(upbeat music) >> Hello, CUBE friends and welcome back to Vegas. Lisa Martin here with John Furrier. This is our third day of coverage of AWS re:Invent. There are somewhere between 50,000 and 60, 70,000 people here. The excitement is palpable. The energy in the room has been on fire since Monday night. John, we love talking, we love re:Invent. We love talking about AWS and it's incredible ecosystem of partners and we're going to be doing that next. >> Yeah, I mean 10 years of theCUBE, we've been here since 2013. Watching it grow as the cloud computing invention. And then the ecosystem has just been growing, growing, growing at the same time innovation. And that's this next segment with the company that we both have covered deeply. Boomi is going to be a great segment. Looking forward to it. >> We have, we have. And speaking of innovation and Boomi, we have a four-time cube guests back with us. Ed Macosky joined us, Chief Innovation Officer at Boomi. And it's great to see you in person. >> Yeah, great to be here. Thanks for having me. >> What's going on at Boomi? I mean, I know up and to the right, continues we'll go this way. What's going on? >> Yeah, we continue to grow. We're really focused with AWS on the cloud and app modernization. Most of our projects and many of our customers are in this modernization journey from an enterprise perspective, moving from on-premises, trying to implement multicloud, hybrid cloud, that sort of thing. But what we're really seeing is this modernization choke point that a lot of our customers are facing in that journey where they just can't get over the hump. And a lot of their, they come to us with failing projects where they're saying, "Hey, I've got maybe this anchor of a legacy data source or applications that I need to bring in temporarily or I need to keep filling that." So we help with integrating these workflows, integrating these applications and help that lift and shift and help our customers projects from failing and quickly bringing themselves to the cloud. >> You know, Ed, we've been talking with you guys for many many years with theCUBE and look at the transition, how the market's evolved. If you look at the innovation going on now, I won't say it's an innovator's dilemma because there's a lot of innovation happening. It's becoming an integrator's dilemma. And I was talking with some of your staff. Booth traffic's up, great leads coming in. You mentioned on the keynote in a slide. I mean, the world spun in the direction of Boomi with all your capabilities around integration, understanding how data works. All the themes here at re:Invent kind of like are in that conversation top track that we've been mentioning and Boomi, you guys have been building around. Explain why that's happening. Am I right? Am I getting that right, or can you share your thoughts? >> Yeah, absolutely. We're in a great spot. I mean, given the way the economy's going today, people are, again, trying to do more with less. But there is this modernization journey that I talked about and there's an explosion of SaaS applications, cloud technologies, data sources, et cetera. And not only is it about integrating data sources and automating workflows, but implementing things at scale, making sure you have high data quality, high data governance, security, et cetera. And Boomi sits right in the middle of providing solutions of all of that to make a business more efficient. Not only that, but you can implement things very very quickly 'cause we're a low-code platform. It's not just about this hardcore technology that's really hard to implement. You can do it really quickly with our platform. >> Speaking of transformation, one of the things John does every year ahead of re:Invent is he gets to sit down with the CEO of re:Invent and really does a great, if you haven't seen it, check it out on siliconangle.com. Really kind of a preview of what we're going to expect at the show. And one of the things Adam said to you was CIOs, CEOs are coming to me not wanting to talk about technology. They want to talk about transformation, business transformation. It's no more, not so much about digital transformation anymore, it's about transforming businesses. Are you hearing customers come to you with the same help us transform our business so we can be competitive, so we can meet customer demand? >> Oh, absolutely. It's no longer about tools and technology and providing people with paint to paint on a canvas. We're offering solutions on the AWS marketplace. We have five solutions that we launched this year to get people up and running very quickly based on business problems from disbursement to lead to cash with Salesforce and NetSuite to business-to-business integrations and EDI dashboarding and that sort of thing. We also have our own marketplace that provide these solutions and give our customers the ability to visualize what they can do with our platform to actually solve business problems. Again, not just about tooling and technology and how to connect things. >> How's the marketplace relationship going for you? Are you guys seeing success there? >> Yeah, we're seeing a lot of success. I mean, in fact, we're going to be doubling down in the next year. We're going to be, we haven't announced it yet, but we're going to be announcing some new solutions. >> John: I guess we're announcing it now. >> No, I'm not going to get to specifics. But we're going to be putting more and more solutions on the marketplace and we're going to be offering more ways to consume and purchase our platform on the marketplace in the next couple of months. >> Ed, talk about what's new with Boomi real quick. I know you guys have new connectors Early Access. What's been announced? What have you guys announced? What's coming? What's the new things folks should pay attention from a product standpoint? >> Yeah, so you mentioned the connectors. We have 32 new connectors. And by the way in our ecosystem, our customers have connected 199,970 unique things. Amazon SQS is one of those in that number. So that's the kind of scale. >> What's the number again? >> 199,970. At least that's the last I checked earlier. >> That's a good recall right there. Exact number. >> It's an exciting number 'cause we're scaling very, very rapidly. But the other things that are exciting are we announced our event streaming service that we want to bring to our cloud. We've relied on partners in the past to do that for us, but it's been a very critical need that our customers have asked for. So we're integrating that into our platform. We're also going to be focusing more and more on our data management capabilities because I mentioned it a little earlier, connecting things, if bad data's going in and bad data's going out, bad data's going everywhere. So we have the tools and capability to govern data, manage data, high quality solutions. So we're going to invest more and more in that 'cause that's what our customers are asking us for. >> Data governance is a challenge for any business in any industry. Too much access is a huge risk, not enough access to the right people means you can't really extract the insights from data to be able to make data-driven decisions. How do you help customers really on that fine line of data governance? >> Very specifically, we have as part of our iPaaS platform, we have a data catalog and data prep capability within the platform itself that gives citizens in the organization the ability to catalog data in a secure way based on what they have capabilities to. But not only that, the integrator can use data catalog to actually catalog the data and understand what needs to be integrated and how they can make their business more efficient by automating the movement of data and sharing the data across the organization. >> On the innovation side, I want to get back to that again because I think this integration innovation angle is something that we talked about with Adams Selipsky in our stories hitting SiliconANGLE right now are all about the partner ecosystems. We've been highlighting some of the bigger players emerging. You guys are out there. You got Databricks, Snowflake, MongoDB where they're partnering with Amazon, but they're not just an ISV, they're platforms. You guys have your own ISVs. You have your own customers. You're doing low-code before no-code is popular. So where are you guys at on that wave? You got a good customer base, share some names. What's going on with the customers? Are they becoming more developer oriented? 'Cause let's face it, your customers that working on Boomi, they're developers. >> Yes. >> And so they got tools. You're enablers, so you're a platform on Amazon. >> We are a platform on Amazon. >> We call that supercloud, but that's where this new shift is happening. What's your reaction to that? >> Yes, so I guess we are a supercloud on Amazon and our customers and our partners are developers of our platforms themselves. So most of our partners are also customers of ours and they will be implementing their own integrations in the backend of their platforms into their backend systems to do things like billing and monitoring of their own usage of their platforms. But with our customers, they're also Amazon customers who are trying to connect in a multicloud way or many times just within the Amazon ecosystem. Or even customers like Kenco and Tim Heger who did a presentation from HealthBridge. They're also doing B2B connectivity to bring information from their partners into their ecosystem within their platform. So we handle all of the above. So now we are an independent company and it's nice to be a central part of all of these different ecosystems. And where I find myself in my role a lot of times is literally connecting different platforms and applications and SI partners to solve these problems 'cause nobody can really see it themselves. I had a conversation earlier today where someone would say, "Hey, you're going to talk with that SI partner later today. They're a big SI partner of ours. Why don't they develop solutions that we can go to market together to solve problems for our customers?" >> Lisa, this is something that we've been talking about a lot where it's an and conversation. My big takeaway from Adam's one-on-one and re:Invent so far is they're not mutually exclusive. There's an and. You can be an ISV and this platforms in the ecosystem because you're enabling software developers, ISV as they call it. I think that term is old school, but still independent software vendors. That's not a platform. They can coexist and they are, but they're becoming on your platform. So you're one of the most advanced Amazon partners. So as cloud grows and we mature and what, 13 years old Amazon is now, so okay, you're becoming bigger as a platform. That's the next wave. What happens in that next five years from there? What happens next? Because if your platform continues to grow, what happens next? >> So for us, where we're going is connecting platform providers, cloud providers are getting bigger. A lot of these cloud providers are embracing partnerships with other vendors and things and we're helping connect those. So when I talk about business-to-business and sharing data between those, there are still some folks that have legacy applications that need to connect and bring things in and they're just going to ride them until they go away. That is a requirement, but at some point that's all going to fall by the wayside. But where the industry is really going for us is it is about automation and quickly automating things and again, doing more with less. I think Tim Heger had a quote where he said, "I don't need to use Michelangelo to come paint my living room." And that's the way he thinks about low-code. It's not about, you don't want to just sit there and code things and make an art out of coding. You want to get things done quickly and you want to keep automating your business to keep pushing things forward. So a lot of the things we're looking at is not just about connecting and automating data transformation and that's all valuable, but how do I get someone more productive? How do I automate the business in an intelligent way more and more to push them forward. >> Out of the box solutions versus platforms. You can do both. You can build a platform. >> Yes. >> Or you can just buy out of the box. >> Well, that's what's great about us too is because we don't just provide solutions. We provide solutions many times as a starting point or the way I look at it, it's art of the possible a lot of what we give 'cause then our customers can take our low-code tooling and say, wow, I like this solution, but I can really take it to the next step, almost in like an open source model and just quickly iterate and drive innovation that way. And I just love seeing our, a lot of it for me is just our ecosystem and our partners driving the innovation for us. >> And driving that speed for customers. When I had the chance to interview Tim Heger myself last month and he was talking about Boomi integration and Flow are enabling him to do integration 10x faster than before and HealthBridge built their business on Boomi. They didn't replace the legacy solution, but he had experience with some of your big competitors and chose Boomi and said, "It is 10x faster." So he's able to deliver to those and it's a great business helping people pay for health issues if they don't have the funds to do that. So much faster than they could have if had they chosen a different technology. >> Yeah, and also what I like about the HealthBridge story is you said they started with Boomi's technology. So I like to think we scale up and scale down. So many times when I talk to prospects or new customers, they think that our technology is too advanced or too expensive or too big for them to go after and they don't think they can solve these problems like we do with enterprises. We can start with you as a startup going with SaaS applications, trying to be innovative in your organization to automate things and scale. As you scale the company will be right there along with you to scale into very very advanced solutions all in a low-code way. >> And also helping folks to scale up and down during what we're facing these macroeconomic headwinds. That's really important for businesses to be able to do for cost optimization. But at the end of the day, that company has to be a data company. They have to be able to make sure that the data matches. It's there. They know what they have. They can actually facilitate communications, conversations and deliver the end user customer is demanding whether it's a retailer, a healthcare organization, a bank, you name it. >> Exactly. And another thing with today's economy, a lot of people forget with integration or automation tooling, once you get things implemented, in many traditional forms you got to manage that long term. You have to have a team to do that. Our technology runs autonomously. I hear from our customers over and over again. I just said it, sometimes I'll walk away for a month and come back and wow, Boomi's still running. I didn't realize it. 'Cause we have technology that continues to patch itself, heal itself, continue running autonomously. That also saves in a time like now where you don't have to worry about sending teams out to patch and upgrade things on a continuous basis. We take care of that for our customers. >> I think you guys can see a lot of growth with this recession and looming. You guys fit well in the marketplace. As people figure out how to right size, you guys fit right nicely into that equation. I got to ask you, what's ahead for 2023 for Boomi? What can we expect to see? >> Yeah, what's ahead? I briefly mentioned it earlier, but the new service we're really excited about that 'cause it's going to help our customers to scale even further and bring more workloads into AWS and more workloads that we can solve challenges for our customers. We've also got additional solutions. We're looking at launching on AWS marketplace. We're going to continue working with SIs and GSIs and our ISV ecosystem to identify more and more enterprise great solutions and verticals and industry-based solutions that we can take out of the box and give to our customers. So we're just going to keep growing. >> What are some of those key verticals? Just curious. >> So we're focusing on manufacturing, the financial services industry. I don't know, maybe it's vertical, but higher ed's another big one for us. So we have over a hundred universities that use our technology in order to automate, grant submissions, student management of different aspects, that sort of thing. Boise State is one of them that's modernized on AWS with Boomi technology. So we're going to continue rolling in that front as well. >> Okay. Is it time for the challenge? >> It's time for the challenge. Are you ready for the challenge, Ed? We're springing this on you, but we know you so we know you can nail this. >> Oh no. >> If you were going to create your own sizzle reel and we're creating sizzle reel that's going to go on Instagram reels and you're going to be a star of it, what would that sizzle reel say? Like if you had a billboard or a bumper sticker, what's that about Boomi boom powerful story? >> Well, we joked about this earlier, but I'd have to say, Go Boomi it. This isn't real. >> Go Boomi it, why? >> Go Boomi it because it's such a succinct way of saying our customer, that terminology came to us from our customers because Boomi becomes a verb within an organization. They'll typically start with us and they'll solve an integration challenge or something like that. And then we become viral in a good way with an organization where our customers, Lisa, you mentioned it earlier before the show, you love talking to our customers 'cause they're so excited and happy and love our technology. They just keep finding more ways to solve challenges and push their business forward. And when a problem comes up, an employee will typically say to another, go Boomi it. >> When you're a verb, that's a good thing. >> Ed: Yes it is. >> Splunk, go Splunk it. That was a verb for log files. Kleenex, tissue. >> Go Boomi it. Ed, thank you so much for coming back on your fourth time. So next time we see you will be fifth time. We'll get you that five-timers club jacket like they have on SNL next time. >> Perfect, can't wait. >> We appreciate your insight, your time. It's great to hear what's going on at Boomi. We appreciate it. >> Ed: Cool. Thank you. >> For Ed Macosky and John Furrier, I'm Lisa Martin. You're watching theCUBE, the leader in live enterprise and emerging tech coverage. (upbeat music)

>> From theCUBE Studios in Palo Alto in Boston, connecting with thought leaders all around the world. This is a CUBE conversation. >> Hi, I'm Stuart Miniman. and welcome to this special CUBE conversation. Always love when we get to talk to founders of companies, when they're drilling into some interesting technologies. I want to welcome a new guest to theCUBE as well as one of our CUBE alumni, sitting right next to me on the screen. First of all, we have Martin Casado, who is a general partner within Andreessen Horowitz. Martin great to see you. >> Its great to be here. >> And you've brought along Mike Del Balso also who is the co founder and CEO of Tecton recently out of stealth going to dig into a lot of the ML discussion. Mike, thanks for joining us. >> Thanks for having me on. Alright, so Martin look, you're no stranger to being a founder yourself, we've loved having you on theCUBE over the years. I have to get since we're getting you on here in 2020, we of course need to start with the fact that there's a global pandemic going on. and I'm curious from our standpoint, from an investment standpoint and looking at technology How does this make it a little bit different in 2020, say than you would've thought coming into the year? >> Yeah, so I think there's kind of a near term answer and a long term answer. I think the near term answer is people don't really understand what the broad impact is going to be. And so companies in the portfolio and the guidance that we do is to be conservative with cash. let's see how Q2 plays out and then let's figure out the right way to kind of operate the company in light of the macro changes. Long term however, it's very clear that every digital transformation project right now is being fast tracked. And as a result we think it's a huge boom to infrastructure. And as who been for the software. Right? like where in the past you could deal with kind of legacy setups that were on print. This is just not the case anymore. So take for a company like Tecton, Mike's company, like there's a lot of conversations that happen now. where the company is like, wow, we really need to have our infrastructure digitized and it all needs to be in the cloud, and all need to be remote and so forth. So we're actually seeing a ton of tailwinds even though there's uncertainty on the macro environment in the near term. >> Yeah. You make some great points, Martin absolutely. The companies that have actually gone through some digital transformation, the goals of that is number one I should be data-driven, number two I should be able to be much more agile. And that's what we need in uncertain times is to be able to react fast and answer it. Mike unfortunately I've talked to plenty of companies, you can't necessarily choose when's the right time to launch a company. When's the right time to do an IPO, trying to time the market. But sorry to say interesting times are upon us. So let's talk a bit about Tecton, give us a little bit of your background the team, the core team I believe coming out of Uber with the Michelangelo project that led to Tecton? >> Yeah. Great. So at Tecton we really focus on what we call operational machine learning, which is really about helping organizations really use machine learning and applied context, really powering customer experiences, powering business processes, things that really make it to production. And so we help these machine learning, AI efforts get past the finish line. And a little bit about the background of me, I used to work at Google as a product manager for the machine learning teams that power the ads auction. So the models that choose which ads to show. and run in real time and are highly productionized. and are really core to the business. And then I was at Uber after that and Uber helped start their first centralized machine learning team. And it was really the whole journey for Uber going from just starting to getting to tens of thousands of models in production. And a big component of that was a lot of the technology that we built there, the platforms and infrastructure that we built to support the different business teams. To be able to embed machine learning and AI products. And so what we're talking about, all these very applied use cases real time, fraud detection, ETA estimation, search pricing. All these things, when you think about with Uber. so through that journey of supporting and helping them get to 100 with machine learning. We built out this platform called Michelangelo, which is really a machine learning platform. It's really an end to end machine learning platform. Learned a lot of lessons as we helped out, dozens of teams. go through the full life cycle, start with starting a project. What is this, what does this mean? What does my business problem, how does it translate to a machine learning problem all the way to having a model in production monitored, and really fully productionized and kind of a growing core to that business. So we learned a lot of lessons from building that at Tecton. My co-founders are the other leaders of that project and we learned a lot of really important lessons that lead to the success of these machine learning projects and we're now focused on helping a lot of other organizations really start up their machine learning efforts and get these things into production. >> Yeah Martin, maybe you could give us a little bit of context here. When I think about repeatability of solutions, how much they scale, there's only so many Google's and Uber's out there. when I look back at the big data world, there wasn't a lot of repeatability, it seemed like everything was custom. What did you see with Tecton? What are you looking at in the ML space that made them such an attractive investment? >> Sure. So maybe let's just pull back and talk about what's going on in systems and infrastructure in general. And I actually think this is probably the biggest shift. Certainly I've seen in my career which is, it used to be, if you looked at a system, let's say a super but whatever system, the correctness of that system. and the performance of that system and the compliance of that system, and the security was dictated by the code that you wrote, right? You wrote bad code you made bugs, you had vulnerabilities in your code that would dictate the system. But more and more that's actually not the case. I mean these days kind of performance, accuracy, security compliance is actually dictated by the data that you feed into. Right? You create these models, you feed the data models, the data gives you output and the data that you feed in and like your work flows around those models who are really dictating things like pricing or things like fraud, these really important things. And unlike code, we don't have the tools to manage data in the same way. And so if you think of it we're moving kind of from this code economy, to this data economy more and more techniques to correct dictates the correctness of all of these systems. and we're talking about trillions of dollars of market cap But if you actually look at the tooling around it, it still feels like the seventies around code, which is like you've got fiefdoms and you've got a lot of tribal knowledge. And so we've been tracking this trend for a long time. We're investors in Databricks We've got a large data portfolio. I mean, it's very obvious if you look at what's happening with the cloud data warehouses, if you think like Redshift, BigQuery and Snowflake. The world is going data extracting information out of data. And so on the backdrop of that, we're like okay, you need to be able to think of data like you think of code. and have the tooling around it that helped makes the lives of people working with this stuff simpler, especially for the core use cases which is ML and AI. And to that end I think that this is broadly known in the industry but like looking in the leading companies is like a crystal ball into the future, right? Because they tackle a lot of the problems before the rest of the industry did. And Michelangelo was very well know as the leading project in this. It had a broad set of respect from the community and kind of created this notion of a feature store which has now been replicated. And so really this is like the preeminent project in one of the biggest macro transformations. Beyond that, we met the team that are fantastic. We've got great chemistry, we've got a lot of similar backgrounds. And so the investment was pretty straight forward from that. But I do think it's important to frame it in the context of this macro shift that's going on. >> Yeah. it can't be overstated how important data is. I do think we need a new analogy probably with what happened with the global pandemic. Everybody was talking about data being the new oil and oil is pretty deep right now. And data is definitely not losing its value. Mike, when I read some of the discussion about Tecton enables data scientists turn raw data into production ready features and predictive singles as signals it sounds really impressive. So help us understand kind of the core thing that you do and where we are in the product life cycle. >> Great. Well so a machine learning application there's fundamentally two components. Right? There's a model that you have to build that's going to make the decisions, given a certain set of inputs. And then there's the features which ended up being those inputs. that the model uses making decision. common machine learning infrastructure stats, really are split into two layers. There's a model management layer and a feature management layer. And that's an emerging pattern in some of the more sophisticated machine learning. stacks that are out there. And what we build at Michelangelo we really had this model management layer, this feature management layer, and we recognize that that feature management layer was the thing that really allowed us to go from not just zero to one, but one to end and scale out machine learning across a number of different used cases and allow individual data scientists to own more than just one model in production. And so really what's at the core of that is a few components. The first is just feature pipelines. So these are data pipelines that plug into the businesses raw data via batch streaming, real time data and turn those into features that are these predictive signals and models consume. The second part of that is a feature store, which catalogs these feature transformations, catalog these pipelines and draws, the output raw data or the output feature data. And then the third component is feature service. Making those features accessible to a data scientist when they're building their models. And to the models in the production environment so they can make these decisions sometimes needed in milliseconds for real time decisioning that is quite common. and a lot of high value machine learning applications. what Tecton really is, it's a data platform for machine learning that manages all the feature data and feature transformations that allow an organization to share the predictive signals. These features across use cases in reading catalog needs and understand what they are. And secondly get these into production so they don't get hung up in that final stage right before they're trying to cross the finish line with the machine learning project. >> Stuart: All right. And Mike the product today, my understanding of private beta. You do have some customers at that point, tell us a little bit about that. >> Yeah, we're at private beta with a number of customers. We just went into full production with it last month. A couple of other customers that I maybe shouldn't name on the air, but we are spending time engaging in kind of like deep hands-on engagements. with different teams who are really trying to set up their machine learning on the cloud. Figuring out how to get their machine learning in production. And it tends to be teams that are trying to really use machine learning for operational use cases. Really trying to drive real business decisions and power their product customer experiences. And not as much as a lot of the kind of like research algorithm research stuff, but we're really just trying to solve these core data problems that are preventing machine learning projects from being successful. >> Yeah. And it was interesting Martin. I was listening to some of what Mike was saying I'm like, okay. It's not quite the analogy of micro-segmentation. or separating the control plane or the network plane and networking, but there were some analogies there. What I want to ask you though is the role of data? I talked to Andy Jassy a couple of years ago. I asked him the flywheel for AWS for years was customers. How many customers they could get and I was wondering does data become that new flywheel? And there's the center of gravity's and the customers that can happen and monetize with going there. So I'm just curious your thoughts on that. >> So I think people don't appreciate how different data is than code. And so I just want to start there because I think it's really germane to this topic. So listen code is like a finite state. Right? It's like, it's lines of code. You can build it, you can modularize it, It's like building a house. And so the tools that you put around code kind of reign in, what's already a fairly low entropy system, like a fairly orderly system. Data On the other hand, data is like the natural world. It's all of the complexity of the universe. Right? It's the behavior of humans. It's temperature readings and there's so much more complexity. and there's so much more entropy in data that the way that you deal with it is so fundamentally different than you have to deal with code. And so we've all of these and so I wanted to start that with we've heard all of these analogies about data, data is the new oil data is for the value, et cetera, et cetera, et cetera. But a lot of it's tautological, meaning yes, of course there's value in data. Yeah. Yes. If you have proprietary access to data, you've got proprietary access to data. But what we don't really know is how do you take data and reign it in? So you can use it in the same way that you use software system. We actually don't even know how to do that and so talking about things like data network effects and extracting data is a little bit preliminary because we still actually don't even understand, like how much work it takes to mine insights from data. What I do know you need, I do know you need the tools to do it and I do know that those tools are quite different. and so I think that we're now in this era building the tooling that is required to extract the insights of that data. And I think that's a very necessary step and this is where a Tecton comes in, to provide that tooling. And I think once we have a better handle on that then we can start asking these deeper questions, which I think are great questions. But the things like how defensible is data? Do you have network effects of the data? can you put in a finite amount of effort and extract signal at all times? Like how messy is data, et cetera. So I think that's kind of where we are in this journey, which is exactly why you need companies like Tecton to help answer. >> Alright. So Mike there's been the promise of really unlocking data now. There has been a really interesting discussion point for the last five or 10 years. The company is named Tecton, I've read some of the blog posts and talk about the Cambrian explosion and changes there. So give us if we're looking forward, you've just come out of stealth. What is success for Tecton two to three years out from now? >> Yeah, I think the biggest thing is we're trying to help organizations. Recognize that their data really is an asset and treat their features like assets. And when we can get to a point where organizations that teams that want to use machine learning and production don't need to throw a million data engineers at a problem. And we get added to a point where machine learning is not, a special team of experts that are super expensive that you kind of leave in the corner of your building and hope they come back 18 months later with some project that is showing some value, that would be success for us. we really are dead focused on the problems that are preventing these projects just from getting into production. And when we see the industry as a whole have seen success with these machine learning projects, I think we will have our mission accomplished. >> All right, Martin, I'll give you the final word to the opportunity you see in front of Tecton. >> I honestly think the data industry is going to be 10 X the computer industry. I just think like with compute you're building houses from the ground up and there's a ton of value there. I think with data is you're extracting insight and value from the universe, right? It's like the natural system. And every company has data and lots of data and all of it has some information. And so I think that this is a chance to be a very, very pivotal company. in democratizing access to data. So I think that the opportunity is enormous. >> Well, Martin, thank you for joining us again on the update, Mike, thank you Welcome to being a CUBE alum. Definitely hope to have you back soon to track the journey, congrats on step one out the door and best of luck going forward. >> Thank you. >> That's great. Thanks too >> All right. Be sure to check out the cube.net. for the upcoming events that we have today they're all virtual, but the interviews are all there as well as all the archive. I'm Stuart Miniman and thank you for watching theCUBE. (soft upbeat music)

>> Announcer: Live from Orlando, Florida. It's theCube covering PentahoWorld 2017, brought to you buy Hitachi Ventara. >> Welcome back to theCube's live coverage of PentahoWorld, brought to you of course by Hitachi Ventara. I'm your host, Rebecca Knight, along with my cohost James Kobielus. We are joined by Stephano Celati. He is a Pentaho Solutions consultant at BNova. Thanks so much for coming on theCube, Stephano. >> Thank you for having me. >> So I should say congratulations are in order because you are here to accept the Pentaho Excellence Award for the ROI category on behalf of LAZIOcrea. Tell us about the award. >> Yes, as I was saying, I'm really proud of this award because it is something that is related to public administration savings, which is a good thing, first of all for me as a citizen, let's say. This project is about healthcare spending. In Italy the National Healthcare Services allows the drugstore to sell medicines to total or partial reimbursement by NHS itself. And they also have the possibility to replace the medicine with a generic drug which normally costs less to the people and also to the health service itself. So a couple of years ago (speaks in foreign language) which is the political area to which Rome belongs just to explain, launched a new project to monitor, analyze and inspect the spending flow in drugs. So we partnered with LAZIOcrea to create a business analytics platform based on Pentaho obviously, and which collects all the data coming from the prescriptions and store it in an analytical database that is Vertica, and uses PDI/ETL tools to store this data. >> That's for Pentaho Data Integration. >> Yes, PDI is Pentaho Data Integration, good point. And after that we present the data in terms of reporting, analysis, dashboards, to all the people that are interested in this data. So we talk about regional managers, we talk about auditors, and also to local district users which are in charge of managing the expenditure for drugs. The outcome of this project was real impressive because we had an expenditure fell by 3.6%, which in a region where we have more than 200 million prescriptions every year means 34 million Euros in a years. >> Rebecca: Wow. >> So it was really huge result. We were very happy about that. And it was so simple because simply monitoring better the expenditure, monitoring how they deliver the drugs out, what kind of medicine they prescribe and targeting what pharmacies sell to the end user just gave these impressive results. And this year they are forecasting for 41 million Euros in savings more, so it's a huge result. It's something that is for us really a good result. >> So here in the U.S., I mean we have problems very similar to what you just described in Italy. And just putting the transparency around the data would be a huge revelation for the United States, too. How big a departure was it in Italy? >> Well, it was a really a big problem to start because they didn't have any system to collect all this data. So they had to set up everything from scratch, let's say, just by acquiring the paper where the physician writes the recipe, so it was not that easy to build it from scratch. But after that the region has had the opportunity to monitor this data and also to publish this data, which is something that in Italy is really relevant in this moment because we are talking about open government, we are talking about open data, and so again, the result was really impressive. >> Do you see any follow on opportunities to use this data for other purposes other than the initial application? >> Yes, we already experienced a different usage of this data because during the last major earthquake we have in 2016 in this area, those guys from LAZIOcrea were able to produce a list of mostly the drugs in that area just in a couple of hours, just by using the ETL and setting up this list that somehow help the first aid units in giving the right assistance on time. And next steps will be about hyper prescriptions because we want to monitor if there are any doctors that prescribe drugs that are not really necessary. And we also try to move our inspection also to hospitals because when you do a surgery, you get medicine, you get a lot of assistance in the hospital. So we want also to monitor that kind of the aspect, which is again in charge of the health system. >> To make sure that the right medicines are being distributed to the right regions at the right time for the intent to likely-- >> Yes, this could also lead to something that is a correlation analysis, meaning what is your pain and what are you assuming so that they can have an historical data they can use to prescribe better medicines. >> But the anecdote he was sharing about the earthquake too is really compelling too, if you think about a public health crisis and outbreak of some sort, to be able to get drugs quickly to those in needs, it's really astonishing. >> Again, this morning we were talking about data lake. This is a sort of data lake. We found several ways to use that data, to fish them back from the data, let's say from the lake, and it's really impressive what you can do if you have the right information and you know how to use it. >> How do you see the market developing over the next year, next five years? >> Yes, the problem in Italy is that the market is not so responsive to innovation like others, let's say U.S. or U.K. and Europe. So for this reason my company Bnova set up annual event which is called Big Data Tech, and the purpose of this event is to spread knowledge about big data systems, products, architecture and so on, which helps companies in knowing better what they can do with these platforms. So in the next month we see a lot of opportunities. Generically speaking data mining field, we start talking about predictive analysis, we start talking about smart cities and other stuff like that. So again, we will need maybe to enter in a new phase of let's say (mumbling) because companies like BNova and others that operate in this field of business analytics need to put to general knowledge what other innovative companies are doing. So in the next month we will for sure move to newer architectures, new technology, and we will have to support all the companies with this kind of stuff. >> In terms of the new technology you're moving to, is there a role for the internet of things, both in your plans and really in terms of the Italian market. What sort of potential applications are there for IOT related perhaps to the use of it with health data going forward in Italy? >> Yes, also for healthcare, but in Italy the IOT team is a parallel line that is growing thanks to a governmental initiative which is called Industry 4.0, which encourages the usag of interconnected machines, connected to the internet, so classical approach of the IOT field. So with this new approach and the government sustain we believe that the IOT will have a big improvement in the next years. Again, we are talking about Italy, so we are not so fast in growing. But again, we are starting to talk about smart cities for energy saving, sustainable energy and other stuff in which the IOT plays a key role. So as far as our business is concerned, that is business analytics, so on top of that we see a lot of opportunities coming from predictive analysis, which means to prevent the maintenance of a machine, for example, or to use virtual reality to simulate a laboratory test and other stuff. So with these opportunities for sure the usage of data mining tools, such Wake Up when we're talking about Pentaho Solutions, could be a great advantage because you will apply the knowledge to your data. So you will not only analyze the data, but you will also extract some sort of knowledge from the data which can help companies. >> Of course, Italy is where the renaissance began, and it just sounds like you, I mean renaissance use of analytics to help the Italian people and the Italian economy to continue to grow and innovate. >> Stephano: Yes, yes. >> So I want to see not a data lake, a data colosseum, that should be on your to do list. >> I want a data gallery with lots of data masterpieces hanging on the walls all around Italy. >> Exactly. >> You'll be the new Leonardo and Michelangelo. >> Stefano , I love it. Well, thank you so much for coming on theCube. >> Thank you for having me. >> I am Rebecca Knight for Jim Kubielus. We will have more from PentahoWorld just after this.

>> Live from Boston, Massachusetts. It's theCUBE covering Red Hat Summit 2017. Brought to you by Red Hat. {Electronic music} >> Welcome to the session wrap of the Red Hat Summit. I am your host, Rebecca Knight, along with my co-host Stu Miniman. Wrapping up three great days of open source talk. Where are we, Stu? Tell us the state of Red Hat, the state of open source. What have we learned? >> You mean, beyond we're in the seaport district of Boston, Massachusetts, you know, a couple blocks away from >> or the heart of open source the new open innovation lab coming from Red Hat. So, Rebecca it's been a lot of fun with you these last couple of days. >> I feel the same way. >> Did over thirty interviews: executives from Jim Whitehurst you know on down to many of the product teams many people participating greatly in open source, open innovation award winners, the women of open source award winners, open invest in lab participants. A lot of topics but okay Red Hat itself. I've worked with Red Hat in various roles in my career for quite a long time. We didn't talk a lot about Linux this week. >> Stu, Stu, Stu I've got to stop you Linux is containers, containers is Linux. So we're hearing so much about containers it's the same diff. >> Yeah, well I got the t-shirt "Linux is containers, containers are Linux" however, if I even look at Red Hat's messaging Red Hat Enterprise Linux is like the first platform what they built around and it's a little surprising that they didn't at least in the conversation we had, it was very much about some of the newer things coming into the show I said What's the progress that they've made around some of the cloud offerings, some of the management offerings, Ansible, weaving its way into a lot of the products. OpenShift really maturing and expanding the portfolio with things like the OpenShift io to be able to really help with application modernization. Middleware progressing even heard a little bit of the future where they are doing things like server lists. So Red Hat's making good progress. We love when we do these shows multiple years is they talk about it, they deliver on it, and in the way a couple guests talked about there's a little more transparency in open source and being part of all of these communities you have some visibility as to where you're going it doesn't mean that things don't slip every now and again and not every piece makes it into the product lease that they're expecting, but they've made great progress. Linux still is just a mainstay. It's a piece of lots of environments. The ecosystem reminds me of the same way I talk about OpenStack which we'll go into next week. We had a great session with Radhesh towards the end here talking about OpenStack in many ways is like that it's weaving its way into lots of infrastructure pieces some we'll dig into more this week, but let's focus on this week for now. >> Right, so you said we didn't talk a lot about Linux. I set you straight there. But what else did we, what else did you not hear? What do you remain skeptical of? As you said, Red Hat seems to be going from strength to strength. It had two point four billion in revenue this year. >> Yeah it did. For 2016, two point four billion in revenue and three billion in bookings >> Right And there was, I read a financial report that Jim Whithurst said Golda Company is five billion within five years. And you look at it and you say okay from two point four to five well, you know >> yeah actually if it was three billion in bookings and I think back to three years ago when we first started it was around two billion dollars that was almost a 50% growth rate in three years. So, if three years from now we do 50% growth rate we're going to have three to four point five. Of course the math is not linear, there's scaling of the company, there's lots of products in here, but they've got a big tam. >> Ambitious but achievable. >> Ambitious but achievable. The question we've had for a bunch of years is when I look at the cloud. Public cloud is affecting a lot of the traditional infrastructure companies. Red Hat is a software company. They're an open source company. We heard the cloud messaging. Microsoft and Google up on stage. Andy Jassy on video. That was a big question coming in. What about Amazon? How close will Red Hat do? Amazon actually has their own AMI for Linux which means I can get a package for Linux from Amazon not only that I could take that package outside of Amazon and put it in a data center so I could use the same type of Linux for AWS to work with Red Hat to take RedShift make what's deeper integration in the public cloud with AWS and if I put that on premises I'm going to have access to the AWS services so that tighter application integration for what they're laying out really the open hybrid cloud. Red Hat terminology, we'll see if other people take that up. But really it's a multi-cloud world and Red Hat has a good position to live in lots of those environments and provide and really help solutionize and give really almost that almost adult supervision that the enterprise wants for all of these open packages. So I was heartened to see the progress made. Strong ecosystem. As always, you know passionate customers, developers, and really just heartwarming stories of you know making the world a better place. What was your take on those pieces? >> Yes, absolutely. Those are really what you come away remembering. It is the story of a woman saving a man's life in a park in Singapore. It is the story of an emergency room doing a better job of serving its patients. It is scaling up technology use in the developing world. This is what you come away. And you say that is open source. >> Maybe next year that apple you get at the grocery store won't have been sitting there 18 months. >> Well maybe. But in a code climate. Boston going to be beautiful year round. No, but so I really do agree and that is I think what Red Hat did so brilliantly at this summit. Is really showcasing the ways in which this technology is having an impact at transforming industries obviously, helping businesses make more money, but also really doing a lot of good. >> Yeah, absolutely. And Rebecca I want a big shout out to the community here. This is a community show. Red Hat is a great participant of the community. We talked to Jim Whitehurst they want to help raise up the community it's not about Red Hat leadership. We don't hear number one at a show like this, we hear where they're participating and when they get involved they go deep. We heard about OpenPOWER. How excited they are that Red Hat you know getting involved and working in some of these pieces. So, we could not be here without Red Hat support. It's our fourth year doing the show. We had a blast with it. We see Red Hat at a lot of shows. They bring us great customers, their ecosystem partners and their executives. And it's been a pleasure to cover it. >> Yeah. No, I couldn't agree more and I do think, just in terms of what your talking about, the humility of the Red Hat folks is that they aren't going banging drums of we're number one in this and number one in that and you sort of think, "okay, blah, blah." No, they don't at all. They really are saying, "No we're about making our partners and our customers shine." >> Yeah, yeah. What's going to happen with the future of jobs? You know where are people going to work these days in the future? >> How will they work? >> Rebecca: What kind of processes will they work with? >> We've all said it's very much a global ecosystem here. Got to interview quite a few international guests here and hear how technology is spreading, how people are interacting, how innovation happens in a global environment. I'm sure ties back to a lot of the things that you write about. >> Absolutely. And I think, that Radhesh some of his words of wisdom was technology is the easy part what we need to be fundamentally rethinking is how we write these applications, how we develop these applications, how we design them, and how we deliver them. And, also really bearing in mind the end user. And, that is what we learned in a lot of our other sessions. Is really thinking about that. We heard from another person you know your competitor is maybe not necessarily the competitor you're thinking of it's the last app you opened or the last application that that company was using and what is drawing them toward that application or that technology or that infrastructure and not yours? [Stu]- Right. >> And so it's really thinking much more broadly about technology and who you're competing with and how you're working. >> Yeah, that was it was a bank. I loved that. They're like we're not competing against other banks it's like where's that other attention span that you have. >> Rebecca: Right, where are your eyeballs. >> One of my favorite lines is you know what you, Michelangelo, and Einstein have in common? You only have 24 hours in the day so you need to make sure you take advantage of that. That's the kind of thing that >> That's depressing Stu, when you leverage >> I don't know. the community. I thought it's inspiring. >> Okay. You know we can do >> Good great things when we work together and do that. So, we're always like oh I'm too busy or I don't have time it's like hogwash. >> Right. >> That's not the case. I'm inspired and fired up after all the conversations we had especially some of these great users here and looking forward to the next one. >> You're looking forward to the next one, you're looking forward to OpenStack coming up. >> Yeah, oh my gosh so right. >> Got to plug it. >> So Rebecca next week we're both going to be on theCUBE but in two different locales. Our team is in the midst of the sprint that is the spring tour. So we had the Micron event and we're here. Next week our team is at Service Now Knowledge, we're also at DELL EMC World in Vegas, we're at OpenStack Summit back in Boston. We've got some of our teams going to Microsoft Build. I'm sure we'll have analyst reports follow up from there. Boy do we have more shows than I can mention through the rest of May and June and beyond. Check out siliconangle.tv to catch all of them. Rebecca I'm going to let you do the close, but I have to say a big thanks to our team here and remote. >> Yes, yes. Leonard, Chuck, Alex, Ava. >> We love you all. Jeff and the team back there. You know we were doing some cool things playing with Facebook Live as part of this event, we always love playing around with some of the new technologies finding more ways that we can help reach you. We always appreciate your feedback. And Rebecca take us on home. >> Thank you so much for joining us here at theCUBE Red Hat Summit, Boston, Massachusetts. I'm Rebecca Knight for Stu Miniman, Thanks so much. {Electronic 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)

>> A cube at DMC World twenty fourteen is brought to you by D. M. C. Redefine, see innovating the world's first converged infrastructure solution for private cloud computing brocade. Say goodbye to the status quo and hello to Brocade. >> Welcome back to the Cube. This silken angle TVs live wall to wall Coverage of DMC World twenty fourteen here in the Sands Convention Center in Las Vegas. We've got three days to stage is over eighty guests. Lots of practitioners, execs, business leaders got a special segment. I'm bringing you today, bringing onto two thirds of the geek whispers, podcasts, Those in the story for the virtual ization and Claude Communities. No art is to guess. Well, let me introduce it's John Troyer, who's making his debut as the founder of tech reckoning. >> Thanks for having me. >> And we've got Amy Lewis influence marketing from Cisco. Name is your first time on the Cube, so, you know, welcome to the program. >> Thank you for having me on. >> All right, so So, guys, you know, we've been to a lot of conferences way we've hung out with, You know, the various influencers bloggers. It's changed a lot. This is my twelfth year coming M. C World. If you had told me twelve years ago some of things I'd be doing at this show, I wouldn't have believed you. I mean, I was one of the guys in a polo that only got out of out of the office once a year to give a presentation and, you know, talks in people about some cool tak um, and you know, social media is one of those things that, you know turn my career. Eleven. So you know what? Let's have a conversation about what's going on in the industry with kind of community influences and everything. John, maybe you could start us often. You know, Maybe if it leads in tow your new gigs? >> Sure, sure, on one on one, and things have changed. On the other hand, the same dynamics are playing out. Buying the buying cycle has changed. The buying process has changed. Customers are looking much more to their peers and not to traditional media analysts. Marketing folks, they can't find more ads. You can't send out more E mail. So what do you do? You need to get part of the conversation. We've been saying that for five or ten years, that's actually happened. Now the folks that were early on into the blogging space have turned themselves into communicators as well as technologists. We've seen, you know, their careers have have gone and all sorts of interesting places, for instance, you. But I think now that even we could talk about his art Is blogging dead? But I think now we're seeing it. We're seeing social media not as a trade or a practice practice, but simply a tool set that we all use. So that's all I'm saying is it's a It's more of a it spread throughout our organization. Not so much in one tiny niche, right? >> Yeah, Jonah, I love that point. I I I've been preaching for a bunch of years that this is an important skill, something you have to have their wonderful tools. But you've been doing community for a lot longer than Social Media has been around, and, you know, so it's peace, Amy, your influence marketing. What would please way out on this? >> Yeah, I chose the title, actually myself on purpose. To say it's not just social media, think social Media is very important, but like John was saying that to me is a set of tools. They're important platforms or important communications channels, but influencers the people who between the term citizen analysts they are unpaid analyst. But people are very passionate about technology, and they want to write on block and share, really engage their community. That's an important group of people. It's a really a buying center, and we have to find new ways to address them. So community is more important than >> ever. Citizen analysts thought, Let's focus that >> some of the >> people you know, I say some people goto event and they get it, get it, get wined and dined and they get to, you know, write about a bunch of stuff I'm like, you know you're better than journalists, you know, you'll You know you do some really good stuff and sometimes it's a little bit too friendly to the people that are doing it. So you know where do you see the role of kind of the press? You know, the analyst and the influencer? >> It's a great question I've been checking. We need to abstract the or chart. It is. It is a complicated question, but I think the traditional presses really trained and rightfully so in giving us that neutrality. So that is still a very important role. I think the analysts are paid Tio Tio, analyze particular sets, etcetera. They have nation specialty. I think the citizen analyst is interesting because they are what you don't know about the neutrality. But you do know that there are people who roll up their sleeves and really touched the technology. So that becomes a very interesting set because they really care about the technology Kazakh but could become their problem if they don't, you know, raise our voice and sort of engaged with technology and let the community know what, what the new trends are, what they need, what business needs. Our etcetera gives us a really applied version, the PR in the e R outside. >> Don't you want to comment on matter? >> I mean, these are the folks that they lose their jobs if they picked the wrong technology. So they have much more. Their discussions have it. They have more skin in the game. >> Aye, that's right. If you've got the practitioner, you know whether it be the end user sometime times it's the you know channel guy that they do that that's good, You know? What about the people inside the corporations that are also using these? >> I'm super bullish about the use of employees as advocates and evangelists in our community, both for technical education. And for the commercial part of our conversation in the enterprise space, we don't sell solutions with Russia. Your hair's a pressure and very nice calm. Give me a call. We sell it with relationships with people. I've been working on the social media since it existed, I suppose. And what we've seen over and over again is the social channels are really great for getting the word out. But without that personal component, it's like just handing out brochures. So you need your employees out there. You need your employees talking to folks. You need your employees without their representing your brand, just like they would have an event. I've seen that at something. On one hand, it's something that's so trivial that we all agree it's true. On the other hand, I don't. I think a lot of people are just realizing that now. >> So, John, you know, there's some some big companies, you know, creative certification programs to do some of this. There's some companies that just, you know, sign everybody up and, you know, it could be kind of an echo chamber or things like that. You know what? What do you see in these days? To kind of help out. You know the community >> well. There's a lot of software and a lot of programmatic things you could do. Those may be useful in terms of organizing you. It comes down to the people in the culture of the company and help much. You trust your people to go out. I think the best thing we can do is sit up platform for folks to be able to, to communicate. I think that's actually what Amy does really well at Cisco. >> X. It's, um I always talk about influence marketing as being people, platforms in content. And so I agree. I think that we sorted out some of the platform issues as we've learned about social media and grew up with it. I think that we are still working out the people in the content side and what's appropriate, how we can join together and do that and how we can creates a mute platforms may be using the tools of social tio to drive the conversation forward. >> All right. So, I mean, I got one for you. You know, how do we balance the kind of creation of information and kind of the community and fund? I mean, you do a lot of fun event you've got, you know, awful club this week. You've got, you know, bacon, stack and B bacon and bacon. I e I mean, I can't keep track of you, deport vacants and everything. And, you know, there'd be some executives here that would be like that, That social stuff. And they're playing games and things like that. So how do we balance kind of attic business value and greeting, you know, value to the community. And, you know, having fun in building community. >> No, it's a great question. A couple of years ago, I got a text in the middle of the night that said, Please explain to me how the bacon is a marketing play. Please explain this and you know, I need a power point slide. So if you've never had to explain, be bacon on the power points, I for that challenge out to everyone. But I think in the last couple of years people started to see it more and more as we're, uh, we're similar to the sales role, and that's how we've sort of changed the language. So I perform a sales like function, except I don't carry a quota. So it is about building the relationship like John was saying, and it is about balancing fun with your intent. So I think that if you create a fun environment, if you create an openness and willingness to listen, then the good things will follow. So you form the relationships of people. You open up their ability to create content with you because they don't feel under attack. They're ready to share. And again, it's it's kind of a magical formula. Be nice and create opportunity. >> Yeah, so >> I think we'll part of it's a generational ship. I think part of it a generational shift and part of it is a temperamental she So tradition again, going back to sales traditional enterprise sales. You might go and play golf with somebody, cause that's what you enjoy doing for our kind of geeks. Our golf is eating bacon and talking about the duplication strategies, right? That's where we're having the most fun. So it's It's just it's same sort of thing. Just a shift in generations. >> Yeah, I wonder if you know what, what role this community help in kind of careers. You know, I think you know, we're talking so much of these shows about, you know, if your storage admin. If you're networking admin and you know you're down there, you know, configuring Luns or setting up the land, you know, we're going to have a job in a couple of years because automation is gonna change. You know, how much does the community help in kind of those career paths and education? >> So, John, I think we should interview stew on this one. Should we have the geek whispers takeover. I think this is your great example. You've talked about you, you were on a career path and we hear this a lot, and when you raise your hand to volunteer, we sort of jokingly call the spokes uniforms. You both really enjoy the technology and like to communicate about it. When you raise your hand and make yourself known to the community, to your employers, to the world at large, it gives you different opportunities. And I think I don't think you go into technology really without wanting to have an evolving, exciting career. So I think that he's becoming proficient in these tools. Joining your community is an opportunity to learn from your peers to get back to your peers and to raise her profile and open yourself up to the possibility of a new opportunity or a new idea or different engagement. A new way to learn >> In today's business environment, communication is a key part of whatever you do, even if you're the guy sitting there configuring the lungs, because if you're not communicating with your teams and the application teams and the storage of network virtualization team, you're not going to succeed so I think that's an important part of it, right? Being a communicator, absolutely critical and art. Barney. >> All right, so either one of you feel free to answer, but I think back to my early days, you know, two thousand eight, I was so excited when I got invited to a couple of conferences. A blogger, you could kind of get a pass, and I would, You know, ten might take my own vacation time and usually spend that on expenses because my employer at the time didn't get it. It was this innovation conference in, like, in a New York City with four hundred people, and it was like, kind of amazing. I've seen people go to B m world on their own dime where they can get a pass. I mean, you know, it's great to see when you when you got the passion. So I guess the question I wanted to ask is, you know, with companies today, who should they be inviting? How do they do it? You know? You know. Is it you know, the blogger Or is it the, you know, empty Alexis co expert? You know, bm where be expert, you know, What? How's that? How's that changing? Or is it >> changing? Well, I think what you've seen happen over the years is something that was a little more unstructured, which was a kind of blogger relations program. Working with both customers partners, employees in your ecosystem has turned into something a little more formal. We created the V Expert program in two thousand nine to formalize what we were already doing. It's an analogy to the endless relations, press relations, investor relations, sorts of programs. So I mean, it's it's it's a little more buttoned up. It's a little more of a membership thing, but we I know both of DMC and BM where and it Cisco, Francisco champions to try to embrace all the folks that are out there blogging. I think you know, if you're a market or you need to make sure that you're keep your eyes open and you don't just talk to the people that you've gathered in your living room, Bye. You know, a lot of it's pretty easy if you're enthusiastic about technology, if you're engaged with the technology, if you put some effort into it, it's actually pretty easy to get involved with one of these programs there, there, there and there, there, fourth of people in them right there. They're not there to say the glory of the emcee and glory of Cisco and glory of'em, where they're there to help you with your career. They're there to give you tools to give you networking and, you know, hopefully get you to places like this. So I encourage everybody that that's interested in starting, you know, go ahead and get started. It's easier than you think to get involved. >> I agree with that, and I think that way want to be almost like an airline program that you'd actually want to participate. And it's sort of my job like this is a customer service activity, and I often talk about if you talk about the large pool of influencers. Maybe they haven't identified yet. Or maybe they prefer to stay independent. Or maybe they do have interest in a lot of different technologies. Me for them to engage in one of these programs, that stolen, important set of people that you have to deal with the mark, you know, and again set up these blogger days have longer briefings. But like John was saying, When you have the group of people that you name and give it a program name, this is a little bit of inside baseball if we don't talk about giving program a name and funding can follow. So if you're working in a corporate marketing environment, it's really important to explain to people that marketing structure behind what you're doing and when you treat them as a class, it gives you some advantage is you can scale out a little easier. You can provide more assets to those individuals, and it frees you up to Dio. What I love to do, which is is to really engage with those individuals and create content with them. So, >> yeah, so how is engagement these days? You know, I think back, you know, that you know, ten years ago, you talk. You know, one percent of the community would, you know, be doing almost all the contribution. Ten percent might be a little active and everybody else's lurker. You know, when we founded Wicked Bond Day, Volonte actually has on his business card that he's a one percenter which goes back to you know it. It's, you know, the one percent that causes all the trouble, the one percent that causes all all of the commotion. So, you know, with this wave, I mean, we were founded off of, you know, economics in crowd sourcing and everything else, and the Cube is all about, you know, sharing information. We put it all out there. We want everybody to contribute and, you know, give that feedback. You know, How are we along now? You know that that journey to get more people involved. >> I think the opportunity is there more than ever. I think you're right. I mean, there's always gonna be a percentage of people who want to raise her hand, the class that want to give up their PTO to go to a conference that that had this other life they just can't help themselves. And so in some ways it's finding the most impassioned and giving them opportunities. But I think that with the platforms and the scale, there is a greater opportunity for people. They don't want to start their own block. For instance, one of the things we do it Cisco champions is allowed people to guess, block or allow them to come post a podcast. So I think there are more more ways to and there, you know, that's one example. There's lots of other groups that provide people again a little bit a dose of it so they might not want to run a full media company on their own. They don't wanna build Q, but they want to participate. And I think that we have so many more opportunities for them to do that that we're seeing group. >> We're seeing platform ships over the years. I think we as technologists human beings have a tendency to forget their past relatively quickly, as people have moved from the MySpace world to the Facebook Twitter world. I think actually, we're headed for I don't call it I don't want to call it post Facebook, but it certainly is. A multi platform world made >> it just like >> it's a multi device world. We're not opposed PC world in that. I think you're seeing the rise of more specialized communities. They come back again from from our from our origins back ten or twenty years ago. I think we're seeing that people want more deeper engagement along the company. A lot of the report building and kind of conversation. And hey, how are you? Goes on on Twitter. But I think people are really looking for a place where they can have a better conversation, more interaction, more lasting death that might not be on their own. Blogger in their own kind of indie web sort of style, roll your own block. But there are more and more platforms that people are making available for this kind of connection again. What was once niche eventually permeates the whole >> yes. So, you know, the concern I have is it's tough because it is so dispersed right now, you know? You know, I love Twitter, you know? Hi, I'm stew, you know, on Twitter. And I know you guys are big on it, too. And I don't love the multi platform discussion. You know, I always love when you dropped that kind information on the community. But, you know, how >> do we How do we get that >> depth? It's one of the things I always worry about is, you know, people will read the headline and, you know, just react at it and, you know, they might even share it a bunch, but they haven't read it. Uh, so how do we get that deeper engagement? Deeper understanding. I mean, you know, I always say, you know, the I'm too busy is a poor excuse because, you know, you know Michelangelo and I'd sign that many hours in the day way we did and, you know, sure they didn't have their phone buzzing all over >> the place. >> I actually think we should do less. Not more. I think I think too much information, too many channels, too many corporate channels, too many personal channels, too much bad content. The world does not need more crappy content. So whether you're a individual, blogger or marketer, I'd say just turn the dial back a little bit. Did work on better, longer pieces that add more? I think that's the only way that we can shift the conversation. >> Yeah, long for love it. Oh, no, absolutely. I still read so >> well. It's a curatorial function as well, that we have to be responsible. And that's yet one more way people can participate. We see people rise and in the community because they're really great curator Sze, because they syndicate the content in ways are interesting to others because time is of a value so that becomes a real asset. And the skill is Well, >> yeah, great. Great point. Could you know, so many times I'm like I really like to do a thousand word post on this, but, you know, sometimes all I'll come out of this show and take, you know, I did a year ago. I did it. I didn't article on the federation. You know, the ZPM were pivotal and coming out of the show, I've got a lot of new data, and I could really quickly take some photos. I've done. Takes some of the notes. I take some of the tweets and, you know, put together an order. Won't take me as long. I mean, I'll probably do it on the plane ride home. So what I wanna ask next is, you know, you guys see a lot of things out there. What coolest thing you're seeing either at a at a conference or event or you know what? What? What's catching, right? What? What's interesting? Done. >> There's a whole new side out there called Tech, right? I don't know what's cool out there again. I'm seeing multi channel multi, a lot of experiments. There's some cool stuff going on with the indie web. There's I mean, everything is mobile. I don't know. There's just a lot of places. It >> sounds like you Let's give the plug. Integrity has finally cool things and, you know, solid. But something >> like that tech reckoning is a site that's gonna bring. It's an independent site. It's not associate with any vendor. It's going to bring some of the community and enterprise community together to talk about some of these things about Where is it going as a whole? Where's technology going, where our career is going to try to help us get to whatever this you know, it is a service. Third platform, Whatever you wanna call it, where the heck were going? It looks pretty interesting, and it looks like it isn't gonna be quite the same thing. So we're trying to bring together a set of people and just tackle some of those problem and also work together and collaborate. It's so much easier with open source with cloud. With all the tools we have available, it's so cheap and easy to build new pieces of technology, not just a type of each other words online, but to actually build stuff that I'm very excited about. The power taking going far. This from open source, right? Taking the power of people to come together and build cool new stuff. That's what I would like to. >> Still, I'm just angry that you scooped Matt and I on getting to interview John first about >> tech recognition. So, Amy, you you do some cool things that some of events we talk about, the waffle bacon, you What have you seen out there that that's kind of interesting? Or, you know, how do you find some of the cool new ideas? >> Yeah, I think you always I'm working with a really talented events team right now. And I think one of the things I've seen them sort of transform is that social is not other, you know? And we're seeing the social and this concept of community permeate and really think about our audience to really engage that core base, those those tech enthusiasts, and to see what you can do to in engage them. So I'm saying it in real life and in these community platforms. So I think that's been one of the other great trends is watching people band together and various kinds of consortiums. I won't name names, but there's a few folks outlook community. We're seeing a lot of this happen where they're sort of grouping together, and they're saying if they pull their resource is what happens, they might be able to gather enough money to go to a conference or to fund a buddy or to get a hotel room that they've got extra spaces somebody can crash. So I'm saying it's very cool, sort of stitching together opportunity and working together to learn more. So again, the combination of the platforms, using the technology and then in real life connection. >> All right, so I've been asking all the questions here. So before we wrap up, you know, Amy, anything you want, Johnny, when as me, John same, we throw it open. When Whenever >> you first signed up for your Twitter account, did you think it would lead you here because you have the best Twitter >> account? No, actually, a friend of mine for me and Steve Todd, who was blogging before I was, and he said, You know, when there's trepidation when you're gonna get published and you never know where it leads. And we were talking about this after he and I were on the stage at Radio City Music Hall right after Bill Clinton had been on because they brought the bloggers down when we were there. And it's like, Come on, you know, I'm, you know, I'm an engineer by training, you know, I've done. You know, I've done some sales. I've done engineering. I've done you no operations. Technologist is hard. So you know, some of the places the people I've met. I mean, if you just reach out to people, it still, even though there's so many people on Twitter, you know, the people that right and our authors and bloggers, If you comment or you reach out to them, a lot of them reach back. I mean, you know, I still amazed at some of the people I've met get to rub elbows with. No, just just have had a blast with him. So >> get another one. So do you think unicorns can be trained? Do you think people have to be born with the skill set, Or do you think you can be a uniformed rancher? >> No, I think I think I think they could be trained. You know, it's absolutely it's Ah, it's a tough skill set. I mean, you know, doing video is not easy. First couple of times you do it. It's different there's there's all these muscles. You know, Writing is one of those things that you know. I thought I was an okay writer, but hadn't done a lot of it. They're things you do. So try it out. And that thing I tell you, you got to stick with it for a while. I thought Twitter was pretty stupid. First Go on it. But, you know, I stuck on it for another six months and have some fun with it. No, here we are six years later and you know it is a lot and, you know, blocking of writing and blogging and everything else you know all over. I >> like the muscle memory idea. >> It's hard. You were on camera, have remember not to scratch my face. Strange. He'll set, I ask. I actually, I'm seeing a lot of interest in short form video. I know the kids are all doing it. I mean, obviously, we're doing it here. You do it. It's part of your practice. But in talking with people about our new activities, it's just so easy to take a chair. I think that's actually, even though it's been coming up for years, I think where I think that's an interesting thing >> on all right now, I'll give one of those inside tips videos. Great. Some people don't like to watch video. Yeah, broadcaster great. Some people don't like to listen to him, you know, writing's great. Some people won't read. So you know what? One of the early lessons I had is when I was, you know, being a, you know, active member on standard evangelizing of solution. I did it everywhere it you know that give presentations that shows you put it up on slide chair. You do you two videos, you blogged about it. You talk to everybody, you bet that you can everywhere. And you know, it just permeates out there. It could be a bunch of works and then there's tools that are out there. >> They're all connected events, right? I've discovered recently, and I can't believe I just realized this. But it was with the conversation with Amy on our Christmas broadcast that even though I've been part of an online group for years, I'm part of digital marketing for BM. Where for years, Uh, actually, most of my work. Half of my work is off line having my workers meeting people in person, getting to meet them and connecting that online and offline. And the synergy there is just is immense. >> Yeah, absolutely. I mean, other than the keynotes, my phone stays in my pocket for the most time. Unless I'm going between events. It's the in real life and nearly getting to know things. I was joking, You know, Twitter went away. Tomorrow might be a little sad, but I can connect the most. All those people, we got him on LinkedIn, Facebook and, you know, email. I still use something. Don't taking their holds. Absolutely. So you know, to wrap. I guess if you want to, just You know what people find more on your podcast. Find your website. You know Amy, Like it start? Well, >> where >> are Equus? Versace, of course. Geek hyphen whispers dot com on way, published every week. So give us a listen. See what you think. And I'm >> Matthew Brender. Sorry you couldn't join this time, but it's a lot as it were. A DMC world and you two are here in Matthew's. >> It's hard. We're going toe to toe. It's true. We're going to record with him like it's a Max headroom figure on a yes tomorrow, so and also I'm on Twitter as calms mention and I block under that same constantly dot com girls have engineers. That's true. I have engineers, unplug dot com as well. And now sixty second Tech, the short first on the popcorn version >> and I. J. Troia on Twitter and tech reckoning dot com. I went inside. >> Hey, Amy, John. Thanks so much. We We love taking the podcast. Inception. Sile inside the Cube. Look forward to seeing you lost events connecting with the community and everybody. Definitely check out their stuff. I'm at stew on Twitter with yvonne dot org's is where most of my articles go, and, of course, silicon angled on TV is where you can find all the video. Thanks for joining us. We will be back with the rest of DMC world covered.