

• 20:54Harry Stebbings?

  And I'd love to dive into a quickfire round with you now.

• 20:56Speaker 02

  Yeah.

• 20:57Harry Stebbings?

  Let's start with your favorite kind of academic text. Is there kind of a big data AI book that you most recommend for us to take away today? Mine would be Nick Bostrom's. I don't know if you've read Nick Bostrom's.

• 21:08Speaker 02

  Yeah, so there's a whole series of those. I find the difficulty of a lot of those books is that there's two types of books. There's one book which is deeply technical and for the business user or somebody who's curious about AI, then you end up feeling overwhelmed fairly quickly. And then there's a different type of book, which is talks about very fundamentally important issue around, you know, is AI doing us, are the machine arriving and taking control of our lives? And I think that's, you know, I'm not saying this facetiously, but it's a fundamentally important question. So I actually find that I've learned a lot more around AI by just reading a lot of stuff

• 21:53Speaker 02

  online in shorter formats. You know, there's all sorts of people to follow on Twitter and blogs to read and all those things. And I would recommend that as an approach as opposed to just people.

• 22:04Harry Stebbings?

  What blogs would you say we should be reading? What are your must reads when they come in?

• 22:08Speaker 02

  You know, in general, that's an interesting question, you know, so starting with the classics, you know, Fred Wilson, Chris Dixon, Radfell and all the things, and I think that's really, you know, part of what all of us should be reading on a daily basis. There are, you know, a handful of blogs that I find particularly interesting and thoughtful. One that comes to mind is Strategery. I also like broader blogs, not necessarily about tech or that touch upon tech, but not all the time. I think Waitbird Why is particularly interesting. And actually, to your question about AI, there's two or three blog posts that he did there on AI

• 22:50Speaker 02

  that were a particularly good introduction to AI for anybody that's curious about the topic, but doesn't want to spend, you know, hours and hours reading books.

• 23:00Harry Stebbings?

  What's the most challenging aspect of your role as Managing Director at First Mark?

• 23:04Speaker 02

  That's a great question, I think. There's one very tactical thing about managing bandwidth. There is this image of VCs that maybe had some basis a few years ago as, you know, people who sort of decide when they want to have a meeting and.

• 23:25Harry Stebbings?

  Play golf.

• 23:26Speaker 02

  Play some golf and all the things. I think that the reality of it is, at least from my perspective, is much more in the trenches and it's a combination of, you know, generating and then analyzing and, you know, processing a massive amount of deal flow. If you're so lucky that, you know, people want to reach out, that's one aspect. The second aspect is that we make a very strong commitment to our founders. So First Mark is one of those firms that doesn't make tons of new investments a year, you know, very much of a sort of classic series eight type model, but spends a lot of time with, you know, with our companies and our founders.

• 24:09Speaker 02

  So as you build a portfolio, that tends to add up quite a bit. And the last part is that being a partner at a firm especially, you know, not a firm that has a very large number of partners and billions of dollars in the management is that you essentially run something that in many ways feels like a startup itself, including the fact that we need to own farm raising every two or three years and then hire great people to be part of the team. And in some ways, there are challenges or milestones that startups can experience themselves. So that's a very tactical aspect.

• 24:46Speaker 02

  I think the higher level aspect is that I find there is this interesting tension between becoming a deep student of a specific industry like big data, internet of things, when there's these different areas, and the industry in general, so really understanding all the nuances of how companies become successful. So all of that on the one hand. And the other hand, remaining deeply optimistic and almost cultivating a little bit of a form of naivete where you want to think about not just all the potential obstacles when you meet a founder

• 25:26Speaker 02

  in a company, but also think about, you know, what this could look like if everything goes right. And the more you do this, the more you work with companies, the more reasons you have to be, you know, seeing all the bad stuff that can happen to those companies. But you want to constantly just keep this fundamental and strong into the atom at the core of what you do every day.

• 25:52Harry Stebbings?

  So two more questions. Your deep dives are some of my favorites. So how do you approach the deep dive? Is there kind of a content creation plan, a research schedule? How do you approach a big topic like big data or IOT?

• 26:05Speaker 02

  Yeah, so I think I do it as a thought exercise. Part of the idea is really to open source the effort. And it's been tremendous how those deep dives just generate all sorts of new conversations with people I hadn't had a chance to meet. That has a forcing function as well of just making me just constantly read and constantly meet new companies and whether, you know, small or large companies, really stay on top of any industry. So it's, you know, I find it interesting to do. I find that it pays all sorts of rewards. It works very well with all the things I do on the community building front and event front.

• 26:50Speaker 02

  So as perhaps you know, I run a couple of large events, one called Data Driven NYC, which has almost 11,000 members and is the largest community of its kind in the country. And another one called Hardware NYC, which covers hardware and software startups and same thing, that has, I think, something close to 5,000 members. So all of this, you know, works very well together and it forces me to basically stay on top of an industry that I'm passionate about. But at the same time, given what I was mentioning earlier, that, you know, constant bandwidth challenge, it's, it's, yeah, I would sort of kick my one butt, if you want.

• 27:32Harry Stebbings?

  And then I want to talk about your most recent investment and why you said yes.

• 27:36Speaker 02

  Yeah, it's a semi-public one. And interestingly enough, I haven't made investments in quite a while now. And that's, you know, part of the reflection of broader concerns around the environment and finding the right opportunity, doing the right thing at the right price and all the things. The most recent investment I made, which is in semi-stealth, but, you know, coming out of stealth very, very in a matter of days now, is a company called HyperScience, which is based here in New York and that does precisely AI for the enterprise. So it's a variation on deep learning.

• 28:17Speaker 02

  It's given me an opportunity to observe firsthand truly the power of those technologies. It's really quite amazing. So those guys. Offer potentially predictive analytics across a range of different use cases and companies and the quality of the results again is truly impressive and I think as an industry just gives a sense of what is truly possible there.

• 28:46Harry Stebbings?

  Well Matt, thank you so much for joining me on the show today. It's been absolutely brilliant to hear your incredible wealth of knowledge on the data industry and I'm so grateful for you for sharing with us.

• 28:55Speaker 02

  Great, thanks so much Harry for having me and for the kind words and congratulations on everything you do with this podcast. It's been a lot of fun to listen to.

• 29:05Harry Stebbings?

  What a fantastic deep dive that was on the rise of big data and if you have not already, you must check out Matt's blog at mattturk.com for incredible deep dives and if you'd like to stay in the loop from all things the 20 minute VC, then you can follow me on Snapchat at H Stebbings with two Bs and you can sign up for the newsletter on the 20minutevc.com so you never have to miss an episode or an update from us. But before we leave you today, today's episode was brought to you by Leesa, in my opinion, the world's best mattress company and what I find so fascinating about Leesa is actually how they deliver their mattress. Not only do they deliver to anywhere in the UK, US and Canada for free, but the mattresses are compressed, boxed and then shipped.

• 29:46Harry Stebbings?

  And although crazy, it's actually amazing and it really extends the life of the mattress apparently. And another thing I love about Leesa is that they've not only measured their success by looking at their bottom line, but also the social impact that they have with their donation of one mattress to a shelter for every 10 they sell. Also, all their mattresses are made in the US and the UK, so join the movement and get yours by heading over to leesa.com/vc and enter the promo code VC75 to get a wicked $75 off at checkout. That's L E E S A dot com. As always, thank you so much for tuning in. I always love to hear your feedback, so hit me up on [email protected]

• 30:25Harry Stebbings?

  and I look very forward to bringing you our next episode on Wednesday with Nikhil at Shasta on the rise of big data as an enabler for the autonomous vehicle industry. So stay tuned.