>> Live from Las Vegas, it's theCUBE! Covering the AWS Accenture Executive Summit. Brought to you by Accenture. >> Welcome back everyone to theCUBE's live coverage of the AWS Executive Summit here in Las Vegas. I'm your host, Rebecca Knight. We have three guests for this segment. We have Joe Donahue, managing director at Accenture. Hal Stern, AVP, IT Engineering Merck Research Labs. And Derek Seymour, Global Partner Leader Industry Verticals at AWS. Thank you so much for coming on theCUBE. >> Thank you! >> So, we're talking today about a new informatics research platform in the pharmaceutical/medical research industry. Will you paint a picture for us right now, Joe, of what it's like today. Sort of what medical research the time frame we're thinking about, the clunkiness of it all. >> Yeah, so it's a great question Rebecca. Drug discovery today generally takes more than a decade, it costs billions of dollars and has a lot of failures in excess of 90%. So it's not an exact science, we're generating more and more data. And at the same time, just our understanding of human disease biology continues to increase. These metrics haven't really changed. If you look back at the last coupe of decades, it's a 10 year plus process and that much money. So we're looking for ways that we can apply technology to really improve the odds of discovering a new drug that could help patients sooner and faster. >> And that will ultimately save lives. So it's a real social problem, a real problem. Why a platform for this? >> I think if you look at basic research, and you talk about basic blood sciences research, the lingua franca there is chemistry and biology. And we still don't really understand all the aspects, all the mechanisms of action that lead to chronic disease or lead to specific disease that we're interested in. So very, very much research is driven by the scientific method. You formulate a hypothesis based on some data, you run an experiment, you collect the data, you analyze it, and you start over again. So your ability to essentially cycle your data through that discovery process is absolutely critical. The problem is that we buy a lot of applications. And the applications were not designed to be able to interchange data freely. There is no platform to the sense of you have one on your phone, or you have one on your server operating system, where things were designed with a fairly small set of standards that say this is how you share data, this is how you represent it, this is how you access it. Instead we have these very top to bottom integrated applications that, quite honestly, they work together through a variety of copy and paste. Sometimes quite literal copy and paste mechanisms. And our goal in producing a platform is we would like to be able to first separate data from the applications to allow it to flow more freely around the cycle, that basic scientific method. Number two, to now start to allow component substitution. So we'll actually start to encourage more innovation in the space, bring in some of the new players. Make it easier to bring in new ideas is there better ways of analyzing the data or better ways of helping shape and formulate and curate those hypothesis. And finally, there's just a lot of parts of this that are fairly common. They're what we call pre competitive. Everybody has to do them. Everybody has to store data, everybody has to get lab instrument information. Everybody has to be able to go capture assay information. It's very hard to do it better than one of your competitors. So we should just all do it the same way. You see this happen in the cable industry, you see this happen at a variety of other industries where there are industry standards for how you accomplish basic commoditized things, and we haven't really had that. So one of the goals is, let's just sit down and find the first things to commoditize and go drive that economic advantage of being able to buy them as opposed to having to go build them bespoke each time. >> So this pre competitive element is really important. Derek, can you talk a little bit about how this platform in particular operates? >> Certainly. Our goal collectively as partners is to help pharma companies and researchers improve their efficiency and effectiveness in the drug discovery process. So the platform that we built brings together content and service and data from the pharma companies in a way that allows them, the researchers, a greater access to share that information. To do analysis, and to spend their time on researching the data and using their science and less on the work of managing an IT environment. So in that way we can both elevate their work and also take away, what we at AWS, call the undifferentiated heavy lifting of managing an IT environment. >> So you're doing the heavy lifting behind the scenes so that the researchers themselves can do what they do, which is focus on the science. So what have we seen so far? What kind of outcomes are we seeing? Particularly because it is in this pre competitive time. >> Well we've just really started, but we're getting a lot of excitement. Merck obviously is our first client, but our intent is that we'll have other pharmaceutical and biotech companies coming on board. And right now we've effectively started to create this two sided marketplace of pharma and biotech companies on one side and the key technology providers and content providers on the other side. We've effectively created that environment where the technology companies can plug in their secret sauce, you know via standardized APIs and micro services, and then the pharmaceutical and biotech companies can leverage those capabilities as part of this industry standard open platform that we're co creating. And so far we've started that process. The results are really encouraging. And the key thing is, you know really two fold. Get the word out there, we're doing that today here. Talking to other pharmaceutical and biotech companies. As well as not only the established technology providers in this space, but also the new comers. 'Cause this type of infrastructure, this type of platform, will enable the new innovative companies, the startup companies, to enter a market that traditionally has been very challenging to get into. Because there's so much data, there's so much legacy infrastructure. We're creating a mechanism that pharmaceutical researchers can take advantage of new technologies faster. For example, the latest algorithms on artificial intelligence and machine learning analyze all of this diverse data that's being generated. >> So that's for the startups, and that's sort of the promise of this kind of platform approach. But what about for a Merck, a established player in this. What kinds of things are you feeling and seeing inside the company? >> You think about this efficient frontier of what does is cost us to run the underlying technology systems that are foundational to our science? And you think about it, there are some things we do which are highly commoditized, we want them to be very efficient. And some things we do, which are very highly specialized, they're highly competitive, and it's okay if they're less efficient. You want to invest your money there. And you really want to invest more in things that are going to drive you a unique competitive advantage. And less in the things that are highly commoditized. The example I use frequently is you could go out and buy a barrel of oil, bring it home, refine it in your backyard, make your own gasoline. It's not recommended. It's messy, it really annoys the neighbors. Especially when it goes wrong. And it's not nearly as cost effective or as convenient as driving over to Exxon Mobil and filling up at the pump. If you're in New Jersey, having someone else even pump it for you. That's kind of the environment we're in right now today where we're refining that barrel of oil for every single application we have. So in doing this, we start to establish the base line of really thinking about refactoring our core applications into those things which can be driven by the economics of the commodity platform and those things which are going to give us unique advantage. We will see things I think, like improved adoption of data standards. We're going to see a lower barrier to entry for new applications, for new ideas. We're also going to see a lower barrier to exit. It'll be easier for us to adopt new ideas. Or to change or to substitute components because they really are built as part of a platform. And you see this, you look at, I would say over time things that have sedimented into AWS. It's been a remarkable story of starting with things that were basically resting our faces on a pausics file system and turned all the sudden into a seamless data base. By sedimenting well defined open source projects, we would like to see some of the same thing happen, where some of the core things we have to go do, entity registration, assay data captured, data management. They should be part of the platform. It's really hard to register an entity better than your competitor. What you do with it, how you describe what you're registering, how you capture intellectual property, how it drives your next invention. Completely bespoke, completely highly competitive. I'm going to keep that. But the underlying mechanics of it, to me it's file system stuff, it's data base stuff. We should leverage the economics of our industry. And again, leverage it as technologist ingredient. It's not the top level brand, chemistry and biology are the top level brand, technology's an ingredient brand we should really use the best ingredients we can. >> When you're hearing this conversation so related to life sciences, medical, bio/pharma research, what are sort of the best practices that have emerged, in terms of the way life sciences approaches its platform, and how it can be applied to other industries? >> What we've seen through the early collaboration with Merck and with Accenture is that bringing together these items in a secure environment, multi talent environment, managed by Accenture, run by AWS. We can put those tools in the hands of the researchers. We can provide them with work flow data analytics capabilities, reporting capabilities, to cover the areas that Hal is talking about so that they can elevate the work that they are doing. Over time, we expect to bring in more components. The application, the platform, will become more feature rich as we add additional third parties. And that's a key element in life science is that the science itself, while it may take place in (mumbles), it's a considerable collaboration across a number of research institutes. Both within the pharma and biotech community. Having this infrastructure in place where those companies and the researchers can come together in a secure manner, we're very proud to be supporting of that. >> So Joe, we started this conversation with you describing the state of medical research today, can you describe what you think it will be in 10 years from now as more pharmaceutical companies adopt this platform approach. And we're talking about the Mercks of the world, but then also those hungry start ups that are also. >> Sure, I think we're starting to see that transition actually happen now. And I think it's the recognition and you start to hear it as you hear some of the pharmaceutical CEO's talking about their business and the transformation. They've always talked about the science. They've always talked about the research. Now they're talking about data and informatics and they're realizing being a pharmaceutical company is not just about the science, it's about the data and you have to be as good and as efficient on the informatics and the IT side as you are on the science side. And that's the transition that we're going through right now. In 10 years, where we all hope we should be, is leveraging modern computing architectures. Existing platform technology to let the organizations focus on what's really important. And that's the science and the data that they generate for the benefit potentially of saving patient's lives in the future. >> So not only focusing on their core competencies, but then also that means that drug discovery will be quicker, that failure rates will go down. >> Even a 10 or 20% improvement in failure rates would be incredibly dramatic to the industry. >> And could save millions of lives. And improve lives and outcomes. Great, well thank you all so much for coming on theCUBE. It's been a really fun and interesting conversation. >> Same here, thank you Rebecca. >> Thank you, thank you. >> Thank you. >> I'm Rebecca Knight, we will have more of the AWS Executive Summit and theCUBE's live coverage coming up in just a little bit. (upbeat music)

>>from around the globe. It's the Cube presenting Cuban cloud brought to you by silicon angle in 2000 >>nine. Hal Varian, Google's chief economist, said that statisticians would be the sexiest job in the coming decade. The modern big data movement >>really >>took off later in the following year. After the Second Hadoop World, which was hosted by Claudette Cloudera in New York City. Jeff Ham Abakar famously declared to me and John further in the Cube that the best minds of his generation, we're trying to figure out how to get people to click on ads. And he said that sucks. The industry was abuzz with the realization that data was the new competitive weapon. Hadoop was heralded as the new data management paradigm. Now, what actually transpired Over the next 10 years on Lee, a small handful of companies could really master the complexities of big data and attract the data science talent really necessary to realize massive returns as well. Back then, Cloud was in the early stages of its adoption. When you think about it at the beginning of the last decade and as the years passed, Maurin Mawr data got moved to the cloud and the number of data sources absolutely exploded. Experimentation accelerated, as did the pace of change. Complexity just overwhelmed big data infrastructures and data teams, leading to a continuous stream of incremental technical improvements designed to try and keep pace things like data Lakes, data hubs, new open source projects, new tools which piled on even Mawr complexity. And as we reported, we believe what's needed is a comm pleat bit flip and how we approach data architectures. Our next guest is Jean Marc de Connie, who is the director of emerging technologies That thought works. John Mark is a software engineer, architect, thought leader and adviser to some of the world's most prominent enterprises. She's, in my view, one of the foremost advocates for rethinking and changing the way we create and manage data architectures. Favoring a decentralized over monolithic structure and elevating domain knowledge is a primary criterion. And how we organize so called big data teams and platforms. Chamakh. Welcome to the Cube. It's a pleasure to have you on the program. >>Hi, David. This wonderful to be here. >>Well, okay, so >>you're >>pretty outspoken about the need for a paradigm shift in how we manage our data and our platforms that scale. Why do you feel we need such a radical change? What's your thoughts there? >>Well, I think if you just look back over the last decades you gave us, you know, a summary of what happened since 2000 and 10. But if even if we go before then what we have done over the last few decades is basically repeating and, as you mentioned, incrementally improving how we've managed data based on a certain assumptions around. As you mentioned, centralization data has to be in one place so we can get value from it. But if you look at the parallel movement off our industry in general since the birth of Internet, we are actually moving towards decentralization. If we think today, like if this move data side, if he said the only way Web would work the only way we get access to you know various applications on the Web pages is to centralize it. We would laugh at that idea, but for some reason we don't. We don't question that when it comes to data, right? So I think it's time to embrace the complexity that comes with the growth of number of sources, the proliferation of sources and consumptions models, you know, embrace the distribution of sources of data that they're not just within one part of organization. They're not just within even bounds of organization there beyond the bounds of organization. And then look back and say Okay, if that's the trend off our industry in general, Um, given the fabric of computation and data that we put in, you know globally in place, then how the architecture and technology and organizational structure incentives need to move to embrace that complexity. And to me, that requires a paradigm shift, a full stack from how we organize our organizations, how we organize our teams, how we, you know, put a technology in place, um, to to look at it from a decentralized angle. >>Okay, so let's let's unpack that a little bit. I mean, you've spoken about and written that today's big architecture and you basically just mentioned that it's flawed, So I wanna bring up. I love your diagrams of a simple diagram, guys, if you could bring up ah, figure one. So on the left here we're adjusting data from the operational systems and other enterprise data sets and, of course, external data. We cleanse it, you know, you've gotta do the do the quality thing and then serve them up to the business. So So what's wrong with that picture that we just described and give granted? It's a simplified form. >>Yeah, quite a few things. So, yeah, I would flip the question may be back to you or the audience if we said that. You know, there are so many sources off the data on the Actually, the data comes from systems and from teams that are very diverse in terms off domains. Right? Domain. If if you just think about, I don't know retail, Uh, the the E Commerce versus Order Management versus customer This is a very diverse domains. The data comes from many different diverse domains. And then we expect to put them under the control off a centralized team, a centralized system. And I know that centralization. Probably if you zoom out, it's centralized. If you zoom in it z compartmentalized based on functions that we can talk about that and we assume that the centralized model will be served, you know, getting that data, making sense of it, cleansing and transforming it then to satisfy in need of very diverse set of consumers without really understanding the domains, because the teams responsible for it or not close to the source of the data. So there is a bit of it, um, cognitive gap and domain understanding Gap, um, you know, without really understanding of how the data is going to be used, I've talked to numerous. When we came to this, I came up with the idea. I talked to a lot of data teams globally just to see, you know, what are the pain points? How are they doing it? And one thing that was evident in all of those conversations that they actually didn't know after they built these pipelines and put the data in whether the data warehouse tables or like, they didn't know how the data was being used. But yet the responsible for making the data available for these diverse set of these cases, So s centralized system. A monolithic system often is a bottleneck. So what you find is, a lot of the teams are struggling with satisfying the needs of the consumers, the struggling with really understanding the data. The domain knowledge is lost there is a los off understanding and kind of in that in that transformation. Often, you know, we end up training machine learning models on data that is not really representative off the reality off the business. And then we put them to production and they don't work because the semantic and the same tax off the data gets lost within that translation. So we're struggling with finding people thio, you know, to manage a centralized system because there's still the technology is fairly, in my opinion, fairly low level and exposes the users of those technologies. I said, Let's say warehouse a lot off, you know, complexity. So in summary, I think it's a bottleneck is not gonna, you know, satisfy the pace of change, of pace, of innovation and the pace of, you know, availability of sources. Um, it's disconnected and fragmented, even though the centralizes disconnected and fragmented from where the data comes from and where the data gets used on is managed by, you know, a team off hyper specialized people that you know, they're struggling to understand the actual value of the data, the actual format of the data, so it's not gonna get us where our aspirations and ambitions need to be. >>Yes. So the big data platform is essentially I think you call it, uh, context agnostic. And so is data becomes, you know, more important, our lives. You've got all these new data sources, you know, injected into the system. Experimentation as we said it with the cloud becomes much, much easier. So one of the blockers that you've started, you just mentioned it is you've got these hyper specialized roles the data engineer, the quality engineer, data scientists and and the It's illusory. I mean, it's like an illusion. These guys air, they seemingly they're independent and in scale independently. But I think you've made the point that in fact, they can't that a change in the data source has an effect across the entire data lifecycle entire data pipeline. So maybe you could maybe you could add some color to why that's problematic for some of the organizations that you work with and maybe give some examples. >>Yeah, absolutely so in fact, that initially the hypothesis around that image came from a Siris of requests that we received from our both large scale and progressive clients and progressive in terms of their investment in data architectures. So this is where clients that they were there were larger scale. They had divers and reached out of domains. Some of them were big technology tech companies. Some of them were retail companies, big health care companies. So they had that diversity off the data and the number off. You know, the sources of the domains they had invested for quite a few years in, you know, generations. If they had multi generations of proprietary data warehouses on print that they were moving to cloud, they had moved to the barriers, you know, revisions of the Hadoop clusters and they were moving to the cloud. And they the challenges that they were facing were simply there were not like, if I want to just, like, you know, simplifying in one phrase, they were not getting value from the data that they were collecting. There were continuously struggling Thio shift the culture because there was so much friction between all of these three phases of both consumption of the data and transformation and making it available consumption from sources and then providing it and serving it to the consumer. So that whole process was full of friction. Everybody was unhappy. So its bottom line is that you're collecting all this data. There is delay. There is lack of trust in the data itself because the data is not representative of the reality has gone through a transformation. But people that didn't understand really what the data was got delayed on bond. So there is no trust. It's hard to get to the data. It's hard to create. Ultimately, it's hard to create value from the data, and people are working really hard and under a lot of pressure. But it's still, you know, struggling. So we often you know, our solutions like we are. You know, Technologies will often pointed to technology. So we go. Okay, This this version of you know, some some proprietary data warehouse we're using is not the right thing. We should go to the cloud, and that certainly will solve our problems. Right? Or warehouse wasn't a good one. Let's make a deal Lake version. So instead of you know, extracting and then transforming and loading into the little bits. And that transformation is that, you know, heavy process, because you fundamentally made an assumption using warehouses that if I transform this data into this multi dimensional, perfectly designed schema that then everybody can run whatever choir they want that's gonna solve. You know everybody's problem, but in reality it doesn't because you you are delayed and there is no universal model that serves everybody's need. Everybody that needs the divers data scientists necessarily don't don't like the perfectly modeled data. They're looking for both signals and the noise. So then, you know, we've We've just gone from, uh, et elles to let's say now to Lake, which is okay, let's move the transformation to the to the last mile. Let's just get load the data into, uh into the object stores into semi structured files and get the data. Scientists use it, but they're still struggling because the problems that we mentioned eso then with the solution. What is the solution? Well, next generation data platform, let's put it on the cloud, and we sell clients that actually had gone through, you know, a year or multiple years of migration to the cloud. But with it was great. 18 months I've seen, you know, nine months migrations of the warehouse versus two year migrations of the various data sources to the clubhouse. But ultimately, the result is the same on satisfy frustrated data users, data providers, um, you know, with lack of ability to innovate quickly on relevant data and have have have an experience that they deserve toe have have a delightful experience off discovering and exploring data that they trust. And all of that was still a missed so something something else more fundamentally needed to change than just the technology. >>So then the linchpin to your scenario is this notion of context and you you pointed out you made the other observation that look, we've made our operational systems context aware. But our data platforms are not on bond like CRM system sales guys very comfortable with what's in the CRM system. They own the data. So let's talk about the answer that you and your colleagues are proposing. You're essentially flipping the architecture whereby those domain knowledge workers, the builders, if you will, of data products or data services there now, first class citizens in the data flow and they're injecting by design domain knowledge into the system. So So I wanna put up another one of your charts. Guys, bring up the figure to their, um it talks about, you know, convergence. You showed data distributed domain, dream and architecture. Er this self serve platform design and this notion of product thinking. So maybe you could explain why this approach is is so desirable, in your view, >>sure. The motivation and inspiration for the approach came from studying what has happened over the last few decades in operational systems. We had a very similar problem prior to micro services with monolithic systems, monolithic systems where you know the bottleneck. Um, the changes we needed to make was always, you know, our fellow Noto, how the architecture was centralized and we found a nice nation. I'm not saying this is the perfect way of decoupling a monolith, but it's a way that currently where we are in our journey to become data driven, um is a nice place to be, um, which is distribution or decomposition off your system as well as organization. I think when we whenever we talk about systems, we've got to talk about people and teams that's responsible for managing those systems. So the decomposition off the systems and the teams on the data around domains because that's how today we are decoupling our business, right? We're decoupling our businesses around domains, and that's a that's a good thing and that What does that do really for us? What it does? Is it localizes change to the bounded context of fact business. It creates clear boundary and interfaces and contracts between the rest of the universe of the organization on that particular team, so removes the friction that often we have for both managing the change and both serving data or capability. So it's the first principle of data meshes. Let's decouple this world off analytical data the same to mirror the same way we have to couple their systems and teams and business why data is any different. And the moment you do that, So you, the moment you bring the ownership to people who understands the data best, then you get questions that well, how is that any different from silence that's connected databases that we have today and nobody can get to the data? So then the rest of the principles is really to address all of the challenges that comes with this first principle of decomposition around domain Context on the second principle is well, we have to expect a certain level off quality and accountability and responsibility for the teams that provide the data. So let's bring product thinking and treating data as a product to the data that these teams now, um share and let's put accountability around. And we need a new set of incentives and metrics for domain teams to share the data. We need to have a new set off kind of quality metrics that define what it means for the data to be a product. And we can go through that conversation perhaps later eso then the second principle is okay. The teams now that are responsible, the domain teams responsible for the analytical data need to provide that data with a certain level of quality and assurance. Let's call that a product and bring products thinking to that. And then the next question you get asked off by C. E. O s or city or the people who build the infrastructure and, you know, spend the money. They said, Well, it's actually quite complex to manage big data, and now we're We want everybody, every independent team to manage the full stack of, you know, storage and computation and pipelines and, you know, access, control and all of that. And that's well, we have solved that problem in operational world. And that requires really a new level of platform thinking toe provide infrastructure and tooling to the domain teams to now be able to manage and serve their big data. And that I think that requires reimagining the world of our tooling and technology. But for now, let's just assume that we need a new level of abstraction to hide away ton of complexity that unnecessarily people get exposed to and that that's the third principle of creating Selves of infrastructure, um, to allow autonomous teams to build their domains. But then the last pillar, the last you know, fundamental pillar is okay. Once you distributed problem into a smaller problems that you found yourself with another set of problems, which is how I'm gonna connect this data, how I'm gonna you know, that the insights happens and emerges from the interconnection of the data domains right? It does not necessarily locked into one domain. So the concerns around interoperability and standardization and getting value as a result of composition and interconnection of these domains requires a new approach to governance. And we have to think about governance very differently based on a Federated model and based on a computational model. Like once we have this powerful self serve platform, we can computational e automate a lot of governance decisions. Um, that security decisions and policy decisions that applies to you know, this fabric of mesh not just a single domain or not in a centralized. Also, really. As you mentioned that the most important component of the emissions distribution of ownership and distribution of architecture and data the rest of them is to solve all the problems that come with that. >>So very powerful guys. We actually have a picture of what Jamaat just described. Bring up, bring up figure three, if you would tell me it. Essentially, you're advocating for the pushing of the pipeline and all its various functions into the lines of business and abstracting that complexity of the underlying infrastructure, which you kind of show here in this figure, data infrastructure is a platform down below. And you know what I love about this Jama is it to me, it underscores the data is not the new oil because I could put oil in my car I can put in my house, but I can't put the same court in both places. But I think you call it polyglot data, which is really different forms, batch or whatever. But the same data data doesn't follow the laws of scarcity. I can use the same data for many, many uses, and that's what this sort of graphic shows. And then you brought in the really important, you know, sticking problem, which is that you know the governance which is now not a command and control. It's it's Federated governance. So maybe you could add some thoughts on that. >>Sure, absolutely. It's one of those I think I keep referring to data much as a paradigm shift. And it's not just to make it sound ground and, you know, like, kind of ground and exciting or in court. And it's really because I want to point out, we need to question every moment when we make a decision around how we're going to design security or governance or modeling off the data, we need to reflect and go back and say, um, I applying some of my cognitive biases around how I have worked for the last 40 years, I have seen it work. Or do I do I really need to question. And we do need to question the way we have applied governance. I think at the end of the day, the rule of the data governance and objective remains the same. I mean, we all want quality data accessible to a diverse set of users. And these users now have different personas, like David, Personal data, analyst data, scientists, data application, Um, you know, user, very diverse personal. So at the end of the day, we want quality data accessible to them, um, trustworthy in in an easy consumable way. Um, however, how we get there looks very different in as you mentioned that the governance model in the old world has been very commander control, very centralized. Um, you know, they were responsible for quality. They were responsible for certification off the data, you know, applying making sure the data complies. But also such regulations Make sure you know, data gets discovered and made available in the world of the data mesh. Really. The job of the data governance as a function becomes finding that equilibrium between what decisions need to be um, you know, made and enforced globally. And what decisions need to be made locally so that we can have an interoperable measure. If data sets that can move fast and can change fast like it's really about instead of hardest, you know, kind of putting the putting those systems in a straitjacket of being constant and don't change, embrace, change and continuous change of landscape because that's that's just the reality we can't escape. So the role of governance really the governance model called Federated and Computational. And by that I mean, um, every domain needs to have a representative in the governance team. So the role of the data or domain data product owner who really were understand the data that domain really well but also wears that hacks of a product owner. It is an important role that had has to have a representation in the governance. So it's a federation off domains coming together, plus the SMEs and people have, you know, subject matter. Experts who understands the regulations in that environmental understands the data security concerns, but instead off trying to enforce and do this as a central team. They make decisions as what need to be standardized, what need to be enforced. And let's push that into that computational E and in an automated fashion into the into the camp platform itself. For example, instead of trying to do that, you know, be part of the data quality pipeline and inject ourselves as people in that process, let's actually, as a group, define what constitutes quality, like, how do we measure quality? And then let's automate that and let Z codify that into the platform so that every native products will have a C I City pipeline on as part of that pipeline. Those quality metrics gets validated and every day to product needs to publish those SLOC or service level objectives. So you know, whatever we choose as a measure of quality, maybe it's the, you know, the integrity of the data, the delay in the data, the liveliness of it, whatever the are the decisions that you're making, let's codify that. So it's, um, it's really, um, the role of the governance. The objectives of the governance team tried to satisfies the same, but how they do it. It is very, very different. I wrote a new article recently trying to explain the logical architecture that would emerge from applying these principles. And I put a kind of light table to compare and contrast the roll off the You know how we do governance today versus how we will do it differently to just give people a flavor of what does it mean to embrace the centralization? And what does it mean to embrace change and continuous change? Eso hopefully that that that could be helpful. >>Yes, very so many questions I haven't but the point you make it to data quality. Sometimes I feel like quality is the end game. Where is the end game? Should be how fast you could go from idea to monetization with the data service. What happens again? You sort of address this, but what happens to the underlying infrastructure? I mean, spinning a PC to S and S three buckets and my pie torches and tensor flows. And where does that that lives in the business? And who's responsible for that? >>Yeah, that's I'm glad you're asking this question. Maybe because, um, I truly believe we need to re imagine that world. I think there are many pieces that we can use Aziz utilities on foundational pieces, but I but I can see for myself a 5 to 7 year roadmap of building this new tooling. I think, in terms of the ownership, the question around ownership, if that would remains with the platform team, but and perhaps the domain agnostic, technology focused team right that there are providing instead of products themselves. And but the products are the users off those products are data product developers, right? Data domain teams that now have really high expectations in terms of low friction in terms of lead time to create a new data product. Eso We need a new set off tooling, and I think with the language needs to shift from, You know, I need a storage buckets. So I need a storage account. So I need a cluster to run my, you know, spark jobs, too. Here's the declaration of my data products. This is where the data for it will come from. This is the data that I want to serve. These are the policies that I need toe apply in terms of perhaps encryption or access control. Um, go make it happen. Platform, go provision, Everything that I mean so that as a data product developer. All I can focus on is the data itself, representation of semantic and representation of the syntax. And make sure that data meets the quality that I have that I have to assure and it's available. The rest of provisioning of everything that sits underneath will have to get taken care of by the platform. And that's what I mean by requires a re imagination and in fact, Andi, there will be a data platform team, the data platform teams that we set up for our clients. In fact, themselves have a favorite of complexity. Internally, they divide into multiple teams multiple planes, eso there would be a plane, as in a group of capabilities that satisfied that data product developer experience, there would be a set of capabilities that deal with those need a greatly underlying utilities. I call it at this point, utilities, because to me that the level of abstraction of the platform is to go higher than where it is. So what we call platform today are a set of utilities will be continuing to using will be continuing to using object storage, will continue using relation of databases and so on so there will be a plane and a group of people responsible for that. There will be a group of people responsible for capabilities that you know enable the mesh level functionality, for example, be able to correlate and connects. And query data from multiple knows. That's a measure level capability to be able to discover and explore the measure data products as a measure of capability. So it would be set of teams as part of platforms with a strong again platform product thinking embedded and product ownership embedded into that. To satisfy the experience of this now business oriented domain data team teams s way have a lot of work to do. >>I could go on. Unfortunately, we're out of time. But I guess my first I want to tell people there's two pieces that you put out so far. One is, uh, how to move beyond a monolithic data lake to a distributed data mesh. You guys should read that in a data mesh principles and logical architectures kind of part two. I guess my last question in the very limited time we have is our organization is ready for this. >>E think the desire is there I've bean overwhelmed with number off large and medium and small and private and public governments and federal, you know, organizations that reached out to us globally. I mean, it's not This is this is a global movement and I'm humbled by the response of the industry. I think they're the desire is there. The pains are really people acknowledge that something needs to change. Here s so that's the first step. I think that awareness isa spreading organizations. They're more and more becoming aware. In fact, many technology providers are reach out to us asking what you know, what shall we do? Because our clients are asking us, You know, people are already asking We need the data vision. We need the tooling to support. It s oh, that awareness is there In terms of the first step of being ready, However, the ingredients of a successful transformation requires top down and bottom up support. So it requires, you know, support from Chief Data Analytics officers or above the most successful clients that we have with data. Make sure the ones that you know the CEOs have made a statement that, you know, we want to change the experience of every single customer using data and we're going to do, we're going to commit to this. So the investment and support, you know, exists from top to all layers. The engineers are excited that maybe perhaps the traditional data teams are open to change. So there are a lot of ingredients. Substance to transformation is to come together. Um, are we really ready for it? I think I think the pioneers, perhaps the innovators. If you think about that innovation, careful. My doctors, probably pioneers and innovators and leaders. Doctors are making making move towards it. And hopefully, as the technology becomes more available, organizations that are less or in, you know, engineering oriented, they don't have the capability in house today, but they can buy it. They would come next. Maybe those are not the ones who aren't quite ready for it because the technology is not readily available. Requires, you know, internal investment today. >>I think you're right on. I think the leaders are gonna lead in hard, and they're gonna show us the path over the next several years. And I think the the end of this decade is gonna be defined a lot differently than the beginning. Jammeh. Thanks so much for coming in. The Cuban. Participate in the >>program. Pleasure head. >>Alright, Keep it right. Everybody went back right after this short break.

(bright music) >> In 2009, Hal Varian, Google's Chief Economist said that statisticians would be the sexiest job in the coming decade. The modern big data movement really took off later in the following year, after the second Hadoop World, which was hosted by Cloudera, in New York city. Jeff Hama Bachar, famously declared to me and John Furrie, in "theCUBE," that the best minds of his generation were trying to figure out how to get people to click on ads. And he said that sucks. The industry was abuzz with the realization that data was the new competitive weapon. Hadoop was heralded as the new data management paradigm. Now what actually transpired over the next 10 years was only a small handful of companies could really master the complexities of big data and attract the data science talent, really necessary to realize massive returns. As well, back then, cloud was in the early stages of its adoption. When you think about it at the beginning of the last decade, and as the years passed, more and more data got moved to the cloud, and the number of data sources absolutely exploded, experimentation accelerated, as did the pace of change. Complexity just overwhelmed big data infrastructures and data teams, leading to a continuous stream of incremental technical improvements designed to try and keep pace, things like data lakes, data hubs, new open source projects, new tools, which piled on even more complexity. And as we reported, we believe what's needed is a complete bit flip and how we approach data architectures. Our next guest is Zhamak Dehgani, who is the Director of Emerging Technologies at ThoughtWorks. Zhamak is a software engineer, architect, thought leader and advisor, to some of the world's most prominent enterprises. She's in my view, one of the foremost advocates for rethinking and changing the way we create and manage data architectures, favoring a decentralized over monolithic structure, and elevating domain knowledge as a primary criterion, and how we organize so-called big data teams and platforms. Zhamak, welcome to the cube, it's a pleasure to have you on the program. >> Hi David, it's wonderful to be here. >> Okay. So you're pretty outspoken about the need for a paradigm shift, in how we manage our data, and our platforms at scale. Why do you feel we need such a radical change? What's your thoughts there? >> Well, I think if you just look back over the last decades, you gave us a summary of what happened since 2010. But even if we got it before then, what we have done over the last few decades is basically repeating, and as you mentioned, incrementally improving how we manage data, based on certain assumptions around, as you mentioned, centralization. Data has to be in one place so we can get value from it. But if you look at the parallel movement of our industry in general, since the birth of internet, we are actually moving towards decentralization. If we think today, like if in this move data side, if we said, the only way web would work, the only way we get access to various applications on the web or pages is to centralize it, we would laugh at that idea, but for some reason, we don't question that when it comes to data, right? So I think it's time to embrace the complexity that comes with the growth of number of sources, the proliferation of sources and consumptions models, embrace the distribution of sources of data, that they're not just within one part of organization. They're not just within even bounds of organizations. They're beyond the bounds of organization, and then look back and say, okay, if that's the trend of our industry in general, given the fabric of compensation and data that we put in globally in place, then how the architecture and technology and organizational structure incentives need to move, to embrace that complexity. And to me, that requires a paradigm shift. A full stack from how we organize our organizations, how we organize our teams, how we put a technology in place to look at it from a decentralized angle. >> Okay, so let's unpack that a little bit. I mean, you've spoken about and written today's big architecture, and you've basically just mentioned that it's flawed. So I want to bring up, I love your diagrams, you have a simple diagram, guys if you could bring up figure one. So on the left here, we're adjusting data from the operational systems, and other enterprise data sets. And of course, external data, we cleanse it, you've got to do the quality thing, and then serve them up to the business. So what's wrong with that picture that we just described, and give granted it's a simplified form. >> Yeah. Quite a few things. So, and I would flip the question maybe back to you or the audience. If we said that there are so many sources of the data and actually data comes from systems and from teams that are very diverse in terms of domains, right? Domain. If you just think about, I don't know, retail, the E-Commerce versus auto management, versus customer. These are very diverse domains. The data comes from many different diverse domains, and then we expect to put them under the control of a centralized team, a centralized system. And I know that centralization probably, if you zoom out is centralized, if you zoom in it's compartmentalized based on functions, and we can talk about that. And we assume that the centralized model, will be getting that data, making sense of it, cleansing and transforming it, then to satisfy a need of very diverse set of consumers without really understanding the domains because the teams responsible for it are not close to the source of the data. So there is a bit of a cognitive gap and domain understanding gap, without really understanding how the data is going to be used. I've talked to numerous, when we came to this, I came up with the idea. I talked to a lot of data teams globally, just to see, what are the pain points? How are they doing it? And one thing that was evident in all of those conversations, that they actually didn't know, after they built these pipelines and put the data in, whether the data warehouse tables or linked, they didn't know how the data was being used. But yet they're responsible for making the data available for this diverse set of use cases. So essentially system and monolithic system, often is a bottleneck. So what you find is that a lot of the teams are struggling with satisfying the needs of the consumers, are struggling with really understanding the data, the domain knowledge is lost, there is a loss of understanding and kind of it in that transformation, often we end up training machine learning models on data, that is not really representative of the reality of the business, and then we put them to production and they don't work because the semantic and the syntax of the data gets lost within that translation. So, and we are struggling with finding people to manage a centralized system because still the technology's fairly, in my opinion, fairly low level and exposes the users of those technology sets and let's say they warehouse a lot of complexity. So in summary, I think it's a bottleneck, it's not going to satisfy the pace of change or pace of innovation, and the availability of sources. It's disconnected and fragmented, even though there's centralized, it's disconnected and fragmented from where the data comes from and where the data gets used, and is managed by a team of hyper specialized people, they're struggling to understand the actual value of the data, the actual format of the data. So it's not going to get us where our aspirations, our ambitions need to be. >> Yeah, so the big data platform is essentially, I think you call it context agnostic. And so as data becomes more important in our lives, you've got all these new data sources injected into the system, experimentation as we said, the cloud becomes much, much easier. So one of the blockers that you've cited and you just mentioned it, is you've got these hyper specialized roles, the data engineer, the quality engineer, data scientist. And it's a losery. I mean, it's like an illusion. These guys, they seemingly they're independent, and can scale independently, but I think you've made the point that in fact, they can't. That a change in a data source has an effect across the entire data life cycle, entire data pipeline. So maybe you could add some some color to why that's problematic for some of the organizations that you work with, and maybe give some examples. >> Yeah, absolutely. So in fact initially, the hypothesis around data mesh came from a series of requests that we received from our both large scale and progressive clients, and progressive in terms of their investment in data architecture. So these were clients that were larger scale, they had diverse and rich set of domain, some of them were big technology, tech companies, some of them were big retail companies, big healthcare companies. So they had that diversity of the data and a number of the sources of the domains. They had invested for quite a few years in generations, of they had multi-generations of PROPRICER data warehouses on prem that were moving to cloud. They had moved through the various revisions of the Hadoop clusters, and they were moving to that to cloud, and then the challenges that they were facing were simply... If I want to just simplify it in one phrase, they we're not getting value from the data that they were collecting. They were continuously struggling to shift the culture because there was so much friction between all of these three phases of both consumption of the data, then transformation and making it available. Consumption from sources and then providing it and serving it to the consumer. So that whole process was full of friction. Everybody was unhappy. So it's bottom line is that you're collecting all this data, there is delay, there is lack of trust in the data itself, because the data is not representative of the reality, it's gone through the transformation, but people that didn't understand really what the data was got delayed. And so there's no trust, it's hard to get to the data. Ultimately, it's hard to create value from the data, and people are working really hard and under a lot of pressure, but it's still struggling. So we often, our solutions, like we are... Technologies, we will often point out to technology. So we go. Okay, this version of some proprietary data warehouse we're using is not the right thing. We should go to the cloud and that certainly will solve our problem, right? Or warehouse wasn't a good one, let's make a data Lake version. So instead of extracting and then transforming and loading into the database, and that transformation is that heavy process because you fundamentally made an assumption using warehouses that if I transform this data into this multidimensional perfectly designed schema, that then everybody can draw on whatever query they want, that's going to solve everybody's problem. But in reality, it doesn't because you are delayed and there is no universal model that serves everybody's need, everybody needs are diverse. Data scientists necessarily don't like the perfectly modeled data, they're for both signals and the noise. So then we've just gone from ATLs to let's say now to Lake, which is... Okay, let's move the transformation to the last mile. Let's just get load the data into the object stores and sort of semi-structured files and get the data scientists use it, but they still struggling because of the problems that we mentioned. So then what is the solution? What is the solution? Well, next generation data platform. Let's put it on the cloud. And we saw clients that actually had gone through a year or multiple years of migration to the cloud but it was great, 18 months, I've seen nine months migrations of the warehouse versus two year migrations of various data sources to the cloud. But ultimately the result is the same, unsatisfied, frustrated data users, data providers with lack of ability to innovate quickly on relevant data and have an experience that they deserve to have, have a delightful experience of discovering and exploring data that they trust. And all of that was still amiss. So something else more fundamentally needed to change than just the technology. >> So the linchpin to your scenario is this notion of context. And you pointed out, you made the other observation that "Look we've made our operational systems context aware but our data platforms are not." And like CRM system sales guys are very comfortable with what's in the CRMs system. They own the data. So let's talk about the answer that you and your colleagues are proposing. You're essentially flipping the architecture whereby those domain knowledge workers, the builders if you will, of data products or data services, they are now first-class citizens in the data flow, and they're injecting by design domain knowledge into the system. So I want to put up another one of your charts guys, bring up the figure two there. It talks about convergence. She showed data distributed, domain driven architecture, the self-serve platform design, and this notion of product thinking. So maybe you could explain why this approach is so desirable in your view. >> Sure. The motivation and inspirations for that approach came from studying what has happened over the last few decades in operational systems. We had a very similar problem prior to microservices with monolithic systems. One of the things systems where the bottleneck, the changes we needed to make was always on vertical now to how the architecture was centralized. And we found a nice niche. And I'm not saying this is a perfect way of decoupling your monolith, but it's a way that currently where we are in our journey to become data driven, it is a nice place to be, which is distribution or a decomposition of your system as well as organization. I think whenever we talk about systems, we've got to talk about people and teams that are responsible for managing those systems. So the decomposition of the systems and the teams, and the data around domains. Because that's how today we are decoupling our business, right? We are decoupling our businesses around domains, and that's a good thing. And what does that do really for us? What it does is it localizes change to the bounded context of that business. It creates clear boundary and interfaces and contracts between the rest of the universe of the organization, and that particular team, so removes the friction that often we have for both managing the change, and both serving data or capability. So if the first principle of data meshes, let's decouple this world of analytical data the same to mirror. The same way we have decoupled our systems and teams, and business. Why data is any different. And the moment you do that, so the moment you bring the ownership to people who understands the data best, then you get questions that well, how is that any different from silos of disconnected databases that we have today and nobody can get to the data? So then the rest of the principles is really to address all of the challenges that comes with this first principle of decomposition around domain context. And the second principle is, well, we have to expect a certain level of quality and accountability, and responsibility for the teams that provide the data. So let's bring products thinking and treating data as a product, to the data that these teams now share, and let's put accountability around it. We need a new set of incentives and metrics for domain teams to share the data, we need to have a new set of kind of quality metrics that define what it means for the data to be a product, and we can go through that conversation perhaps later. So then the second principle is, okay, the teams now that are responsible, the domain teams responsible for their analytical data need to provide that data with a certain level of quality and assurance. Let's call that a product, and bring product thinking to that. And then the next question you get asked off at work by CIO or CTO is the people who build the infrastructure and spend the money. They say, well, "It's actually quite complex to manage big data, now where we want everybody, every independent team to manage the full stack of storage and computation and pipelines and access control and all of that." Well, we've solved that problem in operational world. And that requires really a new level of platform thinking to provide infrastructure and tooling to the domain teams to now be able to manage and serve their big data, and I think that requires re-imagining the world of our tooling and technology. But for now, let's just assume that we need a new level of abstraction to hide away a ton of complexity that unnecessarily people get exposed to. And that's the third principle of creating self-serve infrastructure to allow autonomous teams to build their domains. But then the last pillar, the last fundamental pillar is okay, once he distributed a problem into smaller problems that you found yourself with another set of problems, which is how I'm going to connect this data. The insights happens and emerges from the interconnection of the data domains, right? It's just not necessarily locked into one domain. So the concerns around interoperability and standardization and getting value as a result of composition and interconnection of these domains requires a new approach to governance. And we have to think about governance very differently based on a federated model. And based on a computational model. Like once we have this powerful self-serve platform, we can computationally automate a lot of covenants decisions and security decisions, and policy decisions, that applies to this fabric of mesh, not just a single domain or not in a centralized. So really, as you mentioned, the most important component of the data mesh is distribution of ownership and distribution of architecture in data, the rest of them is to solve all the problems that come with that. >> So, very powerful. And guys, we actually have a picture of what Zhamak just described. Bring up figure three, if you would. So I mean, essentially, you're advocating for the pushing of the pipeline and all its various functions into the lines of business and abstracting that complexity of the underlying infrastructure which you kind of show here in this figure, data infrastructure as a platform down below. And you know why I love about this, Zhamak, is, to me it underscores the data is not the new oil. Because I can put oil in my car, I can put it in my house but I can't put the same code in both places. But I think you call it polyglot data, which is really different forms, batch or whatever. But the same data doesn't follow the laws of scarcity. I can use the same data for many, many uses, and that's what this sort of graphic shows. And then you brought in the really important, sticking problem, which is that the governance which is now not a command and control, it's federated governance. So maybe you could add some thoughts on that. >> Sure, absolutely. It's one of those, I think I keep referring to data mesh as a paradigm shift, and it's not just to make it sound grand and like kind of grand and exciting or important, it's really because I want to point out, we need to question every moment when we make a decision around, how we're going to design security, or governance or modeling of the data. We need to reflect and go back and say, "Am I applying some of my cognitive biases around how I have worked for the last 40 years?" I've seen it work? Or "Do I do I really need to question?" And do need to question the way we have applied governance. I think at the end of the day, the role of the data governance and the objective remains the same. I mean, we all want quality data accessible to a diverse set of users and its users now know have different personas, like data persona, data analysts, data scientists, data application user. These are very diverse personas. So at the end of the day, we want quality data accessible to them, trustworthy in an easy consumable way. However, how we get there looks very different in as you mentioned that the governance model in the old world has been very command and control, very centralized. They were responsible for quality, they were responsible for certification of the data, applying and making sure the data complies with all sorts of regulations, make sure data gets discovered and made available. In the world of data mesh, really the job of the data governance as a function becomes finding the equilibrium between what decisions need to be made and enforced globally, and what decisions need to be made locally so that we can have an interoperable mesh of data sets that can move fast and can change fast. It's really about, instead of kind of putting those systems in a straight jacket of being constantly and don't change, embrace change, and continuous change of landscape because that's just the reality we can't escape. So the role of governance really, the modern governance model I called federated and computational. And by that I mean, every domain needs to have a representative in the governance team. So the role of the data or domain data product owner who really were understands that domain really well, but also wears that hats of the product owner. It's an important role that has to have a representation in the governance. So it's a federation of domains coming together. Plus the SMEs, and people have Subject Matter Experts who understand the regulations in that environment, who understands the data security concerns. But instead of trying to enforce and do this as a central team, they make decisions as what needs to be standardized. What needs to be enforced. And let's push that into that computationally and in an automated fashion into the platform itself, For example. Instead of trying to be part of the data quality pipeline and inject ourselves as people in that process, let's actually as a group, define what constitutes quality. How do we measure quality? And then let's automate that, and let's codify that into the platform, so that every day the products will have a CICD pipeline, and as part of that pipeline, law's quality metrics gets validated, and every day to product needs to publish those SLOs or Service Level Objectives, or whatever we choose as a measure of quality, maybe it's the integrity of the data, or the delay in the data, the liveliness of the data, whatever are the decisions that you're making. Let's codify that. So it's really the objectives of the governance team trying to satisfies the same, but how they do it, it's very, very different. And I wrote a new article recently, trying to explain the logical architecture that would emerge from applying these principles, and I put a kind of a light table to compare and contrast how we do governance today, versus how we'll do it differently, to just give people a flavor of what does it mean to embrace decentralization, and what does it mean to embrace change, and continuous change. So hopefully that could be helpful. >> Yes. There's so many questions I have. But the point you make it too on data quality, sometimes I feel like quality is the end game, Where the end game should be how fast you can go from idea to monetization with a data service. What happens again? And you've sort of addressed this, but what happens to the underlying infrastructure? I mean, spinning up EC2s and S3 buckets, and MyPytorches and TensorFlows. That lives in the business, and who's responding for that? >> Yeah, that's why I'm glad you're asking this question, David, because I truly believe we need to reimagine that world. I think there are many pieces that we can use as utilities are foundational pieces, but I can see for myself at five to seven year road map building this new tooling. I think in terms of the ownership, the question around ownership, that would remain with the platform team, but I don't perhaps a domain agnostic technology focused team, right? That there are providing a set of products themselves, but the users of those products are data product developers, right? Data domain teams that now have really high expectations, in terms of low friction, in terms of a lead time to create a new data products. So we need a new set of tooling and I think the language needs to shift from I need a storage bucket, or I need a storage account, to I need a cluster to run my spark jobs. Too, here's the declaration of my data products. This is where the data file will come from, this is a data that I want to serve, these are the policies that I need to apply in terms of perhaps encryption or access control, go make it happen platform, go provision everything that I need, so that as a data product developer, all I can focus on is the data itself. Representation of semantic and representation of the syntax, and make sure that data meets the quality that I have to assure and it's available. The rest of provisioning of everything that sits underneath will have to get taken care of by the platform. And that's what I mean by requires a reimagination. And there will be a data platform team. The data platform teams that we set up for our clients, in fact themselves have a fair bit of complexity internally, they divide into multiple teams, multiple planes. So there would be a plane, as in a group of capabilities that satisfied that data product developer experience. There would be a set of capabilities that deal with those nitty gritty underlying utilities, I call them (indistinct) utilities because to me, the level of abstraction of the platform needs to go higher than where it is. So what we call platform today are a set of utilities we'll be continuing to using. We'll be continuing to using object storage, we will continue to using relational databases and so on. So there will be a plane and a group of people responsible for that. There will be a group of people responsible for capabilities that enable the mesh level functionality, for example, be able to correlate and connect and query data from multiple nodes, that's a mesh level capability, to be able to discover and explore the mesh of data products, that's the mesh of capability. So it would be a set of teams as part of platform. So we use a strong, again, products thinking embedded in a product and ownership embedded into that to satisfy the experience of this now business oriented domain data teams. So we have a lot of work to do. >> I could go on, unfortunately, we're out of time, but I guess, first of all, I want to tell people there's two pieces that you've put out so far. One is how to move beyond a Monolithic Data Lake to a distributed data mesh. You guys should read that in the "Data Mesh Principles and Logical Architecture," is kind of part two. I guess my last question in the very limited time we have is are organizations ready for this? >> I think how the desire is there. I've been overwhelmed with the number of large and medium and small and private and public, and governments and federal organizations that reached out to us globally. I mean, this is a global movement and I'm humbled by the response of the industry. I think, the desire is there, the pains are real, people acknowledge that something needs to change here. So that's the first step. I think awareness is spreading, organizations are more and more becoming aware, in fact, many technology providers are reaching to us asking what shall we do because our clients are asking us, people are already asking, we need the data mesh and we need the tooling to support it. So that awareness is there in terms of the first step of being ready. However, the ingredients of a successful transformation requires top-down and bottom-up support. So it requires support from chief data analytics officers, all above, the most successful clients that we have with data mesh are the ones that, the CEOs have made a statement that, "We'd want to change the experience of every single customer using data, and we're going to commit to this." So the investment and support exists from top to all layers, the engineers are excited, the maybe perhaps the traditional data teams are open to change. So there are a lot of ingredients of transformations that come together. Are we really ready for it? I think the pioneers, perhaps, the innovators if you think about that innovation curve of adopters, probably pioneers and innovators and lead adopters are making moves towards it, and hopefully as the technology becomes more available, organizations that are less engineering oriented, they don't have the capability in-house today, but they can buy it, they would come next. Maybe those are not the ones who are quite ready for it because the technology is not readily available and requires internal investments to make. >> I think you're right on. I think the leaders are going to lean in hard and they're going to show us the path over the next several years. And I think that the end of this decade is going to be defined a lot differently than the beginning. Zhamak, thanks so much for coming to "theCUBE" and participating in the program. >> Thank you for hosting me, David. >> Pleasure having you. >> It's been wonderful. >> All right, keep it right there everybody, we'll be back right after this short break. (slow music)

>>My name is Dave Volonte, and with me are two world class technologists, visionaries and entrepreneurs. And Wa Dodgeville is the he co founded Snowflake, and he's now the president of the product division. And Florian Duetto is the co founder and CEO of Data Aiko. Gentlemen, welcome to the Cube to first timers. Love it. >>Great to be here >>now, Florian you and Ben Wa You have a number of customers in common. And I have said many times on the Cube that you know, the first era of cloud was really about infrastructure, making it more agile, taking out costs. And the next generation of innovation is really coming from the application of machine intelligence to data with the cloud is really the scale platform. So is that premise your relevant to you? Do you buy that? And and why do you think snowflake and data ICU make a good match for customers? >>I think that because it's our values that are aligned when it's all about actually today allowing complexity for customers. So you close the gap or the democratizing access to data access to technology. It's not only about data data is important, but it's also about the impact of data. Who can you make the best out of data as fast as possible as easily as possible within an organization. And another value is about just the openness of the platform building the future together? Uh, I think a platform that is not just about the platform but also full ecosystem of partners around it, bringing the level off accessibility and flexibility you need for the 10 years away. >>Yeah, so that's key. But it's not just data. It's turning data into insights. Have been why you came out of the world of very powerful but highly complex databases. And we know we all know that you and the snowflake team you get very high marks for really radically simplifying customers lives. But can you talk specifically about the types of challenges that your customers air using snowflake to solve? >>Yeah, so So the really the challenge, you know, be four. Snowflake. I would say waas really? To put all the data, you know, in one place and run all the computers, all the workloads that you wanted to run, You know, against that data and off course, you know, existing legacy platforms. We're not able to support. You know that level of concurrency, Many workload. You know, we we talk about machine learning that a science that are engendering, you know, that our house big data were closed or running in one place didn't make sense at all. And therefore, you know what customers did is to create silos, silos of data everywhere, you know, with different system having a subset of the data. And of course, now you cannot analyze this data in one place. So, snowflake, we really solve that problem by creating a single, you know, architectural where you can put all the data in the cloud. So it's a really cloud native we really thought about You know how to solve that problem, how to create, you know, leverage, Cloud and the lessee cc off cloud to really put all the die in one place, but at the same time not run all workload at the same place. So each workload that runs in Snowflake that is dedicated, You know, computer resource is to run, and that makes it very Ajai, right? You know, Floyd and talk about, you know, data scientists having to run analysis, so they need you know a lot of compute resources, but only for, you know, a few hours on. Do you know, with snowflake they can run these new work lord at this workload to the system, get the compute resources that they need to run this workload. And when it's over, they can shut down. You know that their system, it will be automatically shut down. Therefore, they would not pay for the resources that they don't use. So it's a very Ajai system where you can do this, analyzes when you need, and you have all the power to run all this workload at the same time. >>Well, it's profound what you guys built to me. I mean, of course, everybody's trying to copy it now. It was like, remember that bringing the notion of bringing compute to the data and the Hadoop days, and I think that that Asai say everybody is sort of following your suit now are trying to Florian I gotta say the first data scientist I ever interviewed on the Cube was amazing. Hilary Mason, right after she started a bit Lee. And, you know, she made data science that sounds so compelling. But data science is hard. So same same question for you. What do you see is the biggest challenges for customers that they're facing with data science. >>The biggest challenge, from my perspective, is that owns you solve the issue of the data. Seidel with snowflake, you don't want to bring another Seidel, which would be a side off skills. Essentially, there is to the talent gap between the talented label of the market, or are it is to actually find recruits trained data scientist on what needs to be done. And so you need actually to simplify the access to technologies such as every organization can make it, whatever the talent, by bridging that gap and to get there, there is a need of actually breaking up the silos. And in a collaborative approach where technologists and business work together and actually put some their hands into those data projects together, >>it makes sense for flooring. Let's stay with you for a minute. If I can your observation spaces, you know it's pretty, pretty global, and and so you have a unique perspective on how companies around the world might be using data and data science. Are you seeing any trends may be differences between regions or maybe within different industries. What are you seeing? >>Yes. Yeah, definitely. I do see trends that are not geographic that much, but much more in terms of maturity of certain industries and certain sectors, which are that certain industries invested a lot in terms of data, data access, ability to start data in the last few years and no age, a level of maturity where they can invest more and get to the next steps. And it's really rely on the ability of certain medial certain organization actually to have built this long term strategy a few years ago and no start raping up the benefits. >>You know, a decade ago, Florian Hal Varian, we, you know, famously said that the sexy job in the next 10 years will be statisticians. And then everybody sort of change that to data scientists and then everybody. All the statisticians became data scientists, and they got a raise. But data science requires more than just statistics acumen. What what skills >>do >>you see as critical for the next generation of data science? >>Yeah, it's a good question because I think the first generation of the patient is became the licenses because they could done some pipe and quickly on be flexible. And I think that the skills or the next generation of data sentences will definitely be different. It will be first about being able to speak the language of the business, meaning, oh, you translate data inside predictive modeling all of this into actionable insight or business impact. And it would be about you collaborate with the rest of the business. It's not just a farce. You can build something off fast. You can do a notebook in python or your credit models off themselves. It's about, oh, you actually build this bridge with the business. And obviously those things are important. But we also has become the center of the fact that technology will evolve in the future. There will be new tools and technologies, and they will still need to keep this level of flexibility and get to understand quickly, quickly. What are the next tools they need to use the new languages or whatever to get there. >>As you look back on 2020 what are you thinking? What are you telling people as we head into next year? >>Yeah, I I think it's Zaveri interesting, right? We did this crisis, as has told us that the world really can change from one day to the next. And this has, you know, dramatic, you know, and perform the, you know, aspect. For example, companies all the sudden, you know, So their revenue line, you know, dropping. And they had to do less meat data. Some of the companies was the reverse, right? All the sudden, you know, they were online, like in stock out, for example, and their business, you know, completely, you know, change, you know, from one day to the other. So this GT off, You know, I, you know, adjusting the resource is that you have tow the task a need that can change, you know, using solution like snowflakes, you know, really has that. And we saw, you know, both in in our customers some customers from one day to the to do the next where, you know, growing like big time because they benefited, you know, from from from from co vid and their business benefited, but also, as you know, had to drop. And what is nice with with with cloud, it allows to, you know, I just compute resources toe, you know, to your business needs, you know, and really adjusted, you know, in our, uh, the the other aspect is is understanding what is happening, right? You need to analyze the we saw all these all our customers basically wanted to understand. What is that going to be the impact on my business? How can I adapt? How can I adjust? And and for that, they needed to analyze data. And, of course, a lot of data which are not necessarily data about, you know, their business, but also data from the outside. You know, for example, coffee data, You know, where is the States? You know, what is the impact? You know, geographic impact from covitz, You know, all the time and access to this data is critical. So this is, you know, the promise off the data crowd, right? You know, having one single place where you can put all the data off the world. So our customers, all the Children you know, started to consume the cov data from our that our marketplace and and we had the literally thousands of customers looking at this data analyzing this data, uh, to make good decisions So this agility and and and this, you know, adapt adapting, you know, from from one hour to the next is really critical. And that goes, you know, with data with crowding adjusting, resource is on and that's, you know, doesn't exist on premise. So So So indeed, I think the lesson learned is is we are living in a world which machines changing all the time and we have for understanding We have to adjust and and And that's why cloud, you know, somewhere it's great. >>Excellent. Thank you. You know the kid we like to talk about disruption, of course. Who doesn't on And also, I mean, you look at a I and and the impact that is beginning to have and kind of pre co vid. You look at some of the industries that were getting disrupted by, you know, we talked about digital transformation and you had on the one end of the spectrum industries like publishing which are highly disrupted or taxis. And you could say Okay, well, that's, you know, bits versus Adam, the old Negroponte thing. But then the flip side of that look at financial services that hadn't been dramatically disrupted. Certainly healthcare, which is ripe for disruption Defense. So the number number of industries that really hadn't leaned into digital transformation If it ain't broke, don't fix it. Not on my watch. There was this complacency and then, >>of >>course, co vid broke everything. So, florian, I wonder if you could comment? You know what industry or industries do you think you're gonna be most impacted by data science and what I call machine intelligence or a I in the coming years and decades? >>Honestly, I think it's all of them artist, most of them because for some industries, the impact is very visible because we're talking about brand new products, drones like cars or whatever that are very visible for us. But for others, we are talking about sport from changes in the way you operate as an organization, even if financial industry itself doesn't seems to be so impacted when you look it from the consumer side or the outside. In fact, internally, it's probably impacted just because the way you use data on developer for flexibility, you need the kind off cost gay you can get by leveraging the latest technologies is just enormous, and so it will actually transform the industry that also and overall, I think that 2020 is only a where, from the perspective of a I and analytics, we understood this idea of maturity and resilience, maturity, meaning that when you've got a crisis, you actually need data and ai more than before. You need to actually call the people from data in the room to take better decisions and look for a while and not background. And I think that's a very important learning from 2020 that will tell things about 2021 and the resilience it's like, Yeah, Data Analytics today is a function consuming every industries and is so important that it's something that needs to work. So the infrastructure is to work in frustration in super resilient. So probably not on prime on a fully and prime at some point and the kind of residence where you need to be able to plan for literally anything like no hypothesis in terms of behaviors can be taken for granted. And that's something that is new and which is just signaling that we're just getting to the next step for the analytics. >>I wonder, Benoit, if you have anything to add to that. I mean, I often wonder, you know, winter machine's gonna be able to make better diagnoses than doctors. Some people say already, you know? Well, the financial services traditional banks lose control of payment systems. Uh, you know what's gonna happen to big retail stores? I mean, maybe bring us home with maybe some of your final thoughts. >>Yeah, I would say, you know, I I don't see that as a negative, right? The human being will always be involved very closely, but the machine and the data can really have, you know, see, Coalition, you know, in the data that that would be impossible for for for human being alone, you know, you know, to to discover so So I think it's going to be a compliment, not a replacement on. Do you know everything that has made us you know faster, you know, doesn't mean that that we have less work to do. It means that we can doom or and and we have so much, you know, to do, uh, that that I would not be worried about, You know, the effect off being more efficient and and and better at at our you know, work. And indeed, you know, I fundamentally think that that data, you know, processing off images and doing, you know, I ai on on on these images and discovering, you know, patterns and and potentially flagging, you know, disease, where all year that then it was possible is going toe have a huge impact in in health care, Onda and And as as as Ryan was saying, every you know, every industry is going to be impacted by by that technology. So So, yeah, I'm very optimistic. >>Great guys. I wish we had more time. I gotta leave it there. But so thanks so much for coming on. The Cube was really a pleasure having you.

>> My name is Dave Vellante. And with me are two world-class technologists, visionaries and entrepreneurs. Benoit Dageville, he co-founded Snowflake and he's now the President of the Product Division, and Florian Douetteau is the Co-founder and CEO of Dataiku. Gentlemen, welcome to the cube to first timers, love it. >> Yup, great to be here. >> Now Florian you and Benoit, you have a number of customers in common, and I've said many times on theCUBE, that the first era of cloud was really about infrastructure, making it more agile, taking out costs. And the next generation of innovation, is really coming from the application of machine intelligence to data with the cloud, is really the scale platform. So is that premise relevant to you, do you buy that? And why do you think Snowflake, and Dataiku make a good match for customers? >> I think that because it's our values that aligned, when it gets all about actually today, and knowing complexity of our customers, so you close the gap. Where we need to commoditize the access to data, the access to technology, it's not only about data. Data is important, but it's also about the impacts of data. How can you make the best out of data as fast as possible, as easily as possible, within an organization. And another value is about just the openness of the platform, building a future together. Having a platform that is not just about the platform, but also for the ecosystem of partners around it, bringing the level of accessibility, and flexibility you need for the 10 years of that. >> Yeah, so that's key, that it's not just data. It's turning data into insights. Now Benoit, you came out of the world of very powerful, but highly complex databases. And we know we all know that you and the Snowflake team, you get very high marks for really radically simplifying customers' lives. But can you talk specifically about the types of challenges that your customers are using Snowflake to solve? >> Yeah, so the challenge before snowflake, I would say, was really to put all the data in one place, and run all the computes, all the workloads that you wanted to run against that data. And of course existing legacy platforms were not able to support that level of concurrency, many workload, we talk about machine learning, data science, data engineering, data warehouse, big data workloads, all running in one place didn't make sense at all. And therefore be what customers did this to create silos, silos of data everywhere, with different system, having a subset of the data. And of course now, you cannot analyze this data in one place. So Snowflake, we really solved that problem by creating a single architecture where you can put all the data into cloud. So it's a really cloud native. We really thought about how solve that problem, how to create, leverage cloud, and the elasticity of cloud to really put all the data in one place. But at the same time, not run all workload at the same place. So each workload that runs in Snowflake, at its dedicated compute resources to run. And that makes it agile, right? Florian talked about data scientist having to run analysis, so they need a lot of compute resources, but only for a few hours. And with Snowflake, they can run these new workload, add this workload to the system, get the compute resources that they need to run this workload. And then when it's over, they can shut down their system, it will automatically shut down. Therefore they would not pay for the resources that they don't use. So it's a very agile system, where you can do this analysis when you need, and you have all the power to run all these workload at the same time. >> Well, it's profound what you guys built. I mean to me, I mean of course everybody's trying to copy it now, it was like, I remember that bringing the notion of bringing compute to the data, in the Hadoop days. And I think that, as I say, everybody is sort of following your suit now or trying to. Florian, I got to say the first data scientist I ever interviewed on theCUBE, it was the amazing Hillary Mason, right after she started at Bitly, and she made data sciences sounds so compelling, but data science is a hard. So same question for you, what do you see as the biggest challenges for customers that they're facing with data science? >> The biggest challenge from my perspective, is that once you solve the issue of the data silo, with Snowflake, you don't want to bring another silo, which will be a silo of skills. And essentially, thanks to the talent gap, between the talent available to the markets, or are released to actually find recruits, train data scientists, and what needs to be done. And so you need actually to simplify the access to technologies such as, every organization can make it, whatever the talent, by bridging that gap. And to get there, there's a need of actually backing up the silos. Having a collaborative approach, where technologies and business work together, and actually all puts up their ends into those data projects together. >> It makes sense, Florain let's stay with you for a minute, if I can. Your observation space, it's pretty, pretty global. And so you have a unique perspective on how can companies around the world might be using data, and data science. Are you seeing any trends, maybe differences between regions, or maybe within different industries? What are you seeing? >> Yeah, definitely I do see trends that are not geographic, that much, but much more in terms of maturity of certain industries and certain sectors. Which are, that certain industries invested a lot, in terms of data, data access, ability to store data. As well as experience, and know region level of maturity, where they can invest more, and get to the next steps. And it's really relying on the ability of certain leaders, certain organizations, actually, to have built these long-term data strategy, a few years ago when no stats reaping of the benefits. >> A decade ago, Florian, Hal Varian famously said that the sexy job in the next 10 years will be statisticians. And then everybody sort of changed that to data scientist. And then everybody, all the statisticians became data scientists, and they got a raise. But data science requires more than just statistics acumen. What skills do you see as critical for the next generation of data science? >> Yeah, it's a great question because I think the first generation of data scientists, became data scientists because they could have done some Python quickly, and be flexible. And I think that the skills of the next generation of data scientists will definitely be different. It will be, first of all, being able to speak the language of the business, meaning how you translates data insight, predictive modeling, all of this into actionable insights of business impact. And it would be about how you collaborate with the rest of the business. It's not just how fast you can build something, how fast you can do a notebook in Python, or do predictive models of some sorts. It's about how you actually build this bridge with the business, and obviously those things are important, but we also must be cognizant of the fact that technology will evolve in the future. There will be new tools, new technologies, and they will still need to keep this level of flexibility to understand quickly what are the next tools they need to use a new languages, or whatever to get there. >> As you look back on 2020, what are you thinking? What are you telling people as we head into next year? >> Yeah, I think it's very interesting, right? This crises has told us that the world really can change from one day to the next. And this has dramatic and perform the aspects. For example companies all of a sudden, show their revenue line dropping, and they had to do less with data. And some other companies was the reverse, right? All of a sudden, they were online like Instacart, for example, and their business completely changed from one day to the other. So this agility of adjusting the resources that you have to do the task, and need that can change, using solution like Snowflake really helps that. Then we saw both in our customers. Some customers from one day to the next, were growing like big time, because they benefited from COVID, and their business benefited. But others had to drop. And what is nice with cloud, it allows you to adjust compute resources to your business needs, and really address it in house. The other aspect is understanding what happening, right? You need to analyze. We saw all our customers basically, wanted to understand what is the going to be the impact on my business? How can I adapt? How can I adjust? And for that, they needed to analyze data. And of course, a lot of data which are not necessarily data about their business, but also they are from the outside. For example, COVID data, where is the States, what is the impact, geographic impact on COVID, the time. And access to this data is critical. So this is the premise of the data cloud, right? Having one single place, where you can put all the data of the world. So our customer obviously then, started to consume the COVID data from that our data marketplace. And we had delete already thousand customers looking at this data, analyzing these data, and to make good decisions. So this agility and this, adapting from one hour to the next is really critical. And that goes with data, with cloud, with interesting resources, and that doesn't exist on premise. So indeed I think the lesson learned is we are living in a world, which is changing all the time, and we have to understand it. We have to adjust, and that's why cloud some ways is great. >> Excellent thank you. In theCUBE we like to talk about disruption, of course, who doesn't? And also, I mean, you look at AI, and the impact that it's beginning to have, and kind of pre-COVID. You look at some of the industries that were getting disrupted by, everyone talks about digital transformation. And you had on the one end of the spectrum, industries like publishing, which are highly disrupted, or taxis. And you can say, okay, well that's Bits versus Adam, the old Negroponte thing. But then the flip side of, you say look at financial services that hadn't been dramatically disrupted, certainly healthcare, which is ripe for disruption, defense. So there a number of industries that really hadn't leaned into digital transformation, if it ain't broke, don't fix it. Not on my watch. There was this complacency. And then of course COVID broke everything. So Florian I wonder if you could comment, what industry or industries do you think are going to be most impacted by data science, and what I call machine intelligence, or AI, in the coming years and decade? >> Honestly, I think it's all of them, or at least most of them, because for some industries, the impact is very visible, because we have talking about brand new products, drones, flying cars, or whatever that are very visible for us. But for others, we are talking about a part from changes in the way you operate as an organization. Even if financial industry itself doesn't seem to be so impacted, when you look at it from the consumer side, or the outside insights in Germany, it's probably impacted just because the way you use data (mumbles) for flexibility you need. Is there kind of the cost gain you can get by leveraging the latest technologies, is just the numbers. And so it's will actually comes from the industry that also. And overall, I think that 2020, is a year where, from the perspective of AI and analytics, we understood this idea of maturity and resilience, maturity meaning that when you've got to crisis you actually need data and AI more than before, you need to actually call the people from data in the room to take better decisions, and look for one and a backlog. And I think that's a very important learning from 2020, that will tell things about 2021. And the resilience, it's like, data analytics today is a function transforming every industries, and is so important that it's something that needs to work. So the infrastructure needs to work, the infrastructure needs to be super resilient, so probably not on prem or not fully on prem, at some point. And the kind of resilience where you need to be able to blend for literally anything, like no hypothesis in terms of BLOs, can be taken for granted. And that's something that is new, and which is just signaling that we are just getting to a next step for data analytics. >> I wonder Benoir if you have anything to add to that. I mean, I often wonder, when are machines going to be able to make better diagnoses than doctors, some people say already. Will the financial services, traditional banks lose control of payment systems? What's going to happen to big retail stores? I mean, maybe bring us home with maybe some of your finals thoughts. >> Yeah, I would say I don't see that as a negative, right? The human being will always be involved very closely, but then the machine, and the data can really help, see correlation in the data that would be impossible for human being alone to discover. So I think it's going to be a compliment not a replacement. And everything that has made us faster, doesn't mean that we have less work to do. It means that we can do more. And we have so much to do, that I will not be worried about the effect of being more efficient, and bare at our work. And indeed, I fundamentally think that data, processing of images, and doing AI on these images, and discovering patterns, and potentially flagging disease way earlier than it was possible. It is going to have a huge impact in health care. And as Florian was saying, every industry is going to be impacted by that technology. So, yeah, I'm very optimistic. >> Great, guys, I wish we had more time. I've got to leave it there, but so thanks so much for coming on theCUBE. It was really a pleasure having you.

>> Hello everyone, welcome back to theCUBE'S wall to wall coverage of the Snowflake Data Cloud Summit. My name is Dave Vellante and with me are two world-class technologists, visionaries, and entrepreneurs. Benoit Dageville is the, he co-founded Snowflake. And he's now the president of the Product division and Florian Douetteau is the co-founder and CEO of Dataiku. Gentlemen, welcome to theCUBE, two first timers, love it. >> Great time to be here. >> Now Florian, you and Benoit, you have a number of customers in common. And I've said many times on theCUBE that, the first era of cloud was really about infrastructure, making it more agile taking out costs. And the next generation of innovation is really coming from the application of machine intelligence to data with the cloud, is really the scale platform. So is that premise relevant to you, do you buy that? And why do you think Snowflake and Dataiku make a good match for customers? >> I think that because it's our values that align. When it gets all about actually today, and knowing complexity per customer, so you close the gap or we need to commoditize the access to data, the access to technology, it's not only about data, data is important, but it's also about the impacts of data. How can you make the best out of data as fast as possible, as easily as possible within an organization? And another value is about just the openness of the platform, building a future together. I think a platform that is not just about the platform but also for the ecosystem of partners around it, bringing the little bit of accessibility and flexibility, you need for the 10 years of that. >> Yes, so that's key, but it's not just data. It's turning data into insights. Now Benoit, you came out of the world of very powerful, but highly complex databases. And we all know that, you and the Snowflake team, you get very high marks for really radically simplifying customers' lives. But can you talk specifically about the types of challenges that your customers are using Snowflake to solve? >> Yeah, so really the challenge before Snowflake, I would say, was really to put all the data, in one place and run all the computes, all the workloads that you wanted to run, against that data. And of course, existing legacy platforms were not able to support that level of concurrency, many workload. We talk about machine learning, data science, data engineering, data warehouse, big data workloads, all running in one place, didn't make sense at all. And therefore, what customers did, is to create silos, silos of data everywhere, with different systems having a subset of the data. And of course now you cannot analyze this data in one place. So Snowflake, we really solved that problem by creating a single architecture where you can put all the data in the cloud. So it's a really cloud native. We really thought about how to solve that problem, how to create leverage cloud and the elasticity of cloud to really put all the data in one place. But at the same time, not run all workload at the same place. So each workload that runs in Snowflake at least dedicate compute resources to run. And that makes it very agile, right. Florian talked about data scientist having to run analysis. So they need a lot of compute resources, but only for few hours and with Snowflake, they can run these new workload, add this workload to the system, get the compute resources that they need to run this workload. And then when it's over, they can shut down their system. It will automatically shut down. Therefore they would not pay for the resources that they don't choose. So it's a very agile system, where you can do these analysis when you need, and you have all the power to run all these workload at the same time. >> Well, it's profound what you guys built. To me, I mean, because everybody's trying to copy it now. It's like, I remember the notion of bringing compute to the data in the Hadoop days. And I think that, as I say, everybody is sort of following your suit now or trying to. Florian, I got to say, the first data scientist I ever interviewed on theCUBE was the amazing Hilary Mason, right after she started at Bitly. And she made data science sounds so compelling, but data science is hard. So same question for you. What do you see is the biggest challenges for customers that they're facing with data science? >> The biggest challenge from my perspective is that once you solve the issue of the data silo with Snowflake, you don't want to bring another silo, which would be a silo of skills. And essentially, thanks to that talent gap between the talent and labor of the markets, or how it is to actually find, recruit and train data scientists and what needs to be done. And so you need actually to simplify the access to technology such as every organization can make it, whatever the talents by bridging that gap. And to get there, there is a need of actually breaking up the silos. I think a collaborative approach, where technologies and business work together and actually all put some of their ends into those data projects together. >> Yeah, it makes sense. So Florian, Let's stay with you for a minute, if I can. Your observation spaces, is pretty, pretty global. And so, you have a unique perspective on how companies around the world might be using data and data science. Are you seeing any trends, maybe differences between regions or maybe within different industries? What are you seeing? >> Yep. Yeah, definitely, I do see trends that are not geographic that much, but much more in terms of maturity of certain industries and certain sectors, which are that certain industries invested a lot in terms of data, data access, ability to store data as well as few years and know each level of maturity where they can invest more and get to the next steps. And it's really reliant to reach out to certain details, certain organization, actually to have built this longterm data strategy a few years ago, and no stocks ripping off the benefits. >> You know, a decade ago, Florian, Hal Varian famously said that the sexy job in the next 10 years will be statisticians. And then everybody sort of changed that to data scientists. And then everybody, all the statisticians became data scientists and they got a raise. But data science requires more than just statistics acumen. What skills do you see is critical for the next generation of data science? >> Yeah, it's a good question because I think the first generation of data scientists became better scientists because they could learn some Python quickly and be flexible. And I think that skills of the next generation of data scientists will definitely be different. It will be first about being able to speak the language of the business, meaning all you translate data insight, predictive modeling, all of this into actionable insights or business impact. And it will be about who you collaborate with the rest of the business. It's not just how fast you can build something, how fast you can do a notebook in Python or do quantity models of some sorts. It's about how you actually build this bridge with the business. And obviously those things are important, but we also must be cognizant of the fact that technology will evolve in the future. There will be new tools in technologies, and they will still need to get this level of flexibility and get to understand quickly what are the next tools, they need to use or new languages or whatever to get there. >> Thank you for that. Benoit, let's come back to you. This year has been tumultuous to say the least for everyone, but it's a good time to be in tech, ironically. And if you're in cloud, it's even better. But you look at Snowflake and Dataiku, you guys had done well, despite the economic uncertainty and the challenges of the pandemic. As you look back on 2020, what are you thinking? What are you telling people as we head into next year? >> Yeah, I think it's very interesting, right. We, this crisis has told us that the world really can change from one day to the next. And this has dramatic and profound aspects. For example, companies all of a sudden, saw their revenue line dropping and they had to do less with data. And some of the companies was the reverse, right? All of a sudden, they were online like Instacart, for example, and their business completely change from one day to the other. So this agility of adjusting the resources that you have to do the task, a need that can change, using solution like Snowflake, really helps that. And we saw both in our customers. Some customers from one day to the next, were growing like big time, because they benefited from COVID and their business benefited, but also, as you know, had to drop and what is nice with cloud, it allows to adjust compute resources to your business needs and really address it in-house. The other aspect is understanding what is happening, right? You need to analyze. So we saw all our customers basically wanted to understand, what is it going to be the impact on my business? How can I adapt? How can I adjust? And for that, they needed to analyze data. And of course, a lot of data, which are not necessarily data about their business, but also data from the outside. For example, COVID data. Where is the state, what is the impact, geographic impact on COVID all the time. And access to this data is critical. So this is the promise of the data cloud, right? Having one single place where you can put all the data of the world. So, our customers all of a sudden, started to consume the COVID data from our data marketplace. And we have the unit already thousands of customers looking at this data, analyzing this data to make good decisions. So this agility and this adapting from one hour to the next is really critical and that goes with data, with cloud, more interesting resources and that's doesn't exist on premise. So, indeed I think the lesson learned is, we are living in a world which is changing all the time, and we have to understand it. We have to adjust and that's why cloud, some way is great. >> Excellent, thank you. You know, in theCUBE, we like to talk about disruption, of course, who doesn't. And also, I mean, you look at AI and the impact that it's beginning to have and kind of pre-COVID, you look at some of the industries that were getting disrupted by, everybody talks about digital transformation and you had on the one end of the spectrum, industries like publishing, which are highly disrupted or taxis, and you can say, "Okay well, that's Bits versus Adam, the old Negroponte thing." But then the flip side of this, it says, "Look at financial services that hadn't been dramatically disrupted, certainly healthcare, which is right for disruption, defense." So the more the number of industries that really hadn't leaned into digital transformation, if it ain't broke, don't fix it. Not on my watch. There was this complacency. And then of course COVID broke everything. So Florian, I wonder if you could comment, what industry or industries do you think are going to be most impacted by data science and what I call machine intelligence or AI in the coming years and decades? >> Honestly, I think it's all of them, or at least most of them. Because for some industries, the impact is very visible because we are talking about brand new products, drones, flying cars, or whatever is that are very visible for us. But for others, we are talking about spectrum changes in the way you operate as an organization. Even if financial industry itself doesn't seem to be so impacted when you look at it from the consumer side or the outside. In fact internally, it's probably impacted just because of the way you use data to develop for flexibility you need, is there kind of a cost gain you can get by leveraging the latest technologies, is just enormous. And so it will, actually comes from the industry, that also. And overall, I think that 2020 is a year where, from the perspective of AI and analytics, we understood this idea of maturity and resilience. Maturity, meaning that when you've got a crisis, you actually need data and AI more than before, you need to actually call the people from data in the room to take better decisions and look forward and not backward. And I think that's a very important learning from 2020 that will tell things about 2021. And resilience, it's like, yeah, data analytics today is a function consuming every industries, and is so important that it's something that needs to work. So the infrastructure needs to work, the infrastructure needs to be super resilient. So probably not on trend and not fully on trend, at some point and the kind of residence where you need to be able to plan for literally anything. like no hypothesis in terms of behaviors can be taken for granted. And that's something that is new and which is just signaling that we are just getting into a next step for all data analytics. >> I wonder Benoit, if you have anything to add to that, I mean, I often wonder, you know, when are machines going to be able to make better diagnoses than doctors, some people say already. Will the financial services, traditional banks lose control of payment systems? You know, what's going to happen to big retail stores? I mean, may be bring us home with maybe some of your final thoughts. >> Yeah, I would say, I don't see that as a negative, right? The human being will always be involved very closely, but then the machine and the data can really help, see correlation in the data that would be impossible for human being alone to discover. So, I think it's going to be a compliment, not a replacement and everything that has made us faster, doesn't mean that we have less work to do. It means that we can do more. And we have so much to do. That I would not be worried about the effect of being more efficient and better at our work. And indeed, I fundamentally think that, data, processing of images and doing AI on these images and discovering patterns and potentially flagging disease, way earlier than it was possible, it is going to have a huge impact in health care. And as Florian was saying, every industry is going to be impacted by that technology. So, yeah, I'm very optimistic. >> Great, Guys, I wish we had more time. We got to leave it there but so thanks so much for coming on theCUBE. It was really a pleasure having you. >> [Benoit & Florian] Thank you. >> You're welcome but keep it right there, everybody. We'll back with our next guest, right after this short break. You're watching theCUBE.

>>from the heart of Silicon Valley. It's the Q covering Comcast Innovation Date to you by Comcast. >>Hey, welcome back it ready? Geoffrey here with the Cube were in the Comcast Silicon Valley Innovation Center here in Sunnyvale, just off the runways here. Moffett feels really cool place, a lot of fun toys and gadgets that I have not got to play with yet, but I got to do before I leave. But the conversation today is really about customer experience. We had a small panel this morning of experts talking about customer experience. What does that mean? How do we do a better job at it? And we're excited. Have an expert brought in just for this conversation. She's a net Franz, the founder and CEO of C X Journey, and it's great to see you. >>Thank you. Thanks for having me. Glad to be here. Absolutely been a fun morning. >>What did you think? >>What were some of your impressions of the conversation this morning? You know >>what? It's always great to sit in a room with so many people who have been living and breathing this customer experience journey. And so it was great to hear what Comcast is doing. It was great to hear from some of the other folks in the room. What are some of the latest trends in terms of data and technology and where customer experiences headed? Yeah, it was awesome. >>So customer experiences, it's >>a little bit over. It's almost kind of digital transformation a little bit. Everyone's like experience, experience, experience. And that's a big, complicated topic. How do you help customers really kind of break it down, make it into something manageable, make it into something they can actually approach and have some success with >>us? So I spent a lot of my time working with clients who are brand new to this field, right? I had a former boss who said that they can't even spell C X. Right. So yes, so So yes. So I go in there and I really listen and understand what their pain points are and what they need help with and then get them started on that journey. Basically, soup did not see X strategy work. We typically start out making sure that the right foundation is in place in terms of the executives that they're all a line that they're all committed to this work. The culture. We've got the right culture in place. We've got, you know, some feedback from employees and from customers of what's going well and what's not. And then from there we dive right into a phase that I call understanding. And that's listening to customers listening to employees developing personas so that we can really understand who customers are and who are employees really are, and then also journey mapping to really walk in their shoes to understand the experience that they're having today and then design. Use that to design a better experience for tomorrow. So there's a lot of work that happens up front to make you know the things that we talked about in there this morning. >>Right? Happen. What's the biggest gap? Because everyone >>always talks about being customer centric. And I'm sure if you talk to any sea, of course, were customer centric and you know, we see it would like would like Amazon Andy Jassy and that team is just crazy hyper customer centric and they executed with specific behavior. So what's the part that's usually missing that they think their customer centric, but they're really not? >>Yeah, I think you just hit the nail on the head with the word execute, right? So there's a stat out there that's been out there for forever, and we know it. Every single company, every single business interviews or surveys us to death, right? So they have all this great feedback, but they do nothing with it. They just don't execute. They just don't act on it. And they've got such rich feedback and and and customers want to tell them, Hey, you're doing this well behaved. This is not going so well. So please fix it because we want to continue doing business with you. And so, yeah, it's about execution. I think that's one problem. The other problem is that they focus on the metrics and not on actually doing something with the feedback >>temporary experience. Do they just ignore it? Do they not have the systems to capture it? Are they are they kind of analysis? Paralysis? He just said they have all this great data, and I'm not doing anything about it. Why >>there it is that, too Analysis, paralysis. Let's just beat the numbers to death and and what's the What's the quote about beating the number until the beating the data until the talks >>kind of thing. You know, I don't know that something. I know I'm just mess that, >>but But yeah, they don't have the system in place to actually. Then take what they learn and go do something with it. And I think a big part of it. We talked about this in the room this morning, too. Was around having that commitment from the top, having the CEO say, Listen, we're doing this and we're going to when we listen to, our customers were going toe act on what we hear, So But they don't They don't have the infrastructure in place to actually go and then do it >>right. It's pretty interesting. You have, Ah, a deck that you shared in advance Eight Principles of customer centric city. Yes. And of the aid three are people people before products people before profits people before metrics. That sounds great, but it sounds contrary to everything we hear these days about measure, measure, measure, measure, measure. Right? It's human resource is it almost feels like we're kind of back to these kind of time. Motion studies in tryingto optimize people as if they're a machine as opposed to being a person. >>Yeah, well, it's It's not, because we have to. The way that we could think about is we have to put the human into this. That's what customer experience is all about, right? It's about putting the human in the experience. And it's interesting that you bring up that back because when I opened that talk, I'm show a comm your commercial from Acura, and it's if you've never seen it. It's called the test. If you can google it and find the video and it's really about. If we don't view them as dummies, something amazing happens. That's the tagline, right? And so it's really about people. The experience is all about people. Our business is all about people. That's why we're in business, right? It's all about the customer. It's for the customer. And who's gonna deliver that? Our employees? And so we've got to put the people first, and then the numbers will come >>right. Another one that you had in there, I just have to touch on was forget the golden rule, which which I always thought the golden rules of us. You know, he has the gold makes a >>rule. You're talking about a different golden, which is really treat. Treat others >>not the way you think they want, that you want to be treated but treat people the way that they want to be treated in such a small It's the pylons, but it's so important. >>It's so important. And I love this example that I share. Thio just recently read a book by Hal Rosenbluth called The Customer Comes Second, right, and to most people, that seems counterintuitive, but he's really referring to The employee comes more first, which I love, and I'm the example that he gives us. He's left handed and he goes into a restaurant. He frequents this restaurant all the time, and until I read this story, I never even thought about this. And now that I go to restaurants, I think about this all the time. The silverware is always on the right hand side, but he's left handed, so this restaurant that he frequents the waitress. He always seemed to have the same waitress she caught on, and so when when he would come into the restaurant, she would set the silvery down on the left hand side. for him that's treating people the way that they want to be treated. And that's what customer experience is all about, >>right? One of the topics that he talked about in the session this morning was, um, the reputation that service experiences really defined by the sum of all your interactions. And it's really important to kind of keep a ah view of that that it's not just an interaction with many, many interactions over a period of time that sounds so hard to manage. And then there's also this kind of the last experience, which is probably overweighted based on the whole. >>How do people >>keep that in mind? How did they How did they, you know, make sure that they're thinking that kind of holistically about the customer engagement across a number of fronts within the company. >>Well, you've got to think >>about it as think about it as a journey, not just touch points, not just a bunch of little touch points, because if you think about just the last experience or just a touch point, then you're thinking about transactions. You're not thinking about a relationship, and what we're trying to get at is customer relationships and not just transactional, you know, it's it's they're in, they're out, they're gone, right? So what? We want relationships. We want them to be customers for life. And and that's the only way that we're gonna do it is if we focus on the journey, >>right? What about the challenge of that which was special suddenly becomes the norm. And we talked a lot about, you know, kind of consumers ations of i t. Because as soon as I get great results on a Google search or, you know, I find exactly what I need on Amazon in two clicks and then to take that into whatever my be to be your B to C application as when Now those expectations are not being driven by what I promised to deliver. But they're being driven by all these third party app said. I have a no control up and they're probably developing at a faster pace of innovation that I can keep up housing people, you know, kind of absorb that deal with it and try to take some lessons from that in the delivery of their own application >>essay. You you brought up two things there which I want to address the 1st 1 to which was about the delighting customers. But to answer your question is really about focusing on your customers and your customers needs on. And that's why I talk a lot about customer understanding, right? It's it's about listening to your customers. It's about developing personas and really understanding who they are, what their pain points are, what their problems are, what needs. Are they trying to solve our problems? Are they trying to solve on and then walking in their shoes through journey, mapping? And that understanding allows us to design an experience for our customers, right for our customers. If we don't solve a problem up for our customers, they will go elsewhere and they'll get their problems solved elsewhere, right? So I think that's really important. The first part of your question was, our point was around delighting our customers, and you're absolutely right. We don't have to delight customers at every touch point. I know that's counter to what a lot of people might say or think, but to your point, once we delighted every touch point, now it becomes the new norm. It's an expectation that has now been set and now delight, Where does it stop? You know, Delight is here, and then it's here. And then it's here. And so So it's It's a whole different. So my thinking on that is that most businesses cannot delight at every touch point, and they certainly don't. Um, I think we need to meet expectations and the and the only way that we can do that is to listen and understand and and and then act on what we hear. And, um, most businesses are still very primitive, even when it comes to that, >>right? Okay. Give you the last word. What's what's the kind of the most consistent, easy to fix stumble that most customers are doing when you when you get engaged and you walk in, what's that one thing that you know with 90% confidence factor that when you walk in, this is gonna be, you know, one of these three or four little things that they should stop doing or that they should do just just just get off the baseline? >>Yeah, I think it's You know what I think it >>za combination of sort of speed and responsiveness. I'll give an example. I won't mean the company, but But I thought, man, in this day and age, this shouldn't be happening, right? It was a company that I contacted. I was supposed to set up an account and they said I couldn't for it just wasn't working. I tried different browsers, just wasn't working. So I sent them and eat. First. I tried to call, but I got stuck in Ivy are hell. And then I sent an email and my the email that I got back was an auto responder. That's I will reply within five business days. >>Five business days, Thio like, really, where? Why don't you just ask me to send a fax, right? You know, So So that's the kind >>of stuff that seriously I I want to solve that e mails like really in 2019. We're still responding in five business days. That's just that's just ludicrous. I think that's one of the and it's such it doesn't cost anything to respond in a timely manner and to respond at all right now. Here it is. It's been I haven't heard from them yet, so it's been like seven days now, so >>there's that just tweet tweet at the CEO going to, hopefully the >>CEO tweets and maybe doesn't tweet. >>I know, right? Yeah, well, in >>that you know nothing about opportunity for you because this is not an easy it's not an easy thing to do is it's hard to stay up with people's expectations and to drive new and innovative products when they don't necessarily even know how to engage with those things. >>Yeah, absolutely. Yeah, The field is wide open because, like I said, there's still so many companies that are still just trying to get the basics right. So >>Well, thanks for taking a few minutes of your time. Thanks for participating. Absolutely, She's in that. I'm Jeff. You're watching the Cube worth the Comcast Silicon Valley Innovation Center. Thanks for watching. We'll see next time.

(upbeat music) >> From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CUBE Conversation. >> Welcome everyone to this special CUBE Conversation Around the CUBE segment, Unpacking AI, number two, sponsored by Juniper Networks. We've got a great lineup here to go around the CUBE and unpack AI. We have Ken Jennings, all-time Jeopardy champion with us. Celebrity, great story there, we'll dig into that. John Hinson, director of AI at Evotek and Charna Parkey, who's the applied scientist at Textio. Thanks for joining us here for Around the CUBE Unpacking AI, appreciate it. First question I want to get to, Ken, you're notable for being beaten by a machine on Jeopardy. Everyone knows that story, but it really brings out the question of AI and the role AI is playing in society around obsolescence. We've been hearing gloom and doom around AI replacing people's jobs, and it's not really that way. What's your take on AI and replacing people's jobs? >> You know, I'm not an economist, so I can't speak to how easy it's going to be to retrain and re-skill tens of millions of people once these clerical and food prep and driving and whatever jobs go away, but I can definitely speak to the personal feeling of being in that situation, kind of watching the machine take your job on the assembly line and realizing that the thing you thought made you special no longer exists. If IBM throws enough money at it, your skill essentially is now obsolete. And it was kind of a disconcerting feeling. I think that what people need is to feel like they matter, and that went away for me very quickly when I realized that a black rectangle can now beat me at a game show. >> Okay John, what's your take on AI replacing jobs? What's your view on this? >> I think, look, we're all going to have to adapt. There's a lot of changes coming. There's changes coming socially, economically, politically. I think it's a disservice to us all to get to too indulgent around the idea that these things are going to change. We have to absorb these things, we have to be really smart about how we approach them. We have to be very open-minded about how these things are going to actually change us all. But ultimately, I think it's going to be positive at the end of the day. It's definitely going to be a little rough for a couple of years as we make all these adjustments, but I think what AI brings to the table is heads above kind of where we are today. >> Charna, your take around this, because the role of humans versus machines are pretty significant, they help each other. But is AI going to dominate over humans? >> Yeah, absolutely. I think there's a thing that we see over and over again in every bubble and collapse where, you know, in the automotive industry we certainly saw a bunch of jobs were lost, but a bunch of jobs were gained. And so we're just now actually getting into the phase where people are realizing that AI isn't just replacement, it has to be augmentation, right? We can't simply use images to replace recognition of people, we can't just use black box to give our FICO credit scores, it has to be inspectable. So there's a new field coming up now called explainable AI that actually is where we're moving towards and it's actually going to help society and create jobs. >> All right so let's stay on that next point for the next round, explainable AI. This points to a golden age. There's a debate around are we in a bubble or a golden age. A lot of people are negative right now on tech. You can see all the tech backlash. Amazon, the big tech companies like Apple and Facebook, there's a huge backlash around this so-called tech for society. Is this an indicator of a golden age coming? >> I think so, absolutely. We can take two examples of this. One would be where, you remember when Amazon built a hiring algorithm based upon their own resume data and they found that it was discriminating against women because they had only had men apply for it. Now with Textio we're building augmented writing across the audience and not from a single company and so companies like Johnson and Johnson are increasing the pipeline by more than nine percent which converts to 90,000 more women applying for their jobs. And so part of the difference there is one is explainable, one isn't, and one is using the right data set representing the audience that is consuming it and not a single company's hiring. So I think we're absolutely headed into more of a golden age, and I think these are some of the signs that people are starting to use it in the right way. >> John, what's your take? Obviously golden age doesn't look that to us right now. You see Facebook approving lies as ads, Twitter banning political ads. AI was supposed to solve all these problems. Is there light at the end of this dark tunnel we're on? >> Yeah, golden age for sure. I'm definitely a big believer in that. I think there's a new era amongst us on how we handle data in general. I think the most important thing we have here though is education around what this stuff is, how it works, how it's affecting our lives individually and at the corporate level. This is a new era of informing and augmenting literally everything we do. I see nothing but positives coming out of this. We have to be obviously very careful with our approaching all the biases that already exist today that are only going to be magnified with these types of algorithms at mass scale. But ultimately if we can get over that hurdle, which I believe collectively we all need to do together, I think we'd live in much better, less wasteful world just by approaching the data that's already at hand. >> Ken, what's your take on this? It's like a daily double question. Is it going to be a golden age? >> Laughs >> It's going to come sooner or later. We have to have catastrophe before, we have to have reality hit us in the face before we realize that tech is good, and shaping it? It's pretty ugly right now in some of the situations out there, especially in the political scene with the election in the US. You're seeing some negative things happening. What's your take on this? >> I'm much more skeptical than John and Charna. I feel like that kind of just blinkered, it's going to be great, is something you have to actually be in the tech industry and hearing all day to actually believe. I remember seeing kind of lay-person's exposure to Watson when Watson was on Jeopardy and hearing the questions reporters would ask and seeing the memes that would appear, and everyone's immediate reaction just to something as innocuous as a AI algorithm playing on a game show was to ask, is this Skynet from Terminator 2? Is this the computer from The Matrix? Is this HAL pushing us out of the airlock? Everybody immediately first goes to the tech is going to kill us. That's like everybody's first reaction, and it's weird. I don't know, you might say it's just because Hollywood has trained us to expect that plot development, but I almost think it's the other way around. Like that's a story we tell because we're deeply worried about our own meaning and obsolescence when we see how little these skills might be valued in 10, 20, 30 years. >> I can't tell you how much, by the way, Star Trek, Star Wars and Terminators probably affected the nomenclature of the technology. Everyone references Skynet. Oh my God, we're going to be taken over and killed by aliens and machines. This is a real fear. I thinks it's an initial reaction. You felt that Ken, so I've got to ask you, where do you think the crossover point is for people to internalize the benefits of say, AI for instance? Because people will say hey, look back at life before the iPhone, look at life before these tools were out there. Some will say society's gotten better, but yet there's this surveillance culture, things... And on and on. So what do you guys think the crossover point is for the reaction to change from oh my God, it's Skynet, gloom and doom to this actually could be good? >> It's incredibly tricky because as we've seen, the perception of AI both in and out of the industry changes as AI advances. As soon as machine learning can actually do a task, there's a tendency to say there's this no true Scotsman problem where we say well, that clearly can't be AI because I see how the trick worked. And yeah, humans lose at chess now. So when these small advances happen, the reaction is often oh, that's not really AI. And by the same token, it's not a game-changer when your email client can start to auto-complete your emails. That's a minor convenience to you. But you don't think oh, maybe Skynet is good. I really do think it's going to have to be, maybe the inflection point is when it starts to become so disruptive that actually public policy has to change. So we get serious about >> And public policy has started changing. >> whatever their reactions are. >> Charna, your thoughts. >> The public policy has started changing though. We just saw, I think it was in September, where California banned the use of AI in the body cameras, both real-time and after the fact. So I think that's part of the pivot point that we're actually seeing is that public policy is changing.` The state of Washington currently has a task force for AI who's making a set of recommendations for policy starting in December. But I think part of what we're missing is that we don't have enough digital natives in office to even attempt to, to your point Ken, predict what we're even going to be able to do with it, right? There is this fear because of misunderstanding, but we also don't have a respect of our political climate right now by a lot of our digital natives, and they need to be there to be making this policy. >> John, weigh in on this because you're director of AI, you're seeing positive, you have to deal with the uncertainty as well, the growth of machine learning. And just this week Google announced more TensorFlow for everybody. You're seeing Open Source. So there's a tech push, almost a democratization, going on with AI. So I think this crossover point might be sooner in front of us than people think. What's your thoughts? >> Yeah it's here right now. All these things can be essentially put into an environment. You can see these into products, or making business decisions or political decisions. These are all available right now. They're available today and its within 10 to 15 lines of code. It's all about the data sets, so you have to be really good stewards of the data that you're using to train your models. But I think the most important thing, back to the Skynet and all this science-fiction side, we have to collectively start telling the right stories. We need better stories than just this robots are going to take us over and destroy all of our jobs. I think more interesting stories really revolve around, what about public defenders who can have this informant augmentation algorithm that's going to help them get their job done? What about tailor-made medicine that's going to tell me exactly what the conditions are based off of a particular treatment plan instead of guessing? What about tailored education that's going to look at all of my strengths and weaknesses and present a plan for me? These are things that AI can do. Charna's exactly right, where if we don't get this into the right political atmosphere that's helping balance the capitalist side with the social side, we're going to be in trouble. So that's got to be embedded in every layer of enterprise as well as society in general. It's here, it's now, and it's real. >> Ken, before we move on to the ethics question, I want to get your thoughts on this because we have an Alexa at home. We had an Alexa at home; my wife made me get rid of it. We had an Apple device, what they're called... the Home pods, that's gone. I bought a Portal from Facebook because I always buy the earliest stuff, that's gone. We don't want listening devices in our house because in order to get that AI, you have to give up listening, and this has been an issue. What do you have to give to get? This has been a big question. What's your thoughts on all this? >> I was at an Amazon event where they were trumpeting how no technology had ever caught on faster than these personal digital assistants, and yet every time I'm in a use case, a household that's trying to use them, something goes terribly wrong. My friend had to rename his because the neighbor kids kept telling Alexa to do awful things. He renamed it computer, and now every time we use the word computer, the wall tells us something we don't want to know. >> (laughs) >> This is just anecdata, but maybe it speaks to something deeper, the fact that we don't necessarily like the feeling of being surveilled. IBM was always trying to push Watson as the star Trek computer that helpfully tells you exactly what you need to know in the right moment, but that's got downsides too. I feel like we're going to, if nothing else, we may start to value individual learning and knowledge less when we feel like a voice from the ceiling can deliver unto us the fact that we need. I think decision-making might suffer in that kind of a world. >> All right, this brings up ethics because I bring up the Amazon and the voice stuff because this is the new interface people want to have with machines. I didn't mention phones, Androids and Apple, they need to listen in order to make decisions. This brings up the ethics question around who sets the laws, what society should do about this, because we want the benefits of AI. John, you point out some of them. You got to give to get. Where are we on ethics? What's the opinion, what's the current view on this? John, we'll start with you on your ethics view on what needs to change now to move the ball faster. >> Data is gold. Data is gold at an exponential rate when you're talking about AI. There should be no situation where these companies get to collect data at no cost or no benefit to the end consumer. So ultimately we should have the option to opt out of any of these products and any of this type of surveillance wherever we can. Public safety is a little bit different situation, but on the commercial side, there is a lot of more expensive and even more difficult ways to train these models with a data set that isn't just basically grabbing everything our of your personal lives. I think that should be an option for consumers and that's one of those ethical check-marks. Again, ethics in general, the way that data's trained, the way that data's handled, the way models actually work, it has to be a primary reason for and approach of how you actually go about developing and delivering AI. That said, we cannot get over-indulgent in the fact that we can't do it because we're so fearful of the ethical outcomes. We have to find some middle ground and we have to find it quickly and collectively. >> Charna, what's your take on this? Ethics is super important to set the agenda for society to take advantage of all this. >> Yeah. I think we've got three ethical components here. We certainly have, as John mentioned, the data sets. However, it's also what behavior we're trying to change. So I believe the industry could benefit from a lot more behavioral science, so that we can understand whether or not the algorithms that we're building are changing behaviors that we actually want to change, right? And if we aren't, that's unethical. There is an entire field of ethics that needs to start getting put into our companies. We need an ethics board internally. A few companies are doing this already actually. I know a lot of the military companies do. I used to be in the defense industry, and so they've got a board of ethics before you can do things. The challenge is also though that as we're democratizing the algorithms themselves, people don't understand that you can't just get a set of data that represents the population. So this is true of image processing, where if we only used 100 images of a black woman, and we used 1,000 images of a white man because that was the distribution in our population, and then the algorithm could not detect the difference between skin tones for people of color, then we end up with situations where we end up in a police state where you put in an image of one black woman and it looks like ten of them and you can't distinguish between them. And yet, the confidence rate for the humans are actually higher, because they now have a machine backing their decision. And so they stop questioning, to your point, Ken, about what is the decision I'm making, they're like I'm so confident, this data told me so. And so there's a little bit of you need some expert in the loop and you also can't just have experts, because then you end up with Cambridge Analytica and all of the political things that happened there, not just in the US, but across 200 different elections and 30 different countries. And we are upset because it happened in the US, but this has been happening for years. So its just this ethical challenge of behavior change. It's not even AI and we do it all the time. Its why the cigarette industry is regulated (laughs). >> So Ken, what's your take on this? Obviously because society needs to have ethics. Who runs that? Companies? The law-makers? Someone's got to be responsible. >> I'm honestly a little pessimistic the general public will even demand this the way we're maybe hoping that they will. When I think about an example like Facebook, people just being able to, being willing to give away insane amounts of data through social media companies for the smallest of benefits: keeping in touch with people from high school they don't like. I mean, it really shows how little we value not being a product in this kind of situation. But I would like to see this kind of ethical decisions being made at the company-level. I feel like Google kind of surreptitiously moved away from it's little don't be evil mantra with the subtext that eh, maybe we'll be a little evil now. It just reminds me of Manhattan Project era thinking, where you could've gone to any of these nuclear scientists and said you're working on a real interesting puzzle here, it might advance the field, but like 200,000 civilians might die this summer. And I feel like they would've just looked at you and thought that's not really my bailiwick. I'm just trying to solve the fission problem. I would like to see these 10 companies actually having that kind of thinking internally. Not being so busy thinking if they can do something that they don't wonder if they should. >> That's a great point. This brings up the point of who is responsible. Almost as if who is less evil than the other person? Google, they don't do evil, but they're less evil than Amazon and Facebook and others. Who is responsible? The companies or the law-makers? Because if you look up some of the hearings in Washington, D.C., some of the law-makers we see up there, they don't know how the internet works, and it's pretty obvious that this is a problem. >> Yeah, well that's why Jack Dorsey of Twitter posted yesterday that he banned not just political ads, but also issue ads. This isn't something that they're making him do, but he understands that when you're using AI to target people, that it's not okay. At some point, while Mark is sitting on (laughs) this committee and giving his testimony, he's essentially asking to be regulated because he can't regulate himself. He's like well, everyone's doing it, so I'm going to do it too. That's not an okay excuse. We see this in the labor market though actually, where there's existing laws that prevent discrimination. It's actually the company's responsibility to make sure that the products that they purchase from any vendor isn't introducing discrimination into that process. So its not even the vendor that's held responsible, it's the company and their use of it. We saw in the NYPD actually that one of those image recognition systems came up and someone said well, he looked like, I forget the name of what the actor was, but some actor's name is what the perpetrator looked like and so they used an image of the actor to try and find the person who actually assaulted someone else. And that's, it's also the user problem that I'm super concerned about. >> So John, what's your take on this? Because these are companies are in business to make money, for profit, they're not the government. And who's the role, what should the government do? AI has to move forward. >> Yeah, we're all responsible. The companies are responsible. The companies that we work with, I have yet to interact with customers, or with our customers here, that have some insidious goal, that they're trying to outsmart their customers. They're not. Everyone's looking to do the best and deliver the most relevant products in the marketplace. The government, they absolutely... The political structure we have, it has to be really intelligent and it's got to get up-skilled in this space and it needs to do it quickly, both at the economy level, as well as for our defense. But the individuals, all of us as individuals, we are already subjected to this type of artificial intelligence in our everyday lives. Look at streaming, streaming media. Right now every single one of us goes out through a streaming source, and we're getting recommendations on what we should watch next. And we're already adapting to these things, I am. I'm like stop showing me all the stuff you know I want to watch, that's not interesting to me. I want to find something I don't know I want to watch, right? So we all have to get educated, we're all responsible for these things. And again, I see a much more positive side of this. I'm not trying to get into the fear-mongering side of all the things that could go wrong, I want to focus on the good stories, the positive stories. If I'm in a courtroom and I lose a court case because I couldn't afford the best attorney and I have the bias of a judge, I would certainly like artificial intelligence to make a determination that allows me to drive an appeal, as one example. Things like that are really creative in the world that we need to do. Tampering down this wild speculation we have on the markets. I mean, we are all victims of really bad data decisions right now, almost the worst data decisions. For me, I see this as a way to actually improve all those things. Fraud fees will be reduced. That helps everybody, right? Less speculation and these wild swings, these are all helpful things. >> Well Ken, John and Charna, thank- (audio feedback) >> Go ahead, finish. Get that word in. >> Sorry. I think that point you were making though John, is we are still a capitalist society, but we're no longer a shareholder capitalist society, we are a stakeholder capitalist society and the stakeholder is the society itself. It is us, it what we want to see. And so yes, I still want money. Obviously there are things that I want to buy, but I also care about well-being. I think it's that little shift that we're seeing that is actually you and I holding our own teams accountable for what they do. >> Yeah, culture first is a whole new shift going on in these companies that's a for-profit, mission-based. Ken, John, Charna, thanks for coming on Around the CUBE, Unpacking AI. Let's go around the CUBE Ken, John and Charna in that order, and just real quickly, unpacking AI, what's your final word? >> (laughs) I really... I'm interested in John's take that there's a democratization coming provided these tools will be available to everyone. I would certainly love to believe that. It seems like in the past, we've seen no, that access to these kind of powerful, paradigm-changing tools tend to be concentrated among a very small group of people and the benefits accrue to a very small group of people. But I hope that doesn't happen here. You know, I'm optimistic as well. I like the utopian side where we all have this amazing access to information and so many new problems can get solved with amazing amounts of data that we never could've touched before. Though you know, I think about that. I try to let that help me sleep at night, and not the fact that, you know... every public figure I see on TV is kind of out of touch about technology and only one candidate suggests the universal basic income, and it's kind of a crackpot idea. Those are the kind of things that keep me up at night. >> All right, John, final word. >> I think it's beautiful, AI's beautiful. We're on the cusp of a whole new world, it's nothing but positivity I see. We have to be careful. We're all nervous about it. None of us know how to approach these things, but as human beings, we've been here before. We're here all the time. And I believe that we can all collectively get a better lives for ourselves, for the environment, for everything that's out there. It's here, it's now, it's definitely real. I encourage everyone to hurry up on their own education. Every company, every layer of government to start really embracing these things and start paying attention. It's catching us all a little bit by surprise, but once you see it in production, you see it real, you'll be impressed. >> Okay, Charna, final word. >> I think one thing I want to leave people with is what we incentivize is what we end up optimizing for. This is the same for human behavior. You're training a new employee, you put incentives on the way that they sell, and that's, they game the system. AI's specifically find the optimum route, that is their job. So if we don't understand more complex cost functions, more complex representative ways of training, we're going to end up in a space, before we know it, that we can't get out of. And especially if we're using uninspectable AI. We really need to move towards augmentation. There are some companies that are implementing this now that you may not even know. Zillow, for example, is using AI to give you a cost for your home just by the photos and the words that you describe it, but they're also purchasing houses without a human in the loop in certain markets, based upon an inspection later by a human. And so there are these big bets that we're making within these massive corporations, but if you're going to do it as an individual, take a Coursera class on AI and take a Coursera class on ethics so that you can understand what the pitfalls are going to be, because that cost function is incredibly important. >> Okay, that's a wrap. Looks like we have a winner here. Charna, you got 18, John 16. Ken came in with 12, beaten again! (both laugh) Okay, Ken, seriously, great to have you guys on, a pleasure to meet everyone. Thanks for sharing on Around the CUBE Unpacking AI, panel number two. Thank you. >> Thanks a lot. >> Thank you. >> Thanks. I've been defeated by artificial intelligence again! (all laugh) (upbeat music)

>> Live from Washington, D.C., it's theCUBE! Covering Inforum D.C. 2018. Brought to you by Infor. >> Well, welcome back to Washington, D.C., We are alive here at the Convention Center at Inforum 18, along with Dave Vellante, I'm John Walls. It's a pleasure now, welcome to theCUBE, Leigh Martin, who is the Senior Director of the Dynamic Science Labs at Infor, and good afternoon to you Leigh! >> Good afternoon, thank you for having me. >> Thanks for comin' on. >> Thank you for being here. Alright, well tell us about the Labs first off, obviously, data science is a big push at Infor. What do you do there, and then why is data science such a big deal? >> So Dynamic Science Labs is based in Cambridge, Massachusetts, we have about 20 scientists with backgrounds in math and science areas, so typically PhDs in Statistics and Operations Research, and those types of areas. And, we've really been working over the last several years to build solutions for Infor customers that are Math and Science based. So, we work directly with customers, typically through proof of concept, so we'll work directly with customers, we'll bring in their data, and we will build a solution around it. We like to see them implement it, and make sure we understand that they're getting the value back that we expect them to have. Once we prove out that piece of it, then we look for ways to deliver it to the larger group of Infor customers, typically through one of the Cloud Suites, perhaps functionality, that's built into a Cloud Suite, or something like that. >> Well, give me an example, I mean it's so, as you think-- you're saying that you're using data that's math and science based, but, for application development or solution development if you will. How? >> So, I'll give you an example, so we have a solution called Inventory Intelligence for Healthcare, it's moving towards a more generalized name of Inventory Intelligence, because we're going to move it out of the healthcare space and into other industries, but this is a product that we built over the last couple of years. We worked with a couple of customers, we brought in their loss and data, so their loss in customers, we bring the data into an area where we can work on it, we have a scientist in our team, actually, she's one of the Senior Directors in the team, Dawn Rose, who led the effort to design and build this, design and build the algorithm underlying the product; and what it essentially does is, it allows hospitals to find the right level of inventory. Most hospitals are overstocked, so this gives them an opportunity to bring down their inventory levels, to a manageable place without increasing stockouts, so obviously, it's very important in healthcare, that you're not having a lot of stockouts. And so, we spent a lot of time working with these customers, really understanding what the data was like that they were giving to us, and then Dawn and her team built the algorithm that essentially says, here's what you've done historically, right? So it's based on historic data, at the item level, at the location level. What've you done historically, and how can we project out the levels you should have going forward, so that they're at the right level where you're saving money, but again, you're not increasing stockouts, so. So, it's a lot of time and effort to bring those pieces together and build that algorithm, and then test it out with the customers, try it out a couple of times, you make some tweaks based on their business process and exactly how it works. And then, like I said, we've now built that out into originally a stand-alone application, and in about a month, we're going to go live in Cloud Suite Financials, so it's going to be a piece of functionality inside of Cloud Suite Financials. >> So, John, if I may, >> Please. >> I'm going to digress for a moment here because the first data scientist that I ever interviewed was the famous Hilary Mason, who's of course now at Cloudera, but, and she told me at the time that the data scientist is a part mathematician, part scientist, part statistician, part data hacker, part developer, and part artist. >> Right. (laughs) >> So, you know it's an amazing field that Hal Varian, who is the Google Economist said, "It's going to be the hottest field, in the next 10 years." And this is sort of proven true, but Leigh, my question is, so you guys are practitioners of data science, and then you bring that into your product, and what we hear from a lot of data scientists, other than that sort of, you know, panoply of skill sets, is, they spend more time wrangling data, and the tooling isn't there for collaboration. How are you guys dealing with that? How has that changed inside of Infor? >> It is true. And we actually really focus on first making sure we understand the data and the context of the data, so it's really important if you want to solve a particular business problem that a customer has, to make sure you understand exactly what is the definition of each and every piece of data that's in all of those fields that they sent over to you, before you try to put 'em inside an algorithm and make them do something for you. So it is very true that we spend a lot of time cleaning and understanding data before we ever dive into the problem solving aspect of it. And to your point, there is a whole list of other things that we do after we get through that phase, but it's still something we spend a lot of time on today, and that has been the case for, a long time now. We, wherever we can, we apply new tools and new techniques, but actually just the simple act of going in there and saying, "What am I looking at, how does it relate?" Let me ask the customer to clarify this to make sure I understand exactly what it means. That part doesn't go away, because we're really focused on solving the customer solution and then making sure that we can apply that to other customers, so really knowing what the data is that we're working with is key. So I don't think that part has actually changed too much, there are certainly tools that you can look at. People talk a lot about visualization, so you can start thinking, "Okay, how can I use some visualization to help me understand the data better?" But, just that, that whole act of understanding data is key and core to what we do, because, we want to build the solution that really answers the answers the business problem. >> The other thing that we hear a lot from data scientists is that, they help you figure out what questions you actually have to ask. So, it sort of starts with the data, they analyze the data, maybe you visualize the data, as you just pointed out, and all these questions pop out. So what is the process that you guys use? You have the data, you've got the data scientist, you're looking at the data, you're probably asking all these questions. You get, of course, get questions from your customers as well. You're building models maybe to address those questions, training the models to get better and better and better, and then you infuse that into your software. So, maybe, is that the process? Is it a little more complicated than that? Maybe you could fill in the gaps. >> Yeah, so, I, my personal opinion, and I think many of my colleagues would agree with me on this is, starting with the business problem, for us, is really the key. There are ways to go about looking at the data and then pulling out the questions from the data, but generally, that is a long and involved process. Because, it takes a lot of time to really get that deep into the data. So when we work, we really start with, what's the business problem that the customer's trying to solve? And then, what's the data that needs to be available for us to be able to solve that? And then, build the algorithm around that. So for us, it's really starting with the business problem. >> Okay, so what are some of the big problems? We heard this morning, that there's a problem in that, there's more job openings than there are candidates, and productivity, business productivity is not being impacted. So there are two big chewy problems that data scientists could maybe attack, and you guys seem to be passionate about those, so. How does data science help solve those problems? >> So, I think that, at Infor, I'll start off by saying at Infor there's actually, I talked about the folks that are in our office in Cambridge, but there's quite a bit of data science going on outside of our team, and we are the data science team, but there are lots of places inside of Infor where this is happening. Either in products that contains some sort of algorithmic approach, the HCM team for sure, the talent science team which works on HCM, that's a team that's led by Jill Strange, and we work with them on certain projects in certain areas. They are very focused on solving some of those people-related problems. For us, we work a little bit more on the, some of the other areas we work on is sort of the manufacturing and distribution areas, we work with the healthcare side of things, >> So supply chain, healthcare? >> Exactly. So some of the other areas, because they are, like I said, there are some strong teams out there that do data science, it's just, it's also incorporated with other things, like the talent science team. So, there's lots of examples of it out there. In terms of how we go about building it, so we, like I was saying, we work on answering the business, the business question upfront, understanding the data, and then, really sitting with the customer and building that out, and, so the problems that come to us are often through customers who have particular things that they want to answer. So, a lot of it is driven by customer questions, and particular problems that they're facing. Some of it is driven by us. We have some ideas about things that we think, would be really useful to customers. Either way, it ends up being a customer collaboration with us, with the product team, that eventually we'll want to roll it out too, to make sure that we're answering the problem in the way that the product team really feels it can be rolled out to customers, and better used, and more easily used by them. >> I presume it's a non-linear process, it's not like, that somebody comes to you with a problem, and it's okay, we're going to go look at that. Okay now, we got an answer, I mean it's-- Are you more embedded into the development process than that? Can you just explain that? >> So, we do have, we have a development team in Prague that does work with us, and it's depending on whether we think we're going to actually build a more-- a product with aspects to it like a UI, versus just a back end solution. Depends on how we've decided we want to proceed with it. so, for example, I was talking about Inventory Intelligence for Healthcare, we also have Pricing Science for Distribution, both of those were built initially with UIs on them, and customers could buy those separately. Now that we're in the Cloud Suites, that those are both being incorporated into the Cloud Suite. So, we have, going back to where I was talking about our team in Prague, we sometimes build product, sort of a fully encased product, working with them, and sometimes we work very closely with the development teams from the various Cloud Suites. And the product management team is always there to help us, to figure out sort of the long term plan and how the different pieces fit together. >> You know, kind of big picture, you've got AI right, and then machine learning, pumping all kinds of data your way. So, in a historical time frame, this is all pretty new, this confluence right? And in terms of development, but, where do you see it like 10 years from now, 20 years from now? What potential is there, we've talked about human potential, unlocking human potential, we'll unlock it with that kind of technology, what are we looking at, do you think? >> You know, I think that's such a fascinating area, and area of discussion, and sort of thinking, forward thinking. I do believe in sort of this idea of augmented intelligence, and I think Charles was talking a little bit about, about that this morning, although not in those particular terms; but this idea that computers and machines and technology will actually help us do better, and be better, and being more productive. So this idea of doing sort of the rote everyday tasks, that we no longer have to spend time doing, that'll free us up to think about the bigger problems, and hopefully, and my best self wants to say we'll work on famine, and poverty, and all those problems in the world that, really need our brains to focus on, and work. And the other interesting part of it is, if you think about, sort of the concept of singularity, and are computers ever going to actually be able to think for themselves? That's sort of another interesting piece when you talk about what's going to happen down the line. Maybe it won't happen in 10 years, maybe it will never happen, but there's definitely a lot of people out there, who are well known in sort of tech and science who talk about that, and talk about the fears related to that. That's a whole other piece, but it's fascinating to think about 10 years, 20 years from now, where we are going to be on that spectrum? >> How do you guys think about bias in AI and data science, because, humans express bias, tribalism, that's inherent in human nature. If machines are sort of mimicking humans, how do you deal with that and adjudicate? >> Yeah, and it's definitely a concern, it's another, there's a lot of writings out there and articles out there right now about bias in machine learning and in AI, and it's definitely a concern. I actually read, so, just being aware of it, I think is the first step, right? Because, as scientists and developers develop these algorithms, going into it consciously knowing that this is something they have to protect against, I think is the first step, for sure. And then, I was just reading an article just recently about another company (laughs) who is building sort of a, a bias tracker, so, a way to actually monitor your algorithm and identify places where there is perhaps bias coming in. So, I do think we'll see, we'll start to see more of those things, it gets very complicated, because when you start talking about deep learning and networks and AI, it's very difficult to actually understand what's going on under the covers, right? It's really hard to get in and say this is the reason why, your AI told you this, that's very hard to do. So, it's not going to be an easy process but, I think that we're going to start to see that kind of technology come. >> Well, we heard this morning about some sort of systems that could help, my interpretation, automate, speed up, and minimize the hassle of performance reviews. >> Yes. (laughs) >> And that's the classic example of, an assertive woman is called abrasive or aggressive, an assertive man is called a great leader, so it's just a classic example of bias. I mentioned Hilary Mason, rock star data scientist happens to be a woman, you happen to be a woman. Your thoughts as a woman in tech, and maybe, can AI help resolve some of those biases? >> Yeah. Well, first of all I want to say, I'm very pleased to work in an organization where we have some very strong leaders, who happen to be women, so I mentioned Dawn Rose, who designed our IIH solution, I mentioned Jill Strange, who runs the talent science organization. Half of my team is women, so, particularly inside of sort of the science area inside of Infor, I've been very pleased with the way we've built out some of that skill set. And, I'm also an active member of WIN, so the Women's Infor Network is something I'm very involved with, so, I meet a lot of people across our organization, a lot of women across our organization who have, are just really strong technology supporters, really intelligent, sort of go-getter type of people, and it's great to see that inside of Infor. I think there's a lot of work to be done, for sure. And you can always find stories, from other, whether it's coming out of Silicon Valley, or other places where you hear some, really sort of arcane sounding things that are still happening in the industry, and so, some of those things it's, it's disappointing, certainly to hear that. But I think, Van Jones said something this morning about how, and I liked the way he said it, and I'm not going to be able say it exactly, but he said something along the lines of, "The ground is there, the formation is starting, to get us moving in the right direction." and I think, I'm hopeful for the future, that we're heading in that way, and I think, you know, again, he sort of said something like, "Once the ground swell starts going in that direction, people will really jump in, and will see the benefits of being more diverse." Whether it's across, having more women, or having more people of color, however things expand, and that's just going to make us all better, and more efficient, and more productive, and I think that's a great thing. >> Well, and I think there's a spectrum, right? And on one side of the spectrum, there's intolerable and unacceptable behavior, which is just, should be zero tolerance in my opinion, and the passion of ours in theCUBE. The other side of that spectrum is inclusion, and it's a challenge that we have as a small company, and I remember having a conversation, earlier this year with an individual. And we talk about quotas, and I don't think that's the answer. Her comment was, "No, that's not the answer, you have to endeavor to reach deeper beyond your existing network." Which is hard sometimes for us, 'cause you're so busy, you're running around, it's like okay it's the convenient thing to do. But you got to peel the onion on that network, and actually take the extra time and make it a priority. I mean, your thoughts on that? >> No, I think that's a good point, I mean, if I think about who my circle is, right? And the people that I know and I interact with. If I only reach out to the smallest group of people, I'm not getting really out beyond my initial circle. So I think that's a very good point, and I think that that's-- we have to find ways to be more interactive, and pull from different areas. And I think it's interesting, so coming back to data science for a minute, if you sort of think about the evolution of where we got to, how we got to today where, now we're really pulling people from science areas, and math areas, and technology areas, and data scientists are coming from lots of places, right? And you don't always have to have a PhD, right? You don't necessary have to come up through that system to be a good data scientist, and I think, to see more of that, and really people going beyond, beyond just sort of the traditional circles and the traditional paths to really find people that you wouldn't normally identify, to bring into that, that path, is going to help us, just in general, be more diverse in our approach. >> Well it certainly it seems like it's embedded in the company culture. I think the great reason for you to be so optimistic going forward, not only about your job, but about the way companies going into that doing your job. >> What would you advise, young people generally, who want to crack into the data science field, but specifically, women, who have clearly, are underrepresented in technology? >> Yeah, so, I think the, I think we're starting to see more and more women enter the field, again it's one of those, people know it, and so there's less of a-- because people are aware of it, there's more tendency to be more inclusive. But I definitely think, just go for it, right? I mean if it's something you're interested in, and you want to try it out, go to a coding camp, and take a science class, and there's so many online resources now, I mean there's, the massive online courses that you can take. So, even if you're hesitant about it, there are ways you can kind of be at home, and try it out, and see if that's the right thing for you. >> Just dip your toe in the water. >> Yes, exactly, exactly! Try it out and see, and then just decide if that's the right thing for you, but I think there's a lot of different ways to sort of check it out. Again, you can take a course, you can actually get a degree, there's a wide range of things that you can do to kind of experiment with it, and then find out if that's right for you. >> And if you're not happy with the hiring opportunities out there, just start a company, that's my advice. >> That's right. (laughing together) >> Agreed, I definitely agree! >> We thank you-- we appreciate the time, and great advice, too. >> Thank you so much. >> Leigh Martin joining us here at Inforum 18, we are live in Washington, D.C., you're watching the exclusive coverage, right here, on theCUBE. (bubbly music)

(energetic music) >> Narrator: Live from San Juan, Puerto Rico, it's theCUBE, covering CoinAgenda. Brought to you by SiliconANGLE. >> Hello and welcome to our exclusive Puerto Rico coverage of CoinAgenda, I'm John Furrier with theCUBE. We're here covering all the action at Restart, we've got a ton of events, all the thoughts leaders, influencers, decision makers, you name it, in the industry, pioneers making it happen. My next guest is Toni Lane, who's the founder of CoinGraph. She's a true influencer with a lot of impact in this market. Welcome to theCUBE. >> Thank you for having me. >> We're so glad to have you on. Like the little joke at the beginning about being an influencer, you actually are an influencer. You've done such great work in the industry, well regarded in the community. You have publication and you do a lot of great content. Thanks for coming on. >> Oh, for sure, yeah, thanks for having me. >> So being the influencer, what does that mean these days? Because we were just talking before the camera on, we came on camera, influence changes. You can't be an influencer all the time. You can be super or expert at something, but your expertise could change, you move to a new topic, learn something. And there's a lot of people in the digital marketing world saying I'm an influencer. It's kind of half baked, and really, I mean, it's not about the followers, your thoughts? >> Well, I mean, most of those followers are purchased. So there's a big difference between being an influencer and having actual influence. Because if you're, you know, if you have a million followers on Twitter, that's nice. How much engagement do you have? And that's actually what you look for, it's like when you look at someone's, whether it's, you know, social media, their digital presence, it's not about followers, it's all about engagement. You know, I don't even have that many, like I don't spend a lot of time doing that, at least I haven't so far, it's something I'm getting more into. But I have people that are really engaged, and so I look at people that have 15 million followers and I'm like, you have just as many likes on your things as I do, right. Because these people aren't real people. And it's less about, having influence in general is in many ways about having authenticity. And so influence is your ability to get something done. Being an influencer is your ability to hold someone's attention for a fragment of time. But being an influencer is not the same as having influence. >> And this community here, certainly, with decentralization here, you get the decentralized applications coming up blockchain, you got ICOs booming. It's all about the network effect, if you look at network effect, that is a new concept that ad technology does not know because you can't cookie a network connection. The only way to measure someone's true network is through malware today, and that's not good, no one does that. Well, they do, they're-- >> Toni: Unfortunately, yeah. >> But you can't you do it at business price, not sustainable. So the point is, it's not about how many followers you have. It could be that one follower, maybe 200 or 2,000, that opens up more. This is the network effect. This is what this community is all about, so I want to get your thoughts on this community's vibe. A lot of mission-driven, impact-oriented, merged with tech. So you have a fusion of technology, artistry, craftsmanship and mission-driven societal change in one melting pot. This is your wheelhouse. Share your thoughts on this. >> Well, so all of the different digital currencies have different value systems and they attract a different breed. And there are different incentives for each of these based on how the technology is designed, each protocol, right? So if you look at Bitcoin, in Bitcoin, the incentives are, you know, mining is done by computers, so your only incentive is like having social influence? And this is, I think, why we've seen a lot of kind of I would call it a scarcity mentality in terms of the way, why we see even more trolls in Bitcoin is because social influence is a huge way that success is measured, because as a developer, you can't have, you can't achieve a level of status any other way as a developer or as an influencer in Bitcoin, because the Bitcoin network is so far removed from that. And that's actually a perverse incentive in and of itself, and not only that, but early days in Bitcoin, there were major organizations who would hire people to man 100 Reddit and Twitter accounts and go into the Bitcoin community and actually fragment the public opinion using a technique grassroots psychological insurgency. So buying Reddit accounts that had been active for the last 10 years and going through and, you know, essentially just stabbing at people and creating, even having conversations with themselves to empower the voice of trolls. And what happens is you start bringing out what we call, actually, what the former Assad called, after Henry Kissinger, there was a big move that happened in the Middle East, where Kissinger realized the Middle East was becoming too powerful, and he saw it as a threat to American democracy. And so Kissinger organized a deal that fragmented the Middle East. And Assad said to Kissinger that his actions would be, he played Assad, basically. And Assad said to Kissinger, "Your actions "will bring up demons hidden underneath "the surface of the Arab world." And that strategy is actually something used in the Bitcoin community to leverage the incentives that are created, which is why we have seen previously so much, even from our industry leaders, so much fragmentation and so much tension. But the network is the most secure and the least corruptible, hands down, fundamentally. It's real cryptography. >> But let's talk about that, I love this conversation, because with networks, you have the concept of self-heal, and this gets nerdy on the packets, how packets move, at that level, self-healing networks has been a paradigm that's been proven. So that's out there, that's got to go to a societal level. The other one is the incentive system, if you have an immune system, if you will, in a network, this is a cultural thing. So actions, the Reddit's obvious, right. Weaponizing content has been well-documented, it's coming now mainstream, people are getting it that this outcome was actually manufactured by bad behavior. Now, I argue that there's an exact opposite effect. You can actually weaponize for good, 'cause everything has a polar opposite. So what is your view on that, because this is something that we've been teasing out for the first time. How do you weaponize content for good, (mumbles) not the right word, but look for the opposite value? >> Right, yeah, I mean, it is in so many ways, right. So I think it's about, there's a professor at Stanford whose name is BJ Fogg, and he's a behavioral researcher and he talks about essentially, you know, he writes a lot about habits. But something that's even more interesting about his understanding of propaganda is I studied a lot of Edward Bernays, he's responsible, he created the theory of propaganda, right. And he's the nephew of Sigmund Freud, he's responsible for essentially every consumptive theory in like leading up to the last century, he's actually, I would say he's responsible for the state of advertising and the economy today, almost really single handedly. And what's fascinating about this theory is that you can use propaganda to get women to smoke by unearthing what it is unconsciously in men that makes them not want to smoke. You can also use propaganda to get people to invest in health and wellness. You can also use propaganda to get people to stop their bad habits. So it's understanding that a technique works in a cognitive capacity in a way that affects a large amount of people. And it's really about the intention behind why a person who has influence, as we were saying, is leveraging that relationship. So I would say it's more about-- >> So we have to reimagine influence. Because the signalings that are igniting the cognitive brain can be tweaked. So that's what you're getting at here, right, so that's what we have to do. >> And it's an illusion from almost every angle. It's even the idea that, in the United States, the level of influence the president has and who's running, you know, and who, yeah, and who's at the wheel, right. So it's, we live in a world that is built on manufactured consent, and manufactured consent is enabled through thinkers like Bernays and through what I call the illusion of things like our former construct of even American democracy. That these things we've imagined to be so, the foundation and the structure for the way that we live. All of those things have become so far removed from their theory that they're no longer serving the principles under which they were founded, and that disconnect is actually a huge, it's a gap, it's an inertia gap for exploitation or it's an inertia gap for growth, and usually what happens is you have the exploitation first. Someone says oh, here's a big gap of information asymmetry, so I'm going to exploit the information asymmetry. And then once people start realizing that that information asymmetry is being exploited, you experience a huge inversion of that and you have enough kind of, you have enough inertia behind that slingshot to launch it into something totally different. >> Yeah, this is a great concept, I interviewed the founder of the Halcyon HAL in Washington, DC, and she's an amazing woman. And she had a great conscious about this, and what she postulated was, bubbles that burst, exploitation's always, we've seen it in all trends. The underbelly, 'cause it's motivated, no dogma. They don't care about structural incentives, they just want to make cash. But she had an interesting theory, she was talking about you can let the air out of the bubble with community and data. So all the societal entrepreneurship activities now that are mission-driven, now getting back to mission-driven is interesting. There might be a way to actually avoid the pop. Because, depending upon what the backlash might be on the exploitation side, as we saw in the dotcom bubble, you can actually let the air out a little bit through things like data. I mean, how do you see, in your mind, just thinking out loud, how do you see that playing out, because we have community now. We have access to open data. Blockchain is all about immutability. It's all about power to the user's data. This is a mega trend. Your thoughts? >> So interdependence is huge in the blockchain community, and that's actually to touch back on the incentives in Bitcoin, I think that that's actually one of Bitcoin's, it's not that it's a wrong or a right, it just is, right, like sidechains will be launched eventually, but the idea that Ethereum created something that was adaptable and empowered people to be creative, and yet they're creating incentives for her people to launch products that are, I believe, 'causing, in some ways, could cause some serious harm to the ecosystem once the air is let out of that bubble. >> John: The data. >> The data, so data, yes yes yes. >> How do you let the air out of the bubble, because the pop will be massively implosion, it'll leave a crater. >> So data is a non-scarce resource. This is actually how I describe blockchain to people. And this is actually, I think, one of the, the challenge, if you want to look at it from the perspective of challenge, and then I'll talk about for the benefit, just between Bitcoin and Ethereum, there are obviously other blockchains, EOS is like coming out super soon, Holochain. There are tons, Steem has actually its own infrastructure, tons of other blockchains to speak about. But just to take these two main blockchains, which are not competitors. In Bitcoin, you have, it's really cryptography. Cryptography is not about, you know, like let's do some rapid prototyping, cryptography is about let's like put a lot of thought into this thing and have mathematical certainty that this is not exploitatable. And Ethereum is just kind of like, well, let's build a framework and then let people play as much as they can. And so there are challenges and benefits to both of those models, the challenge of Ethereum being that you've let all of this capital into the industry which is not actually, 46% of ICOs have already failed. Already failed. And then if you look at Bitcoin-- >> And a person with your industry (mumbles) at 1,200, so it's a 50% discount. >> Oh yeah, oh yeah. And then if you do the same thing and you're looking at the Bitcoin blockchain, we've seen that the capacity for innovation, Bitcoin could have done what, they could've been the first to market for what Ethereum is doing. And they chose a different route, and I think there are some pros and cons to both of those things, but I think there is an intentionality behind why the world played out in the way that it did. And I think it's the right strategy for both products. So the way I describe applications using blockchain technology to people and what I call the future of an infinite economy is that, if you think about why are Facebook and Google these multi billion dollars companies, it's really simple. It's because what do they own, right? The data, the data. And they're some of the last companies that are still stewarding these things in a way that is taking vast amount of aggregated ownership over an asset that people are generating every day that's extremely valuable to companies in the private sector. So the way that I describe blockchain is that, if we being to own our self-sovereign identity, then when we're owning our data, that's the foundation for universal basic income. If we take a non-scarce resource like data that's being generated every day, not just from us, right, but the data in the health of the ocean, right. The stewardship of the ocean, the health of the fish, actually saying okay, fish are thriving in this area, and so there's a healthy ecosystem, and so this coin is trading higher because we're stewarding this area of the ocean so we don't overfish. The quality of the air so that, when we're actually de-polluting the air collectively, everyone around us is creating and generating data to say we're making the air better. The air, actually, the health of our bodies, of our Earth, of our minds, of our planet, of even the health of our innovation. Right, what are the incentives behind our innovation, those are all forms of data. And that's a non-scarce resource, so if we take all of these different applications and make many different blockchains. Which I fundamentally believe that there's a powerful theory in having blockchains that are economically scarce, because I believe you're going to empower more diverse spectrums and also have a level of difficulty in creating the coin. You're going to have more innovation. And so-- >> Well, this is a key area. I mean, this is super important. Well, I mean, you step back for a second, you zoom out, you say okay, we have data, data's super valuable, if you take it to the individual's levels, which has not been, quite frankly, the individual's been exploited. Facebooks of the world, these siloed platforms, have been using the data for advertising. That's just what everyone knows, but there's other examples. The point is, when you put the data in the hands of the users, combine that with cloud computing and the Internet of Things when you can have an edge of the network high powered computer, the use cases have never been pushed before. The envelope that we're pushing now has never been in this configuration. You could never have a decentralized network, immutable, storing users' data, you've never had the ability to write the kind of software you can today, you've never had cloud computing, you've never had compute at the edge, which is where the users live, they are the edge. You have the ability where the user's role can enable a new kind of collective intelligence. This is like mind blowing. So I mean, just how would you explain that to a common person? I mean, 'cause this is the challenge, 'cause collective intelligence has been well documented in data science. User generated content is kind of the beginning of what we see in user wearables. But if you can control the data streaming into the network, with all the self-healing and all the geek stuff we're talking about, it's going to change structural things. How do you explain that to a normal person? >> You don't, you don't, right. So you show them. Because I can sit here all day and I can talk to you about, you know, I could talk to you about all of these things, but at the end of the day, with normal people, it's not something you want to explain. You want to show them, because with my, actually, my grandma gets Bitcoin. My grandma hit me up in like 2012 and she was like, "Do you know what that Bitcoin thing is?" I'm like, "Mimi," I'm like, "How do you know "what Bitcoin is, Mimi?" And she's just like, "I don't know, I read." You know, I was like, "This is, so what are you reading? "Like, are you hanging out on like libertarian forums, "like what's up?" And so-- >> What's going on in the club there, I mean, are they playing-- >> Yeah, but she is a really unique lady. So I would say that, for most people, they are not going to, when you explain things to people-- >> What would you show them, I mean, what's an example? >> The way that, so when I was, so I got into Bitcoin in 2011, and the way that I would explain Bitcoin to people is I would just send it to them. I would be like, "Here's Bitcoin, like take this Bitcoin, "here's some Bitcoin for you." And that was, people got it, because they were like, I have five dollars now in my hands that was not there. And this person just sent it to me. And for some people even still, you know, to be honest, even then, I remember how much energy it took for me to do that. Everywhere I go, I'd be like, in cabs, I'd be checking out grocery stores and I would try, I would essentially pitch Bitcoin to every person that I met. >> John: You were evangelizing a lot of it. >> It took so much energy though, and even after that, there was a period-- >> It was hard for people to receive it, they would have to do what at that time? Think about what the process was back then. >> Oh yeah. There were very few people who, even after doing that, really got it. But you know what happened. This is so much perspective for me, I remember doing that in 2013 and I remember, in 2018, actually, I think it was the end of 2017. I went to a gas station, it's the only gas station in San Francisco with a Bitcoin ATM. And I was like, I need to get some cash and I'm running on Bitcoin. >> John: You guys want a mountain view now. >> Yeah, yeah. And so I go in and these guys, I'm like frustrated, I'm like oh, the ATM is like the worst user experience ever, I'm like (groans). That's literally, I'm like, it's just, it was like eyes rolling in the back of my head, like just so frustrated because I'm a super privacy freak. And so it was just a super complex process, but the guys that, the guy's (mumbles) he looks at me and he goes, "Yo." And I was like, "What's up, man?" And he goes, "Are you trying to buy some Bitcoin?" I was like, "I'm trying to sell some Bitcoin right now." (John laughs) >> You're dispensing it, they're like yeah. >> Yeah, he's like, "Oh, word." And he's like, "How much are you trying to sell?" And I'm like, "I don't know, like 2K." And so he goes, "Aight." And he's like, "Let me hit up my friends," he literally calls three of his friends who come down and they just like, they're like, "Do you want to sell more?" They're like, all they just peer to peer. It's like we bypass the ATM and it was actually a peer to peer exchange. And I didn't have to explain anything. You know what made people get it? You showed them the money, you showed them the money. And sometimes people don't, you can explain these concepts that are world-changing, super high level or whatever. People are not actually going to get it until it's useful to them. And that's why a user interface is so important. Like, if you even look at the Internet. Who made the money on the Internet, right, it was the people who understood how to own the user interface. >> I had a conversation with Fred Kruger from WorkCoin, he's been around the block for a long time, great guy. We were riffing on the old days. But we talked about the killer app for the mini computer and the mainframe, the mini computer and then the PC, it was email, for 20 years, the killer app was email. We were like, what's the killer app for blockchain? It's money, the killer app is money. And it's going to be 50 year killer app. Now, the marketplace is certainly maybe tier two killer app, but the killer app is money. >> For sure, that's amazing. >> That's the killer app. Okay, so we're talking about money, let's talk about wallets and whatnot. There's a lot of people that I know personally that had been, wallets had been hacked. Double authentication (mumbles) news articles on this, but even early on, you got to protect yourself. It's something that you're an advocate of, I know recently, you've been sharing some stuff on Telegram. Share your thoughts on newbies coming in, be careful. Your wallet can be hacked, and you got to take care of yourself online. Is there a best practice, can you share some color commentary on when you get into the system, when you get Bitcoin or crypto, what are some of the best practices? >> It's not even, I think you need to remember a key principle of cryptography when you're dealing with digital currency, which was like don't really trust anything unless you call someone, you have like first hand verification from a person that you trust. Because these things are, I mean, I've had, literally last week, I had seven friends contact me, actually more than that once I posted about it, and they were like, "Is this you?" And I was like what, like people would literally just go online, they would scrape my Facebook photo, they'd go on Telegram and they would make, my name is @ToniLaneC, T-O-N-I-L-A-N-E-C, and so is my Twitter, and people would scrape my photos from my Twitter or my Telegram or my Facebook and they would create fake accounts. And they would start messaging people and say "Hey, like "what's up, how are you, that's cool, great, awesome. "So like, I need like 20 BTC for a loan. "Can you help me?" And all my friends were like, "I was just talking to you, is this you?" And I'm like no. And so I think that there's, the other thing you have to, it's not just security in terms of, and this is actually a problem Blockchain has to solve, right. It's not just security in terms of protecting your wallet and, you know, getting like a Ledger or a Trezor and making sure that you're keeping things like in cold storage, that you're going, there are so many, keeping your money in a hard wallet, not keeping your private keys on your computer, like keeping everything, storing your passwords in multiple places that you know are safe. Both handwritten, like in lock boxes, putting it in your safe deposit box or, you know, there are so many different ways that we can get into like the complexities of protecting yourself and security. Not using centralized cell networks is one of the big ways that I do this. Because if you are using two factor-- >> John: What's a centralized cell network? >> AT&T, Verizon, T-Mobile. Because you are putting yourself in a situation where, if you're using a centralized system, those centralized systems are really easily exploitable. I know because my mom, when I was a kid one time, she put a password on my account so I couldn't buy games. I was not happy about it, it was my money that I was using, it was my money I was using to buy games, she was like, "You should just spend your money on better things." And so I remember going in when I was a kid, and I was like, this is my money, I totally want to buy this upgrade on this game. And so I went in and I essentially figured out how to hack into my own phone to be able to use my own money to buy the games that I wanted to buy-- >> Highly motivated learning opportunity there (laughs). >> But I realized that, in the same way we were talking about things that can be used for good can be used for bad, in the same way that someone can do something like that, you can also say, well, I'm in a call and say that I'm this person and take their phone and then get their two factor auth. So I don't use centralized cell networks, I don't use cell networks at all. >> John: What do you use? >> So, I mean, I have different kinds of like strategies or different things that mostly-- >> You might not want to say it here, okay, all right. >> Yeah yeah yeah, they're different, I'm happy to talk about those privately. The way that I've kind of handled that situation, and then the other thing that I would say is like, we really need hardcore reputation systems in our industry and for the world. And not social reputation systems like what is happening in China right now, where you can have someone leave you, like let's say I get into an Uber and I'm 30 seconds late. I can end up in a situation where I'm like not able to be admitted into a hospital or I'm not able to take a public train. Because someone rates me lower on this reputation system, I think that's a huge human rights issue. >> John: Yeah, that's a huge problem. >> And so not reputation systems like this, but reputations like the one we're working on at CULTU.RE that are really based more on the idea of restoration and humanization, rather than continued social exploitation to create some kind of collective norm, I think that kind of model is, it's not only a-- >> Well, the network should reject that by-- >> Toni: Exactly, exactly. >> All right, so let's talk about digital nations, we have China, so there's some bad behavior going on there. I mean, some will argue that there's really no R&D over there, and now they're trying to export the R&D that they stole into other countries, again, that's my personal rant. But the innovation there is clear, we chat and other things are happening. They finally turned the corner where they're driving a lot of, you know, mainly because of the mobile. But there's other nations out there that are kind of left behind. The UK just signed this week with Coinbase a pretty instrumental landmark licensing deal, which is a signal, 'cause I know Estonia, Armenia, you name every country wants to, Bahrain's got, you know, Dubai envy. So I mean, every country wants to be the crypto country. Every country wants to be the smart cities digital nation. I know this is something that you liked, and you and I were talking about 'cause we both are interested in. Your reaction, your thoughts on where that's going, I see, it's a good sign. What are the thresholds there, what are some of the keys things that they need to do to be a real digital nation? >> Well, I think it's less about digital nations in terms of like a nation is a series of borders, and more about first nations that we are, this is what we work on at CULTU.RE, that we are actually a nation of people and a lot of those nations have overlap and we should be able to participate in many different nations who have many different economies that are all really cooperating interdependently to create the best possible life for all human good, rather than just saying like I care about me and mine, because that strategy, the way government works now, it's a closed network with low trust that is extremely inefficient in management of resources. And the only way you can really-- >> That's the opposite, by the way, of what this movement's about. >> Yeah, exactly. And the only way you can have influence in government is to go in government and to work through government. All right. So it's the idea that, look at how much food we waste in the United States. If we took the food we wasted in the United States and repurposed it, we could literally cure world hunger. That is how bad it has gotten, right. And there are people starving in the US. There are people on food stamps in the US. >> Well, I mean, every institution, education, healthcare, you name it, it's all, you know, FUBAR, big time. >> Yeah, but we're throwing away tons of lettuce and all of this different kinds of produce because it like looks funky. Like this peach looks a little too much like a bottom. So we're like not able to sell it. >> Or lettuce got a little brown on it, throw the whole thing away. >> Yes, exactly, exactly, and that waste is unacceptable. So what we need to move toward is a model of open networks of governance where we have peer to peer distribution of finance and of resources in a way that allows people to aggregate around the marketplaces that are actually benefiting the way that they believe the world should work. So it's about creating a collective strategy of collective non-violence and eliminating harm, so obviously, you know, having a society that has enough proper incentives so that people are well off and that people are provided for, and I think blockchain will-- >> I noticed you're wearing a United Nations pin. >> Woo-hoo, yeah. And blockchain, I think, will also create this. >> John: I have one too. >> Let's up top. (slap) Yeah, I think blockchain will also help create universal basic income, but in addition to that, it's the idea that, if I'm living next door, I'll give two examples. So one is about the legality of the way that we contribute to the society. So let's say I have a next door neighbor. And let's say that this next door neighbor and I feel literally, we totally get along on everything, there's just one issue we feel we're like, I totally disagree with this, I totally disagree. And that issue is the use of, and I hope this isn't controversial to say, but anyway. So the use of medical marijuana, right. And it shouldn't be, because we can have two different opinions and the world can still work and that's the point. >> Well, in California, it's now legal to own marijuana. >> Yeah, for sure, it's legal here as well. So it's the idea that, if I, so let's say I'm a woman who, you know, I have someone in my life who was injured by a driver who was driving under the influence of marijuana. And so that's all I know about marijuana because I don't really do drugs, I've never been around drugs. So when I hear that word, I immediately think about the person in my life who was harmed because of, yes, and so immediately triggered, and I'm like, I don't want to support anything, I don't want to support anything to do with marijuana, I think marijuana is like the Devil's lettuce. And I have no interest in supporting marijuana. She never has to support marijuana, she doesn't have to. But her next door neighbor is a veteran with Parkinson's disease, her, me, whatever, is a veteran with Parkinson's disease, okay. And the only way that this man can move is, he's literally shaking, but when he smokes medical marijuana, he's actually able to, you watch and literally 30-45 minutes, he's upright, he looks like a normal healthy man. And so he says, "I believe that every, "after I fought in this, I believe every person "should have access to medical marijuana, "because this is the only way I'm able "to even operate my life." >> The different context. >> And I'm so, yes, exactly. And so what culture is really about is about understanding each other's context, that's even how reputation works. It's contextual awareness that provides greater understanding of who we are as individuals and the way we work together to make society work. So maybe they can mutually agree that he is not going to smoke while he's driving and he can pay to support everyone to have access who needs access to medical marijuana. >> Or he could finance Uber rides for them. You know, or whatever, I mean, these are mechanisms. >> Yes, yes, but it's the, yes, exactly, exactly. It's the idea that we are all, we're coming together to share context is a way that's not aggressive and not accusatory, so two people can believe two totally different things and still develop enough mutual respect to live together peacefully in a society. >> You know, the other too I'm riffing on that is that now KYC is a concept (mumbles) kicked around here, know your customer. I've been riffing on the notion of KYC for know your neighbor. And what we're seeing in these communities, even the analog world, people don't know who their neighbors are. Like, they don't actually even like care about them. >> Toni: For sure. >> You know, maybe I grew up in, you know, a different culture where, you know, everyone played freely, the parents were on the porch having their cocktail or socializing and watching the kids from the porch play on the lawn. Now I call that Snapchat, right. So I can see my kids Snapchat, so I'm not involved, but I have peripheral view. >> Toni: For sure. >> But we took care of each other. That doesn't happen much anymore, and I think one of the things that's interesting in some of these community dynamics that's been successful is this empathy about respect. They kind of get to know people in a non-judgmental way. And I think that is something that you see in some of these fragmented communities, where it's just like, if they just did things a little bit different. Do you agree, I see you're shaking your head, your thoughts on this? This super interesting social science thing that's, now you can measure it with digital or you can measure that kind of-- >> We can incentivize it. We can incentivize it. And that's the difference, measurement is one thing. Incentive is a behavior changer. Incentive is a behavior changer. And that is what we actually have to do in the way we think about the foundation of these systems, is it's not incentivizing competitive marketplaces that are like my way of thinking about this is right and your way of thinking about this thing is wrong, and like ah, it's not about that. At the end of the day like, I think we forget or misquote so much of, so many of the great thinkers of the last generation, like if you think about Darwin. What does everyone know about Darwin, right, it's like survival of the fittest. It's not what Darwin said, okay. It's misquoted and it's used, it's like one of those things where people who want to exploit-- >> It's a meme, basically. >> Yeah, people who want to exploit someone else's knowledge for their own ends will use that to, in some way, uplift the kind of like strategy of, you know, incentives of the time. What Darwin actually said was that human beings with the highest capacity for sympathy, qualities we now identify as altruism, compassion, empathy, reciprocity, will be the most likely to survive during hardship. Fundamentally, I mean, look at the state of the world today. It doesn't look good, it's like, you look at the way people interact with each other, it's like a virus that's attacking itself in an ecosystem that is our planet Earth, and we need to be, you know what is the antibody, our own sense of consideration for our fellow man. That is the antibody to violence. And so we can incentivize this, and we're going to have to because we're going to, AI, automation, these will fundamentally transform the way we think about jobs in a way that will liberate us like we've never known before. And once given the freedom, I think that we'll see the world start to change. >> Toni, I really appreciate you spending the time in this thought leadership conversation, riffing back and forth. Feels great and it's a great productive conversation. I got to ask you, how did you get there? I mean, who are you? I mean, you're amazing. Like, how did you get here, you obviously, Coin Telegraph's one of the projects you're running, great content. You're wearing the UN pin, I'm aligning with that. Got a great perspective. What's your story? Where did you come from originally, I mean... How did you get here? >> I think, you know, I don't know. I'm really connected to Saturn, I don't know where my home planet is. >> Which spaceship did you come in on? No, I mean, seriously, what's your background? How did you weave into this? 'Cause you have a holistic view on things, it's impressive. But you also can get down and dirty on the tech, and you have a good, strong network. Did you kind of back into this by accident on purpose, or was it something that you studied? What's the evolution that you have? >> Yeah, you know. I studied performance art and I was an artist all of my life. And I had a really big existential crisis, because I realized, as I was looking around, that technology was replacing every form. I remember the first time I watched an AI generate, this was maybe in like, I don't remember how, this was a long time ago, but I was essentially watching, before like the deep dream stuff, maybe like 2009 or 10. And I remember watching computers generate art. And I just was like, I was like mic drop, I was like anything that could ever be created can and will be created by computers, because these are, you are looking at this data, you can scan every art piece in the world and create an amalgamation of this in a way that extends so far beyond team and capacity that the form that we have used to express artistic integrity, all forms will, in some way, become obsolete as a form of creative expression. And I had this huge existential crisis as a performer, realizing that the value of my work was essentially, like, how long would the value of my work live on if no one is, I am not alive to continue singing the song. You don't remember the people who played Carmen, you remember Bizet who wrote the opera, you remember Carmen the character, but the life of the performer is like that of a butterfly. It's like you emerge from the cocoon, you fly around the world beautifully for a very short amount of time. And then you just, you know, stardust again. And so I had this huge existential moment, and it was a really big awakening call. It was as though the gravity of the universe came into the entire dimension of my being and said these, what you have learned has given you a skill, but this is not your path. So I went okay, I just need some time to like process that and so, 'cause this is my entire life, it's the only thing I ever imagined I would ever do. And so I ended up spending three months in silence meditating. And people are like whoa, like how did you do that? And I don't think people, I don't know, not that people don't understand, but I'm not certain that a lot of people have the level of this kind of existential moment that I experienced. And I couldn't have done anything else, I really just needed to take that time to process that I was actually reformulating every construct at the foundation of my own reality. And that was going to take, that's not something you just do overnight, right, like some people can do it more fluidly, but this was a real shift, a conscious shift. And so I asked myself three questions in that meditation, it was what is my purpose, what is the paradigm shift and where is my love. And so I just meditated on these three questions and started to, I don't know how deeply you've studied lucid dreaming or out of body experiences, but that's another, a conversation we can get into in another time, that was my area of study during that period. And so I ended up leaving the three months in silence and I just kind of, I started following my intuition. So I would just, essentially, sometimes I'd walk into a library and I would just shut my eyes and I would just walk around and I would touch books. And I would just feel what they felt like to me, like the density of their knowledge. And I would just feel something that I felt called to, and I would just pull it out of the shelf and just read it. And I don't know how to explain it-- >> (mumbles) Energy, basically-- >> I was guided, I was guided to this. This was in 2011. And so what I started getting into was propaganda theory, the dissolution of Aristotelian politics as an idea of citizen and state when we're really all consumers in a Keynesian economy structured by Edward Bernays, the inventor of propaganda, who essentially based our entire attitude of economic health on, you know, a dissolving human well being. Like, the evolution of our economic well being and our human well being were fundamentally at odds, and not only was that system non-sustainable, but it was a complete illusion. At every touch, point and turn, that the systems we lived in were illusions. And so is all of the world, right, like this whole world is an illusion, but these illusions in particular have some serious implications in terms of people who don't have the capacity, or not the capacity, everyone has the capacity, but who have not explored that deeply, right, who haven't gone that deep with themselves. >> And one of those books was like a tech book or was like-- >> It was just multiple, no, it was multiple books. And it's not that I would even read all of the books all of the way through. Sometimes I would just pick up a book and I would just open it to a certain page and I would read like a passage or a couple pages, and I'd just feel like that's all I need to read out of that book. It's, you just tune into it. >> When was your first trade on Bitcoin, first buy, 2011? >> You want to know something nuts? People always, people are like, "When did you first buy Bitcoin?" I was not, I didn't. So after I started, once you know, all this knowledge came to me, I just started talking about it, I was like, I've been given some wisdom, I just have to share it. So I started going out into the world and finding podiums and sharing. And that was when someone put a USB full of Bitcoin into my hands. I very rarely, I don't necessarily buy, I've just been gifted a lot. >> Good gifts. >> Toni: They've been great gifts, yeah. >> And then when did you start Coin Telegraph, when did that come online? >> So that was in 2013. I joined, the property had been operable for I think like three or four months. And some guys called me and they said, "We're just really impressed with you "and we want to work with you." And I said, "Well, that's nice," I was like, "But you don't have a business, right?" And they were like, "What do you mean?" And I was like, "Well, you have a blog, right?" And so I went in and I said, essentially like, here's, to scale the property, I was like, "Here's a plan for the next three years. "If we really want to get this property to where "it needs to be." I'm like, "Here are the programs that we need "to institute, here's like this entire, "countries we can be operable in "and then other acquisitions of other properties." I essentially went in and said like, "Here's the business model and the plan at scale," and they were just like, I think they were a little like, the first call that we had, I think they were just like, "We just called you to," it was a bold move, like, "We just called you to offer you something, "and you countered our offer by saying "we don't have a business?" It was one of those things, but they-- >> Well, it was the labor of love for them, right, I mean-- >> Well, for all of us, yeah, for all of us. >> When all you do is you're blogging, you're just sharing. And then you start thinking about, you know, how to grow, and you got to nurture it, you need cash. >> Yes, and so I essentially came in and then started, I was both editor in chief and CEO and co-founder of the property who helped bring in a lot of the network, build the reputation for the brand, create a scaling strategy. A lot of mergers and acquisitions, a lot of franchises and-- >> How many properties did you buy roughly, handful, six, less than six? >> So I would also say that-- >> Little blogs and kind of (mumbles) them together, bring people together, was that the thinking? >> Yeah, you know, what's interesting is media from all shapes and sizes, 15 to 20 offices in 25 different countries. I always say this when I talk about this, a very important lesson that I learned. How do you manage a team of 40 anarchists? You don't, you don't, that's the answer, you don't, you don't even like, you're like oh. I remember when I was like, "We're a team!" And someone was like, "No, we're not, "I don't believe in teams, I work for myself "and I don't need," I was like oh, wow. I was like oh-- >> John: The power of we, no. >> I was just like, all right, but it was a good learning experience, because I was like well, this is the way, these are your needs. So if that's your, I was like, well, let's embrace that, let's embrace the idea-- >> But that's the culture, you can't change it. >> And let's create the economy around that, let's actually do direct incentive for it, if you think that you're, if you want to be in this on your own, then let's say okay, we're going to make this fully free market economics and we're going to have a matter of consensus on whether or not someone who's exploiting the system, you write an article, you send it out, the number of views and shares that it gets from accounts that are, you know, proven verified, that is how much you get out of the bounty that's created from our ad sales, and if the community comes together in a consensus and says that someone wrote an article that was basically exploiting the system, like beer, guns, tits and weed plus Bitcoin and then they just shared it with everyone, then obviously, they would be weighted differently because the community would reach consensus so-- >> Change the incentive system. >> We just, I started, yeah, I started redesigning, essentially, once I had that moment, I was like okay, I was like, well, we really got to change the incentives here then because the incentives are not going to work like that. If that's the, if there's a consensus that that is the way you guys want to do things, then I got to change things around that. All right, cool, and so yeah, it was a really interesting awesome learning experience from like, you know, a team of like, maybe like 20 to 40 into, probably took it up 40, and then with all of the other, you know, companies and franchises, to about 435 people. And then just took the revenue from, yeah, just took, it was like skating revenue and then rocketing revenue. So that was really my role in the growth of the business and we're all, you know, it's amazing to see how these kind of blockchain holacracies work, you know, at a micro scale and at a macro scale. And what it really takes to build a movement, right. And then, in some ways, I guess it'd either become or create a meme. >> Well, I really appreciate the movement you've been supporting, we're here to bring theCUBE to the movement, our second show, third show we've been doing. And getting a lot more this year, as the ecosystem is coming together, the norms are forming, they're storming, they're forming, it's great stuff. You've been a great thought leader, and thanks for sharing the awesome range of topics here for theCUBE. >> For sure. >> Toni Lane here inside theCUBE, I'm John Furrier. Thanks for watching our exclusive Puerto Rico coverage of CoinAgenda, we'll be right back. (energetic music)

>> Narrator: Live from Las Vegas, it's theCUBE, covering AWS re:Invent 2017, presented by AWS, Intel, and our ecosystem of partners. >> Hey, welcome back everyone, this is theCUBE's exclusive live coverage here in Las Vegas for AWS Amazon web services re:Invent 2017. I'm John Furrier with Keith Townsend. Our next guest is Laura Stevens, data scientist at the American Heart Association, an AWS customer, welcome to theCUBE. >> Hi, it's nice to be here. >> So, the new architecture, we're seeing all this great stuff, but one of the things that they mention is data is the killer app, that's my word, Verna didn't say that, but essentially saying that. You guys are doing some good work with AWS and precision medicine, what's the story? How does this all work, what are you working with them on? >> Yeah, so the American Heart Association was founded in 1924, and it is the oldest and largest voluntary organization dedicated to curing heart disease and stroke, and I think in the past few years what the American Heart Association has realized is that the potential of technology and data can really help us create innovative ways and really launch precision medicine in a fashion that hasn't been capable to do before. >> What are you guys doing with AWS? What's that, what's the solution? >> Yeah so the HA has strategically partnered with Amazon Web Services to basically use technology as a way to power precision medicine, and so when I say precision medicine, I mean identifying individual treatments, based on one's genetics, their environmental factors, their life factors, that then results in preventative and treatment that's catered to you as an individual rather than kind of a one size fits all approach that is currently happening. >> So more tailored? >> Yeah, specifically tailored to you as an individual. >> What do I do, get a genome sequence? I walk in, they throw a high force computing, sequence my genomes, maybe edit some genes while they're at it, I mean what's going on. There's some cutting edge conversations out there we see in some of the academic areas, course per that was me just throwing that in for fun, but data has to be there. What kind of data do you guys look at? Is it personal data, is it like how big is the data? Give us a sense of some of the data science work that you're doing? >> Yeah so the American Heart Association has launched the Institute for Precision Cardiovascular Medicine, and as a result, with Amazon, they created the precision medicine platform, which is a data marketplace that houses and provides analytic tools that enable high performance computing and data sharing for all sorts of different types of data, whether it be personal data, clinical trial data, pharmaceutical data, other data that's collected in different industries, hospital data, so a variety of data. >> So Laura, there's a lot of think fud out there around the ability to store data in a cloud, but there's also some valid concerns. A lot of individual researchers, I would imagine, don't have the skillset to properly protect data. What is the Heart Association doing with the framework to help your customers protect data? >> Yeah so the I guess security of data, the security of the individual, and the privacy of the individual is at the heart of the AHA, and it's their number one concern, and making anything that they provide that a number one priority, and the way that we do that in partnering with AWS is with this cloud environment we've been able to create even if you have data that you'd like to use sort of a walled garden behind your data so that it's not accessible to people who don't have access to the data, and it's also HIPAA compliant, it meets the standards that the utmost secure standards of health care today. >> So I want to make sure we're clear on this, the Heart Association doesn't collect data themselves. Are you guys creating a platform for your members to leverage this technology? >> So there's, I would so maybe both actually. The American Heart Association does have data that it is associated with, with its volunteers and the hospitals that it's associated with, and then on top of that, we've actually just launched My Research Legacy, which allows individuals of the community to, who want to share their data, whether you're healthy or just sick, either one, they want to share their data and help in aiding to cure heart disease and stroke, and so they can share their own data, and then on top of that, anybody, we are committed to strategically partnering with anybody who's involved and wants to share their data and make their data accessible. >> So I can share my data? >> Yes, you can share your data. >> Wow, so what type of tools do you guys use against that data set and what are some of the outcomes? >> Yeah so I think the foundation is the cloud, and that's where the data is stored and housed, and then from there, we have a variety of different tools that enable researchers to kind of custom build data sets that they want to answer the specific research questions they have, and so some of those tools, they range from common tools that are already in use today on your personal computer, such as Python or R Bioconductor, and then they have more high performance computing tools, such as Hal or any kind of s3 environment, or Amazon services, and then on top of that I think what is so awesome about the platform is that it's very dynamic, so a tool that's needed to use for high performance computing or a tool that's needed even just as a on a smaller data set, that can easily be installed and may be available to researchers, and so that they can use it for their research. >> So kind of data as a service. I would love to know about the community itself. How are you guys sharing the results of kind of oh this process worked great for this type of analysis amongst your members? >> Yeah so I think that there's kind of two different targets in that sense that you can think of is that there's the researchers and the researchers that come to the platform and then there's actually the patient itself, and ultimately the HA's goal is to make, to use the data and use the researcher for patient centered care, so with the researchers specifically, we have a variety of tutorials available so that researchers can one, learn how to perform high performance computing analysis, see what other people have done. We have a forum where researchers can log on and enable, I guess access other researchers and talk to them about different analysis, and then additionally we have My Research Legacy, which is patient centered, so it's this is what's been found and this is what we can give back to you as the patient about your specific individualized treatment. >> What do you do on a daily basis? Take us through your job, are you writing code, are you slinging API's around? What are some of the things that you're doing? >> I think I might say all of the above. I think right now my main effort is focused on one, conducting research using the platform, so I do use the platform to answer my own research questions, and those we have presented at different conferences, for example the American Heart Association, we had a talk here about the precision medicine platform, and then two, I'm focused on strategically making the precision medicine platform better by getting more data, adding data to the platform, improving the way that data is harmonized in the platform, and improving the amount of data that we have, and the diversity, and the variety. >> Alright, we'll help you with that, so let's help you get some people recruited, so what do they got to do to volunteer, volunteer their data, because I think this is one of those things where you know people do want to help. So, how do they, how you onboard? You use the website, is it easy, one click? Do they have to wear an iWatch, I mean what I mean? >> Yeah. >> What's the deal? What do I got to do? >> So I think I would encourage researchers and scientists and anybody who is data centric to go to precision.heart.org, and they can just sign up for an account, they can contact us through that, there's plenty of different ways to get in touch with us and plenty of ways to help. >> Precision.heart.org. >> Yup, precision.heart.org. >> Stu: Register now. >> Register now click, >> Powered by AWS. >> Yup. >> Alright so I gotta ask you as an AWS customer, okay take your customer hat off, put your citizen's hat on, what is Amazon mean to you, I mean is it, how do you describe it to people who don't use it? >> Okay yeah, so I think... the HA's ultimate mission right, is to provide individualized treatment and cures for cardiovascular disease and stroke. Amazon is a way to enable that and make that actually happen so that we can mine extremely large data sets, identify those individualized patterns. It allows us to store data in a fashion where we can provide a market place where there's extremely large amounts of data, extremely diverse amounts of data, and data that can be processed effectively, so that it can be directly used for research. >> What's your favorite tool or product or service within Amazon? >> That's a good question. I think, I mean the cloud and s3 buckets are definitely in a sense they're my favorites because there's so much that can be stored right there, Athena I think is also pretty awesome, and then the EMR clusters with Spark. >> The list is too long. >> My jam. >> It is. (laughs) >> So, one of the interesting things that I love is a lot of my friends are in non-profits, fundraising is a big, big challenge, grants are again, a big challenge, have you guys seen any new opportunities as a result of the results of the research coming out of HA and AWS in the cloud? >> Yeah so I think one of the coolest things about the HA is that they have this Institute for Precision Cardiovascular Medicine, and the strategic partnership between the HA and AWS, even just this year we've launched 13 new grants, where the HA kind of backs the research behind, and the AWS provides credit so that people can come to the cloud and use the cloud and use the tools available on a grant funded basis. >> So tell me a little bit more about that program. Anybody specifically that you, kind of like saying, seeing that's used these credits from AWS to do some cool research? >> Yeah definitely, so I think specifically we have one grantee right now that is really focused on identifying outcomes across multiple clinical trials, so currently clinical trials take 20 years, and there's a large variety of them. I don't know if any of you are familiar with the Framingham heart study, the Dallas heart study, the Jackson heart study, and trying to determine how those trials compare, and what outcomes we can generate, and research insights we can generate across multiple data sets is something that's been challenging due to the ability to not being able to necessarily access that data, all of those different data sets together, and then two, trying to find ways to actually compare them, and so with the precision medicine platform, we have a grantee at the University of Colorado-Denver, who has been able to find those synchronicities across data sets and has actually created kind of a framework that then can be implemented in the precision medicine platform. >> Well I just registered, it takes really two seconds to register, that's cool. Thanks so much for pointing out precision.heart.org. Final question, you said EMR's your jam. (laughing) >> Why, why is it? Why do you like it so much, is it fast, is it easy to use? >> I think the speed is one of the things. When it comes to using genetic data and multiple biological levels of data, whether it be your genetics, your lifestyle, your environment factors, there's... it just ends up being extremely large amounts of data, and to be able to implement things like server-less AI, and artificial intelligence, and machine learning on that data set is time consuming, and having the power of an EMR cluster that is scalable makes that so much faster so that we can then answer our research questions faster and identify those insights and get them to out in the world. >> Gotta love the new services they're launching, too. It just builds on top of it. Doesn't it? >> Yes. >> Yeah, soon everyone's gonna be jamming on AWS in our opinion. Thanks so much for coming on, appreciate the stories and commentary. >> Yeah. >> Precision.heart.org, you want to volunteer if you're a researcher or a user, want to share your data, they've got a lot of data science mojo going on over there, so check it out. It's theCUBE bringing a lot of data here, tons of data from the show, three days of wall to wall coverage, we'll be back with more live coverage after this short break. (upbeat music)

>> Hey, welcome back everybody. Jeff Frick here with theCube. We're in Palo Alto, California, at one of the Chertoff events. It's called Security in the Boardroom. They have these events all over the country, and this is really kind of elevating the security conversation beyond the edge, and beyond CISOs to really the boardroom, which is really where the conversation needs to happen. And our next guest, really excited to have We've got Chad Sweet, he's the co-founder and CEO of the Chertoff Group. Welcome Chad. >> Great to be here. >> And with him also Reggie Brothers, he's the principal at the Chertoff Group, and spent a lot of time in Washington. Again you can check his LinkedIn and find out his whole history. I won't go through it here. First off, welcome gentlemen. >> Thank you. >> Thank you. >> So, before we jump in a little bit of-- What are these events about? Why should people come? >> Well, basically they're a form in which we bring together both practitioners and consumers of security. Often it's around a pragmatic issue that the industry or government's facing, and this one, as you just said, priority of security, cyber screening in particular, in the boardroom, which is obviously what we're reading about everyday in the papers with the Petya and NotPetya and the WannaCry attacks, these are basically, I think, teachable moments that are affecting the whole nation. And so this is a great opportunity for folks to come together in a intimate form, and we welcome everybody who wants to come. Check out our website at chertoffgroup.com >> Okay, great, and the other kind of theme here, that we're hearing over and over is the AI theme, right? >> Yeah. >> We hear about AI and machine learning all over the place and we're in Mountain View and there's self-driving cars driving all over the place and Google tells me, like, "you're home now." And I'm like, "Ah, that's great." But there's much bigger fish to fry with AI and there's a much higher level. And Reggie you just came off a panel talking about some much higher level-- I don't know if issues is the right word, maybe issues is the right word, around AI for security. So, I wonder if you can share some of those insights. >> I think issues, challenges, are the right words. >> Challenges, that's probably a better word. >> Those are good words, because particularly you're talking about security application. Whether it's corporate or government the issue becomes trust. How do you trust that this machine has made the right kind of decision, how do you make it traceable. One of the challenges with the current AI technology is it's mostly based on machine-learning. Machine-learning tends to be kind of a black box where you know know what goes in and you train what comes out. That doesn't necessarily mean you understand what's going inside the box. >> Right. >> So then if you have a situation where you really need to be able to trust this decision this machine's making How do you trust it? What's the traceability? So, in the panel we started discussing that. Why is it so important to have this level of trust? You brought up autonomous-vehicles, well of course, you want to make sure that you can trust your vehicle to make the right decision if it has to make a decision at an intersection. Who's it going to save? How do you trust that machine becomes a really big issue. I think it's something that in the machine-learning community, as we learn in the panel, is really starting to grapple with and face that challenge. So I think there's good news, but I think it's a question that when think about what we have to ask when we're adopting these kind of machine-learning AI solutions we have to make sure we do ourself. >> So, it's really interesting, the trust issue, because there's so many layers to it, right? We all get on airplanes and fly across country all the time, right? And those planes are being flown by machines, for the most part. And at the same time if you start to unpack some of these crazy algorithms, even if you could open up the black box, unless you're a data scientist and you have a PhD, in some of these statistical analysis could you really understand it anyway? So how do you balance it? We're talking about the boardroom. What's the level of discovery? What's the level of knowledge that's appropriate without necessarily being a full-fledged data scientist who are the ones that are actually writing those algorithms? >> So I think that's a challenge, right, because I think when you look at the types of ways that people are addressing this trust challenge it is highly technical, alright. People are making hybrid systems where you can do some type of traceability but that's highly technical for the boardroom. I think what's important is that the-- and one thing that we did talk about on the panel and even prior to panel was on cybersecurity and governance, we talked about the importance of being able to speak in a language that everyone-- that the laborers can understand. You can't just speak in a computer science jargon kind of manner. You have to be able to speak to the person that's actually making the decision. Which means you have to really understand the problem, because I think my experience the people that can speak in the plainest language understand the problem the best. So these problems are things that can be explained they just tend not to be explained, because they're in this super technical domain. >> But you know, Reggie is being very humble. He's got a PhD from MIT and worked at the defense advanced research-- >> Well he can open the box. >> He can open the box. I'm a simple guy from Beaumont, Texas, so I can kind of dumb it down for the average person. I think on the trust issue over time whether, and you just mentioned some of it, if you use the analogy of a car or the board room or a war scenario, it's the result. So you get comfortable, you know the first time, I have a Tesla, the first time I let go of the wheel and let it drive it's self was a scary experience but then when you actually see the result and get to enjoy and experience the actual performance of the vehicle that's when the trust can begin. And I think in a similar vein, in the military context, you know, we're seeing automation start to take hold. The big issue will be in that moment of ultimate trust, i.e. do you allow a weapon actually to have lethal decision-making authority, and we just talked about that on the panel, which is the ultimate trust is-- is not really today in the military something that we're prepared to trust yet. I think we've seen in, there's only a couple places, like the DMZ in North Korea where we actually do have a few systems that are, if they actually detect an attack because there's such a short response time, those are the rare exceptions of where lethal authority is at least being considered. I think Elon Musk has talked about how the threat of AI, and how this could, if it's not, we don't have some norms put around it then that trust could not be developed, cause there wouldn't be this checks and balances. So, in the boardroom that last scenario, I think, the boards are going to be facing these cyber attacks and the more that they experience once the attack happens how the AI is providing some immediate response in mitigation and hopefully even prevention, that's where the trust will begin. >> The interesting thing, though, is that the sophistication of the attacks is going up dramatically, right? >> Chad: Yep. >> Why do we have machine-learning in AI? Because it's fast. It can react to a ton of data and move at speeds that we as people can't, such as your self-driving car. And now we're seeing an increase in state-sponsored threats that are coming in, it's not just the crazy kid in the basement, you know, hacking away to show his friend, but you know, now they're trying to get much more significant information, trying to go after much more significant systems. So, it almost begs then that you have to have the North Korean example when your time windows are shorter, when the assets are more valuable and when the sophistication of the attacking party goes up, can people manage it, you know, I would assume that the people role, you know, will continue to get further and further up the stack where the automation takes an increasing piece of it. >> So let's pull on that, right. So if you talk to the Air Force, cause the Air Force does a lot of work on autonomy, DoD General does, but the Air Force has this chart where they show that over time the resource that will be dedicated by a machine, autonomous machine, will increase and resources to a human decrease, to a certain level, to a certain level. And that level is really governed by policy issues, compliance issues. So there's some level over which because of policy and compliance the human will always be in the loop. You just don't let the machine run totally open loop, but the point is it has to run at machine speed. So let's go back to your example, with the high speed cyber attacks. You need to have some type of defensive mechanism that can react at machine speed, which means at some level the humans are out of that part of the loop, but you still have to have the corporate board person, as Chad said, have trust in that machine to operate at this machine speed, out of the loop. >> In that human oversight one of the things that was discussed on on the panel was that interestingly AI can actually be used in training of humans to upgrade their own skills, and so right now in the Department of Defense, they do these exercises on cyber ranges and there's about a 4 month waiting period just to get on the ranges, that's how congested they are. And even if you get on it, if you think about it, right now there's a limited number of human talent, human instructors that can simulate the adversary and oversee that, and so actually using AI to create a simulated adversary and being able to do it in a gamified environment is something that's increasingly going to be necessary to make it, to keep everyone's skills, and to do it real-time 24/7 against active threats that are being morphed over time. That's really where we have to get our game up to. So, watch for companies like Circadence, which are doing this right now with the Air Force, Army, DISA, and also see them applying this, as Reggie said, in the corporate sphere where a lot of the folks who will tell you today they're facing this asymmetric threat, they have a lot of tools, but they don't necessarily trust or have the confidence that when the balloon goes up, when the attack is happening, is my team ready? And so being able to use AI to help simulate these attacks against their own teams so they can show the board actually our guys are at this level of tested-ness and readiness. >> It's interesting Hal's talking to me in the background as you're talking about the cyber threat, but there's another twist on that, right, which is where machines aren't tired, they didn't have a bad day, they didn't have a fight with the kids in the morning. So you've got that kind of human frailty which machines don't have, right, that's not part of the algorithm generally. But it's interesting to me that it usually comes down to, as most things of any importance, right, it's not really a technical decision. The technical pieces was actually pretty easy. The hard part is what are the moral considerations, what are the legal considerations, what are the governance considerations, and those are what really ultimately drive the decision to go or no-go. >> I absolutely agree. One of the challenges that we face is what is our level of interaction between the machine and the human, and how does that evolve over time. You know, people talk about the centaur model, where the centaur, the mythical horse and human, where you have this same kind of thing with the machine and human, right? You want this seamless type of interaction, but what does that really mean, and who does what? What they've found is you've got machines have beaten, obviously, our human chest masters, they've beaten our goal masters. But the things that seems to work best is when there's some level of teaming between the human and the machine. What does that mean? And I think that's going to be a challenge going forward is how we start understanding what that frontier is where the human and machine have to have this really seamless interaction. How do we train for that, how do we build for that? >> So, give your last thoughts before I let you go. The chime is running, they want you back. As you look down the road, just a couple years, I would never say more than a couple years, and, you know, Moore's Law is not slowing down people argue will argue they're crazy, you know, chips are getting faster, networks are getting faster, data systems are getting faster, computers are getting faster, we're all carrying around mobile phones and just blowing off tons of digital exhaust as our systems. What do you tell people, how do boards react in this rapidly evolving, you know, on like an exponential curve environment in which we're living, how do they not just freeze? >> Well if you look at it, I think, to use a financial analogy and almost every board knows the basic foundational formula for accounting which is assets equals liabilities plus equity. I think in the future because no business today is immune from the digital economy every business is being disrupted by the digital economy and it's-- there are businesses that are underpinned by the trust of the digital economy. So, every board I think going forward has to become literate on cybersecurity and Artificial Intelligence will be part of that board conversation, and they'll need to learn that fundamental formula of risk, which is risk equals threat, times vulnerability, times consequence. So in the months ahead part of what the Chertoff Group will be doing is playing a key role in helping to be an educator of those boards and a facilitator in these important strategic discussions. >> Alright, we'll leave it there. Chad Sweet, Reggie Brothers thanks for stopping by. >> Thank you. >> Thank you, appreciate it. >> Alright, I'm Jeff Frick, you're watching theCube. We're at the Chertoff event, it's security in the boardroom. Think about it, we'll catch ya next time.

>> 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)