foreign okay we're back at Falcon 2022 crowdstrike's big user conference first time in a couple of years obviously because of kova this is thecube's coverage Dave vellante and Dave Nicholson wall-to-wall coverage two days in a row Michael Rogers the series the newly minted vice president of global alliances at crowdstrike Michael first of all congratulations on the new appointment and welcome to the cube thank you very much it's an honor to be here so dial back just a bit like think about your first hundred days in this new role what was it like who'd you talk to what'd you learn wow well the first hundred days were filled with uh excitement uh I would say 18 plus hours a day getting to know the team across the globe a wonderful team across all of the partner types that we cover and um just digging in and spending time with people and understanding uh what the partner needs were and and and and it was just a it was a blur but a blast I agree with any common patterns that you heard that you could sort of coalesce around yeah I mean I think that uh really what a common thing that we hear at crowdstrike whether it's internal is extra external is getting to the market as fast as possible there's so much opportunity and every time we open a door the resource investment we need we continue to invest in resources and that was an area that we identified and quickly pivoted and started making some of those new investments in a structure of the organization how we cover Partners uh how we optimize uh the different routes to Market with our partners and yeah just a just a it's been a wonderful experience and in my 25 years of cyber security uh actually 24 and a half as of Saturday uh I can tell you that I have never felt and had a better experience in terms of culture people and a greater mission for our customers and our partners you'll Max funny a lot of times Dave we talk about this is we you know we learned a lot from Amazon AWS with the cloud you know taking something you did internally pointing it externally to Pizza teams there's shared responsibility model we talk about that and and one of the things is blockers you know Amazon uses that term blocker so were there any blockers that you identified that you're you're sort of working with the partner ecosystem to knock down to accelerate that go to market well I mean if I think about what we had put in place prior and I had the benefit of being vice president of America's prior to the appointment um and had the pleasure of succeeding my dear friend and Mentor Matthew Pauley um a lot of that groundwork was put in place and we work collectively as a leadership team to knock down a lot of those blockers and I think it really as I came into the opportunity and we made new Investments going into the fiscal year it's really getting to Market as fast as possible it's a massive Target addressable market and identifying the right routes and how to how to harness that power of we to drive the most value to the marketplace yeah what is it what does that look like in terms of alliances alliances can take a lot of shape we've we've talked to uh service providers today as an example um our Global Systems integrators in that group also what what is what does the range look like yeah I mean alliances at crowdstrike and it's a great question because a lot of times people think alliances and they only think of Technology alliances and for us it spans really any and all routes to Market it could be your traditional solution providers which might be regionally focused it could be nationally focused larger solution providers or Lars as you noted service providers and telcos global system integrators mssps iot Partners OEM Partners um and store crouchstrike store Partners so you look across that broad spectrum and we cover it all so the mssps we heard a lot about that on the recent earnings call we've heard this is a consistent theme we've interviewed a couple here today what's driving that I mean is it the fact that csos are just you know drowning for talent um and why crowdstrike why is there such an affinity between mssps and crowdstrike yeah a great question we um and you noted that uh succinctly that csos today are faced with the number one challenge is lack of resources and cyber security the last that I heard was you know in the hundreds of thousands like 350 000 and that's an old stat so I would venture to Guess that the open positions in cyber security are north of a half a million uh as we sit here today and um service providers and mssps are focused on providing service to those customers that are understaffed and have that Personnel need and they are harnessing the crowdstrike platform to bring a cloud native best of breed solution to their customers to augment and enhance the services that they bring to those customers so partner survey what tell us about the I love surveys I love data you know this what was the Genesis of the survey who took it give us the breakdown yeah that's a great question no uh nothing is more important than the feedback that we get from our partners so every single year we do a partner survey it reaches all partner types in the uh in the ecosystem and we use the net promoter score model and so we look at ourselves in terms of how we how we uh rate against other SAS solution providers and then we look at how we did last year and in the next year and so I'm happy to say that we increased our net promoter score by 16 percent year over year but my philosophy is there's always room for improvement so the feedback from our partners on the positive side they love the Falcon platform they love the crowdstrike technology they love the people that they work with at crowdstrike and they like our enablement programs the areas that they like us to see more investment in is the partner program uh better and enhanced enablement making it easier to work with crowdstrike and more opportunities to offer services enhance services to their customers dramatic differences between the types of Partners and and if so you know why do you think those were I mean like you mentioned you know iot Partners that's kind of a new area you know so maybe maybe there was less awareness there were there any sort of differences that you noticed by type of partner I would say that you know the areas or the part the partners that identified areas for improvement were the partners that that uh either were new to crowdstrike or they're areas that we're just investing in uh as as we expand as a company and a demand from the market is you know pull this thing into these new routes to Market um not not one in particular I mean iot is something that we're looking to really blow up in the next uh 12 to 18 months um but no no Common Thread uh consistent feedback across the partner base speaking of iot he brought it up before it's is it in a you see it as an adjacency to i-team it seems like it and OT used to never talk to each other and now they're increasingly doing so but they're still it still seems like different worlds what have you found and learned in that iot partner space yeah I mean I think the key and we the way we look at the journey is it starts with um Discovery discovering the assets that are in the OT environment um it then uh transitions to uh detection and response and really prevention and once you can solve that and you build that trust through certifications in the industry um you know it really is a game changer anytime you have Global in your job title first word that comes to mind for me anyway is sovereignty issues is that something that you deal with in this space uh in terms of partners that you're working with uh focusing on Partners in certain regions so that they can comply with any governance or sovereignty yeah that's that's a great question Dave I mean we have a fantastic and deep bench on our compliance team and there are certain uh you know parameters and processes that have been put in place to make sure that we have a solid understanding in all markets in terms of sovereignty and and uh where we're able to play and how that were you North America before or Americas uh Americas America so you're familiar with the sovereignty issue yeah a little already Latin America is certainly uh exposed me plenty of plenty of that yes 100 so you mentioned uh uh Tam before I think it was total available Market you had a different word for the t uh total addressable Mark still addressable Market okay fine so I'm hearing Global that's a tam expansion opportunity iot is definitely you know the OT piece and then just working better um you know better Groove swing with the partners for higher velocity when you think about the total available total addressable market and and accelerating penetration and growing your Tam I've seen the the charts in your investor presentation and you know starts out small and then grows to you know I think it could be 100 billion I do a lot of Tam analysis but just my back a napkin had you guys approaching 100 billion anyway how do you think about the Tam and what role do Partners play in terms of uh increasing your team yeah that's a great question I mean if you think about it today uh George announced on the day after our 11th anniversary as a company uh 20 000 customers and and if you look at that addressable Market just in the SMB space it's north of 50 million companies that are running on Legacy on-prem Solutions and it really provides us an opportunity to provide those customers with uh Next Generation uh threat protection and and detection and and response partners are the route to get there there is no doubt that we cannot cover 50 50 million companies requires a span of of uh of of of a number of service providers and mssps to get to that market and that's where we're making our bets what what's an SMB that is a candidate for crowdstrike like employee size or how do you look at that like what's the sort of minimum range yeah the way we segment out the SMB space it's 250 seats or endpoints and below 250 endpoints yes right and so it's going to be fairly significant so math changes with xdr with the X and xdr being extended the greater number of endpoints means that a customer today when you talk about total addressable Market that market can expand even without expanding the number of net new customers is that a fair yeah Fair assessment yep yeah you got that way in that way but but map that to like company size can you roughly what's the what's the smallest s that would do business with crowdstrike yeah I mean we have uh companies as small as five employees that will leverage crowd strike yeah 100 and they've got hundreds of endpoints oh no I'm sorry five uh five endpoints is oh okay so it's kind of 250 endpoints as well like the app that's the sweets that's it's that's kind of the Top Line we look at and then we focus oh okay when we Define SMB it's below so five to 250 endpoints right yes and so roughly so you're talking to companies with less than 100 employees right yeah yeah so I mean this is what I was talking about before I say I look around the the ecosystem myself it kind of reminds me of service now in 2013 but servicenow never had a SMB play right and and you know very kind of proprietary closed platform not that you don't have a lot of propriety in your platform you do but you they were never going to get down Market there and their Tam is not as big in my view but I mean your team is when you start bringing an iot it's it's mind-boggling it's endless how large it could be yeah all right so what's your vision for the Elevate program partner program well I I look at uh a couple things that we've we've have in place today one is um one is we've we've established for the first time ever at crowdstrike the Alliance program management office apmo and that team is focused on building out our next Generation partner program and that's you know processes it's you know uh it's it's ring fencing but it's most important importantly identifying capabilities for partners to expand to reduce friction and uh grow their business together with crowdstrike we also look at uh what we call program Harmony and that's taking all of the partner types or the majority of the partner types and starting to look at it with the customer in the middle and so multiple partners can play a role on the journey to bringing a customer on board initially to supporting that customer going forward and they can all participate and be rewarded for their contribution to that opportunity so it's really a key area for us going forward Hub and spoke model with the center of the that model is the customer you're saying that's good okay so you're not like necessarily fighting each other for for a sort of ownership of that model but uh cool Michael Rogers thanks so much for coming on thecube it was great to have you my pleasure thank you for having me you're welcome all right keep it right there Dave Nicholson and Dave vellante we'll be right back to Falcon 22 from the Aria in Las Vegas you're watching thecube foreign [Music]

>>Mm. >>Welcome back to the Cube. Special presentation. Simplifying Hybrid Cloud Brought to You by Cisco We're here with Vegas Rattana, who's the director of product management for you? CSS Cisco and James Leach, who was director of business development at Cisco. Gents, welcome back to the Cube. Good to see you again. >>Hey, thanks for having us. >>Okay, Jim, let's start. We know that when it comes to navigating a transition to hybrid cloud, it's a complicated situation for a lot of customers and as organisations that they hit the pavement for their hybrid cloud journeys, one of the most common challenges that they face. What are they telling you? How is Cisco specifically UCS helping them deal with these problems? >>Well, you know, first, I think that's a That's a great question. And, you know, the customer centric view is is the way that we've taken. Um, it's kind of the approach we've taken from Day one, right? So I think that if you look at the challenges that we're solving for their customers are facing, you could break them into just a few kind of broader buckets. The first would definitely be applications, right? That's the That's where the rubber meets your proverbial road. Um, with the customer. And I would say that you know, what we're seeing is the challenges customers are facing within applications come from the way that applications have evolved. So what we're seeing now is more data centric applications. For example, um, those require that we are able to move, um, and process large datasets really in real time. Um, and the other aspect of application, I think, to give our customers kind of some pose some challenges would be around the fact that they're changing so quickly. So the application that exists today or the day that they make a purchase of infrastructure to be able to support that application. That application is most likely changing so much more rapidly than the infrastructure can't keep up with today. So, um, that creates some some challenges around. How do I build the infrastructure? How do I write? Size it without over provisioning, for example. But also there's a need for some flexibility around life cycle and planting those purchase cycles based on the life cycle of the different hardware elements and within the infrastructure, which I think is the second bucket of challenges. We see customers who are being forced to move away from the like a modular or blade approach, which offers a lot of operational and consolidation benefits. And they have to move to something like, um, Iraq server model for some applications because of these needs that these data centric applications have. And that creates a lot of opportunity for silo going. The infrastructure and those silos, in turn, create multiple operating models within the A data centre environment that, you know, again drive a lot of complexity. So that complexity is definitely the the enemy here. Um, and then finally, I think life cycles. We're seeing this democratisation of of processing, if you will, right, so it's no longer just CPU focus. We have GPU. We have F p g A. We have things that are being done in storage and the fabrics that stitch them together that are all changing rapidly and have very different life cycles. So when those life cycles don't align for a lot of our customers, they see a challenge in how they can can manage this these different life cycles and still make a purchase without having to make too big of a compromise in one area or another because of the misalignment of life cycles. So that is a kind of the other bucket. And then finally, I think management is huge, right? So management at its core is really right size for for our customers and give them the most value when it when it meets the mark around scale and scope. Um, back in 2000 and nine, we weren't meeting that mark in the industry and UCS came about and took management outside the chassis, right? We put at the top of the rack, and that works great for the scale and scope we needed at that time. However, as things have changed, we're seeing a very new scale and scope needed, Right? So we're talking about hybrid cloud world that has to manage across data centres across clouds. And, um, you know, having to stitch things together for some of our customers poses a huge challenge. So there are tools for all of those those operational pieces that that touched the application that touched the infrastructure. But they're not the same tool. They tend to be, um, disparate tools that have to be put together. So our customers, you know, don't really enjoy being in the business of building their own tools. So, um, so that creates a huge challenge. And one where I think that they really crave that full hybrid cloud stack that has that application visibility but also can reach down into the infrastructure. >>Right? You know, Jim, I said in my my Open that you guys, Cisco sort of changed the server game with the original UCS. But the X Series is the next generation, the generation of the next decade, which is really important cause you touched on a lot of things. These data intensive workloads, alternative processors to sort of meet those needs. The whole cloud operating model and hybrid cloud has really changed. So how's it going with the X Series? You made a big splash last year. What's the reception been in the field? >>Actually, it's been great. Um, you know, we're finding that customers can absolutely relate to our UCS X series story. Um, I think that the main reason they relate to it as they helped create it, right, it was their feedback and their partnership that they gave us Really, those problem areas, those, uh, those areas that we could solve for the customer that actually add significant value. So, you know, since we brought you see s to market back in 2000 and nine, we had this unique architectural, um uh, paradigm that we created. And I think that created a product which was the fastest in Cisco history. Um, in terms of growth, Um, what we're seeing now is X series is actually on a faster trajectory. So we're seeing a tremendous amount of uptake. We're seeing, uh, both in terms of the number of customers. But also, more importantly, the number of workloads that our customers are using and the types of workloads are growing. Right? So we're growing this modular segment that exists not just, um, you know, bringing customers onto a new product, But we're actually bringing them into the product in the way that we had envisioned, which is one infrastructure that can run any application and do it seamlessly. So we're really excited to be growing this modular segment. Um, I think the other piece, you know that, you know, we judge ourselves is, you know, sort of not just within Cisco, but also within the industry and I think right now is a You know, a great example. Our competitors have taken kind of swings and misses over the past five years at this, um, at a kind of a new next architecture, and we're seeing a tremendous amount of growth even faster than any any of our competitors have seen. When they announced something, um, that was new to this space. So I think that the ground up work that we did is really paying off. Um, and I think that what we're also seeing is it's not really a leapfrog game, Um, as it may have been in the past, Um, X series is out in front today, and we're extending that lead with some of the new features and capabilities we have. So we're delivering on the story that's already been resonating with customers, and we're pretty excited that we're seeing the results as well. So as our competitors hit walls, I think we're you know, we're executing on the plan that we laid out back in June when we launched that series to the world. And, uh, you know, as we as we continue to do that, um, we're seeing, you know, again tremendous uptake from our customers. >>So thank you for that, Jim. So viscous. I was just on Twitter just today, actually talking about the gravitational pull. You've got the public clouds pulling C x o is one way. And you know I'm Prem folks pulling the other way and hybrid cloud So organisations are struggling with a lot of different systems and architectures and and ways to do things. And I said that what they're trying to do is abstract all that complexity away, and they need infrastructure to support that. And I think your stated aim is really to try to help with that with that confusion with the X series. Right? So how so? Can you explain that? >>Sure. And and and that's the right, Uh, the context that you built up right there, Dave, if you walk into Enterprise Data Centre, you see platform of computer systems spread all across because every application has its unique needs. And hence you find Dr Note Driving system memory system, computing system, coordinate system and a variety of farm factors. When you do, you, for you and every one of them typically come with a variety of adapters and cables and so forth Just create silence of resources. Fabric is broad. The actress brought the power and cooling implications the rack, you know, the space challenges and above all, the multiple management plane that they come of it, which makes it very difficult for I t to have one common centre policy and enforce it all across across the firmware and software and so forth and then think about the great challenges of the baroness makes it even more complex as these go through the great references of their own. As a result, we observe quite a few of our customers. Uh, you know, really, uh, seeing Anna slowness in that agility and high burden, uh, in the cost of overall ownership, this is where the X rays powered by inter side. We have one simple goal. We want to make sure our customers get out of that complexities. They become more Asyl and drive lower tco and we are delivering it by doing three things. Three aspects of simplification first simplify their whole infrastructure by enabling them to run their entire workload on single infrastructure and infrastructure, which removes the narrowness of fun factor and infrastructure which reduces direct from footprint that is required infrastructure were power and cooling better served in the Lord. Second, we want to simplify it with by delivering a cloud operating model where they can create the policy ones across compute network stories and deployed all across. And third, we want to take away the pain they have by simplifying the process of upgrade and any platform evolution that they are going to go through the next 23 years. So that's where the focus is on just driving down the simplicity lowering down there. >>That's key. Less friction is is always a good thing now, of course, because we heard from the hyper flex guys earlier, they had news. Not to be outdone, you have hard news as well. What innovations are you announcing around X series today? >>Absolutely. So we are following up on the excited, exciting extras announcement that we made in June last year. Day and we are now introducing three innovation on experience with the bowl of three things First, expand the supported World War and extra days. Second, take the performance to new levels. Third dramatically reduced the complex cities in the data centre by driving down the number of adapters and cables. To that end, three new innovations are coming in. First, we are introducing the support for the GPU note using a cable list and very unique X fabric architecture. This is the most elegant design to add the GPS to the compute note in the model of form factor thereby, our customers can now power in AML workload on any workload that needs many more number of GPS. Second, we are bringing in GPS right onto the computer note and thereby the our customers can now fire up the accelerated video upload, for example, and turf, which is what you know we are extremely proud about, is we are innovating again by introducing the fifth generation of our very popular unified fabric technology with the increased bandwidth that it brings in, coupled with the local drive capacity and density is that we have on the computer note our customers can now fire up the big data workloads the F C I work. Lord, uh, the FDA has worked with all these workloads that have historically not lived in the model of form. Factor can be run over there and benefit from the architectural benefits that we have. Second, with the announcement of fifth generation fabric, we become the only vendor to now finally enable 100 gig and two and single board banned word and the multiple of those that are coming in there. And we are working very closely with our partners to deliver the benefit of these performance through our Cisco validated design to oversee a franchise. And third, the innovations in, uh, in the in the fifth and public again allow our customers to have fewer physical adapters, made the Internet adapter made with our general doctors or maybe the other stories adapters. They reduced it down and coupled with the reduction in the cable so very, very excited about these three big announcements that we're making in this part of the great >>A lot There. You guys have been busy. So thank you for that. Because so, Jim, you talked a little bit about the momentum that you have. Customers are adopting. What problems are they telling you that X series addresses and and how do they align with where where they want to go in the future? >>Um, that's a great question. I think if you go back to um and think about some of the things that we mentioned before. Um, in terms of the problems that we originally set out to solve, we're seeing a lot of traction. So what the cost mentioned, I think, is really important, right? Those pieces that we just announced really enhanced that story and really move again to kind of to the next level of, of taking advantage of some of these problem solving for our customers. You know, if you look, you know, I think the cost mentioned accelerated VD. That's a great example. Um, these are where customers you know, they need to have this dense compute. They need video acceleration, they need type policy management, right. And they need to be able to deploy these, um, these systems anywhere in the world. Well, that's exactly what we're hitting on here with X series right now, we're hitting the mark in every every single way, right? We have the highest compute config density that we can offer across the, you know, the very top end configurations of CPUs. Um, and a lot of room to grow. Um, we have the the premier cloud based management. You know, hybrid cloud suite. Um uh, in the industry. Right. So check there. We have the flexible GPU accelerators that that the cost just talked about that we're announcing both on the system and also adding additional ones to the through the use of the X fabric, which is really, really critical to this launch as well. And, uh, you know, I think finally, the fifth generation of fabric interconnect and virtual interface card, um, and an intelligent fabric module go hand in hand in creating this 100 gig and end bandwidth story that we can move a lot of data again. You know, having all this performance is only as good as what we can get in and out of it, right? So giving customers the ability to manage it anywhere be able to get the bandwidth that they need to be able to get the accelerators that are flexible to that fit exactly their needs. This is huge, right? This solves a lot of the problems we can take off right away with the infrastructure. As I mentioned, X fabric is really critical here because it opens a lot of doors here. We're talking about GPS today, but in the future, there are other elements that we can disaggregate like the GPS that solve these lifecycle mismanagement issues. They solve issues around the form factor limitations. It solves all these issues for like it does for GPU. We can do that with storage or memory in the future, So that's going to be huge, right? This is disaggregate Asian that actually delivers right. It's not just a gimmicky bar trick here that we're doing. This is something that that customers can really get value out of Day one. And then finally, I think the future readiness here. You know, we avoid saying future proof because we're kind of embracing the future here. We know that not only are the GPS going to evolve, the CPUs are going to evolve the drives, the storage modules are going to evolve. All of these things are changing very rapidly. The fabric that stitches them together. It's critical, and we know that we're just on the edge of some of the developments that are coming with C XL with with some of the the PC express changes that are coming in the in the very near future. So we're ready to go X and the X fabric is exactly the vehicle that's going to be able to deliver those technologies to our customers. Our customers are out there saying that you know, they want to buy into something like X Series that has all the operational benefits, but at the same time, they have to have the comfort in knowing that they're protected against being locked out of some technology that's coming in the future. We want our customers to take these disruptive technologies and not be disrupted, but use them to disrupt, um, their competition as well. So, um, you know, we're really excited about the pieces today, and I think it goes a long way towards continuing to tell the customer benefit story that X Series brings And, um, again, stay tuned because it's going to keep getting better as we go. >>A lot of headroom, uh, for scale and the management piece is key. There just have time for one more question because talk to give us some nuggets on the road map. What's next for? For X X series that we can look forward to? >>Absolutely Dave, as as we talked about. And James also hinted this is the future radio architecture, a lot of focus and innovation that we are going through is about enabling our customers to seamlessly and painlessly adopt very disruptive hardware technologies that are coming up no infantry place. And there we are, looking into enabling the customer journey as the transition from PCH in less than 4 to 5 to six without rip and replace as they embraced the Excel without rip and replace as they embrace the newer paradigm of computing through the desegregated memory desegregated P. C, A, r N B and dance drives and so forth. We're also looking forward to extract Brick Next Generation, which will and now that dynamic assignment of GPS anywhere within the chassis and much more. Um, so this this is again all about focusing on the innovation that will make the Enterprise Data Centre operations a lot more simpler and drive down the PCO by keeping them not only covered for today, but also for future. So that's where some of the focus is on there. >>Okay, Thank you guys. We'll leave it there in a moment. I'll have some closing thoughts. >>Mhm

>> Announcer: From around the globe, it's "theCUBE" covering Space and Cybersecurity Symposium 2020 hosted by Cal Poly. >> I want to welcome to theCUBE's coverage, we're here hosting with Cal Poly an amazing event, space and the intersection of cyber security. This session is Defending Satellite and Space Infrastructure from Cyber Threats. We've got two great guests. We've got Major General John Shaw of combined force space component commander, U.S. space command at Vandenberg Air Force Base in California and Roland Coelho, who's the CEO of Maverick Space Systems. Gentlemen, thank you for spending the time to come on to this session for the Cal Poly Space and Cybersecurity Symposium. Appreciate it. >> Absolutely. >> Guys defending satellites and space infrastructure is the new domain, obviously it's a war-fighting domain. It's also the future of the world. And this is an important topic because we rely on space now for our everyday life and it's becoming more and more critical. Everyone knows how their phones work and GPS, just small examples of all the impacts. I'd like to discuss with this hour, this topic with you guys. So if we can have you guys do an opening statement. General if you can start with your opening statement, we'll take it from there. >> Thanks John and greetings from Vandenberg Air Force Base. We are just down the road from Cal Poly here on the central coast of California, and very proud to be part of this effort and part of the partnership that we have with Cal Poly on a number of fronts. In my job here, I actually have two hats that I wear and it's I think, worth talking briefly about those to set the context for our discussion. You know, we had two major organizational events within our Department of Defense with regard to space last year in 2019. And probably the one that made the most headlines was the standup of the United States Space Force. That happened December 20th, last year, and again momentous, the first new branch in our military since 1947. And it's just over nine months old now, as we're makin' this recording. And already we're seein' a lot of change with regard to how we are approaching organizing, training, and equipping on a service side for space capabilities. And so, with regard to the Space Force, the hat I wear there is Commander of Space Operations Command. That was what was once 14th Air Force, when we were still part of the Air Force here at Vandenberg. And in that role, I'm responsible for the operational capabilities that we bring to the joint warfighter and to the world from a space perspective. Didn't make quite as many headlines, but another major change that happened last year was the reincarnation, I guess I would say, of United States Space Command. And that is a combatant command. It's how our Department of Defense organizes to actually conduct war-fighting operations. Most people are more familiar perhaps with Central Command, CENTCOM or Northern Command, NORTHCOM, or even Strategic Command, STRATCOM. Well, now we have a SPACECOM. We actually had one from 1985 until 2002, and then stood it down in the wake of the 9/11 attacks and a reorganization of Homeland Security. But we've now stood up a separate command again operationally, to conduct joint space operations. And in that organization, I wear a hat as a component commander, and that's the combined force-based component command working with other, all the additional capabilities that other services bring, as well as our allies. The combined in that title means that under certain circumstances, I would lead in an allied effort in space operations. And so it's actually a terrific job to have here on the central coast of California. Both working how we bring space capabilities to the fight on the Space Force side, and then how we actually operate those capabilities in support of joint warfighters around the world and national security interests. So that's the context. Now what also I should mention and you kind of alluded to John at your beginning, we're kind of in a changed situation than we were a number of years ago, in that we now see space as a war-fighting domain. For most of my career, goin' back a little ways, most of my focus in my jobs was making sure I could bring space capabilities to those that needed them. Bringing GPS to that special operations soldier on the ground somewhere in the world, bringing satellite communications for our nuclear command and control, bringing those capabilities for other uses. But I didn't have to worry in most of my career, about actually defending those space capabilities themselves. Well, now we do. We've actually gone to a point where we're are being threatened in space. We now are treating it more like any other domain, normalizing in that regard as a war-fighting domain. And so we're going through some relatively emergent efforts to protect and defend our capabilities in space, to design our capabilities to be defended, and perhaps most of all, to train our people for this new mission set. So it's a very exciting time, and I know we'll get into it, but you can't get very far into talking about all these space capabilities and how we want to protect and defend them and how we're going to continue their ability to deliver to warfighters around the globe, without talking about cyber, because they fit together very closely. So anyway, thanks for the chance to be here today. And I look forward to the discussion. >> General, thank you so much for that opening statement. And I would just say that not only is it historic with the Space Force, it's super exciting because it opens up so much more challenges and opportunities to do more and to do things differently. So I appreciate that statement. Roland in your opening statement. Your job is to put stuff in space, faster, cheaper, smaller, better, your opening statement, please. >> Yes, thank you, John. And yes, to General Shaw's point with the space domain and the need to protect it now is incredibly important. And I hope that we are more of a help than a thorn in your side in terms of building satellites smaller, faster, cheaper. Definitely looking forward to this discussion and figuring out ways where the entire space domain can work together, from industry to U.S. government, even to the academic environment as well. So first, I would like to say, and preface this by saying, I am not a cybersecurity expert. We build satellites and we launch them into orbit, but we are by no means cybersecurity experts. And that's why we like to partner with organizations like the California Cybersecurity Institute because they help us navigate these requirements. So I'm the CEO of Maverick Space Systems. We are a small aerospace business in San Luis Obispo, California. And we provide small satellite hardware and service solutions to a wide range of customers. All the way from the academic environment to the U.S. government and everything in between. We support customers through an entire program life cycle, from mission architecture and formulation, all the way to getting these customer satellites in orbit. And so what we try to do is provide hardware and services that basically make it easier for customers to get their satellites into orbit and to operate. So whether it be reducing mass or volume, creating greater launch opportunities, or providing the infrastructure and the technology to help those innovations mature in orbit, that's what we do. Our team has experience over the last 20 years, working with small satellites. And I'm definitely fortunate to be part of the team that invented the CubeSat standard by Cal Poly and Stanford back in 2000. And so, we are in VandenBerg's backyard. We came from Cal Poly San Luis Obispo and our hearts are fond of this area, and working with the local community. A lot of that success that we have had is directly attributable to the experiences that we learned as students, working on satellite programs from our professors and mentors. And that's all thanks to Cal Poly. So just wanted to tell a quick story. So back in 2000, just imagine a small group of undergraduate students, myself included, with the daunting task of launching multiple satellites from five different countries on a Russian launch vehicle. Many of us were only 18 or 19, not even at the legal age to drink yet, but as essentially teenagers we were managing million-dollar budgets. And we were coordinating groups from around the world. And we knew what we needed to accomplish, yet we didn't really know what we were doing when we first started. The university was extremely supportive and that's the Cal Poly learn-by-doing philosophy. I remember the first time we had a meeting with our university chief legal counsel, and we were discussing the need to register with the State Department for ITAR. Nobody really knew what ITAR was back then. And discussing this with the chief legal counsel, she was asking, "What is ITAR?" And we essentially had to explain, this is, launching satellites is part of the U.S. munitions list. And essentially we had a similar situation exporting munitions. We are in similar categories as weapons. And so, after that initial shock, everybody jumped in both feet forward, the university, our head legal counsel, professors, mentors, and the students knew we needed to tackle this problem because the need was there to launch these small satellites. And the reason this is important to capture the entire spectrum of users of the community, is that the technology and the innovation of the small satellite industry occurs at all levels, so we have academia, commercial, national governments. We even have high schools and middle schools getting involved and building satellite hardware. And the thing is the importance of cybersecurity is incredibly important because it touches all of these programs and it touches people at a very young age. And so, we hope to have a conversation today to figure out how do we create an environment where we allow these programs to thrive, but we also protect and keep their data safe as well. >> Thank you very much Roland. Appreciate that a story too as well. Thanks for your opening statement. Gentlemen, I mean I love this topic because defending the assets in space is obvious, if you look at it. But there's a bigger picture going on in our world right now. And general, you kind of pointed out the historic nature of Space Force and how it's changing already, operationally, training, skills, tools, all that stuff is evolving. You know in the tech world that I live in, change the world is a topic they use, gets thrown around a lot, you can change the world. A lot of young people, and we have other panels on this where we're talkin' about how to motivate young people, changing the world is what it's all about technology, for the better. Evolution is just an extension of another domain. In this case, space is just an extension of other domains, similar things are happening, but it's different. There's huge opportunity to change the world, so it's faster. There's an expanded commercial landscape out there. Certainly government space systems are moving and changing. How do we address the importance of cybersecurity in space? General, we'll start with you because this is real, it's exciting. If you're a young person, there's touch points of things to jump into, tech, building hardware, to changing laws, and everything in between is an opportunity, and it's exciting. And it is truly a chance to change the world. How does the commercial government space systems teams, address the importance of cybersecurity? >> So, John, I think it starts with the realization that as I like to say, that cyber and space are BFFs. There's nothing that we do on the cutting edge of space that isn't heavily reliant on the cutting edge of cyber. And frankly, there's probably nothing on the cutting edge of cyber that doesn't have a space application. And when you realize that and you see how closely those are intertwined as we need to move forward at speed, it becomes fundamental to answering your question. Let me give a couple examples. One of the biggest challenges I have on a daily basis is understanding what's going on in the space domain. Those on the surface of the planet talk about tyranny of distance across the oceans or across large land masses. And I talk about the tyranny of volume. And right now, we're looking out as far as the lunar sphere. There's activity that's extending out there. We expect NASA to be conducting perhaps human operations in the lunar environment in the next few years. So it extends out that far. When you do the math that's a huge volume. How do you do that? How do you understand what's happening in real time within that volume? It is a big data problem by the very definition of that kind of effort and that kind of challenge. And to do it successfully in the years ahead, it's going to require many, many sensors and the fusion of data of all kinds, to present a picture and then analytics and predictive analytics that are going to deliver an idea of what's going on in the space arena. And that's just if people are not up to mischief. Once you have threats introduced into that environment, it is even more challenging. So I'd say it's a big data problem that we'll enjoy tackling in the years ahead. Now, a second example is, if we had to take a vote of what were the most amazing robots that have ever been designed by humans, I think that spacecraft would have to be up there on the list. Whether it's the NASA spacecraft that explore other planets, or GPS satellites that amazingly provide a wonderful service to the entire globe and beyond. They are amazing technological machines. That's not going to stop. I mean, all the work that Roland talked about, even that we're doin' at the kind of the microsat level is putting cutting-edge technology into small a package as you can to get some sort of capability out of that. As we expand our activities further and further into space for national security purposes, or for exploration or commercial or civil, the cutting-edge technologies of artificial intelligence and machine-to-machine engagements and machine learning are going to be part of that design work moving forward. And then there's the threat piece. As we operate these capabilities, as these constellations grow, that's going to be done via networks. And as I've already pointed out space is a war-fighting domain. That means those networks will come under attack. We expect that they will and that may happen early on in a conflict. It may happen during peace time in the same way that we see cyber attacks all the time, everywhere in many sectors of activity. And so by painting that picture, we start to see how it's intertwined at the very, very most basic level, the cutting edge of cyber and cutting edge of space. With that then comes the need to, any cutting edge cybersecurity capability that we have is naturally going to be needed as we develop space capabilities. And we're going to have to bake that in from the very beginning. We haven't done that in the past as well as we should, but moving forward from this point on, it will be an essential ingredient that we work into all of our capability. >> Roland, we're talkin' about now, critical infrastructure. We're talkin' about new capabilities being addressed really fast. So, it's kind of chaotic now there's threats. So it's not as easy as just having capabilities, 'cause you've got to deal with the threats the general just pointed out. But now you've got critical infrastructure, which then will enable other things down the line. How do you protect it? How do we address this? How do you see this being addressed from a security standpoint? Because malware, these techniques can be mapped in, extended into space and takeovers, wartime, peace time, these things are all going to be under threat. That's pretty well understood, and I think people kind of get that. How do we address it? What's your take? >> Yeah, yeah, absolutely. And I couldn't agree more with General Shaw, with cybersecurity and space being so intertwined. And, I think with fast and rapid innovation comes the opportunity for threats, especially if you have bad actors that want to cause harm. And so, as a technology innovator and you're pushing the bounds, you kind of have a common goal of doing the best you can, and pushing the technology bounds, making it smaller, faster, cheaper. But a lot of times what entrepreneurs and small businesses and supply chains are doing, and don't realize it, is a lot of these components are dual use. I mean, you could have a very benign commercial application, but then a small modification to it, can turn it into a military application. And if you do have these bad actors, they can exploit that. And so, I think that the big thing is creating a organization that is non-biased, that just wants to kind of level the playing field for everybody to create a set standard for cybersecurity in space. I think one group that would be perfect for that is CCI. They understand both the cybersecurity side of things, and they also have at Cal Poly the small satellite group. And just having kind of a clearing house or an agency where can provide information that is free, you don't need a membership for. And to be able to kind of collect that, but also reach out to the entire value chain for a mission, and making them aware of what potential capabilities are and then how it might be potentially used as a weapon. And keeping them informed, because I think the vast majority of people in the space industry just want to do the right thing. And so, how do we get that information free flowing to the U.S. government so that they can take that information, create assessments, and be able to, not necessarily stop threats from occurring presently, but identify them long before that they would ever even happen. Yeah, that's- >> General, I want to follow up on that real quick before we move to the next top track. Critical infrastructure you mentioned, across the oceans long distance, volume. When you look at the physical world, you had power grids here in the United States, you had geography, you had perimeters, the notion of a perimeter and a moat, and then you had digital comes in. Then you have, we saw software open up, and essentially take down this idea of a perimeter, and from a defense standpoint, and everything changed. And we have to fortify those critical assets in the U.S. Space increases the same problem statement significantly, because you can't just have a perimeter, you can't have a moat, it's open, it's everywhere. Like what digital's done, and that's why we've seen a surge of cyber in the past two decades, attacks with software. So, this isn't going to go away. You need the critical infrastructure, you're putting it up there, you're formulating it, and you got to protect it. How do you view that? Because it's going to be an ongoing problem statement. What's the current thinking? >> Yeah, I think my sense is that a mindset that you can build a firewall, or a defense, or some other system that isn't dynamic in its own right, is probably not headed in the right direction. I think cybersecurity in the future, whether it's for space systems, or for other critical infrastructure is going to be a dynamic fight that happens at a machine-to-machine speed and dynamic. I don't think that it's too far off where we will have machines writing their own code in real time to fight off attacks that are coming at them. And by the way, the offense will probably be doing the same kind of thing. And so, I guess I would not want to think that the answer is something that you just build it and you leave it alone and it's good enough. It's probably going to be a constantly-evolving capability, constantly reacting to new threats and staying ahead of those threats. >> That's the kind of use case, you know as you were, kind of anecdotal example is the exciting new software opportunities for computer science majors. I mean, I tell my young kids and everyone, man it's more exciting now. I wish I was 18 again, it's so exciting with AI. Roland, I want to get your thoughts. We were joking on another panel with the DoD around space and the importance of it obviously, and we're going to have that here. And then we had a joke. It's like, oh software's defined everything. Software's everything, AI. And I said, "Well here in the United States, companies had data centers and then they went to the cloud." And then he said, "You can do break, fix, it's hard to do break, fix in space. You can't just send a tech up." I get that today, but soon maybe robotics. The general mentions robotics technologies, in referencing some of the accomplishments. Fixing things is almost impossible in space. But maybe form factors might get better. Certainly software will play a role. What's your thoughts on that landscape? >> Yeah, absolutely. You know, for software in orbit, there's a push for software-defined radios to basically go from hardware to software. And that's a critical link. If you can infiltrate that and a small satellite has propulsion on board, you could take control of that satellite and cause a lot of havoc. And so, creating standards and that kind of initial threshold of security, for let's say these radios, or communications and making that available to the entire supply chain, to the satellite builders, and operators is incredibly key. And that's again, one of the initiatives that CCI is tackling right now as well. >> General, I want to get your thoughts on best practices around cybersecurity, state-of-the-art today, and then some guiding principles, and kind of how the, if you shoot the trajectory forward, what might happen around supply chain? There's been many stories where, we outsource the chips and there's a little chip sittin' in a thing and it's built by someone else in China, and the software is written from someone in Europe, and the United States assembles it, it gets shipped and it's corrupt, and it has some cyber, I'm making it up, I'm oversimplifying the statement. But this is what when you have space systems that involve intellectual property from multiple partners, whether it's from software to creation and then deployment. You got supply chain tiers. What are some of best practices that you see involving, that don't stunt the innovation, but continues to innovate, but people can operate safely. What's your thoughts? >> Yeah, so on supply chain, I think the symposium here is going to get to hear from General JT Thompson from space and missile system center down in Los Angeles, and he's just down the road from us there on the coast. And his team is the one that we look to to really focus on, as he fires and develops to again bake in cybersecurity from the beginning and knowing where the components are coming from, and properly assessing those as you put together your space systems, is a key piece of what his team is focused on. So I expect, we'll hear him talk about that. When it talks to, I think, so you asked the question a little more deeply about how do the best practices in terms of how we now develop moving forward. Well, another way that we don't do it right, is if we take a long time to build something and then General JT Thompson's folks take a while to build something, and then they hand it over to me, and my team operate and then they go hands free. And then that's what I have for years to operate until the next thing comes along. That's a little old school. What we're going to have to do moving forward with our space capabilities, and with the cyber piece baked in is continually developing new capability sets as we go. We actually have partnership between General Thompson's team and mine here at Vandenberg on our ops floor, or our combined space operation center, that are actually working in real time together, better tools that we can use to understand what's going on in the space environment to better command and control our capabilities anywhere from military satellite communications, to space domain awareness, sensors, and such. And we're developing those capabilities in real time. And with the security pieces. So DevSecOps is we're practicing that in real time. I think that is probably the standard today that we're trying to live up to as we continue to evolve. But it has to be done again, in close partnership all the time. It's not a sequential, industrial-age process. While I'm on the subject of partnerships. So, General Thompson's team and mine have good partnerships. It's partnerships across the board are going to be another way that we are successful. And that it means with academia and some of the relationships that we have here with Cal Poly. It's with the commercial sector in ways that we haven't done before. The old style business was to work with just a few large companies that had a lot of space experience. Well, we need a lot of kinds of different experience and technologies now in order to really field good space capabilities. And I expect we'll see more and more non-traditional companies being part of, and organizations, being part of that partnership that will work goin' forward. I mentioned at the beginning that allies are important to us. So everything that Roland and I have been talking about I think you have to extrapolate out to allied partnerships. It doesn't help me as a combined force component commander, which is again, one of my jobs. It doesn't help me if the United States capabilities are cybersecure, but I'm tryin' to integrate them with capabilities from an ally that are not cybersecure. So that partnership has to be dynamic and continually evolving together. So again, close partnering, continually developing together from the acquisition to the operational sectors, with as many different sectors of our economy as possible, are the ingredients to success. >> General, I'd love to just follow up real quick. I was having just a quick reminder for a conversation I had with last year with General Keith Alexander, who does a lot of cybersecurity work, and he was talking about the need to share faster. And the new school is you got to share faster to get the data, you mentioned observability earlier, you need to see what everything's out there. He's a real passionate person around getting the data, getting it fast and having trusted partners. So that's not, it's kind of evolving as, I mean, sharing's a well known practice, but with cyber it's sensitive data potentially. So there's a trust relationship. There's now a new ecosystem. That's new for government. How do you view all that and your thoughts on that trend of the sharing piece of it on cyber? >> So, I don't know if it's necessarily new, but it's at a scale that we've never seen before. And by the way, it's vastly more complicated and complex when you overlay from a national security perspective, classification of data and information at various levels. And then that is again complicated by the fact you have different sharing relationships with different actors, whether it's commercial, academic, or allies. So it gets very, very complex web very quickly. So that's part of the challenge we're workin' through. How can we effectively share information at multiple classification levels with multiple partners in an optimal fashion? It is certainly not optimal today. It's very difficult, even with maybe one industry partner for me to be able to talk about data at an unclassified level, and then various other levels of classification to have the traditional networks in place to do that. I could see a solution in the future where our cybersecurity is good enough that maybe I only really need one network and the information that is allowed to flow to the players within the right security environment to make that all happen as quickly as possible. So you've actually, John you've hit on yet another big challenge that we have, is evolving our networks to properly share, with the right people, at the right clearance levels at the speed of war, which is what we're going to need. >> Yeah, and I wanted to call that out because this is an opportunity, again, this discussion here at Cal Poly and around the world is for new capabilities and new people to solve the problems. It's again, it's super exciting if you're geeking out on this. If you have a tech degree or you're interested in changin' the world, there's so many new things that could be applied right now. Roland, I want to get your thoughts on this, because one of the things in the tech trends we're seeing, and this is a massive shift, all the theaters of the tech industry are changing rapidly at the same time. And it affects policy law, but also deep tech. The startup communities are super important in all this too. We can't forget them. Obviously, the big trusted players that are partnering certainly on these initiatives, but your story about being in the dorm room. Now you've got the boardroom and now you got everything in between. You have startups out there that want to and can contribute. You know, what's an ITAR? I mean, I got all these acronym certifications. Is there a community motion to bring startups in, in a safe way, but also give them ability to contribute? Because you look at open source, that proved everyone wrong on software. That's happening now with this now open network concept, the general was kind of alluding to. Which is, it's a changing landscape. Your thoughts, I know you're passionate about this. >> Yeah, absolutely. And I think as General Shaw mentioned, we need to get information out there faster, more timely and to the right people, and involving not only just stakeholders in the U.S., but internationally as well. And as entrepreneurs, we have this very lofty vision or goal to change the world. And oftentimes, entrepreneurs, including myself, we put our heads down and we just run as fast as we can. And we don't necessarily always kind of take a breath and take a step back and kind of look at what we're doing and how it's touching other folks. And in terms of a community, I don't know of any formal community out there, it's mostly ad hoc. And, these ad hoc communities are folks who let's say was a student working on a satellite in college. And they loved that entrepreneurial spirit. And so they said, "Well, I'm going to start my own company." And so, a lot of these ad hoc networks are just from relationships that have been built over the last two decades from colleagues at the university. I do think formalizing this and creating kind of a clearing house to handle all of this is incredibly important. >> And there's going to be a lot of entrepreneurial activity, no doubt, I mean there's too many things to work on and not enough time. I mean this brings up the question that I'm going to, while we're on this topic, you got the remote work with COVID, everyone's workin' remotely, we're doin' this remote interview rather than being on stage. Work's changing, how people work and engage. Certainly physical will come back. But if you looked at historically the space industry and the talent, they're all clustered around the bases. And there's always been these areas where you're a space person. You kind of work in there and the job's there. And if you were cyber, you were generally in other areas. Over the past decade, there's been a cross-pollination of talent and location. As you see the intersection of space, general we'll start with you, first of all, central coast is a great place to live. I know that's where you guys live. But you can start to bring together these two cultures. Sometimes they're not the same. Maybe they're getting better. We know they're being integrated. So general, can you just share your thoughts because this is one of those topics that everyone's talkin' about, but no one's actually kind of addressed directly. >> Yeah, John, I think so. I think I want to answer this by talkin' about where I think the Space Force is going. Because I think if there was ever an opportunity or an inflection point in our Department of Defense to sort of change culture and try to bring in non-traditional kinds of thinking and really kind of change maybe some of the ways that the Department of Defense does things that are probably archaic, Space Force is an inflection point for that. General Raymond, our Chief of Space Operations, has said publicly for awhile now, he wants the U.S. Space Force to be the first truly digital service. And what we mean by that is we want the folks that are in the Space Force to be the ones that are the first adopters, the early adopters of technology. To be the ones most fluent in the cutting edge, technologic developments on space and cyber and other sectors of the economy that are technologically focused. And I think there's some, that can generate some excitement, I think. And it means that we'll probably ended up recruiting people into the Space Force that are not from the traditional recruiting areas that the rest of the Department of Defense looks to. And I think it allows us to bring in a diversity of thought and diversity of perspective and a new kind of motivation into the service, that I think is frankly really exciting. So if you put together everything I mentioned about how space and cyber are going to be best friends forever. And I think there's always been an excitement from the very beginning in the American psyche about space. You start to put all these ingredients together, and I think you see where I'm goin' with this. That this is a chance to really change that cultural mindset that you were describing. >> It's an exciting time for sure. And again, changing the world. And this is what you're seeing today. People do want to change the world. They want a modern world that's changing. Roland, I'll get your thoughts on this. I was having an interview a few years back with a technology entrepreneur, a techie, and we were joking, we were just kind of riffing. And I said, "Everything that's on "Star Trek" will be invented." And we're almost there actually, if you think about it, except for the transporter room. You got video, you got communicators. So, not to bring in the "Star Trek" reference with Space Force, this is digital. And you start thinking about some of the important trends, it's going to be up and down the stack, from hardware to software, to user experience, everything. Your thoughts and reaction. >> Yeah, absolutely. And so, what we're seeing is timelines shrinking dramatically because of the barrier to entry for new entrants and even your existing aerospace companies is incredibly low, right? So if you take previously where you had a technology on the ground and you wanted it in orbit, it would take years. Because you would test it on the ground. You would verify that it can operate in a space environment. And then you would go ahead and launch it. And we're talking tens, if not hundreds of millions of dollars to do that. Now, we've cut that down from years to months. When you have a prototype on the ground and you want to get it launched, you don't necessarily care if it fails on orbit the first time, because you're getting valuable data back. And so, we're seeing technology being developed for the first time on the ground and in orbit in a matter of a few months. And the whole kind of process that we're doing as a small business is trying to enable that. And so, allowing these entrepreneurs and small companies to get their technology in orbit at a price that is sometimes even cheaper than testing on the ground. >> You know this is a great point. I think this is really an important point to call out because we mentioned partnerships earlier, the economics and the business model of space is doable. I mean, you do a mission study. You get paid for that. You have technology that you get stuff up quickly, and there's a cost structure there. And again, the alternative was waterfall planning, years and millions. Now the form factors are doing, now, again, there may be different payloads involved, but you can standardize payloads. You've got robotic arms. This is all available. This brings up the congestion problem. This is going to be on the top of mind of the generals of course, but you've got the proliferation of these constellation systems. You're going to have more and more tech vectors. I mean, essentially that's malware. I mean, that's a probe. You throw something up in space that could cause some interference. Maybe a takeover. General, this is the real elephant in the room, the threat matrix from new stuff and new configurations. So general, how does the proliferation of constellation systems change the threat matrix? >> So I think the, you know I guess I'm going to be a little more optimistic John than I think you pitched that. I'm actually excited about these new mega constellations in LEO. I'm excited about the growing number of actors that are going into space for various reasons. And why is that? It's because we're starting to realize a new economic engine for the nation and for human society. So the question is, so I think we want that to happen. When we could go to almost any other domain in history and when air travel started to become much, much more commonplace with many kinds of actors from private pilots flying their small planes, all the way up to large airliners, there was a problem with congestion. There was a problem about, challenges about behavior, and are we going to be able to manage this? And yes we did. And it was for the great benefit of society. I could probably look to the maritime domain for similar kinds of things. And so this is actually exciting about space. We are just going to have to find the ways as a society, and it's not just the Department of Defense, it's going to be civil, it's going to be international, find the mechanisms to encourage this continued investment in the space domain. I do think that Space Force will play a role in providing security in the space environment, as we venture further out, as economic opportunities emerge, wherever they are in the lunar, Earth, lunar system, or even within the solar system. Space Force is going to play a role in that. But I'm actually really excited about those possibilities. Hey, by the way, I got to say, you made me think of this when you talked about "Star Trek" and Space Force and our technologies, I remember when I was younger watchin' the Next Generation series. I thought one of the coolest things, 'cause bein' a musician in my spare time, I thought one of the coolest things was when Commander Riker would walk into his quarters and say, "Computer play soft jazz." And there would just be, the computer would just play music. And this was an age when we had hard media. Like how will that, that is awesome. Man, I can't wait for the 23rd century when I can do that. And where we are today is so incredible on those lines. The things that I can ask Alexa or Siri to play. >> Well that's the thing, everything that's on "Star Trek," think about it, it's almost invented. I mean, you got the computers, you got, the only thing really is, holograms are startin' to come in, you got, now the transporter room. Now that's physics. We'll work on that. >> So there is this balance between physics and imagination, but we have not exhausted either. >> Well, firstly, everyone that knows me knows I'm a huge "Star Trek" fan, all the series. Of course, I'm an original purist, but at that level. But this is about economic incentive as well. Roland, I want to get your thoughts, 'cause the gloom and doom, we got to think about the bad stuff to make it good. If I put my glass half full on the table, this economic incentives, just like the example of the plane and the air traffic. There's more actors that are incented to have a secure system. What's your thoughts to general's comments around the optimism and the potential threat matrix that needs to be managed. >> Absolutely, so one of the things that we've seen over the years, as we build these small satellites is a lot of that technology that the General's talking about, voice recognition, miniaturized chips, and sensors, started on the ground. And I mean, you have your iPhone, that, about 15 years ago before the first iPhone came out, we were building small satellites in the lab and we were looking at cutting-edge, state-of-the-art magnetometers and sensors that we were putting in our satellites back then. We didn't know if they were going to work. And then a few years later, as these students graduate, they go off and they go out to other industries. And so, some of the technology that was first kind of put in these CubeSats in the early 2000s, kind of ended up in the first generation iPhone, smartphones. And so being able to take that technology, rapidly incorporate that into space and vice versa gives you an incredible economic advantage. Because not only are your costs going down because you're mass producing these types of terrestrial technologies, but then you can also increase revenue and profit by having smaller and cheaper systems. >> General, let's talk about that real quickly, that's a good point, I want to just shift it into the playbook. I mean, everyone talks about playbooks for management, for tech, for startups, for success. I mean, one of the playbooks that's clear from your history is investment in R&D around military and/or innovation that has a long view, spurs innovation, commercially. I mean, just there's a huge, many decades of history that shows that, hey we got to start thinking about these challenges. And next thing you know it's in an iPhone. This is history, this is not like a one off. And now with Space Force you're driving the main engine of innovation to be all digital. You know, we riff about "Star Trek" which is fun, the reality is you're going to be on the front lines of some really new, cool, mind-blowing things. Could you share your thoughts on how you sell that to the people who write the checks or recruit more talent? >> First, I totally agree with your thesis that national security, well, could probably go back an awful long way, hundreds to thousands of years, that security matters tend to drive an awful lot of innovation and creativity. You know I think probably the two things that drive people the most are probably an opportunity to make money, but beating that out are trying to stay alive. And so, I don't think that's going to go away. And I do think that Space Force can play a role as it pursues security structures, within the space domain to further encourage economic investment and to protect our space capabilities for national security purposes, are going to be at the cutting edge. This isn't the first time. I think we can point back to the origins of the internet, really started in the Department of Defense, with a partnership I should add, with academia. That's how the internet got started. That was the creativity in order to meet some needs there. Cryptography has its roots in security, in national security, but now we use it for economic reasons and a host of other kinds of reasons. And then space itself, I mean, we still look back to Apollo era as an inspiration for so many things that inspired people to either begin careers in technical areas or in space and so on. So I think in that same spirit, you're absolutely right. I guess I'm totally agreeing with your thesis. The Space Force will have a positive, inspirational influence in that way. And we need to realize that. So when we are asking for, when we're looking for how we need to meet capability needs, we need to spread that net very far, look for the most creative solutions and partner early and often with those that can work on those. >> When you're on the new frontier, you got to have a team sport, it's a team effort. And you mentioned the internet, just anecdotally I'm old enough to remember this 'cause I remember the days that it was goin' on, is that the policy decisions that the U.S. made at that time was to let it go a little bit invisible hand. They didn't try to commercialize it too fast. But there was some policy work that was done, that had a direct effect to the innovation. Versus take it over, and the next thing you know it's out of control. So I think there's this cross-disciplinary skillset becomes a big thing where you need to have more people involved. And that's one of the big themes of this symposium. So it's a great point. Thank you for sharing that. Roland, your thoughts on this because you got policy decisions. We all want to run faster. We want to be more innovative, but you got to have some ops view. Now, most of the ops view people want things very tight, very buttoned up, secure. The innovators want to go faster. It's the ying and yang. That's the world we live in. How's it all balance in your mind? >> Yeah, one of the things that may not be apparently obvious is that the U.S. government and Department of Defense is one of the biggest investors in technology in the aerospace sector. They're not the traditional venture capitalists, but they're the ones that are driving technology innovation because there's funding. And when companies see that the U.S. government is interested in something, businesses will revector to provide that capability. And, I would say the more recent years, we've had a huge influx of private equity, venture capital coming into the markets to kind of help augment the government investment. And I think having a good partnership and a relationship with these private equity, venture capitalists and the U.S. government is incredibly important because the two sides can help collaborate and kind of see a common goal. But then also too, on the other side there's that human element. And as General Shaw was saying, not only do companies obviously want to thrive and do really well, some companies just want to stay alive to see their technology kind of grow into what they've always dreamed of. And oftentimes entrepreneurs are put in a very difficult position because they have to make payroll, they have to keep the lights on. And so, sometimes they'll take investment from places where they may normally would not have, from potentially foreign investment that could potentially cause issues with the U.S. supply chain. >> Well, my final question is the best I wanted to save for last, because I love the idea of human space flight. I'd love to be on Mars. I'm not sure I'm able to make it someday, but how do you guys see the possible impacts of cybersecurity on expanding human space flight operations? I mean, general, this is your wheelhouse. This is your in command, putting humans in space and certainly robots will be there because they're easy to go 'cause they're not human. But humans in space. I mean, you startin' to see the momentum, the discussion, people are scratchin' that itch. What's your take on that? How do we see makin' this more possible? >> Well, I think we will see commercial space tourism in the future. I'm not sure how wide and large a scale it will become, but we will see that. And part of the, I think the mission of the Space Force is going to be probably to again, do what we're doin' today is have really good awareness of what's going on in the domain to ensure that that is done safely. And I think a lot of what we do today will end up in civil organizations to do space traffic management and safety in that arena. And, it is only a matter of time before we see humans going, even beyond the, NASA has their plan, the Artemis program to get back to the moon and the gateway initiative to establish a space station there. And that's going to be a NASA exploration initiative. But it is only a matter of time before we have private citizens or private corporations putting people in space and not only for tourism, but for economic activity. And so it'll be really exciting to watch. It'll be really exciting and Space Force will be a part of it. >> General, Roland, I want to thank you for your valuable time to come on this symposium. Really appreciate it. Final comment, I'd love you to spend a minute to share your personal thoughts on the importance of cybersecurity to space and we'll close it out. We'll start with you Roland. >> Yeah, so I think the biggest thing I would like to try to get out of this from my own personal perspective is creating that environment that allows the aerospace supply chain, small businesses like ourselves, be able to meet all the requirements to protect and safeguard our data, but also create a way that we can still thrive and it won't stifle innovation. I'm looking forward to comments and questions, from the audience to really kind of help, basically drive to that next step. >> General final thoughts, the importance of cybersecurity to space. >> I'll go back to how I started I think John and say that space and cyber are forever intertwined, they're BFFs. And whoever has my job 50 years from now, or a hundred years from now, I predict they're going to be sayin' the exact same thing. Cyber and space are intertwined for good. We will always need the cutting edge, cybersecurity capabilities that we develop as a nation or as a society to protect our space capabilities. And our cyber capabilities are going to need space capabilities in the future as well. >> General John Shaw, thank you very much. Roland Coelho, thank you very much for your great insight. Thank you to Cal Poly for puttin' this together. I want to shout out to the team over there. We couldn't be in-person, but we're doing a virtual remote event. I'm John Furrier with "theCUBE" and SiliconANGLE here in Silicon Valley, thanks for watching. (upbeat music)

>> Narrator: Live from Berlin, Germany It's theCUBE, covering NetApp Insight 2017 Brought to you by NetApp. Welcome back to theCUBE's live coverage of NetApp Insight 2017, here in Berlin, Germany. I'm your host Rebecca Knight along with my co-host Peter Burris. We are joined by Tim Pitcher, he is the Vice President, Next-Generation Data Centre for NetApp. Thanks so much for coming on the program. It's an absolute pleasure, it's a pleasure to be here. So let's start just defining for our viewers the Next-Generation Data Centre, how it's built, how it's founded. Yeah so, if you think about NetApp today we think about our customers really consuming technology in three ways. We've got sort of more, we're modernizing traditional data centers and architectures using data management and flash storage and these sorts of things and this really is our back yard, this is what we've been doing for years and years, been incredibly successful at it. And the big disrupter in many ways is Cloud and so our partnerships with the major hyperscalers are critically important to us as well. But there's a third piece to the jigsaw which is the Next-Generation Data Centre and the way we think about that is that if you imagine that you want to use Cloud services but you want to do a lot of that yourself, you want to take advantage of the sort of simple, scalable, automated nature of Cloud then that's really what we're delivering in the Next-Generation Data Centre for our customers. So the Next-Generation Data Centre is being driven by technology advances, business requirements, the realities of data, what are the practical things that are driving, or indicating, the steps that people should take as they think about new technology and new business practices? I mean, the big driver is really to remove a lot of complexity from their business so if you think about going to the Cloud, you're making a really very simple consumption choice. You're saying I'm going to consume data and services from the public Cloud environment and that drives a similar behavior inside large organizations as well, organizations of all sizes. So they're thinking about how do they build private Cloud, take advantage of both with a hybrid Cloud environment, or they can have multiple public Cloud instances as well. So they're thinking about it all very differently and they're thinking about the most appropriate services that they're trying to deliver or the most appropriate way to deliver that application or that data set, if you will, to their customers. So it's not like everything needs to be in one place, and also critically customers very often want to change that as well so they'll make a decision to put something in a public cloud, it might not be the best fit over time for whatever reason, so they want to bring it back in house and deliver that on their own infrastructure and when they do that they want to take advantage, they like what they've had in the Cloud so they want to put that on premise. So the real drive is they really want simplicity, they're really focused on a much more performant outcome that's focused on simplicity focused on how you scale your business and being able to have truly multi-tenant environments that give you the predictability of your traditional architectures if you will, the architectures you know well and have been using for a long time. You want to be able to do that in a Cloud like environment because you the economics of Cloud but you get the predictability of dedicated environments. So which of the customers that you work with are in fact executing this Next-Generation Data Centre strategy most beautifully in your opinion? Well so, if you think about the strategy that NetApp has for our Next-Gen Data Centre is really based on two companies that they acquired. One is Object Storage platform called StorageGRID Webscale the other one is SolidFire. Which, SolidFire was a young, emerging, hot technology company that was focused on delivering what I've just articulated, simple technologies, simple storage platform operated at scale, completely automated and SolidFire was born out of a service provider, born out of a service provider at the same time as OpenStack so it's kind of unique in that perspective. The company was formed to solve a problem and the problem that Rackspace really were looking to solve was how do they take their managed service clients and move them into the Cloud, what's stopping them doing that? And the answer is obviously customers worry about security and things like that but the key thing that was really stopping them was their concern about performance. So if I'm going to share, put all my stuff in with everybody else's, in a shared environment, how do I know I'm going to get what I'm paying for how do I know that I'm not going to have somebody else's applications consume all the services that are going to be given to me? So as a consequence, this was the thing that prevented people going to the Cloud so this is what the company formed to fix so SolidFire came out of that and that's our background and that's why NetApp acquired us because very different way of looking at things so as a consequence service providers are really at the forefront of how they deliver services to their customers and they leveraged SolidFire and we were very successful as an independent company selling to service providers and have been increasingly successful now that we're part of NetApp. Our very first customer for example is in Jersey and is still a very happy NetApp customer, a company called Calligo and they offer tiered services all on SolidFire, trusted Cloud services in and off-shore kind of environment they're focused on the financial services community and things like that. And now we have also major services providers like 1and1 in Germany, which is one of the largest services providers in Europe, long time NetApp customer and they're a SolidFire customer for their public Cloud services as well for the Cloud that they offer. And in the UK as well, Interoute, major service provider. What I like about them is one, they deal with a massive amount of traffic, they've got a huge network so very traffic intensive, but also they really take advantage of NetApp being, sorry, SolidFire being part of NetApp now so they use the on-tap base products in their manage services which those products are optimized for that kind of environment but for their Cloud environment where they're offering tiered services they use SolidFire so they've got us on both sides of the house if you will and so its a great example of SolidFire being part of NetApp, why that's so powerful, why that's so successful. And companies like Internet Solutions in South Africa is one major service provider in South Africa, big consumer of SolidFire and now is part of NetApp, it's a much better place for them because we've got a big business in South Africa, we're very successful there, so we're part of that team now and they go from strength to strength. So now the next challenge is taking some of the best practices that have emerged from what you've learned from working with these service providers and transferring them to other industries. Yeah so, we're seeing a lot in Fin-tech right now, Farmer is a good market for us, Astrozeneca uses SolidFire so a great example of one of NetApps long-term and major customers that's now consuming products and services from other business units and other offerings that we have across a much broader portfolio so they're very happy customers now. That's part of our global account business. Business Wire in the UAE is another example of a successful business transformation that they're doing as well. We've seen a lot of activity in Dev-ops, these products are perfect for Dev-ops because they're so simple, they don't require management they're completely automated, you're not building those large infrastructures of people to support these environments. And it's much quicker to be able to launch applications because of the simple nature of the technology you can launch applications, new products, new services so your time to market is an awful lot quicker as well. Great, well thanks so much for coming on the show Tim, it's been really fun talking to you. It's been a pleasure, thanks very much. I'm Rebecca Knight for Peter Burris, we will have more from NetApp Insight just after this. (electronic music)

(upbeat techno music) >> Announcer: Live from San Jose, California it's theCUBE, covering Innovating to Fuel the Next Decade of Big Data, brought to you by Western Digital. >> Hey, welcome back everybody. Jeff Frick here at theCUBE. We're at the Western Digital Headquarters off Almaden down in San Jose, a really important place. Western Digital's been here for a while, their headquarters. A lot of innovation's been going on here forever. So we're excited to be here really for the next generation. The event's called Innovating to Fuel the Next Generation of big data, and we're joined by many time Cuber, Dave Tang. He is the SVP in corporate marketing from Western Digital. Dave, always great to see you. >> Yeah. Always great to be here, Jeff. Thanks. >> Absolutely. So you got to MC the announcement today. >> Yes. >> So for the people that weren't there, let's give them a quick overview on what the announcement was and then we can dive in a little deeper. >> Great, so what we were announcing was a major breakthrough in technology that's going to allow us to drive the increase in capacity in density to support big data for the next decade and beyond, right? So capacities and densities had been starting to level off in terms of hard drive technology capability. So what we announced was microwave-assisted magnetic recording technology that will allow us to keep growing that areal density up and reducing the cost per terabyte. >> You know, it's fascinating cause everyone loves to talk about Moore's Law and have these silly architectural debates, whether Moore's Law is alive or dead, but, as anyone who's lived here knows, Moore's Law is really an attitude much more it is than the specific physics of microprocessor density growth. And it's interesting to see. As we know the growth of data is growing in giant and the types of data, and not only regular big data, but now streaming data are bigger and bigger and bigger. I think you talked about stuff coming off of people and machines compared to business data is way bigger. >> Right. >> But you guys continue to push limits and break through, and even though we expect everything to be cheaper, faster, and better, you guys actually have to execute it-- >> Dave: Right. >> Back at the factory. >> Right, well it's interesting. There's this healthy tension, right, a push and pull in the environment. So you're right, it's not just Moore's Law that's enabling a technology push, but we have this virtuous cycle, right? We've realized what the value is of data and how to extract the possibilities and value of data, so that means that we want to store more of that data and access more of that data, which drives the need for innovation to be able to support all of that in a cost effective way. But then that triggers another wave of new applications, new ways to tap into the possibilities of data. So it just feeds on itself, and fortunately we have great technologists, great means of innovation, and a great attitude and spirit of innovation to help drive that. >> Yeah, so for people that want more, they can go to the press releases and get the data. We won't dive deep into the weeds here on the technology, but I thought you had Janet George speak, and she's chief data scientist. Phenomenal, phenomenal big brain. >> Dave: Yes. >> A smart lady. But she talked about, from her perspective we're still just barely even getting onto this data opportunity in terms of automation, and we see over and over at theCUBE events, innovation's really not that complicated. Give more people access to the data, give them more access to the tools, and let them try things easier and faster and feel quick, there's actually a ton of innovation that companies can unlock within their own four walls. But the data is such an important piece of it, and there's more and more and more of this. >> Dave: Right, right. >> What used to be digital exhaust now is, I think maybe you said, or maybe it was Dave, that there's a whole economy now built on data like we used to do with petroleum. I thought that was really insightful. >> Yeah, right. It's like a gold mine. So not only are the sources of data increasing, which is driving increased volume, but, as Janet was alluding to, we're starting to come up with the tools and the sophistication with machine learning and artificial intelligence to be able to put that data to new use as well as to find the pieces of data to interconnect, to drive these new capabilities and new insights. >> Yeah, but unlike petroleum it doesn't get used up. I mean that's the beauty of data. (laughing) >> Yeah, that's right. >> It's a digital process that can be used over and over and over again. >> And a self-renewing, renewing resource. And you're right, in that sense that it's being used over and over again that the longevity of that data, the use for life is growing exponentially along with the volume. >> Right, and Western Digital's in a unique position cause you have systems and you have big systems that could be used in data centers, but you also have the media that powers a whole bunch of other people's systems. So I thought one of the real important announcements today was, yes it's an interesting new breakthrough technology that uses energy assist to get more density on the drives, but it's done in such a way that the stuff's all backward compatible. It's plug and play. You've got production scheduled in a couple years I think with test out the customers-- >> Dave: That's right. >> Next year. So, you know, that is such an important piece beyond the technology. What's the commercial acceptance? What are the commercial barriers? And this sounds like a pretty interesting way to skin that cow. >> Right, often times the best answers aren't the most complex answers. They're the more elegant and simplistic answers. So it goes from the standpoint of a user being able to plug and play with older systems, older technologies. That's beautiful, and for us, to be able to, the ability to manufacture it in high volume reliably and cost effectively is equally as important. >> And you also talked, which I think was interesting, is kind of the relationship between hard drives and flash, because, obviously, flash is a, I want to say the sexy new toy, but it's not a sexy new toy anymore. >> Right. >> It's been around for a while, but, with that pressure on flash performance, you're still seeing the massive amounts of big data, which is growing faster than that. And there is a rule for the high density hard drives in that environment, and, based on the forecast you shared, which I'm presuming came from IDC or people that do numbers for a living, still a significant portion of a whole lot of data is not going to be on flash. >> Yeah, that's right. I think we have a tendency, especially in technology, to think either or, right? Something is going to take over from something else, but in this case it's definitely an and, right. And a lot of that is driven by this notion that there's fast data and big data, and, while our attention seems to shift over to maybe some fast data applications like autonomous vehicles and realtime applications, surveillance applications, there's still a need for big data because the algorithms that drive those realtime applications have to come from analysis of vast amounts of data. So big data is here to stay. It's not going away or shifting over. >> I think it's a really interesting kind of cross over, which Janet talked about too, where you need the algorithms to continue sharing the system that are feeding, continuing, and reacting to the real data, but then that just adds more vocabulary to their learning set so they can continue to evolve overtime. >> Yeah, what really helps us out in the market place is that because we have technologies and products across that full spectrum of flash and rotating magnetic recording, and we sell to customers who buy devices as well as platforms and systems, we see a lot of applications, a lot of uses of data, and we're able to then anticipate what those needs are going to be in the near future and in the distant future. >> Right, so we're getting towards the end of 2017, which I find hard to say, but as you look forward kind of to 2018 and this insatiable desire for more storage, cause this insatiable creation of more data, what are some of your priorities for 2018 and what are you kind of looking at as, like I said, I can't believe we're going to actually flip the calendar here-- >> Dave: Right, right. >> In just a few short months. (laughing) >> Well, I think for us, it's the realization that all these applications that are coming at us are more and more diverse, and their needs are very specialized. So it's not just the storage, although we're thought of as a storage company, it's not just about the storage of that data, but you have contrive complete environments to capture and preserve and access and transform that data, which means we have to go well beyond storage and think about how that data is accessed, technical interfaces to our memory products as well as storage products, and then where compute sits. Does it still sit in a centralized place or do you move compute to out closer to where the data sits. So, all this innovation and changing the way that we think about how we can mine that data is top of the mind for us for the next year and beyond. >> It's only job security for you, Dave. (laughing) >> Dave: Funny to think about. >> Alright. He's Dave Tang. Thanks for inviting us and again congratulations on the presentation. >> Always a pleasure. >> Alright, Dave Tang, I'm Jeff Frick. You're watching theCUBE from Western Digital headquarters in San Jose, California. Thanks for watching. (upbeat techno music)

>> Narrator: Live from New York, it's the Cube, covering big data New York City 2016. Brought to you by headline sponsors, CISCO, IBM, NVIDIA, and our ecosystem sponsors, now here's your host, Dave Valante. >> Welcome back to New York City, everybody, this is the Cube, the worldwide leader in live tech coverage, and this is a cube first, we've got a nine person, actually eight person panel of experts, data scientists, all alike. I'm here with my co-host, James Cubelis, who has helped organize this panel of experts. James, welcome. >> Thank you very much, Dave, it's great to be here, and we have some really excellent brain power up there, so I'm going to let them talk. >> Okay, well thank you again-- >> And I'll interject my thoughts now and then, but I want to hear them. >> Okay, great, we know you well, Jim, we know you'll do that, so thank you for that, and appreciate you organizing this. Okay, so what I'm going to do to our panelists is ask you to introduce yourself. I'll introduce you, but tell us a little bit about yourself, and talk a little bit about what data science means to you. A number of you started in the field a long time ago, perhaps data warehouse experts before the term data science was coined. Some of you started probably after Hal Varian said it was the sexiest job in the world. (laughs) So think about how data science has changed and or what it means to you. We're going to start with Greg Piateski, who's from Boston. A Ph.D., KDnuggets, Greg, tell us about yourself and what data science means to you. >> Okay, well thank you Dave and thank you Jim for the invitation. Data science in a sense is the second oldest profession. I think people have this built-in need to find patterns and whatever we find we want to organize the data, but we do it well on a small scale, but we don't do it well on a large scale, so really, data science takes our need and helps us organize what we find, the patterns that we find that are really valid and useful and not just random, I think this is a big challenge of data science. I've actually started in this field before the term Data Science existed. I started as a researcher and organized the first few workshops on data mining and knowledge discovery, and the term data mining became less fashionable, became predictive analytics, now it's data science and it will be something else in a few years. >> Okay, thank you, Eves Mulkearns, Eves, I of course know you from Twitter. A lot of people know you as well. Tell us about your experiences and what data scientist means to you. >> Well, data science to me is if you take the two words, the data and the science, the science it holds a lot of expertise and skills there, it's statistics, it's mathematics, it's understanding the business and putting that together with the digitization of what we have. It's not only the structured data or the unstructured data what you store in the database try to get out and try to understand what is in there, but even video what is coming on and then trying to find, like George already said, the patterns in there and bringing value to the business but looking from a technical perspective, but still linking that to the business insights and you can do that on a technical level, but then you don't know yet what you need to find, or what you're looking for. >> Okay great, thank you. Craig Brown, Cube alum. How many people have been on the Cube actually before? >> I have. >> Okay, good. I always like to ask that question. So Craig, tell us a little bit about your background and, you know, data science, how has it changed, what's it all mean to you? >> Sure, so I'm Craig Brown, I've been in IT for almost 28 years, and that was obviously before the term data science, but I've evolved from, I started out as a developer. And evolved through the data ranks, as I called it, working with data structures, working with data systems, data technologies, and now we're working with data pure and simple. Data science to me is an individual or team of individuals that dissect the data, understand the data, help folks look at the data differently than just the information that, you know, we usually use in reports, and get more insights on, how to utilize it and better leverage it as an asset within an organization. >> Great, thank you Craig, okay, Jennifer Shin? Math is obviously part of being a data scientist. You're good at math I understand. Tell us about yourself. >> Yeah, so I'm a senior principle data scientist at the Nielsen Company. I'm also the founder of 8 Path Solutions, which is a data science, analytics, and technology company, and I'm also on the faculty in the Master of Information and Data Science program at UC Berkeley. So math is part of the IT statistics for data science actually this semester, and I think for me, I consider myself a scientist primarily, and data science is a nice day job to have, right? Something where there's industry need for people with my skill set in the sciences, and data gives us a great way of being able to communicate sort of what we know in science in a way that can be used out there in the real world. I think the best benefit for me is that now that I'm a data scientist, people know what my job is, whereas before, maybe five ten years ago, no one understood what I did. Now, people don't necessarily understand what I do now, but at least they understand kind of what I do, so it's still an improvement. >> Excellent. Thank you Jennifer. Joe Caserta, you're somebody who started in the data warehouse business, and saw that snake swallow a basketball and grow into what we now know as big data, so tell us about yourself. >> So I've been doing data for 30 years now, and I wrote the Data Warehouse ETL Toolkit with Ralph Timbal, which is the best selling book in the industry on preparing data for analytics, and with the big paradigm shift that's happened, you know for me the past seven years has been, instead of preparing data for people to analyze data to make decisions, now we're preparing data for machines to make the decisions, and I think that's the big shift from data analysis to data analytics and data science. >> Great, thank you. Miriam, Miriam Fridell, welcome. >> Thank you. I'm Miriam Fridell, I work for Elder Research, we are a data science consultancy, and I came to data science, sort of through a very circuitous route. I started off as a physicist, went to work as a consultant and software engineer, then became a research analyst, and finally came to data science. And I think one of the most interesting things to me about data science is that it's not simply about building an interesting model and doing some interesting mathematics, or maybe wrangling the data, all of which I love to do, but it's really the entire analytics lifecycle, and a value that you can actually extract from data at the end, and that's one of the things that I enjoy most is seeing a client's eyes light up or a wow, I didn't really know we could look at data that way, that's really interesting. I can actually do something with that, so I think that, to me, is one of the most interesting things about it. >> Great, thank you. Justin Sadeen, welcome. >> Absolutely, than you, thank you. So my name is Justin Sadeen, I work for Morph EDU, an artificial intelligence company in Atlanta, Georgia, and we develop learning platforms for non-profit and private educational institutions. So I'm a Marine Corp veteran turned data enthusiast, and so what I think about data science is the intersection of information, intelligence, and analysis, and I'm really excited about the transition from big data into smart data, and that's what I see data science as. >> Great, and last but not least, Dez Blanchfield, welcome mate. >> Good day. Yeah, I'm the one with the funny accent. So data science for me is probably the funniest job I've ever to describe to my mom. I've had quite a few different jobs, and she's never understood any of them, and this one she understands the least. I think a fun way to describe what we're trying to do in the world of data science and analytics now is it's the equivalent of high altitude mountain climbing. It's like the extreme sport version of the computer science world, because we have to be this magical unicorn of a human that can understand plain english problems from C-suite down and then translate it into code, either as soles or as teams of developers. And so there's this black art that we're expected to be able to transmogrify from something that we just in plain english say I would like to know X, and we have to go and figure it out, so there's this neat extreme sport view I have of rushing down the side of a mountain on a mountain bike and just dodging rocks and trees and things occasionally, because invariably, we do have things that go wrong, and they don't quite give us the answers we want. But I think we're at an interesting point in time now with the explosion in the types of technology that are at our fingertips, and the scale at which we can do things now, once upon a time we would sit at a terminal and write code and just look at data and watch it in columns, and then we ended up with spreadsheet technologies at our fingertips. Nowadays it's quite normal to instantiate a small high performance distributed cluster of computers, effectively a super computer in a public cloud, and throw some data at it and see what comes back. And we can do that on a credit card. So I think we're at a really interesting tipping point now where this coinage of data science needs to be slightly better defined, so that we can help organizations who have weird and strange questions that they want to ask, tell them solutions to those questions, and deliver on them in, I guess, a commodity deliverable. I want to know xyz and I want to know it in this time frame and I want to spend this much amount of money to do it, and I don't really care how you're going to do it. And there's so many tools we can choose from and there's so many platforms we can choose from, it's this little black art of computing, if you'd like, we're effectively making it up as we go in many ways, so I think it's one of the most exciting challenges that I've had, and I think I'm pretty sure I speak for most of us in that we're lucky that we get paid to do this amazing job. That we get make up on a daily basis in some cases. >> Excellent, well okay. So we'll just get right into it. I'm going to go off script-- >> Do they have unicorns down under? I think they have some strange species right? >> Well we put the pointy bit on the back. You guys have in on the front. >> So I was at an IBM event on Friday. It was a chief data officer summit, and I attended what was called the Data Divas' breakfast. It was a women in tech thing, and one of the CDOs, she said that 25% of chief data officers are women, which is much higher than you would normally see in the profile of IT. We happen to have 25% of our panelists are women. Is that common? Miriam and Jennifer, is that common for the data science field? Or is this a higher percentage than you would normally see-- >> James: Or a lower percentage? >> I think certainly for us, we have hired a number of additional women in the last year, and they are phenomenal data scientists. I don't know that I would say, I mean I think it's certainly typical that this is still a male-dominated field, but I think like many male-dominated fields, physics, mathematics, computer science, I think that that is slowly changing and evolving, and I think certainly, that's something that we've noticed in our firm over the years at our consultancy, as we're hiring new people. So I don't know if I would say 25% is the right number, but hopefully we can get it closer to 50. Jennifer, I don't know if you have... >> Yeah, so I know at Nielsen we have actually more than 25% of our team is women, at least the team I work with, so there seems to be a lot of women who are going into the field. Which isn't too surprising, because with a lot of the issues that come up in STEM, one of the reasons why a lot of women drop out is because they want real world jobs and they feel like they want to be in the workforce, and so I think this is a great opportunity with data science being so popular for these women to actually have a job where they can still maintain that engineering and science view background that they learned in school. >> Great, well Hillary Mason, I think, was the first data scientist that I ever interviewed, and I asked her what are the sort of skills required and the first question that we wanted to ask, I just threw other women in tech in there, 'cause we love women in tech, is about this notion of the unicorn data scientist, right? It's been put forth that there's the skill sets required to be a date scientist are so numerous that it's virtually impossible to have a data scientist with all those skills. >> And I love Dez's extreme sports analogy, because that plays into the whole notion of data science, we like to talk about the theme now of data science as a team sport. Must it be an extreme sport is what I'm wondering, you know. The unicorns of the world seem to be... Is that realistic now in this new era? >> I mean when automobiles first came out, they were concerned that there wouldn't be enough chauffeurs to drive all the people around. Is there an analogy with data, to be a data-driven company. Do I need a data scientist, and does that data scientist, you know, need to have these unbelievable mixture of skills? Or are we doomed to always have a skill shortage? Open it up. >> I'd like to have a crack at that, so it's interesting, when automobiles were a thing, when they first bought cars out, and before they, sort of, were modernized by the likes of Ford's Model T, when we got away from the horse and carriage, they actually had human beings walking down the street with a flag warning the public that the horseless carriage was coming, and I think data scientists are very much like that. That we're kind of expected to go ahead of the organization and try and take the challenges we're faced with today and see what's going to come around the corner. And so we're like the little flag-bearers, if you'd like, in many ways of this is where we're at today, tell me where I'm going to be tomorrow, and try and predict the day after as well. It is very much becoming a team sport though. But I think the concept of data science being a unicorn has come about because the coinage hasn't been very well defined, you know, if you were to ask 10 people what a data scientist were, you'd get 11 answers, and I think this is a really challenging issue for hiring managers and C-suites when the generants say I was data science, I want big data, I want an analyst. They don't actually really know what they're asking for. Generally, if you ask for a database administrator, it's a well-described job spec, and you can just advertise it and some 20 people will turn up and you interview to decide whether you like the look and feel and smell of 'em. When you ask for a data scientist, there's 20 different definitions of what that one data science role could be. So we don't initially know what the job is, we don't know what the deliverable is, and we're still trying to figure that out, so yeah. >> Craig what about you? >> So from my experience, when we talk about data science, we're really talking about a collection of experiences with multiple people I've yet to find, at least from my experience, a data science effort with a lone wolf. So you're talking about a combination of skills, and so you don't have, no one individual needs to have all that makes a data scientist a data scientist, but you definitely have to have the right combination of skills amongst a team in order to accomplish the goals of data science team. So from my experiences and from the clients that I've worked with, we refer to the data science effort as a data science team. And I believe that's very appropriate to the team sport analogy. >> For us, we look at a data scientist as a full stack web developer, a jack of all trades, I mean they need to have a multitude of background coming from a programmer from an analyst. You can't find one subject matter expert, it's very difficult. And if you're able to find a subject matter expert, you know, through the lifecycle of product development, you're going to require that individual to interact with a number of other members from your team who are analysts and then you just end up well training this person to be, again, a jack of all trades, so it comes full circle. >> I own a business that does nothing but data solutions, and we've been in business 15 years, and it's been, the transition over time has been going from being a conventional wisdom run company with a bunch of experts at the top to becoming more of a data-driven company using data warehousing and BI, but now the trend is absolutely analytics driven. So if you're not becoming an analytics-driven company, you are going to be behind the curve very very soon, and it's interesting that IBM is now coining the phrase of a cognitive business. I think that is absolutely the future. If you're not a cognitive business from a technology perspective, and an analytics-driven perspective, you're going to be left behind, that's for sure. So in order to stay competitive, you know, you need to really think about data science think about how you're using your data, and I also see that what's considered the data expert has evolved over time too where it used to be just someone really good at writing SQL, or someone really good at writing queries in any language, but now it's becoming more of a interdisciplinary action where you need soft skills and you also need the hard skills, and that's why I think there's more females in the industry now than ever. Because you really need to have a really broad width of experiences that really wasn't required in the past. >> Greg Piateski, you have a comment? >> So there are not too many unicorns in nature or as data scientists, so I think organizations that want to hire data scientists have to look for teams, and there are a few unicorns like Hillary Mason or maybe Osama Faiat, but they generally tend to start companies and very hard to retain them as data scientists. What I see is in other evolution, automation, and you know, steps like IBM, Watson, the first platform is eventually a great advance for data scientists in the short term, but probably what's likely to happen in the longer term kind of more and more of those skills becoming subsumed by machine unique layer within the software. How long will it take, I don't know, but I have a feeling that the paradise for data scientists may not be very long lived. >> Greg, I have a follow up question to what I just heard you say. When a data scientist, let's say a unicorn data scientist starts a company, as you've phrased it, and the company's product is built on data science, do they give up becoming a data scientist in the process? It would seem that they become a data scientist of a higher order if they've built a product based on that knowledge. What is your thoughts on that? >> Well, I know a few people like that, so I think maybe they remain data scientists at heart, but they don't really have the time to do the analysis and they really have to focus more on strategic things. For example, today actually is the birthday of Google, 18 years ago, so Larry Page and Sergey Brin wrote a very influential paper back in the '90s About page rank. Have they remained data scientist, perhaps a very very small part, but that's not really what they do, so I think those unicorn data scientists could quickly evolve to have to look for really teams to capture those skills. >> Clearly they come to a point in their career where they build a company based on teams of data scientists and data engineers and so forth, which relates to the topic of team data science. What is the right division of roles and responsibilities for team data science? >> Before we go, Jennifer, did you have a comment on that? >> Yeah, so I guess I would say for me, when data science came out and there was, you know, the Venn Diagram that came out about all the skills you were supposed to have? I took a very different approach than all of the people who I knew who were going into data science. Most people started interviewing immediately, they were like this is great, I'm going to get a job. I went and learned how to develop applications, and learned computer science, 'cause I had never taken a computer science course in college, and made sure I trued up that one part where I didn't know these things or had the skills from school, so I went headfirst and just learned it, and then now I have actually a lot of technology patents as a result of that. So to answer Jim's question, actually. I started my company about five years ago. And originally started out as a consulting firm slash data science company, then it evolved, and one of the reasons I went back in the industry and now I'm at Nielsen is because you really can't do the same sort of data science work when you're actually doing product development. It's a very very different sort of world. You know, when you're developing a product you're developing a core feature or functionality that you're going to offer clients and customers, so I think definitely you really don't get to have that wide range of sort of looking at 8 million models and testing things out. That flexibility really isn't there as your product starts getting developed. >> Before we go into the team sport, the hard skills that you have, are you all good at math? Are you all computer science types? How about math? Are you all math? >> What were your GPAs? (laughs) >> David: Anybody not math oriented? Anybody not love math? You don't love math? >> I love math, I think it's required. >> David: So math yes, check. >> You dream in equations, right? You dream. >> Computer science? Do I have to have computer science skills? At least the basic knowledge? >> I don't know that you need to have formal classes in any of these things, but I think certainly as Jennifer was saying, if you have no skills in programming whatsoever and you have no interest in learning how to write SQL queries or RR Python, you're probably going to struggle a little bit. >> James: It would be a challenge. >> So I think yes, I have a Ph.D. in physics, I did a lot of math, it's my love language, but I think you don't necessarily need to have formal training in all of these things, but I think you need to have a curiosity and a love of learning, and so if you don't have that, you still want to learn and however you gain that knowledge I think, but yeah, if you have no technical interests whatsoever, and don't want to write a line of code, maybe data science is not the field for you. Even if you don't do it everyday. >> And statistics as well? You would put that in that same general category? How about data hacking? You got to love data hacking, is that fair? Eaves, you have a comment? >> Yeah, I think so, while we've been discussing that for me, the most important part is that you have a logical mind and you have the capability to absorb new things and the curiosity you need to dive into that. While I don't have an education in IT or whatever, I have a background in chemistry and those things that I learned there, I apply to information technology as well, and from a part that you say, okay, I'm a tech-savvy guy, I'm interested in the tech part of it, you need to speak that business language and if you can do that crossover and understand what other skill sets or parts of the roles are telling you I think the communication in that aspect is very important. >> I'd like throw just something really quickly, and I think there's an interesting thing that happens in IT, particularly around technology. We tend to forget that we've actually solved a lot of these problems in the past. If we look in history, if we look around the second World War, and Bletchley Park in the UK, where you had a very similar experience as humans that we're having currently around the whole issue of data science, so there was an interesting challenge with the enigma in the shark code, right? And there was a bunch of men put in a room and told, you're mathematicians and you come from universities, and you can crack codes, but they couldn't. And so what they ended up doing was running these ads, and putting challenges, they actually put, I think it was crossword puzzles in the newspaper, and this deluge of women came out of all kinds of different roles without math degrees, without science degrees, but could solve problems, and they were thrown at the challenge of cracking codes, and invariably, they did the heavy lifting. On a daily basis for converting messages from one format to another, so that this very small team at the end could actually get in play with the sexy piece of it. And I think we're going through a similar shift now with what we're refer to as data science in the technology and business world. Where the people who are doing the heavy lifting aren't necessarily what we'd think of as the traditional data scientists, and so, there have been some unicorns and we've championed them, and they're great. But I think the shift's going to be to accountants, actuaries, and statisticians who understand the business, and come from an MBA star background that can learn the relevant pieces of math and models that we need to to apply to get the data science outcome. I think we've already been here, we've solved this problem, we've just got to learn not to try and reinvent the wheel, 'cause the media hypes this whole thing of data science is exciting and new, but we've been here a couple times before, and there's a lot to be learned from that, my view. >> I think we had Joe next. >> Yeah, so I was going to say that, data science is a funny thing. To use the word science is kind of a misnomer, because there is definitely a level of art to it, and I like to use the analogy, when Michelangelo would look at a block of marble, everyone else looked at the block of marble to see a block of marble. He looks at a block of marble and he sees a finished sculpture, and then he figures out what tools do I need to actually make my vision? And I think data science is a lot like that. We hear a problem, we see the solution, and then we just need the right tools to do it, and I think part of consulting and data science in particular. It's not so much what we know out of the gate, but it's how quickly we learn. And I think everyone here, what makes them brilliant, is how quickly they could learn any tool that they need to see their vision get accomplished. >> David: Justin? >> Yeah, I think you make a really great point, for me, I'm a Marine Corp veteran, and the reason I mentioned that is 'cause I work with two veterans who are problem solvers. And I think that's what data scientists really are, in the long run are problem solvers, and you mentioned a great point that, yeah, I think just problem solving is the key. You don't have to be a subject matter expert, just be able to take the tools and intelligently use them. >> Now when you look at the whole notion of team data science, what is the right mix of roles, like role definitions within a high-quality or a high-preforming data science teams now IBM, with, of course, our announcement of project, data works and so forth. We're splitting the role division, in terms of data scientist versus data engineers versus application developer versus business analyst, is that the right breakdown of roles? Or what would the panelists recommend in terms of understanding what kind of roles make sense within, like I said, a high performing team that's looking for trying to develop applications that depend on data, machine learning, and so forth? Anybody want to? >> I'll tackle that. So the teams that I have created over the years made up these data science teams that I brought into customer sites have a combination of developer capabilities and some of them are IT developers, but some of them were developers of things other than applications. They designed buildings, they did other things with their technical expertise besides building technology. The other piece besides the developer is the analytics, and analytics can be taught as long as they understand how algorithms work and the code behind the analytics, in other words, how are we analyzing things, and from a data science perspective, we are leveraging technology to do the analyzing through the tool sets, so ultimately as long as they understand how tool sets work, then we can train them on the tools. Having that analytic background is an important piece. >> Craig, is it easier to, I'll go to you in a moment Joe, is it easier to cross train a data scientist to be an app developer, than to cross train an app developer to be a data scientist or does it not matter? >> Yes. (laughs) And not the other way around. It depends on the-- >> It's easier to cross train a data scientist to be an app developer than-- >> Yes. >> The other way around. Why is that? >> Developing code can be as difficult as the tool set one uses to develop code. Today's tool sets are very user friendly. where developing code is very difficult to teach a person to think along the lines of developing code when they don't have any idea of the aspects of code, of building something. >> I think it was Joe, or you next, or Jennifer, who was it? >> I would say that one of the reasons for that is data scientists will probably know if the answer's right after you process data, whereas data engineer might be able to manipulate the data but may not know if the answer's correct. So I think that is one of the reasons why having a data scientist learn the application development skills might be a easier time than the other way around. >> I think Miriam, had a comment? Sorry. >> I think that what we're advising our clients to do is to not think, before data science and before analytics became so required by companies to stay competitive, it was more of a waterfall, you have a data engineer build a solution, you know, then you throw it over the fence and the business analyst would have at it, where now, it must be agile, and you must have a scrum team where you have the data scientist and the data engineer and the project manager and the product owner and someone from the chief data office all at the table at the same time and all accomplishing the same goal. Because all of these skills are required, collectively in order to solve this problem, and it can't be done daisy chained anymore it has to be a collaboration. And that's why I think spark is so awesome, because you know, spark is a single interface that a data engineer can use, a data analyst can use, and a data scientist can use. And now with what we've learned today, having a data catalog on top so that the chief data office can actually manage it, I think is really going to take spark to the next level. >> James: Miriam? >> I wanted to comment on your question to Craig about is it harder to teach a data scientist to build an application or vice versa, and one of the things that we have worked on a lot in our data science team is incorporating a lot of best practices from software development, agile, scrum, that sort of thing, and I think particularly with a focus on deploying models that we don't just want to build an interesting data science model, we want to deploy it, and get some value. You need to really incorporate these processes from someone who might know how to build applications and that, I think for some data scientists can be a challenge, because one of the fun things about data science is you get to get into the data, and you get your hands dirty, and you build a model, and you get to try all these cool things, but then when the time comes for you to actually deploy something, you need deployment-grade code in order to make sure it can go into production at your client side and be useful for instance, so I think that there's an interesting challenge on both ends, but one of the things I've definitely noticed with some of our data scientists is it's very hard to get them to think in that mindset, which is why you have a team of people, because everyone has different skills and you can mitigate that. >> Dev-ops for data science? >> Yeah, exactly. We call it insight ops, but yeah, I hear what you're saying. Data science is becoming increasingly an operational function as opposed to strictly exploratory or developmental. Did some one else have a, Dez? >> One of the things I was going to mention, one of the things I like to do when someone gives me a new problem is take all the laptops and phones away. And we just end up in a room with a whiteboard. And developers find that challenging sometimes, so I had this one line where I said to them don't write the first line of code until you actually understand the problem you're trying to solve right? And I think where the data science focus has changed the game for organizations who are trying to get some systematic repeatable process that they can throw data at and just keep getting answers and things, no matter what the industry might be is that developers will come with a particular mindset on how they're going to codify something without necessarily getting the full spectrum and understanding the problem first place. What I'm finding is the people that come at data science tend to have more of a hacker ethic. They want to hack the problem, they want to understand the challenge, and they want to be able to get it down to plain English simple phrases, and then apply some algorithms and then build models, and then codify it, and so most of the time we sit in a room with whiteboard markers just trying to build a model in a graphical sense and make sure it's going to work and that it's going to flow, and once we can do that, we can codify it. I think when you come at it from the other angle from the developer ethic, and you're like I'm just going to codify this from day one, I'm going to write code. I'm going to hack this thing out and it's just going to run and compile. Often, you don't truly understand what he's trying to get to at the end point, and you can just spend days writing code and I think someone made the comment that sometimes you don't actually know whether the output is actually accurate in the first place. So I think there's a lot of value being provided from the data science practice. Over understanding the problem in plain english at a team level, so what am I trying to do from the business consulting point of view? What are the requirements? How do I build this model? How do I test the model? How do I run a sample set through it? Train the thing and then make sure what I'm going to codify actually makes sense in the first place, because otherwise, what are you trying to solve in the first place? >> Wasn't that Einstein who said if I had an hour to solve a problem, I'd spend 55 minutes understanding the problem and five minutes on the solution, right? It's exactly what you're talking about. >> Well I think, I will say, getting back to the question, the thing with building these teams, I think a lot of times people don't talk about is that engineers are actually very very important for data science projects and data science problems. For instance, if you were just trying to prototype something or just come up with a model, then data science teams are great, however, if you need to actually put that into production, that code that the data scientist has written may not be optimal, so as we scale out, it may be actually very inefficient. At that point, you kind of want an engineer to step in and actually optimize that code, so I think it depends on what you're building and that kind of dictates what kind of division you want among your teammates, but I do think that a lot of times, the engineering component is really undervalued out there. >> Jennifer, it seems that the data engineering function, data discovery and preparation and so forth is becoming automated to a greater degree, but if I'm listening to you, I don't hear that data engineering as a discipline is becoming extinct in terms of a role that people can be hired into. You're saying that there's a strong ongoing need for data engineers to optimize the entire pipeline to deliver the fruits of data science in production applications, is that correct? So they play that very much operational role as the backbone for... >> So I think a lot of times businesses will go to data scientist to build a better model to build a predictive model, but that model may not be something that you really want to implement out there when there's like a million users coming to your website, 'cause it may not be efficient, it may take a very long time, so I think in that sense, it is important to have good engineers, and your whole product may fail, you may build the best model it may have the best output, but if you can't actually implement it, then really what good is it? >> What about calibrating these models? How do you go about doing that and sort of testing that in the real world? Has that changed overtime? Or is it... >> So one of the things that I think can happen, and we found with one of our clients is when you build a model, you do it with the data that you have, and you try to use a very robust cross-validation process to make sure that it's robust and it's sturdy, but one thing that can sometimes happen is after you put your model into production, there can be external factors that, societal or whatever, things that have nothing to do with the data that you have or the quality of the data or the quality of the model, which can actually erode the model's performance over time. So as an example, we think about cell phone contracts right? Those have changed a lot over the years, so maybe five years ago, the type of data plan you had might not be the same that it is today, because a totally different type of plan is offered, so if you're building a model on that to say predict who's going to leave and go to a different cell phone carrier, the validity of your model overtime is going to completely degrade based on nothing that you have, that you put into the model or the data that was available, so I think you need to have this sort of model management and monitoring process to take this factors into account and then know when it's time to do a refresh. >> Cross-validation, even at one point in time, for example, there was an article in the New York Times recently that they gave the same data set to five different data scientists, this is survey data for the presidential election that's upcoming, and five different data scientists came to five different predictions. They were all high quality data scientists, the cross-validation showed a wide variation about who was on top, whether it was Hillary or whether it was Trump so that shows you that even at any point in time, cross-validation is essential to understand how robust the predictions might be. Does somebody else have a comment? Joe? >> I just want to say that this even drives home the fact that having the scrum team for each project and having the engineer and the data scientist, data engineer and data scientist working side by side because it is important that whatever we're building we assume will eventually go into production, and we used to have in the data warehousing world, you'd get the data out of the systems, out of your applications, you do analysis on your data, and the nirvana was maybe that data would go back to the system, but typically it didn't. Nowadays, the applications are dependent on the insight coming from the data science team. With the behavior of the application and the personalization and individual experience for a customer is highly dependent, so it has to be, you said is data science part of the dev-ops team, absolutely now, it has to be. >> Whose job is it to figure out the way in which the data is presented to the business? Where's the sort of presentation, the visualization plan, is that the data scientist role? Does that depend on whether or not you have that gene? Do you need a UI person on your team? Where does that fit? >> Wow, good question. >> Well usually that's the output, I mean, once you get to the point where you're visualizing the data, you've created an algorithm or some sort of code that produces that to be visualized, so at the end of the day that the customers can see what all the fuss is about from a data science perspective. But it's usually post the data science component. >> So do you run into situations where you can see it and it's blatantly obvious, but it doesn't necessarily translate to the business? >> Well there's an interesting challenge with data, and we throw the word data around a lot, and I've got this fun line I like throwing out there. If you torture data long enough, it will talk. So the challenge then is to figure out when to stop torturing it, right? And it's the same with models, and so I think in many other parts of organizations, we'll take something, if someone's doing a financial report on performance of the organization and they're doing it in a spreadsheet, they'll get two or three peers to review it, and validate that they've come up with a working model and the answer actually makes sense. And I think we're rushing so quickly at doing analysis on data that comes to us in various formats and high velocity that I think it's very important for us to actually stop and do peer reviews, of the models and the data and the output as well, because otherwise we start making decisions very quickly about things that may or may not be true. It's very easy to get the data to paint any picture you want, and you gave the example of the five different attempts at that thing, and I had this shoot out thing as well where I'll take in a team, I'll get two different people to do exactly the same thing in completely different rooms, and come back and challenge each other, and it's quite amazing to see the looks on their faces when they're like, oh, I didn't see that, and then go back and do it again until, and then just keep iterating until we get to the point where they both get the same outcome, in fact there's a really interesting anecdote about when the UNIX operation system was being written, and a couple of the authors went away and wrote the same program without realizing that each other were doing it, and when they came back, they actually had line for line, the same piece of C code, 'cause they'd actually gotten to a truth. A perfect version of that program, and I think we need to often look at, when we're building models and playing with data, if we can't come at it from different angles, and get the same answer, then maybe the answer isn't quite true yet, so there's a lot of risk in that. And it's the same with presentation, you know, you can paint any picture you want with the dashboard, but who's actually validating when the dashboard's painting the correct picture? >> James: Go ahead, please. >> There is a science actually, behind data visualization, you know if you're doing trending, it's a line graph, if you're doing comparative analysis, it's bar graph, if you're doing percentages, it's a pie chart, like there is a certain science to it, it's not that much of a mystery as the novice thinks there is, but what makes it challenging is that you also, just like any presentation, you have to consider your audience. And your audience, whenever we're delivering a solution, either insight, or just data in a grid, we really have to consider who is the consumer of this data, and actually cater the visual to that person or to that particular audience. And that is part of the art, and that is what makes a great data scientist. >> The consumer may in fact be the source of the data itself, like in a mobile app, so you're tuning their visualization and then their behavior is changing as a result, and then the data on their changed behavior comes back, so it can be a circular process. >> So Jim, at a recent conference, you were tweeting about the citizen data scientist, and you got emasculated by-- >> I spoke there too. >> Okay. >> TWI on that same topic, I got-- >> Kirk Borne I hear came after you. >> Kirk meant-- >> Called foul, flag on the play. >> Kirk meant well. I love Claudia Emahoff too, but yeah, it's a controversial topic. >> So I wonder what our panel thinks of that notion, citizen data scientist. >> Can I respond about citizen data scientists? >> David: Yeah, please. >> I think this term was introduced by Gartner analyst in 2015, and I think it's a very dangerous and misleading term. I think definitely we want to democratize the data and have access to more people, not just data scientists, but managers, BI analysts, but when there is already a term for such people, we can call the business analysts, because it implies some training, some understanding of the data. If you use the term citizen data scientist, it implies that without any training you take some data and then you find something there, and they think as Dev's mentioned, we've seen many examples, very easy to find completely spurious random correlations in data. So we don't want citizen dentists to treat our teeth or citizen pilots to fly planes, and if data's important, having citizen data scientists is equally dangerous, so I'm hoping that, I think actually Gartner did not use the term citizen data scientist in their 2016 hype course, so hopefully they will put this term to rest. >> So Gregory, you apparently are defining citizen to mean incompetent as opposed to simply self-starting. >> Well self-starting is very different, but that's not what I think what was the intention. I think what we see in terms of data democratization, there is a big trend over automation. There are many tools, for example there are many companies like Data Robot, probably IBM, has interesting machine learning capability towards automation, so I think I recently started a page on KDnuggets for automated data science solutions, and there are already 20 different forums that provide different levels of automation. So one can deliver in full automation maybe some expertise, but it's very dangerous to have part of an automated tool and at some point then ask citizen data scientists to try to take the wheels. >> I want to chime in on that. >> David: Yeah, pile on. >> I totally agree with all of that. I think the comment I just want to quickly put out there is that the space we're in is a very young, and rapidly changing world, and so what we haven't had yet is this time to stop and take a deep breath and actually define ourselves, so if you look at computer science in general, a lot of the traditional roles have sort of had 10 or 20 years of history, and so thorough the hiring process, and the development of those spaces, we've actually had time to breath and define what those jobs are, so we know what a systems programmer is, and we know what a database administrator is, but we haven't yet had a chance as a community to stop and breath and say, well what do we think these roles are, and so to fill that void, the media creates coinages, and I think this is the risk we've got now that the concept of a data scientist was just a term that was coined to fill a void, because no one quite knew what to call somebody who didn't come from a data science background if they were tinkering around data science, and I think that's something that we need to sort of sit up and pay attention to, because if we don't own that and drive it ourselves, then somebody else is going to fill the void and they'll create these very frustrating concepts like data scientist, which drives us all crazy. >> James: Miriam's next. >> So I wanted to comment, I agree with both of the previous comments, but in terms of a citizen data scientist, and I think whether or not you're citizen data scientist or an actual data scientist whatever that means, I think one of the most important things you can have is a sense of skepticism, right? Because you can get spurious correlations and it's like wow, my predictive model is so excellent, you know? And being aware of things like leaks from the future, right? This actually isn't predictive at all, it's a result of the thing I'm trying to predict, and so I think one thing I know that we try and do is if something really looks too good, we need to go back in and make sure, did we not look at the data correctly? Is something missing? Did we have a problem with the ETL? And so I think that a healthy sense of skepticism is important to make sure that you're not taking a spurious correlation and trying to derive some significant meaning from it. >> I think there's a Dilbert cartoon that I saw that described that very well. Joe, did you have a comment? >> I think that in order for citizen data scientists to really exist, I think we do need to have more maturity in the tools that they would use. My vision is that the BI tools of today are all going to be replaced with natural language processing and searching, you know, just be able to open up a search bar and say give me sales by region, and to take that one step into the future even further, it should actually say what are my sales going to be next year? And it should trigger a simple linear regression or be able to say which features of the televisions are actually affecting sales and do a clustering algorithm, you know I think hopefully that will be the future, but I don't see anything of that today, and I think in order to have a true citizen data scientist, you would need to have that, and that is pretty sophisticated stuff. >> I think for me, the idea of citizen data scientist I can relate to that, for instance, when I was in graduate school, I started doing some research on FDA data. It was an open source data set about 4.2 million data points. Technically when I graduated, the paper was still not published, and so in some sense, you could think of me as a citizen data scientist, right? I wasn't getting funding, I wasn't doing it for school, but I was still continuing my research, so I'd like to hope that with all the new data sources out there that there might be scientists or people who are maybe kept out of a field people who wanted to be in STEM and for whatever life circumstance couldn't be in it. That they might be encouraged to actually go and look into the data and maybe build better models or validate information that's out there. >> So Justin, I'm sorry you had one comment? >> It seems data science was termed before academia adopted formalized training for data science. But yeah, you can make, like Dez said, you can make data work for whatever problem you're trying to solve, whatever answer you see, you want data to work around it, you can make it happen. And I kind of consider that like in project management, like data creep, so you're so hyper focused on a solution you're trying to find the answer that you create an answer that works for that solution, but it may not be the correct answer, and I think the crossover discussion works well for that case. >> So but the term comes up 'cause there's a frustration I guess, right? That data science skills are not plentiful, and it's potentially a bottleneck in an organization. Supposedly 80% of your time is spent on cleaning data, is that right? Is that fair? So there's a problem. How much of that can be automated and when? >> I'll have a shot at that. So I think there's a shift that's going to come about where we're going to move from centralized data sets to data at the edge of the network, and this is something that's happening very quickly now where we can't just hold everything back to a central spot. When the internet of things actually wakes up. Things like the Boeing Dreamliner 787, that things got 6,000 sensors in it, produces half a terabyte of data per flight. There are 87,400 flights per day in domestic airspace in the U.S. That's 43.5 petabytes of raw data, now that's about three years worth of disk manufacturing in total, right? We're never going to copy that across one place, we can't process, so I think the challenge we've got ahead of us is looking at how we're going to move the intelligence and the analytics to the edge of the network and pre-cook the data in different tiers, so have a look at the raw material we get, and boil it down to a slightly smaller data set, bring a meta data version of that back, and eventually get to the point where we've only got the very minimum data set and data points we need to make key decisions. Without that, we're already at the point where we have too much data, and we can't munch it fast enough, and we can't spin off enough tin even if we witch the cloud on, and that's just this never ending deluge of noise, right? And you've got that signal versus noise problem so then we're now seeing a shift where people looking at how do we move the intelligence back to the edge of network which we actually solved some time ago in the securities space. You know, spam filtering, if an emails hits Google on the west coast of the U.S. and they create a check some for that spam email, it immediately goes into a database, and nothing gets on the opposite side of the coast, because they already know it's spam. They recognize that email coming in, that's evil, stop it. So we've already fixed its insecurity with intrusion detection, we've fixed it in spam, so we now need to take that learning, and bring it into business analytics, if you like, and see where we're finding patterns and behavior, and brew that out to the edge of the network, so if I'm seeing a demand over here for tickets on a new sale of a show, I need to be able to see where else I'm going to see that demand and start responding to that before the demand comes about. I think that's a shift that we're going to see quickly, because we'll never keep up with the data munching challenge and the volume's just going to explode. >> David: We just have a couple minutes. >> That does sound like a great topic for a future Cube panel which is data science on the edge of the fog. >> I got a hundred questions around that. So we're wrapping up here. Just got a couple minutes. Final thoughts on this conversation or any other pieces that you want to punctuate. >> I think one thing that's been really interesting for me being on this panel is hearing all of my co-panelists talking about common themes and things that we are also experiencing which isn't a surprise, but it's interesting to hear about how ubiquitous some of the challenges are, and also at the announcement earlier today, some of the things that they're talking about and thinking about, we're also talking about and thinking about. So I think it's great to hear we're all in different countries and different places, but we're experiencing a lot of the same challenges, and I think that's been really interesting for me to hear about. >> David: Great, anybody else, final thoughts? >> To echo Dez's thoughts, it's about we're never going to catch up with the amount of data that's produced, so it's about transforming big data into smart data. >> I could just say that with the shift from normal data, small data, to big data, the answer is automate, automate, automate, and we've been talking about advanced algorithms and machine learning for the science for changing the business, but there also needs to be machine learning and advanced algorithms for the backroom where we're actually getting smarter about how we ingestate and how we fix data as it comes in. Because we can actually train the machines to understand data anomalies and what we want to do with them over time. And I think the further upstream we get of data correction, the less work there will be downstream. And I also think that the concept of being able to fix data at the source is gone, that's behind us. Right now the data that we're using to analyze to change the business, typically we have no control over. Like Dez said, they're coming from censors and machines and internet of things and if it's wrong, it's always going to be wrong, so we have to figure out how to do that in our laboratory. >> Eaves, final thoughts? >> I think it's a mind shift being a data scientist if you look back at the time why did you start developing or writing code? Because you like to code, whatever, just for the sake of building a nice algorithm or a piece of software, or whatever, and now I think with the spirit of a data scientist, you're looking at a problem and say this is where I want to go, so you have more the top down approach than the bottom up approach. And have the big picture and that is what you really need as a data scientist, just look across technologies, look across departments, look across everything, and then on top of that, try to apply as much skills as you have available, and that's kind of unicorn that they're trying to look for, because it's pretty hard to find people with that wide vision on everything that is happening within the company, so you need to be aware of technology, you need to be aware of how a business is run, and how it fits within a cultural environment, you have to work with people and all those things together to my belief to make it very difficult to find those good data scientists. >> Jim? Your final thought? >> My final thoughts is this is an awesome panel, and I'm so glad that you've come to New York, and I'm hoping that you all stay, of course, for the the IBM Data First launch event that will take place this evening about a block over at Hudson Mercantile, so that's pretty much it. Thank you, I really learned a lot. >> I want to second Jim's thanks, really, great panel. Awesome expertise, really appreciate you taking the time, and thanks to the folks at IBM for putting this together. >> And I'm big fans of most of you, all of you, on this session here, so it's great just to meet you in person, thank you. >> Okay, and I want to thank Jeff Frick for being a human curtain there with the sun setting here in New York City. Well thanks very much for watching, we are going to be across the street at the IBM announcement, we're going to be on the ground. We open up again tomorrow at 9:30 at Big Data NYC, Big Data Week, Strata plus the Hadoop World, thanks for watching everybody, that's a wrap from here. This is the Cube, we're out. (techno music)