>> From around the globe, it's theCUBE, covering Data Citizens '21. Brought to you by Collibra. >> For the past decade organizations have been effecting very deliberate data strategies and investing quite heavily in people, processes and technology, specifically designed to gain insights from data, better serve customers, drive new revenue streams we've heard this before. The results quite frankly have been mixed. As much of the effort is focused on analytics and technology designed to create a single version of the truth, which in many cases continues to be elusive. Moreover, the world of data is changing. Data is increasingly distributed making collaboration and governance more challenging, especially where operational use cases are a priority. Hello, everyone. My name is Dave Vellante and you're watching theCUBE coverage of Data Citizens '21. And we're pleased to welcome Michele Goetz who's the vice president and principal analyst at Forrester Research. Hello, Michele. Welcome to theCUBE. >> Hi, Dave. Thanks for having me today. >> It's our pleasure. So I want to start, you serve have a wide range of roles including enterprise architects, CDOs, chief data officers that is, analyst, the analyst, et cetera, and many data-related functions. And my first question is what are they thinking about today? What's on their minds, these data experts? >> So there's actually two things happening. One is what is the demand that's placed on data for our new intelligent digital systems. So we're seeing a lot of investment and interest in things like edge computing. And then how does that intersect with artificial intelligence to really run your business intelligently and drive new value propositions to be both adaptive to the market as well as resilient to changes that are unforeseen. The second thing is then you create this massive complexity to managing the data, governing the data, orchestrating the data because it's not just a centralized data warehouse environment anymore. You have a highly diverse and distributed landscape that you both control internally, as well as taking advantage of third party information. So really what the struggle then becomes is how do you trust the data? How do you govern it, and secure, and protect that data? And then how do you ensure that it's hyper contextualized to the types of value propositions that our intelligence systems are going to serve? >> Well, I think you're hitting on the key issues here. I mean, you're right. The data and I sort of refer to this as well is sort of out there, it's distributed at the edge. But generally our data organizations are actually quite centralized and as well you talk about the need to trust the data obviously that's crucial. But are you seeing the organization change? I know you're talking about this to clients, your discussion about collaboration. How are you seeing that change? >> Yeah, so as you have to bring data into context of the insights that you're trying to get or the intelligence that's automating and scaling out the value streams and outcomes within your business, we're actually seeing a federated model emerge in organizations. So while there's still a centralized data management and data services organization led typical enterprise architects for data, a data engineering team that's managing warehouses as in data lakes. They're creating this great platform to access and orchestrate information, but we're also seeing data, and analytics, and governance teams come together under chief data officers or chief data and analytics officers. And this is really where the insights are being generated from either BI and analytics or from data science itself and having dedicated data engineers and stewards that are helping to access and prepare data for analytic efforts. And then lastly, this is the really interesting part is when you push data into the edge the goal is that you're actually driving an experience and an application. And so in that case we are seeing data engineering teams starting to be incorporated into the solutions teams that are aligned to lines of business or divisions themselves. And so really what's happening is if there is a solution consultant who is also overseeing value-based portfolio management when you need to instrument the data to these new use cases and keep up with the pace of the business it's this engineering team that is part of the DevOps work bench to execute on that. So really the balances we need the core, we need to get to the insights and build our models for AI. And then the next piece is how do you activate all that? And there's a team over there to help. So it's really spreading the wealth and expertise where it needs to go. >> Yeah, I love that. You took a couple of things that really resonated with me. You talked about context a couple of times and this notion of a federated model, because historically the sort of big data architecture, the team, they didn't have the context, the business context, and my inference is that's changing and I think that's critical. Your talk at Data Citizens is called how obsessive collaboration fuels scalable DataOps. You talk about the data, the DevOps team. What's the premise you put forth to the audience? >> So the point about obsessive collaboration is sort of taking the hubris out of your expertise on the data. Certainly there's a recognition by data professionals that the business understands and owns their data. They know the semantics, they know the context of it and just receiving the requirements on that was assumed to be okay. And then you could provide a data foundation, whether it's just a lake or whether you have a warehouse environment where you're pulling for your analytics. The reality is that as we move into more of AI machine learning type of model, one, more context is necessary. And you're kind of balancing between what are the things that you can ascribe to the data globally which is what data engineers can support. And then there's what is unique about the data and the context of the data that is related to the business value and outcome as well as the feature engineering that is being done on the machine learning models. So there has to be a really tight link and collaboration between the data engineers, the data scientists, and analysts, and the business stakeholders themselves. You see a lot of pods starting up that way to build the intelligence within the system. And then lastly, what do you do with that model? What do you do with that data? What do you do with that insight? You now have to shift your collaboration over to the work bench that is going to pull all these components together to create the experiences and the automation that you're looking for. And that requires a different collaboration model around software development. And still incorporating the business expertise from those stakeholders, so that you're satisfying, not only the quality of the code to run the solution, but the quality towards the outcome that meets the expectation and the time to value that your stakeholders have. So data teams aren't just sitting in the basement or in another part of the organization and digitally disconnected anymore. You're finding that they're having to work much more closely and side by side with their colleagues and stakeholders. >> I think it's clear that you understand this space really well. Hubris out context in, I mean, that's kind of what's been lacking. And I'm glad you said you used the word anymore because I think it's a recognition that that's kind of what it was. They were down in the basement or out in some kind of silo. And I think, and I want to ask you this. I come back to organization because I think a lot of organizations look the most cost effective way for us to serve the business is to have a single data team with hyper specialized roles. That'll be the cheapest way, the most efficient way that we can serve them. And meanwhile, the business, which as you pointed out has the context is frustrated. They can't get to data. So there's this notion of a federated governance model is actually quite interesting. Are you seeing actual common use cases where this is being operationalized? >> Absolutely, I think the first place that you were seeing it was within the operational technology use cases. There the use cases where a lot of the manufacturing industrial device. Any sort of IOT based use case really recognized that without applying data and intelligence to whatever process was going to be executed. It was really going to be challenging to know that you're creating the right foundation, meeting the SLA requirements, and then ultimately bringing the right quality and integrity to the data, let alone any sort of data protection and regulatory compliance that has to be necessary. So you already started seeing the solution teams coming together with the data engineers, the solution developers, the analysts, and data scientists, and the business stakeholders to drive that. But that is starting to come back down into more of the IT mindset as well. And so DataOps starts to emerge from that paradigm into more of the corporate types of use cases and sort of parrot that because there are customer experience use cases that have an IOT or edge component to though. We live on our smart phones, we live on our smart watches, we've got our laptops. All of us have been put into virtual collaboration. And so we really need to take into account not just the insight of analytics but how do you feed that forward. And so this is really where you're seeing sort of the evolution of DataOps as a competency not only to engineer the data and collaborate but ensure that there sort of an activation and alignment where the value is going to come out, and still being trusted and governed. >> I got kind of a weird question, but I'm going. I was talking to somebody in Israel the other day and they told me masks are off, the economy's booming. And he noted that Israel said, hey, we're going to pay up for the price of a vaccine. The cost per dose out, 28 bucks or whatever it was. And he pointed out that the EU haggled big time and they don't want to pay $19. And as a result they're not as far along. Israel understood that the real value was opening up the economy. And so there's an analogy here which I want to come back to my organization and it relates to the DataOps. Is if the real metric is, hey, I have an idea for a data product. How long does it take to go from idea to monetization? That seems to me to be a better KPI than how much storage I have, or how much geometry petabytes I'm managing. So my question is, and it relates to DataOps. Can that DataOps, should that DataOps individual maybe live, and then maybe even the data engineer live inside of the business and is that even feasible technically with this notion of federated governance? Are you seeing that and maybe talk a little bit more about this DataOps role. Is it. >> Yeah. >> Fungible. >> Yeah, it's definitely fungible. And in fact, when I talked about sort of those three units of there's your core enterprise data services, there's your BI and data, and then there's your line of business. All of those, the engineering and the ops is the DataOps which is living in all of those environments and being as close as possible to where the value proposition is being defined and designed. So absolutely being able to federate that. And I think the other piece on DataOps that is really important is recognizing how the practices around continuous integration and continuous deployment using agile methodologies is really reshaping. A lot of the waterfall approaches that were done before where data was lagging 12 to 18 months behind any sort of insights, but a lot of the platforms today assume that you're moving into a standard mature software development life cycle. And you can start seeing returns on investment within a quarter, really, so that you can iterate and then speed that up so that you're delivering new value every two weeks. But it does change the mindset this DataOps team aligned to solution development, aligned to a broader portfolio management of business capabilities and outcomes needs to understand how to appropriately scope the data products that they're delivering to incremental value-based milestones. So the business feels that they're getting improvements over time and not just waiting. So there's an MVP, you move forward on that and optimize, optimize, extend scale. So again, that CICD mindset is helping to not bottleneck and wait for the complete field of dreams to come from your data and your insights. >> Thank you for that, Michelle. I want to come back to this idea of collaboration because over the last decade we've seen attempts, I've seen software come out to try to help the various roles collaborate and some of it's been okay, but you have these hyper specialized roles. You've got data scientists, data engineers, quality engineers, analysts, et cetera. And they tend to be in their own little worlds. But at the end of the day we rely on them all to get answers. So how can these data scientists, all these stewards, how can they collaborate better? What are you seeing there? >> You need to get them onto the same process. That's really what it comes down to. If you're working from different points of view, that's one thing. But if you're working from different processes collaborating is really challenging. And I think the one thing that's really come out of this move to machine learning and AI is recognizing that you need processes that reinforce collaboration. So that's number one. So you see agile development in CICD not just for DataOps, not just for DevOps, but also encouraging and propelling these projects and iterations for the data science teams as well or even if there's machine learning engineers incorporated. And then certainly the business stakeholders are inserted within there as appropriate to accept what it is that is going to be developed. So processes is number one. And number two is what is the platform that's going to reinforce those processes and collaboration. And it's really about what's being shared. How do you share? So certainly what we're seeing within the platforms themselves is everybody contributing into some sort of a library where their components and products are being ascribed to and then that's able to help different teams grab those components and build out what those solutions are going to be. And in fact, what gets really cool about that is you don't always need hardcore data scientists anymore as you have this social platform for data product and analytic product development. This is where a lot of the auto ML begins because those who are less data science-oriented but can build an insight pipeline, can grab all the different components from the pipelines to the transformations, to capture mechanisms, to bolting into the model itself and allowing that to be delivered to the application. So really kind of balancing out between process and platforms that enable and encourage, and almost force you to collaborate and manage through sharing. >> Thank you for that. I want to ask you about the role data governance. You've mentioned trust and that's data quality, and you've got teams that are focused on and specialists focused on data quality. There's the data catalog. Here's my question. You mentioned edge a couple of times and I can see a lot of that. I mean, today, most AI is are a lot of value, I would say most is modeling. And in the future, you mentioned edge it's going to be a lot of influencing in real time. And people maybe not going to have the time or be involved in that decision. So what are you seeing in terms of data governance, federate. We talked about federated governance, this notion of a data catalog and maybe automating data quality without necessarily having it be so labor intensive. What are you seeing the trends there? >> Yeah, so I think our new environment, our new normal is that you have to be composable, interoperable, and portable. Portability is really the key here. So from a cataloging perspective and governance we would bring everything together into our catalogs and business glossaries. And it would be a reference point, it was like a massive Wiki. Well, that's wonderful, but why just how's it in a museum. You really want to activate that. And I think what's interesting about the technologies today for governance is that you can turn those rules, and business logic, and policies into services that are composable components and bring those into the solutions that you're defining. And in that way what happens is that creates portability. You can drive them wherever they need to go. But from the composability and the interoperability portion of that you can put those services in the right place at the right time for what you need for an outcome so that you start to become behaviorally driven on executing on governance rather than trying to write all of the governance down into transformations and controls to where the data lives. You can have quality and observability of that quality and performance right at the edge and context of behavior and use of that solution. You can run those services and in governance on gateways that are managing and routing information at those edge solutions and we synchronization between the edge and the cloud comes up. And if it's appropriate during synchronization of the data back into the data lake you can run those services there. So there's a lot more flexibility and elasticity for today's modern approaches to cataloging, and glossaries, and governance of data than we had before. And that goes back into what we talked about earlier of like, this is the new wave of DataOps. This is how you bring data products to fruition now. Everything is about activation. >> So how do you see the future of DataOps? I mean, I kind of been pushing you to a more decentralized model where the business has more control 'cause the business has the context. I mean, I feel as though, hey, we've done a great job of contextualizing our operational systems. The sales team they know when the data is crap within my CRM, but our data systems are context agnostic generally. And you obviously understand that problem well. But so how do you see the future of DataOps? >> So I think what's kind of interesting about that is we're going to go to governance on greed versus governance on right more so. What do I mean by that? That means that from a business perspective there's two sides of it. There's ensuring that where governance is run is as we talked about before executing at the appropriate place at the appropriate time. It's semantically domain-centric driven not logical and systems centric. So that's number one. Number two is also recognizing that business owners or business operations actually plays a role in this, because as you're working within your CRM systems, like a Salesforce, for example you're using an iPaaS MuleSoft to connect to other applications, connect to other data sources, connect to other analytics sources. And what's happening there is that the data is being modeled and personalized to whatever view insight our task has to happen within those processes. So even CRM environments where we think of as sort of traditional technologies that we're used to are getting a lift, both in terms of intelligence from the data but also your flexibility and how you execute governance and quality services within that environment. And that actually opens up the data foundations a lot more and avoids you from having to do a lot of moving, copying centralizing data and creating an over-weighted business application and an over, both in terms of the data foundation but also in terms of the types of business services, and status updates, and processes that happen in the application itself. You're drawing those tasks back down to where they should be and where performance can be managed rather than trying to over customize your application environment. And that gives you a lot more flexibility later too for any sort of upgrades or migrations that you want to make because all of the logic is contained back down in a service layer instead. >> Great perspectives, Michelle, you obviously know your stuff and it's been a pleasure having you on. My last question is when you look out there anything that really excites you or any specific research that you're working on that you want to share, that you're super pumped about? >> I think there's two things. One is it's truly incredible the amount of insight and growth that is coming through data profiling and observation. Really understanding and contextualizing data anomalies so that you understand is data helping or hurting the business value and tying it very specifically to processes and metrics, which is fantastic as well as models themselves like really understanding how data inputs and outputs are making a difference whether the model performs or not. And then I think the second thing is really the emergence of more active data, active insights. And as what we talked about before your ability to package up services for governance and quality in particular that allow you to scale your data out towards the edge or where it's needed. And doing so not just so that you can run analytics but that you're also driving overall processes and value. So the research around the operationalization and activation of data is really exciting. And looking at the networks and service mesh to bring those things together is kind of where I'm focusing right now because what's the point of having data in a database if it's not providing any value. >> Michele Goetz, Forrester Research, thanks so much for coming on theCUBE. Really awesome perspectives. You're in an exciting space, so appreciate your time. >> Absolutely, thank you. >> And thank you for watching Data Citizens '21 on theCUBE. My name is Dave Vellante. (upbeat music)

>> From around the globe, it's theCube covering Data Citizens '21, brought to you by Collibra. >> For the past decade, organizations have been effecting very deliberate data strategies investing quite heavily in people, processes, and technology specifically designed to gain insights from data, better serve customers, drive new revenue streams, we've heard this before. The results quite frankly have been mixed. As much of the effort is focused on analytics and technology designed to create a single version of the truth, which in many cases continues to be elusive. Moreover, the world of data is changing, data is increasingly distributed making collaboration in governance more challenging especially where operational use cases are a priority. Hello, everyone, my name is Dave Vellante and you're watching theCube's coverage of Data Citizens '21. And we're pleased to welcome Michele Goetz, who's the Vice President and Principal Analyst at Forrester Research. Hello, Michele, welcome to theCube. >> Hi, Dave thanks for having me today. >> It's our pleasure. So I want to start, you serve have a wide range of roles including enterprise architects, CDOs, chief data officers that is, the analyst et cetera, and many data related functions. And my first question is what are they thinking about today? What's on their minds? These data experts. >> So there's actually two things happening. One is what is the demand that's placed on data for our new intelligent digital systems. So we're seeing a lot of investment and interest in things like edge computing. And then how does that intersect with artificial intelligence to really run your business intelligently and drive new value propositions, to be both adaptive to the market as well as resilient to changes that are unforeseen. The second thing is then you create this massive complexity to managing the data, governing the data, orchestrating the data, because it's not just a centralized data warehouse environment anymore. You have a highly diverse and distributed landscape that you both control internally, as well as taking advantage of third party information. So really what the struggle then becomes is how do you trust the data? How do you govern it and secure or protect that data? And then how do you ensure that it's hyper-contextualized to the types of value propositions that our intelligence systems are going to serve? >> Well, I think you're hitting on the key issues here. I mean, you're right, the data and I sort of refer to this as well as sort of out there it's distributed as at the edge, but generally our data organizations are actually quite centralized. And as well, you talk about the need to trust the data, obviously that's crucial. But are you seeing the organization change? I know you're talking about this to clients, your discussion about collaboration. How are you seeing that change? >> Yeah, so as you have to bring data into context of the insights that you're trying to get or the intelligence that's automating and scaling out the value streams and outcomes within your business. We're actually seeing a federated model emerge in organizations. So while there's still a centralized data management and data services organization led typically by enterprise architects for data, a data engineering team that's managing warehouses and data lakes. They're creating this great platform to access and orchestrate information, but we're also seeing data and analytics and governance teams come together under chief data officers or chief data and analytics officers. And this is really where the insights are being generated from either BI and analytics or from data science itself and having dedicated data engineers and stewards that are helping to access and prepare data for analytic efforts. And then lastly, this is the really interesting part is when you push data into the edge, the goal is that you're actually driving an experience and an application. And so in that case, we are seeing data engineering teams starting to be incorporated into the solutions teams that are aligned to lines of business or divisions themselves. And so really what's happening is if there is a solution consultant who is also overseeing value-based portfolio management when you need to instrument the data to these new use cases and keep up with the pace of the business, it's this engineering team that is part of the DevOps work bench to execute on that. So really the balances we need the core, we need to get to the insights and build our models for AI. And then the next piece is how do you activate all that and there's a team over there to help? So it's really spreading the wealth and expertise where it needs to go. >> Yeah, I love that you to, a couple of things that really resonated with me. You talked about context a couple of times and this notion of a federated model, because historically the sort of big data architecture, the team, they didn't have the context, the business context, and you're the, my inference is that's changing. And I think that's critical. Your talk at Data Citizens is called how obsessive collaboration fuels scalable DataOps. You talk about the data, the DevOps team. What's the premise you put forth to the audience? >> So the point about obsessive collaboration is sort of taking the hubris out of your expertise on the data. Certainly, there's a recognition by data professionals that the business understands and owns their data. They know the semantics, they know the context of it and just receiving the requirements on that was assumed to be okay. And then you could provide a data foundation whether it's just a lake or whether you have a warehouse environment where you're pulling for your analytics. The reality is that as we move into more of AI machine learning type of model, one, more context is necessary and you're kind of balancing between what are the things that you can ascribe to the data globally which is what data engineers can support. And then there's what is unique about the data and the context of about the data that is related to the business value and outcome as well as the feature engineering that is being done on the machine learning models. So there has to be a really tight link and collaboration between the data engineers, the data scientists, and analysts, and the business stakeholders themselves. You see a lot of pods starting up that way to build the intelligence within the system. And then lastly, what do you do with that model? What do you do with that data? What do you do with that insight? You now have to shift your collaboration over to the work bench that is going to pull all these components together to create the experiences and the automation that you're looking for. And that requires a different collaboration model around software development and still incorporating the business expertise from those stakeholders so that you're satisfying, not only the quality of the code to run the solution, but the quality towards the outcome that meets the expectation and the time to value that your stakeholders have. So data teams aren't just sitting in the basement or in another part of the organization and digitally, disconnected anymore. You're finding that they're having to work much more closely and side by side with their colleagues and stakeholders. >> I think it's clear that you understand this space really well, hubris out, context in, I mean, that's kind of what's been lacking. And I'm glad you said, you used the word anymore because I think it's a recognition that that's kind of what it was. They were down in the basement or out in some kind of silo. And I think, and I want to ask you this, I'll come back to organization because I think a lot of organizations, look the most cost effective way for us to serve the businesses to have a single data team with hyper-specialized roles, that'll be the cheapest way, the most efficient way that we can serve them. And meanwhile, the business which as you pointed out has the context is frustrated. They can't get to data. So this notion of a federated governance model is actually quite interesting. Are you seeing actual common use cases where this is being operationalized? >> Absolutely, I think the first place that you were seeing it was within the operational technology use cases. The use cases where a lot of the manufacturing, industrial device, any sort of IoT-based use case really recognized that without applying data and intelligence to whatever process was going to be executed, it was really going to be challenging to know that you're creating the right foundation, meeting the SLA requirements, and then ultimately bringing the right quality and integrity to the data, let alone any sort of data protection and regulatory compliance that has to be necessary. So you already started seeing the solution teams coming together with the data engineers, the solution developers, the analysts, and data scientists, and the business stakeholders to drive that. But that is starting to come back down into more of the IT mindset as well. And so DataOps starts to emerge from that paradigm into more of the corporate types of use cases and sort of parrot that because there are customer experience use cases that have an IoT or edge component to them. We live on our smart phones, we live on our smart watches, we've got our laptops, all of us have been put into virtual collaboration. And so we really need to take into account not just the insight of analytics, but how do you feed that, you know, feed that forward. And so this is really where you're seeing sort of the evolution of DataOps as a competency not only to engineer the data and collaborate, but ensure that there sort of an activation and alignment where the value is going to come out and still being trusted and governed. >> I've got kind of a weird question, but I'm going to (indistinct). I was talking to somebody in Israel the other day and they told me masks are off, the economy's booming. And he noted that Israel said, "Hey, we're going to pay up for the price of a vaccine, the cost per dose around 28 bucks," or whatever it was. And he pointed out that the EU haggled big time and they go, "We're going to pay $19." And as a result, they're not, you know, as far along Israel understood that the real value was opening up the economy. And so there's an analogy here, which I want to come back to my organization and it relates to the DataOps. If the real metric is, "Hey, I have an idea for a data product." How long does it take to go from idea to monetization? That seems to me to be a better KPI than, you know, how much storage I have or how much petabytes I'm managing. So my question is, and it relates to DataOps, can that DataOps, should that DataOps individual maybe live and then maybe even the data engineer live inside of the business and is that even feasible technically with this notion of federated governance? Are you seeing that? And maybe talk a little bit more about this DataOps role. Is it-- >> Yeah. >> Fungible? >> Yeah, it's definitely fungible. And in fact, when I talked about sort of those three units of there's your core enterprise data services, there's your BI and data and then there's your line of business. All of those, the engineering and the ops is the DataOps which is living in all of those environments and being as close as possible to where the value proposition is being defined and designed. So absolutely being able to federate that. And I think the other piece on DataOps that is really important is recognizing how the practices around continuous integration and continuous deployment using agile methodologies is really reshaping a lot of the waterfall approaches that were done before where data was lagging 12 to 18 months behind any sort of insights, but a lot of the platforms today assume that you're moving into a standard mature software development life cycle. And you can start seeing returns on investment within a quarter really, so that you can iterate and then speed that up so that you're delivering new value every two weeks. But it does change the mindset, this DataOps team align to solution development, align to a broader portfolio management of business capabilities and outcomes needs to understand how to appropriately stop the data products that they're delivering to incremental value based milestones. So the business feels that they're getting improvements over time and not just waiting. So there's an MVP, you move forward on that and optimize, optimize, extend scale. So again, that CICD mindset is helping to not bottleneck and wait for the complete field of dreams to come from your data and your insights. >> Thank you for that, Michele. I want to come back to this idea of collaboration 'cause over the last decade, we've seen attempts. I've seen software come out to try to help the various roles, collaborate and some of it's been okay, but you have these hyper-specialized roles. You've got data scientists, data engineers, quality engineers, analysts, et cetera. And they tend to be in their own little worlds. But at the end of the day, we rely on them all to get answers. So how can these data scientists, all these stewards, how can they collaborate better? What are you seeing there? >> You need to get them onto the same process, that's really what it comes down to. If you're working from different points of view, that's one thing. But if you're working from different processes, collaborating is really challenging. And I think the one thing that's really come out of this move to machine learning and AI is recognizing that you need processes that reinforce collaboration. So that's number one. So you see agile development in CICD not just for DataOps, not just for DevOps, but also encouraging and propelling these projects and iterations before the data science teams as well or even if there's machine learning engineers incorporated. And then, certainly the business stakeholders are inserted within there as appropriate to accept what it is that is going to be developed. So process is number one. Number two is what is the platform that's going to reinforce those processes and collaboration. And it's really about what's being shared. How do you share? So certainly what we're seeing within the platforms themselves is everybody contributing into some sort of a library where their components and products are being ascribed to and then that's able to help different teams grab those components and build out what those solutions are going to be. And in fact, what gets really cool about that is you don't always need hardcore data scientists anymore as you have this social platform for data product and analytic product development. This is where a lot of the auto ML begins because those who are less data science oriented but can build an insight pipeline, can grab all the different components from the pipelines to the transformations, to capture mechanisms, to bolting into the model itself and allowing that to be delivered to the application. So really kind of balancing out between process and platforms that enable and encourage and almost force you to collaborate and manage through sharing. >> Thank you for that I want to ask you about the role of data governance. You've mentioned trust and that's data quality and you've got teams that are focused on and specialists focused on data quality. There's the data catalog and here's my question. You mentioned edge a couple of times and I can see a lot of that. I mean, today, most AI is a lot of the AI, I would say most is modeling. And in the future, you mentioned edge. It's going to be a lot of inferencing in real-time. And you know people maybe not going to have the time or be involved in that decision. So what are you seeing in terms of data governance, federate, we talked about federated governance, this notion of a data catalog and maybe automating data quality without necessarily having it be so labor-intensive. What are you seeing trends there? >> Yeah, so I think our new environment, our new normal is that you have to be composable, interoperable, and portable. Portability is really the key here. So from a cataloging perspective, in governance we would bring everything together into our catalogs and business glossaries. And it would be a reference point. It was like a massive Wiki. Well, that's wonderful, but why just how's it in a museum you really want to activate that. And I think what's interesting about the technologies today for governance is that you can turn those rules and business logic and policies into services that are composable components and bring those into the solutions that you're defining. And in that way, what happens is that creates portability. You can drive them wherever they need to go. But from the composability and the interoperability portion of that, you can put those services in the right place at the right time for what you need for an outcome so that you start to become behaviorally-driven on executing on governance, rather than trying to write all of the governance down into transformations and controls to where the data lives. You can have quality and observability of that quality and performance right at the edge in context of behavior and use of that solution. You can run those services and in governance on gateways that are managing and routing information at those edge solutions and where synchronization between the edge and the cloud comes up. And if it's appropriate during synchronization of the data back into the data lake, you can run those services there. So there's a lot more flexibility and elasticity for today's modern approaches to cataloging and glossaries and governance of data than we had before. And that goes back into what we talked about earlier of like this is the new wave of DataOps. This is how you bring data products to fruition now everything is about activation. >> So how do you see the future of DataOps? I mean, I kind of been pushing you to a more decentralized model where the business has more control 'cause the business has the context. I mean, I feel as though, hey, we've done a great job of contextualizing our operational systems. The sales team, they know when the data is crap within my CRM, but our data systems are context agnostic, which you know, generally and you obviously understand that problem well but so how do you see the future of DataOps? >> So I think what's kind of interesting about that is we're going to go to governance on greed versus governance on right, more so. What do I mean by that? That means that from a business perspective there's two sides of it. There's ensuring that where governance is run as we talked about before executing at the appropriate place at the appropriate time. It's semantically domain centric driven not logical and systems centric. So that's number one. Number two is also recognizing that business owners or business operations actually plays a role in this because as you're working within your CRM systems like a Salesforce, for example, you're using an I-PASS environment MuleSoft to connect to other applications, connect to other data sources, connect to other analytics sources, and what's happening there is that the data is being modeled and personalized to whatever view, insight, or task has to happen within those processes. So even CRM environments where we think of as sort of traditional technologies that we're used to are getting a lift to both in terms of intelligence from the data but also your flexibility and how you execute governance and quality services within that environment. And that actually opens up the data foundations a lot more and avoids you from having to do a lot of moving, copying, centralizing data, and creating an over-weighted business application and an over, you know, both in terms of the data foundation but also in terms of the types of business services and status updates and processes that happen in the application itself. You're drawing those tasks back down to where they should be and where performance can be managed rather than trying to over customize your application environment. And that gives you a lot more flexibility later too for any sort of upgrades or migrations that you want to make because all of the logic is contained back down in a service layer instead. >> Great perspectives, Michele, you obviously know your stuff and it's been a pleasure having you on. My last question is when you look out there anything that really excites you or any specific research that you're working on that you want to share that you're super-pumped about. >> I think there's two things. One is it's truly incredible the amount of insight and growth that is coming through data profiling and observation, really understanding and contextualizing data anomalies so that you understand is data helping or hurting the business value. And, you know tying it very specifically to processes and metrics which is fantastic as well as models themselves like really understanding how data inputs and outputs are making a difference whether the model performs or not. And then I think the second thing is really the emergence of more active data, active insights, as what we talked about before your ability to package up services for governance and quality in particular that allow you to scale your data out towards the edge or where it's needed and doing so, you know not just so that you can run analytics but that you're also driving overall processes and value. So the research around the operationalization and activation of data is really exciting. And looking at the networks and service mesh to bring those things is kind of where I'm focusing right now because what's the point of having data in a database if it's not providing any value. >> Michele Goetz, Forrester Research, thanks so much for coming on theCube really awesome perspectives. You're in an exciting space. So appreciate your time. >> Absolutely, thank you. >> And thank you for watching Data Citizens '21 on theCube. My name is Dave Vellante. (upbeat music)

>> Narrator: Live from Las Vegas it's theCUBE. Covering UiPath Forward Americas 2019. Brought to you by UiPath. >> Welcome back everyone to theCUBE's live coverage of UiPath Forward here at the Bellagio in Las Vegas. I'm your host Rebecca Knight along with my co-host Dave Vellante. We are joined by Craig Le Clair, he is the vice president of Forrester and also the author of the book "Invisible Robots in the Quiet of the Night: How AI and Automation will Restructure the Workforce". Thank you so much for coming on theCUBE. >> Craig: Thank you! Thanks for having me. >> And congratulations, it's already made #11 on Amazon's AI and automation bestseller list. >> Wow, it's not quite best seller but OK, that's great, thank you, it's doing well. >> So if anyone calls your book a bestseller you just take 'em on that. >> (Craig) I'll just take it. >> So it is a, it's a bleak story right now, I mean there's a lot, there's so many changes going on in the workforce and there's so much anxiety on the part of workers that they're going to lose their job that all these technologies are going to take away their their livelihood, so how are companies managing this? Are they managing it well, would you say, or is the anxiety misplaced? Give us an overview. >> Yeah, so I don't think companies are really aware of the broader implications of the automation and AI that's developing. They tend to focus on the things that companies focus on. They focus on more efficiency and productivity and so forth, and underlying that is this digital anxiety that we call it, and the fact that a lot of the jobs that we, particularly the middle class have, the working class have, are the targets of the invisible robots, and that's really the point of the invisible robot book is that there's a lot of media attention on the hardware aspects of robotics, in fact the Super Bowl last year had 10 commercials with hardware robots. But if you look at this conference you look at the number of people here. What are these people doing? They're going back to their companies and saying "You know, this UiPath, and there are other providers "in the market, we can build software robotics, "we can build bots to do some of these tasks "that a lot of these humans are doing." And while there is elevation of the human capability in spirit for many of them, there's also a comfort level in employees that do things that they have control over, have incited. And when you extract those you are left with a series of more exciting moments, perhaps, but it's not going to make you more relaxed as an employee. And then you look at the overall job numbers, and our estimates are very conservative compared to some of the other reports, that are 45, 50% of workers over 10 years being displaced. We think it's 16%, but still, when you look at just the US numbers, that's of 160 workers today, 160 million workers, that's a lot of people. >> Rebecca: It is indeed. >> So, displaced and then sort of re-targeted or? >> A percentage, >> Vaporized. >> No, no, well the 16% is the automation, is the net loss of jobs. Now in that, automation's expensive, so there are a tremendous number of new jobs that are created by the work that's been going on here. So we have a formula to calculate that for these 12 different work personas, and the work personas have different relationships to AI and automation, so you would be crossed so many knowledge workers and be very well protected for a long time. >> Rebecca: All right, there we go. >> So you're good, but... for coordinators, people that have clip boards in their hands, for those who work in cubicles, they're going to have a lot of people leaving those cubicles that aren't going to be able to migrate to other personas. And so we have a changed management issue, we need to start driving more education from the workplace through certification, and that's a really critical thing I'll talk about tomorrow, that the refresh of technology with automation is 18 months to 24 months, you can't depend on traditional education to keep up, so we need a different way to look at training and education and for many it's going to be a much better life, but there's going to be many that it will not be. >> What was the time frame for your net 16% loss? >> 10 years. >> 10 years, okay, to me a lower net loss number makes sense, and in fact if you can elongate your timeline it probably shows a net job creation, you can make that argument anyway I don't know if you. >> Craig: It's being made. >> Dave: You don't buy it though? >> I don't, the world economic foundation and others are having huge net new numbers for jobs based on AI. Some of the large integration companies that want to build AI platforms for you are talking about trillions of dollars that would be added value to the world economy, I just don't buy it, and you know the reason I wrote this book was because what's going on here is very quietly preparing to displace a lot of efforts starting with relatively small tasks, it's called task automation but then expanding to more and more work and eventually adding a level of intelligence to the task automation going on here, that's going to take a lot of jobs. And for most of those 20 million cubicle workers, they have high school educations. You know, the bigger problem is this level of anxiety, you know, you go into almost any bookstore and there's a whole section For Dummy books, and it's not, is it because we have this sort of cognitive recession or because there's a, it's because the world's getting faster and more complicated. And unless you have the digital skills to adapt to that, the digital skills gap is growing. And we need to have as much focus that you see here and energy on building automation. We need to have an equal amount of focus on the societal problems. >> Yeah, it really comes down to education, too. I mean if I were able to snap my fingers and transform the educational system, there might be a different outcome but that's very unlikely to happen. Craig, one of the things we talked about last year was you had made the statement that some of these moonshot digital transformations aren't happening for a variety of reasons but our PA is kind of a practical way to achieve automation. >> Still very true. >> Have you seen sort of a greater awareness in your client base that, "You know, hey, maybe we should dial down "some of these moonshots and just try to "pick some clear winners." >> Yeah, we have a number of prediction reports coming out from Forrester and they're all saying basically that. I'm doing reports on what I'm calling the intelligent process automation market and that's really our PA plus AI, but not all aspects of AI. You know, it's AI that you can see in ROI around, you know it's AI that deals with unstructured documents and content and email. It's not the moonshot, more transformative AI that we have been very focused on for a number of years. Now all of that's very very important. You're not going to transform your business by doing task automation even if it's a little more intelligent and handles some decision management, you still need to think about "How do I instantiate "my business algorithmically," with AI that's going to make predictions and move decision management and change the customer experience. All that's still true, as true as it was in 2014/2015, we're just seeing a more realistic pull back in terms of the invested profile. >> Well, and so we've been talking about that all day, it is taking automating processes that have been around for a long time, and you, I think identified this as one of the potential blockers before, if you get old processes that are legacy and I think you, you gave the story of "Hey, I flew out here "on American Airlines in the old SABRE system." How old are those processes, you know? We've, you know the old term "paving the cow path." So the question is, given all the hype around RPA, the valuations, et cetera, what role do you see RPA having in those sort of transformative use cases? >> Well here's the interesting thing that was, I think, somewhat accidental by the, you know what really changed from having simple desktop automation? Well you needed some place to house and essentially manage that automation, so the RPA platforms had to build a central management capability. UiPath calls that the orchestrator, others call it the control tower, but when you think of all the categories of AI none of them have a orchestration capability, so the ability to use events to link in machine intelligence and dispatch digital workers or task automation to coordinate various AI building blocks as we call them and apply it to a use case, that orchestration ability is pretty unique to the RPA platforms. So the sort of secret value of RPA is not in everything that's being talked about here but eventually is going to be as a coordinating mechanism for bringing together machine learning that'll begin in the cloud, conversational intelligence that might be in Google. Having the RPA bots work in conjunction with those. >> But if I recall, I mean that's something that you pointed out last year as well that RPA today struggles with unstructured data that... >> Well it can't do it. >> You're right, we've talked about it NLP versus RPA, RPA, given structured data, I can go after it. >> That's the RPA plus AI bit, though. I mean, you take text analytics layer, and you combine it with RPA bots and now you have the unstructured capability plus the structured capability that RPA does so well. And, with the combination of the two, you can reach. I think what the industry needs to do or the buyers of RPA need to take the pressure off this immediacy of the ROI. In a sense, that's what's driven the value. I can deploy something, I can get value in a few months but, to really make it effective and transformative you need to combine it with these AI components, that's going to take a little longer, so this sort of impatience that you see in a lot of companies, they should really step back and take a look at the more end to end capabilities and take a little hit on the ROI immediately so that you can do that. >> No, I mean I can definitely see a step function, okay, great, we've absorbed that value, we get the quick ROI, but there's, to your point there's got to be some patient capital to allow you to truly transform in order for RPA, I don't want to put words in your mouth, to live up to the hype. >> Absolutely, I totally agree. And I am still very, very high on the market, I think it's going to do extremely well. >> Well, if you look at the spending data, it's quite interesting. I mean RPA as a category is off the charts. You know, UiPath, from the, your last wave kind of took the lead but, Automation Anywhere, Blue Prism spending, even in traditional incumbents, maybe not even RPA, maybe more "process automation" like Pegasystems. Their spending data suggests that this is the rising tide lifting all boats so, my question to you is, how do you see this all shaking out? I mean, huge evaluations, the bankers are swarming around. You saw them in the media yesterday. You know, at some point there's got to be a winner takes most. The number two guy will do pretty well and then everybody else kind of consolidates. What's your outlook? >> Well, there are a lot of emerging players coming into the market and, part of my life is having to fend them off and talk to them, and the RPA wave is coming out in a week. It's going to have four new players in it. Companies like SAP. >> Well, they acquired a company right? >> They acquired and they built internally, and have some interesting approaches to the market. So you are going to see the big players come into the market. Others I won't mention that'll be in the market in a month It's getting a lot of attention. But also I think that there are domains, business domains that, the different platforms can start to specialize in. The majors, the UiPaths to the world, will be horizontal and remain that way. And depend on partners to tailor it for a particular application area. But you're going to see RPA companies come into the testing market, software testing market. You're going to see them come into the contact centers to deal with attended mode in more sophisticated ways perhaps than those that don't have that background. You're going to see tailored robots that are going to be in these robot communities that are springing up. That'll give a lot of juice to others to come into the market. >> And like you say you're going to see, we've talked about this as well Rebecca, the best of breed versus the suite, right? Whether its SAP, Inforce talking about it, I'm sure Oracle will throw its hat in the ring I mean, why not, right? Hey, we have that too. >> Well, if you're those companies that the RPA bots are feasting on, they're slowing the upgrades to your core platforms, in some ways making them less relevant, because their argument has been, let's integrate, you get self integration when you buy SAP, when you buy Oracle, when you buy these big platforms. Well, the bots actually make that argument less powerful because you can use the bots to give you that integration, as a layer, and so they're going to have to come up with some different stories I think if they're going to continue to move forward on their platforms, move them to the cloud and so forth. >> So, finally, your best advice for workers in this new landscape and how it is going to alter their working lives. And also, your best advice for companies and managers who are, as you said, maybe not quite, they're grappling with this issue but maybe not and they're not being disingenuous to workers about who's going to lose their jobs, but this idea of as they're coming to terms with understanding quite all of the implications of this new world. >> Yeah, I know, I'm presenting data tomorrow that shows that organizations, employees, and leaders are not ready and I have data to show that. They're not understanding it. My best advice, I love the concept of, it's not a Forrester concept, it's called constructive ambition. This is the ability in an employee to want to go a little bit out of the box, and learn, and to challenge themselves, and move into more digital to close that digital skills gap. And, we have to get better at, companies need to get better at identifying constructive ambition in people they're hiring, and also, ways to draw it out. And to walk these employees up the mountain in a way that's good for their career and good for the company. I can tell you, I'll tell a few stories on the main stage last night, I interviewed Walmart employees and machinists that could no longer deal with their machine because they had to put codes into it so they had to set it up with programming steps and the digital anxiety was such that they quit the job. So a clear lack of constructive ambition. On the other hand, a Walmart employee graduated from one of their 200 academies and was able to take on more and more responsibility. Somebody with no high school degree at all. She said, "I've never graduated "from anything in my life. "My kids have never seen me "succeed at anything, and I got this certification "from Walmart that said that I was doing this level "of standard work and that felt really, really good." So, you know, we, companies can take a different view towards this but they have to have some model of future of work of what it's going to look like so they can take a more strategic view. >> Well Craig, thank you so much for coming on theCUBE. It was a really great talk. Another plug for the book, "Invisible Robots in the Quiet of the Night" you can buy it on Amazon. >> Craig: Thank you. >> I'm Rebecca Knight for Dave Vellante, stay tuned for more of theCUBE's live coverage of UiPath Forward. (techno music)

>> Live from Miami Beach, Florida, it's theCUBE. Covering UiPathForward Americas. Brought to you by UiPath. >> Welcome back to Miami everybody. You're watching theCUBE, the leader in live tech coverage. We go out to events, we extract the signal from the noise. A lot of noise here but the signal's all around automation and robotic process automation. I'm Dave Vellante, he's Stu Miniman, my co-host. Guy Kirkwood's here he's the UiPath chief evangelist otherwise known as the chief injector of Kool-Aid. Welcome. (guests chuckling) And Craig LeClair, the Vice President at Forrester. Covers this market, wrote the seminal document on this space. Knows it inside out. Craig, great to see you again. >> Yeah, nice to see you again. It's great to be back at theCUBE. >> So let's start with the analyst perspective. Take us back to when you first discovered RPA, why you got excited about it, and what Forrester Research is all about in that space. >> Yeah, it's been a very a interesting ride. Most of these companies, at least that are the higher value ones in the category they've been around for a long time. They've been around for over a decade, and no one ever heard of them three years ago. So I had covered at Forrester, business process management and some of the business rules engines, and I've always been in process. I just got this sense that there was a way that companies could make progress and digital transformation and overcome the technical debt that they had. A lot of the progress has been tepid in digital transformation because it takes tremendous amount of time and tons of consultants to modernize that core system that really runs the company. So along comes this RPA technology that allows you to build human equivalence that patch up the inefficiencies without touching. I came in on American Airlines and the system that cut my ticket was designed in 1960. It's the same Sabre reservation system. That's the big obstacle that a lot of companies have been struggling to really take advantage of AI in general. A lot of the more moonshot and more sophisticated promises haven't been realized. RPA is a very practical form of automation that companies can get a handle on right now, and move the dial for digital transformation. >> So Guy we heard a vision set forth by Daniel this morning. Basically a chicken in every pot, I call it, a robot for every person. Now what Craig was just saying about essentially cutting the line on technical debt, do you have clear evidence of that in your customer base? Maybe you could give some examples. >> What we're really seeing is that as organizations have to deal with the stresses, what Leslie Wilcox professor at LSE describes as the stresses within organizations and particularly in environments where the demographics are changing. What we're seeing is that organizations have to automate. So the best example of that is in Japan where the Japanese population peaked in 2010. It's now falling as a whole, plus all the baby boomers, people of Craig's and my age are now retiring. So we're now in a position where they measure levels of dangerous overwork as being more that 106 hours a week. That isn't 106 hour a week in total, that's 106 hours a week in addition to the 60 hours a week the Japanese people normally work. And there is a word in Japanese, which is (speaking in foreign language), which means to work oneself to death. So there really is no choice. So what we're seeing happening in Japan will be replicated in Western Europe and certainly in the US over the next few years. So what's driving that is the rise of the ecosystems of technologies of which RPA and AI are part, and that's really what we're seeing within the market. >> Craig, sometimes these big waves particularly in infrastructure, you kind of saw it with virtualization and some other wonky techs, like data reduction. They could be a one-time step function, and not an ongoing business value creator. Where does RPA fit in there? How can organizations make sure that this is a continuous business value generator as opposed to a one time hit? >> Good question. >> Well, I like the concept of RPA as a platform that can lead to more intelligence and more integration with AI components. It allows companies to build an automation center or a center of excellence focused on automation. But the next thing they're going to do after building some simple robots that are doing repetitive tasks, is they're going to say "Oh well wouldn't it be better "if my employee could have a textual chat with a chatbot "that then was interacting with the digital worker "that I built with the bot." Or they're going to say "You know what? I really want to use that machine learning algorithm "for my underwriting process, but I can use these bots "to go out and collect all the data from the core systems "and elsewhere and from the web and feed the algorithms "so that I could make a better decision." So again it goes back to that backing off the moonshot approach that we've been talking about that AI has been taking because of the tremendous amount of money spent by the major players to lay out the promise of AI has really been a little dysfunctional in getting organizations' eye off the ball in terms of what could be done with slightly more intelligent automation. So RPA will be a flash in the pan unless it starts to embed these more learning-capable AI modules. But I think it has a very good chance of doing that particularly now with so much investment coming into the category right. >> Craig, it's really interesting. When I heard you describe that it reminds me of the home automation. The Cortanas and Alexas and consumer side where you're seeing this. You've got the consumer side where you can build skills yourself, you know teenagers people can do that. One of the challenges always on the business side is how do you get the momentum when you don't have the consumer side. How do those interact? >> It's the technical debt issue and it's just like the mobile peak in 2011. Consumers in their hands had much better mobility right away than businesses. It took businesses five, they're still not there in building a great mobile environment. So these Alexa in our kitchen snooping on our conversation and to some extent Netflix that observes our behavior. That's a light form of AI. There is a learning from that behavior that's updating an algorithm autonomously in Netflix to understand what you want to watch. There's no one with a spreadsheet back there right. So this has given us in a sense a false sense of progress with all of AI. The reality is business is just getting started. Business is nowhere with AI. RPA is an initial foray on that path. We're in Miami so I'll call it a gateway drug. >> In fact there's also an element that the Siris, the Cortanas, the Alexas, are very poor at understanding specific ontologies that are required for industry, and that's where the limitation is right now. We're working with an organization called Humly, they're focused on those ontologies for specific industries. So if the robot doesn't understand something, then you could say to the robot Okay sit that in the Wells account, if you're in a bank, and it understands that Wells in that case means Wells Fargo it doesn't mean a hole in the ground with water at the bottom or a town in Somerset in the UK, 'cause they're all wells. So it's getting that understanding correct. >> I wonder if you guys could comment on this. Stu and I were at Splunk earlier this week and they were talking up NLP and we were saying one of the problems is that NLP is sometimes not that great. And they made a comment that I thought was very interesting. They said frankly a lot of the stuff that we're ingesting is text and it's actually pretty good. I would imagine the same is true for RPA. Is that what you see? >> You were talking about that on stage. With regards to the text analytics. >> Yes. So RPA doesn't handle unstructured content the way that NLP does. So NLP can handle voice, it can handle text. For the bots to work in RPA today you have to have a layer of analytics that understands those documents, understands those emails and creates a nice clean file that the bots can then work with. But what's happening is the text analytics layer is slowly merging with the RPA bots platforms so it's going to be viewed as one solution. But it's more about categories of use cases that deal with forms and documents and emails rather than natural language, which is where it's at. >> So known business processes really is the starting point. >> Known business-- >> One example we've got live is an insurance company in South Africa called Hollard, and they've used a combination of Microsoft Cognitive Toolkit, plus IBM Watson and it's orchestrated doing NLP and orchestrated by UiPath. So that's dealing with utterly unstructured data. That's the 1.5 million emails that that organization gets in a year. They've managed to automate 98% of that, so it never sees a human. And their reduction in cost is 91% cost in reduction per transaction. And that's done by one of our implementation partners, a company called LarcAI down there. It's superb. >> Yeah, so text analytics is hard. Last several years we have that sentiment out of it, but if I understand it correctly Craig, you're saying if you apply it to a known process it actually could have outcomes that can save money. >> Yes, absolutely yes. >> As Guy was just saying. >> I think it's moving from that rules-based activity to more experience-based activity as more of these technologies become merged. >> Will the technology in your view advance to the point, because the known processes. okay, there's probably a lot of work to be done there, but today there's so many unknown processes. It's like this messy, unpredictable thing. Will machine intelligence combined with robotic process automation get to the point, and if so when, that we can actually be more flexible and adapt to some of these unknown processes or is that just decades off? >> No, no, I think we talk at Forrester about the concept of convergence. Meaning the convergence of the physical world and the digital world. So essentially digital's getting embedded in everything physical that we have right. Think of IoT applications and so forth. But essentially that data coming from those physical devices is unstructured data that the machine learning algorithms are going to make sense of, and make decisions about. So we're very close to seeing that in factory environments. We're seeing that in self-driving cars. The fleet managers that are now understanding where things are based on the signals coming from them. So there's a lot of opportunity that's right here on the horizon. >> Craig, a lot of the technologies you mentioned, we may have had a lot of the technical issues sorted out, but it's the people interactions some things like autonomous vehicles, there's government policies going to be one of the biggest inhibitors out there. When you look at the RPA space, what should workers how do they prepare for this? How do companies, make sure that they can embrace this and be better for it? >> That's a really tough and thoughtful question. The RPA category really attacks what we call the cubicle population. And there are we're estimating four million cubicles will be emptied out in five years by RPA technology specifically. That's how we built the market forecast 'cause each one of the digital workers replacing a cubicle worker will cost $11,000 or what. That's how we built up the market forecast. They're going to be automation deficits. It's not all going to be relocating people. We think that there's going to be a lot of disruption in the outsource community first. So companies are going to look at contractors. They're going to look at the BPO contract. Then they're going to look at their internal staff. Our numbers are pretty clear. We think they're going to be four million automation deficits in five years due to RPA technology specifically. Now there will be better jobs for those that are remaining. But I think it's a big change management issue. When you first talk about robots to employees you can tell them that their jobs are going to get better, they're going to be more human. They're going to have a much more exhilarating experience. And their response to you is, What they're thinking is, "Damn robot's going to take my job." That's what they're thinking. So you have to walk them up the mountain and really understand what their career path is and move them into this motion of adaptive and continual learning and what we call constructive ambition. Which is another whole subject. But there are employees that have a higher level of curiosity and are more willing to adapt to get on the other side of the digital divide. Yep. >> You mentioned the market. You guys did a market forecast. I've seen, read stats, a little over a billion today. I don't know if that's consistent with your numbers? >> Yeah that's about right. >> Is this a 10X market? When does it get to 10 billion? Is it five, seven, 10 years? >> So we go out five years and have it be close to three billion. I think the numbers I presented on stage were 3.2 billion in five years. Now that's just software licenses and it's not the services community that surround that. >> You'd probably triple it if you add in services. >> I think two to three times service license ratio. There's always an issue at this point in emerging markets. Some of the valuations that are there, that market three billion has to be a bit bigger than that in eight or nine years to justify those valuations. That's always the fascinating capital structure questions we create with these sorts of things. >> So you describe this sort of one for one replacement. I'm presuming there's other potential use cases, or maybe not, that you forecast. Is that right? >> Oh no for the cubicles? >> Yes, it's not just cubicle replacement in that three billion right? It's other uplifts. >> No there are use cases that help in factory automation, in supply chain, in guys carrying around clipboards in warehouses. There are a tremendous number of use cases, but the primary focus are back office workers that tend to be in cubicles and contact center employees who are always in cubicles. >> And then we'll see if the non-obvious ones emerge. >> I think ultimately what's going to happen is the number of people doing back office corporate functions, so that's both finance and accounting procurement, HR type roles and indeed the industry specific roles. So claims processing insurance will diminish over time. But I think what we're going to see is an increase in the number of people doing customer experience, because it's the customer intimacy that is really going to differentiate organizations going forward. >> The market's moving very fast. Reading your report, it's like you were saying yesterday's features are now table steaks. Everybody's watching everybody else. You heard Daniel today saying, "Hey our competitors are watching. "We're open they're going to steal from us so be it." The rising tide lifts all boats. What do you advise clients in terms of where they should start, how they should get started? Obviously pick some quick wins. But what do you tell people? >> I always same pretty much the same advice you give almost on any emerging technology. Start with a good solution provider that you trust. Focus on a proof of concept, POC and a pilot. Start small and grow incrementally, and walk people up the mountain as you do that. That's the solution. I also have this report I call The Rule of Fives, that there are certain tasks that are perfect for RPA and they should meet these three rules of five. A relatively small number of decisions, relatively small number of applications involved, and a relatively small number of clicks in the click stream. 500 clicks, five apps, five decisions. Look for those in high volume that have high transaction volume and you'll hit RPA goal. You'll be able to offset 2 1/2 to four FTE's for one bot. And if you follow those rules, follow the proof of concept, good solution partner everyone's winning. >> You have practical advice to get started and actually get to an outcome. Anything you'd add to that? >> In most organizations what they're now doing, is picking one, two, or three different technologies to actually play with to start. And that's a really good way. So we recommend that organizations pick three, four, five processes and do a hackathon and very quickly they work out which organizations they want to work with. It's not necessarily just the technology and in a lot of cases UiPath isn't the right answer. But that is a very good way for them to realize what they want to do and the speed with which they'll want to do it. >> Great, well guys thanks for coming on theCUBE, sharing your knowledge. >> Thank you. >> Pleasure. >> Appreciate your time. >> Thanks very much indeed. >> Alright keep it right there everybody. Stu and I will be back from UiPathForward Americas. This is theCUBE. Be right back. (upbeat music)

>> Live from Bellevue, Washington, it's the CUBE! Covering Smartsheet ENGAGE '18. Brought to you by Smartsheet. >> Welcome back to the CUBE We are live at Smartsheet ENGAGE 2018 from Bellevue, Washington. I'm Lisa Martin with Jeff Frick and we're pleased to welcome to the CUBE for the first time, Margo Visitacion, VP and Principal Analyst at Forrester. Margaret, it's great to have you here. >> Thank you. It's a pleasure to be here. >> You have a session this afternoon, so we'll get a little preview of that. You recently at Forrester were doing a lot of work with some Smartsheet customers on a white paper, regarding digital transformation, looking at how project management has typically been done and how it's evolving. Give us a little bit of an overview of that research and what people are going to hear about today. >> Absolutely, absolutely. Well, what we've seen is that digital transformation is really changing the way that companies need to work today, and that everybody in an organization is now a project manager, whether they recognize it or not. So what we've seen is three quarters of the respondents that we've surveyed, what they've seen is that they've seen their project management activities, and the scale of their projects, increase significantly in size. They've seen projects being far more distributed throughout the organization, so it isn't we have a central group that does project management, it's now everybody does projects. And what we've also seen is that the rate and pace of change creates a lot of uncertainty, and that organizations are dealing with a lot of unplanned tasks, instead of having something that was highly controlled, when you saw more traditional project management. People have to be a lot more flexible, a lot more adaptable, and they need to have a much greater visibility to be able to manage through that rate of change. >> Seems like a dichotomy though, cause on one hand, you're saying that project management is getting more complicated or complex, more pieces, more people need to do it. On the other hand, you need tools that are not for professional project managers. We need the ability to do things for people that aren't trained on those tools. And the amount of work and reach of that order is just growing, so how do you square that circle? >> It is a dichotomy. It really is a dichotomy. The nature of technology and software being central to everything a company does. All companies are software companies today, and what that means is that you have to have more collaboration, and you have a greater need for transparency and interaction between teams so that they can work together more effectively. So while elements of the project are more complex, the fact that you have more stakeholders and more people involved means that you have to create a balance that you have very highly usable technology to get everybody to work together more effectively. Especially when you think about the demographics of the workplace is changing. When I started in a technology world, I expected green screens, I expected difficult, highly complex applications. I thought that went along with the job, but in today's demographics, people want consumer grade applications. I want something that is as pleasing as it is on my device, as it is going on my desktop, and I want to be able to have the same experience no matter where I go, because work isn't nine to five where I'm sitting at a desk any longer. It is wherever I'm going, because the majority of information workers today or knowledge workers today, work on the road. They need to be able to have that experience, so you can balance complexity if you increase accessibility and usability. That allows you to reduce risk within your projects. >> Ultimately, the top line of any enterprise is the same. We got to grow revenue, we've got to do it faster, we've got to deliver better products and services that are based on feedback and data that we can glean. That's a lot of cultural challenge. I imagine in this emerging market of collaborative workforce management versus portfolio program, or project management, how have you seen companies of, and across industry, actually embrace the cultural shift that is essential to drive digital transformation. >> It's a journey and companies are really still moving through this. As we heard in the keynote today, you're seeing pockets of innovation that are growing and as companies are seeing these results, because of accessibility in schools, and because of the transparency and usability of the tools that are on the market today, you're now seeing that, "Oh, you know what, there is value." I get to see it, because it's visible to me. I'm less resistant to the change, so I'm more willing to try and, frankly, sometimes a company really has to get burnt. What we found is if a project fails, half of the respondents said, "Our company lost revenue because a project failed." Well, nobody needs to have that happen. Nobody wants to have that happen, actually. So what they really want to do is say, "What can I do to mitigate that risk?" And they're finding that, because team's today are more willing to work with technology, and more willing to have that transparency, you know everybody's life is an open book now in technology, it actually promotes teamwork. You move from the project manager as the only person, the single throat to choke, to recognize that it is a team that works together more effectively. That's what helps drive that cultural change, because when everybody's empowered to drive to a successful outcome, you're going to see that cultural resistance move away. >> I imagine that sort of, I don't know if shared accountability's the right thing. >> Absolutely. >> Also is a facilitator of that cultural shift? >> Absolutely, absolutely. When you can see the intelligence behind why a decision was being made, and people can contribute to that decision being made, you get better decision making. It's not a decision made in a vacuum, and you don't have people waiting around for someone to make a decision, or you create cost of delay and waste in a process where no company wants that today. Nobody has time for that today. >> It's pretty interesting cause all we see, that diversity of opinions and background, makes better decisions. We've seen that time and time again. And then also, there's this little thing where if people are part of the decision that was made, they generally have a little bit more buy-in. So that's all-- >> Correct. >> All goodness. So you call it collaborative workflow management as a-- >> Collaborative work management. >> Work management. >> Yes. >> Excuse me, work. Not work flow. I'm just curious, in terms of this kind of struggle for the desktop, right, there's so many SAAS tools out there now, whether you're in Slack or you're in Salesforce, or in G Suite or Office 365. As you look at that competition for what is the top level that is driving what I do, how are people sorting through that? Are we just in this multi-app world? Is there a place for something to be on top? Or is it horses for courses depending on where you are in that process? Cause, man oh man, I find myself tapping from app to app to app to app to app. I've got so many browsers open on my desk, just to get through my day. >> Well, we see the average knowledge worker opening between 8 and 13 apps a day to get their job done, and they spend a third to half of their time in email just looking for information. So you're right, it's a morass of applications and it's very difficult. I don't think we're ever going to get to a one stop shop, but what I do think is that organizations can build an operational system of record. When you think about this, you have CRM system where you know everything about your customer. All their contact information, all the deal data, everything that's going on. You have a financial system of record. You know exactly the revenue that your company is generating, the costs that they're incurring, but when you think about how you actually balance that, how you know and deliver to your customers, and know revenue and costs, what's in the middle is just a jumble of different types of applications. And what we're seeing at Forrester is a trend, is that organizations are trying to create an operational system of record. Now as I said, I don't think it's going to be a one-stop shop, but I do think that there will be a planning and delivery ecosystem that will allow organizations to bring together the tools that work for them. As they said in the keynote this morning, as Mark said in the keynote, if you want to tell somebody, "We're going to work together more effectively," stop what you're doing, that's never going to work. So it's really incumbent upon the tools that are able to work with other tools that make people in your organization productive, because employees have to feel productive to really be able to grow a great customer experience. So collaborative work management is an essential element. It's the core part of the execution layer. Project management tools, like I said, are never going to go away. They're going to be for that formal, critical path from building a ship, for building a road or something very plan intensive. They're always going to be there. If you're going to be managing a services organization, you still need to have your people allocated. You don't want people on the bench. You still need that, but to actually get the work done, collaborative work management is really that core that brings together contextual information around the work that's being done. So it gives collaboration purpose. So I really think that's a central core application. >> You guys at Forrester just collaborated, we'll say there in the spirit of marketing terms, with Smartsheet. You interviewed several hundred Smartsheet customers and-- >> Not just Smartsheet customers, really across the industry. >> This was across even some of their competitors. >> Yes. Project managers, professionals, collaboration workers, information workers. >> Okay. >> PMO directors. We really were trying to get into the user community. That's what we were really focusing on. >> Okay, this was agnostic. One of the things Jeff and I were chatting about before we went live is wanting to understand, okay, Smartsheet has a lot of competition, right, so if I'm going to manage a marketing project and I use JIRA, and my sales team is using Salesforce, but I communicate with a lot of people across the company in Slack, how does that integration work? They've got a lot of connectors, and a lot of integrations. What was some of the feedback that you heard from, in this sort of agnostic city, about the workers in terms of confusion, or "I just want to be able to go into one tool and have everything talk to it." >> Right. Depending on the persona there were different requirements. So what we've found is that for PMO leaders, PMO directors, they had a set of tools. They really created a tool kit for their organizations. So you had at the PMO level, they still use project management tools, they still use spreadsheets, but they increasingly used collaborative work management tools. Collaborative work management has only been around for a few years, and a quarter of the respondents that we saw were adding collaborative work management to their tool kits to reach out to that team member, to bring in more information. That became a stronger, a secondary persona, being the team member that was going to be delivering. What was interesting is the high performers, the high maturity organizations that we interviewed, they really latched on to collaborative work management, seeing this as sort of a secret sauce to say, "Okay, now I can get in better data." We don't have people rushing to fill in a time sheet on Friday, we're getting data real time. Where the integration comes in is if you have people happily and actively using tools that are sticky for them, you get better data and you're not running around at 5 o'clock on a Friday saying, "I need your time sheets." "I need your status reports." And speaking with the folks from Office Depot, they have a great saying. They said, "We move from status to progress. We weren't looking backwards, we knew where we were going." And that's a really important element. Speaking of tools like Slack and some of the other messaging tools that are out there, you might be working with somebody in legal, or you might be working with somebody in HR. That doesn't necessarily need to be in a collaborative work management tool. Almost certainly, probably never need to be in a project management tool, but you need input from them. You need to review something. "Is this contract okay? Are we allowed to say this in a marketing campaign?" Slack allows them to share that information, and then you can bring it back into the collaborative work management tool and see the information and the context around the information real time. It takes you from being able to have some transparency into the project, or the work stream that you're working on, to really actually being able to live in that work stream, and have all of that visibility around you. >> Margo, I'm curious in terms of priorities to move into this space, when you talk about all these customers. How much of it was the digital transformation prerogative? How much of it was, "We just can't move fast enough with the old way and our old tools?" How much of it was competitive threats? Either because we have to respond quickly or how much was it, "My goodness, we have so much institutional knowledge and all these greats heads that we're just not leveraging into this process." What are some of those drivers that are moving this next evolution of, well it's project management now into the work management. >> I think it's a little of everything. Digital is definitely accelerating all of those areas. Tribal knowledge, institutional knowledge, being able to move faster, being able to move more efficiently, again, another great phrase I heard in the keynote today was, "Once we move from efficiency to effectiveness, we really were able to drive better outcomes." That, to me, was a very telling statement, because that's a pain point that I hear from my clients all the time and digital is just the accelerant, because, again, customers today are more knowledgeable than ever. They don't interact in one or two ways, physically or over the phone. They now want to interact in multiple ways, and very often the very first way that they're going to interact with a company is online. It's going to be on a device, and they want that same experience throughout every channel that they're interacting with. What that does is that really puts pressure on a company to be able to design experiences for their customers that are consistent throughout their entire journey with a business. With their business. Otherwise, it takes 30 seconds to lose somebody and have them move on to the next company. >> It's so interesting to me, both the consumerization of IT, which you touched on, right. Our expectation is driven by our interaction with a lot of different applications. >> Absolutely. >> And the other thing is how quickly the gold standard becomes baseline. How quickly we just get used to something new and now we just expect that, not only in that application, but now we expect that, "Doesn't that reapplication have that capability?" >> Oh yeah. >> The competitive thread, the competitive speed in which you have to react is way faster than it ever has been before, and you're competing with my Amazon app. You're competing with the way I interact with Netflix. You're not necessarily competing with how I interact with your competitor down the street. It's a completely different paradigm. >> Absolutely. When you think about companies that have been around for a very long time in the banking industry, is such a great example of this. Millennials don't go into branches. Gen Z does not go into a branch. The need for great digital experiences that that demographic requires, needs to also appeal to a generation that was used to going into branches. You need to be able to balance that, and that puts a lot of pressure on a traditional bank, especially when you see that there are digital banking applications that have no real estate. Everything is digital and you have to be competing with that. It really does put pressure on, so that's why the digital transformation was the accelerant that makes all of the other pain points just that much more magnified. >> I like that. I like thinking about digital transformation in that accelerating version. We're out of time, but I have to ask you one more question. >> Sure. >> We're hearing that there's over 50 customers speaking at this event, which is huge. They gave us some great examples of customers in quotes, as well as presenters during the keynote. I heard a lot of strong qualitative, measurable business outcomes. From the survey that you've recently done, the research, can you give us one or two really strong qualitative, like was a company able to increase revenue by 2X or 3X, or reduce costs by 40 percent? >> Sure. What we saw where a lot of productivity increases and satisfaction increases. What we saw was that productivity increased by three to four times. That you were able to reduce the amount of time you were in email. You were enabled to speed up decision making capabilities. When you thought about how organizations were seeing higher customer satisfaction scores coming back, we saw increases there that were 3 to 4X. And from a little tidbit that we saw just from our own research, is that when we interview information workers about what collaborative tools were most valuable to them, over 70% said collaborative work management tools were the most valuable tools for them in how they leverage collaboration to deliver successful outcomes. >> Margo, thanks so much for stopping by. >> Sure, it was my pleasure. >> Sharing with us about collaborative work management in this emerging market. Excited to hear what comes next. >> Great. >> Thank you for your time. >> Thank you very much for having me. >> We want to thank you for watching the CUBE. I'm Lisa Martin with Jeff Frick. We are live from Smartsheet ENGAGE 2018. Stick around, we'll be back. (Outro Music)

>> Narrator: Live from the Wynn Hotel in Las Vegas, it's theCUBE covering Magento Imagine 2018. Brought to you by Magento. >> Hey, welcome back to theCUBE. We are continuing our coverage live from the Wynn Las Vegas at Magento Imagine 2018. We've had a really exciting day talking about commerce and how it's limitless and changing dramatically. Joining me next is Sucharita Kodali, the vice president and principal analyst at Forrester. Sucharita, it's great to have you on theCUBE. >> Thanks for having me, Lisa. >> So commerce is limitless. We've been hearing this thematically all day. You primarily are working with retailers on their digital strategies. And you've been doing this for a long time. Let's talk about the evolution that you've seen in the retail space with everybody expecting to have access to whatever they want to buy in their pockets. >> Right, right, right. I would say, so I've been working in the retail industry for the last two decades. I've been an analyst for the last 10 plus years. I've really seen a number of changes. And if I had to just summarize the biggest changes, one is just the inventory across different retail channels. So, that's definitely been a huge huge one. It's like, how do you, how do you order online, but then fulfill the item from a physical store or fulfill the item from another store? So those are, that's basically the digital transformation of retailers. Those are investments that companies like WalMart and Target have really been doubling down on and focusing on. The second big change is Amazon. And they single-handedly have transformed the retail industry. They have increased consumer expectations. And what Amazon's also done is reinvented retail as a business model. Because it is no longer about just selling product and being profitable selling that product. Amazon actually is not profitable with a lot of the items that it sells. It makes money in other ways. And it is probably what I would describe as America's first retail conglomerate. And that becomes a really interesting question for other companies to compete, do you have to become a retail conglomerate? Then, the third big change is just brand selling direct to consumer. I remember when I started at Forrester, my very first project was with a large consumer electronics company that asked, Well, should we even sell directly to consumers? There's channel conflict and issues with our distributors. And now, that's not even a factor. It's sort of table stakes you have to sell direct to consumer. And that's probably where we'll continue to see a lot of retail sales in the future. >> So the Amazon model, we expect to be able to get whatever we want whenever we want it, have it shipped to us either at home or shipped to us so we can go pick it up at a store. It's really set the bar. In fact, they just announced the other day that a hundred million Amazon Prime members. I know people that won't buy something if it's not available through Prime. But I think this morning the gentleman that was on main stage from Amazon said at least 50% of their sales are not products they sell, they're through all of the other retailers that are using Amazon as a channel as part of their omni-channel strategy. If you think of a retailer from 20 years ago, how do they leverage your services and expertise and advice to become omni-channel? Because as today, you said essentially it's table stakes for companies to have to sell to consumers. >> Yeah, yeah. There are so many questions that really require, I call it destroying the retail orthodoxies. And retail has historically been about buyers and merchandisers buying goods. There's the old expression in retail, You stack 'em high and watch 'em fly. And that is just where buyers would, Take a company like Toys R Us, they would basically take what Mattel and Hasbro told them to buy. They would buy a ton of it, put it in stores. And because there was less competition back in the '80s, consumers actually would buy that merchandise. And unfortunately, the change for retailers is that consumers have so much more choice now. There's so such more innovation. There are small entrepreneurs who are creating fabulous products, consumer tastes have changed. And this old paradigm of Mattel and Hasbro, or kind of fill in the blank with whatever vendors and suppliers, pushing things is no longer relevant. So, there was just an article in the journal today about how Hasbro sales were down by double digits because Toys R Us is now going to go out of business. So those are the kinds of things that retailers who did not adjust to those changes, they are the ones that really suffer. They don't find ways to develop new inventory, they don't find new channels for growth, and they don't protect their own. They don't build a moat around their customers like Amazon has done, or they don't find ways to source inventory creatively. That's where the problems are. >> You think that's more of a function of a legacy organization; having so much technology that they don't know how to integrate it all together? What do you think are some of the forcing functions old orthodoxies that companies that don't do it well are missing? >> Yeah, it's a lot of it is just in the old ways of doing business. So, a lot of it is being heavily dependent, for instance, on buyers and merchandisers buying things. I mean, one of the biggest innovations that Amazon realized was that, look you can sell things without actually owning the inventory. And that is, their entire, what we call the third party marketplace, and that is just so simple. But if you were to ask a buyer at a major retailer a decade or two ago, "Why do you have to buy the inventory?" their response would be, Well, you have to buy the inventory, that's just the way it is. And it's like, well why? Why don't you try to find a new way to do business? And they never did. But it took Amazon to figure that out. And the great irony of why so many retailers continue to struggle is that Amazon has exposed the playbook on how to sell inventory without owning it. And so few retailers to this day have adopted that approach. And that's the great irony I think, is that that's the most profitable part of Amazon's business is that third party marketplace. And every retailer I've talked to is like, Oh, it's really hard. We can't do that. But, the part of Amazon's business that everyone is looking to imitate is their fast shipping. Which, is the most expensive part of their business. Amazon is only able to afford the fast free shipping because of the third party marketplace. Other retailers want to get the fast free shipping without the marketplace. And it just doesn't make any sense. And that's really the heart of the challenge is that they just don't think about alternative business models. They don't want to change the way that they've historically run their businesses. And some of this could mean that merchants are not as powerful in organizations. And maybe that's part of the pushback is that, there could be a lot of people who lose jobs. The future will be robo-buyers and financial services you have robo-advisors, why not robo-planners in retail? >> So one of the keys then, of eliminating some of the old orthodoxies for merchants is to be able to pivot and be flexible. But it has to start from where in an organization from a digital strategy perspective? Where do you help an organization not fall into the Toys R Us bucket? >> Yeah, I think a lot of it does have to start with merchandising and putting in some interesting digital tools to help merchants be more flexible. So, you want to flex to supply and demand. And some of that comes with integrating marketplaces into your own experience. Some of it can be investing in 3D printers that can make things that are plastic or metals based on demand. That's something that I always wondered why Toy R Us didn't, for instance, make Fidget Spinners on demand. Why did you have to get them with a six month leave time from China, it never made any sense. You can scale service, so use technology to match great store associates with a customer who may have a question. And you don't have to be in the same store. It can be a Facetime call with somebody who is far away. But very few retailers do that. And finally, the last bit is really to look at new alternative business models and finding new ways of making money beyond just selling inventory. >> That's really key because there are so many oppurtunities when companies go omni-channel of not just increasing sales and revenue, but also reducing attrition, making the buying process simple and seamless. Everybody wants one click, right? >> Right. >> Super seamless, super fast, and relevant. It's got to be something if you're going to attract my business, you need to be able to offer something where you know me to a degree. >> Absolutely. >> Or know what it is I might have a propensity to buy. >> Absolutely. And that's the entire area of personalization. And that personalization can be anything from a recommendation that I give you. It can be proactively pushing a recommendation. That's what companies like Stitch Fix do is I tell you what I want and then they send you a box in the mail of things I think you would like and oh, by the way are your size and within your budget. It can be customization. One of Nike's most successful parts of their business is their Nike ID program which allows you to customize shoes according to colors and different sort of embellishments that you may like. And that's exactly the kind of thing that more retailers need to be looking at. >> What are some of the trends maybe that a B2B organization might be able to love or some of the conveniences that we have as consumers and we expect in terms of-- Magento, I was looking on their website the other day and a study that they've done suggests 93 percent of B2B buyers want to be able to purchase online. So, new business models, new revenue streams, but it really is a major shift of sales in marketing to be able to deliver this high velocity low touch model. What are some of the things that a business like a Magento, could learn from say a Nike with how they have built this successful omni-channel experience? >> Well, interestingly I think one of the most important things to recognize is that every B2B buyer is also a B2C buyer. And their expectations are set by their experiences in B2C. So, if you have everything from all of the information at your fingertips, all of that information is optimized for mobile devices. You have different ways to view that information, you have all of your loaded costs, like shipping, or tax, or if there's cross-border. All of the information related to the time to ship, any customs and duties, all of that needs to be visible because in any experience that you have with say a site like Amazon, you're going to get that information. So, the expectation is absolutely there to have it in any situation whether it's B2B or whether it's buying components or kind of very long tail items. That's basically the cost of doing business at this point, is that you have to deliver all of the information that the customer wants and needs. And if you don't, the customer is just going to opt to go purchase that product at whatever destination offers it. >> Somewhere else. >> And somebody will. That's the challenge when you have 800 thousand Plus eCommerce sellers out there selling every product imaginable in the both B2B and B2C landscape. >> So, on the data side there's so much data out there that companies have any type of business to be able to take advantage of that. I know that there's, BI has so much potential. Are you hearing retailers start to embrace advanced analytics techniques, AI machine learning, Where are they with starting to do that? I know that some eyeglass companies have virtual reality augmented reality type of apps where you can kind of try on a pair of frames. Where are you seeing advanced analytics start to be successful and help retailers to be able to target buyers that might say, oh, I can't try that on? No, I want to go somewhere that I can touch and feel it. >> Yeah, well, it's emerging still. I mean, retailers have a lot of data. I think they're trying to figure out where is it most useful. And one of the places where it is incredibly useful is in the backend with fraud management. So, after retailers were forced to put in chip cards as a payment form, what you started to see was more of the fraud shifting to eCommerce. I just had two credit cards that had to be shut off because of E-commerce fraud. But that is where you see the fraudsters going to. And what you see as a result of that is some innovators in that space technology companies really leveraging machine learning, AI, other advanced data techniques to identify fraudulent transactions and to better help retailers eliminate or reduce the percent of transactions that have to then be charged back. So, that's probably one of the most promising areas. There are others that are emerging. We're seeing more visual recognition technologies. House for instance, is excellent at that and Pinterest too. If there's part of an image you like you can click on it or you can tap it and see other images like that. And that's incredibly difficult. And it was even more difficult 10-15 years ago, but it's becoming easier. There's the voice element, voice to text or text to voice. I think that the best applications they're often in customer service, there are so many interactions that happen anywhere in a consumer facing world. It doesn't even have to be within retail. You can think about the complaints to the airline industry or to a bank. And a lot of it falls into a black hole. You always hear that oh, This call may be recorded, but it is really difficult to go back and transcribe that. And to really synthesize that into major themes. And what ML in particular can do is to basically pull out those themes, it can automate all of that, and can give insights as to what you could be doing, what you should be doing, what are the opportunities that you may not have even known existed. So there are definitely emerging places. I mean even a visual recognition, so we talked about House and Pinterest. Another great example is the computer vision that you have in the Amazon Go stores. And there's a robot that the Wal Mart stores are now testing to go find if there are gaps in the inventory that need to be filled. Or if something is running low or out of stock. So there are definitely some interesting applications, but it's still early days for sure. >> So last question, we've got to wrap here, but, we're in April 2018, what are some of the, your top three recommendations for merchants, as they prepare for say Black Friday coming up in what, six or eight months. What are you top three recommendations for merchants to be successful and be able to facilitate a seamless online offline experience? >> Well, we always have kind of imbalances between supply and demand, and that's where I do think things like third party sellers, third party marketplaces are huge. So to be able to leverage that is certainly one opportunity. Another is to think creatively about promotions. In Japan they have these promotions called Fukubukuro promotions, and it's basically like grab bags of like all the left over inventory. But then they basically put it into mystery bags where you can buy it for half off. And consumers line up around the block at stores to go buy these grab bags. Because they also have also like a gamified approach where, you know, one of out 10 of the bags will have like an Ipad or some really high value item. So people really like these things, and they have trading parties. So just new ways of having promotions beyond just the typical door busters that retailers think about. And then kind of third I think is just try to pace out the demand. One of the big issues in E-commerce has been just the burst in demand that always happen in December. And that creates a lot of problems from the standpoint of actually shipping the orders. So the more that you can pull those transaction forward into November, the better off you are from a fulfillment and supply chain standpoint. >> Alright Sucharita thank you so much for stopping by theCUBE >> Thanks Lisa >> And sharing your insights on the trends and what's going on in the commerce and E-commerce space. Really enjoy talking with you. >> Nice to talk to you too. >> We want to thank you for watching. You're watching theCUBE live from Magento Imagine 2018, I'm Lisa Martin. Stick around, I'll be back with my next guest after a short break. (upbeat music)

>> Narrator: From downtown San Francisco it's theCUBE covering RSA North America 2018. >> Welcome back everybody, Jeff Frick here with theCUBE. We're at the RSA Conference North America 2018 downtown San Francisco. 40,000 plus people swarming all over Moscone to the north to the south and to the west. We're excited to have our next guest on. He's Chase Cunningham, principal analyst at Forrester. Chase, great to meet you, welcome. >> Thanks for having me. >> Absolutely, so you just had an interesting blog post. Was Zero Trust on a beer budget. >> Yeah. >> What is that all about? >> Well, so Zero Trust is a pretty simple concept about accepting failure, if you will, and focusing on the internal and moving outward. And basically the premise was, I had friend of mine ask me if he could do Zero Trust for his small company. And I said sure, let's go get a beer and we'll figure this out. And literally, in about half an hour we had a Zero Trust strategy in place for less than 40 grand and his infrastructure is way more secure and it's really simple. >> So that's pretty interesting because, you Know it's easy for big companies that have a lot of resources or the big puddle of Cloud companies have a lot of resources to put a lot of implementation into place. But as we look around this conference tons and tons of companies, it's a lot harder for small and medium businesses either to have the expertise or the budgets to really bring in what they need to secure things. So what were some of the insights from your beer exercise? >> Sure, so it was really simple. If you really think about where the majority of the threat comes from, the network is there and everybody uses it but who accesses the network? The users, the individuals, the devices, everything else. So the first thing we did was we're going to lock down identity and access management because I know if I can control that I've made a fundamental shift into power position for myself. And the next thing we did was we said look you guys don't really own intellectual property but you send emails. We're going to put stuff in place to encrypt every email you send whether you like it or not. So between those two simple things, identity access management and sort of data email encryption we put a really strong security platform in place and it didn't break the bank and it wasn't really hard to do and it's something that you can get better as it goes on. >> Right. And I'm curious, had he had an event or he was just trying to get ahead of the curve? >> He had had some weird stuff showing up. He's in esports, right, so he doesn't have actual intellectual property but he's worried because if they get dossed or they get hacked or they get ransomware for every minute they're down they're losing viewers and that's business and money for them. >> Right, so it kind of ties back to this kind of next gen access where it's really important with the identity but the other one is the context. Who is it and where are they trying to get in? Do they usually come in that way? Do they usually have access? So that's another really way to kind of isolate the problems that might come in the front door. >> Yeah, and you know the, years ago the next gen firewall was really the thing to integrate lots of functions across the network and that's all there. It still exists and it's still necessary but really when you break it down and look at historically where the threats have come from and where the compromises have come from, it's access and if you can't control that you don't have the capability of actually stopping bad things from happening. >> Right, right, so as you look around and you've been coming to this probably for a couple years, as this space evolves. You know, kind of what are your general impressions? I mean, on one hand, so many vendors, so many activities. On the other hand, it was like, we've been at this for a while or are we just stuck in this race and we just got to keep running? >> Well I think we're going to continue running the race but interestingly enough there's buses driving by now with Zero Trust all over the side of it. And I'm glad to see that that strategy is starting to take hold because the problem I have is you can Frankenstein technology together all day long but if you don't have a strategic guidepost that everybody understands from the board down to the network engineer you're going to get it wrong. You're going to miss and so I'm a fan of simplicity and force multipliers and to me the Zero Trust strategy sort of drives that forward. >> All right, well Chris thanks for taking a few minutes. Everyone can log onto your site, take a look at the blog. Thanks for stopping by. >> Thanks for having me. >> All right, he's Chris Cunningham from Forrester. I'm Jeff Frick from theCUBE. Thanks for watching from RSAC 2018.

>> Announcer: Live from Boston, Massachusetts, it's theCube, covering IBM Chief Data Officer's Summit, brought to you by IBM. (upbeat music) >> Welcome back to theCUBE's live coverage of the IBM CDO Strategy Summit here in Boston, Massachusetts. I'm your host, Rebecca Knight, along with my co-host, Dave Vellante. >> Hey, hey. We are joined by Gene Leganza, he is the vice president and research director at Forrester Research. Thanks so much for coming on theCUBE. >> Pleasure, thanks for having me. >> So, before the cameras were rolling, we were talking about this transformation, putting data at the front and center of an organization, and you were saying how technology is a piece of the puzzle, a very important piece of the puzzle, but so much of this transformation involves these cultural, social, organizational politics issues that can be just as big and as onerous as the technology, and maybe bigger. >> Bigger in a sense that there can be intractable without any clear path forward. I was just in a session, at a breakout session, at the conference, as I was saying before, we could have had the same discussion 15 or 20 years ago in terms of how do you get people on board for things like data governance, things that sound painful and onerous to business people, something that sound like IT should take care of that, this is not something that a business person should get involved in. But the whole notion of the value of data as an asset to drive an organization forward, to do things you couldn't do before, to be either driven by insights, and if you're even advanced, AI, and cognitive sort of things, really advancing your organization forward, data's obviously very critical. And the things that you can do should be getting business people excited, but they're still having the same complaints about 20 years ago about this is something somebody should do for me. So, clearly the message is not getting throughout the organization that data is a new and fascinating thing that they should care about. There's a disconnect for a lot of organizations, I think. >> So, from your perspective, what is the push back? I mean, as you said, the fact that data is this asset should be getting the business guys' eyes lighting up. What do you see as sort of biggest obstacle and stumbling block here? >> I think it's easy to characterize the people we talk about. I came from IT myself, so the business is always the guys that don't get it, and in this case, the people who are not on board are somehow out of it, they're really bad corporate citizens, they're just not on board in some way that characterizes them as missing something. But I think what no one ever does who's in the position of trying to sell the value of data and data processes and data capabilities, is the fact that these folks are all doing their best to do their job. I mean, nobody thinks about that, right? They just think they're intractable, they like doing things the way they've always done them, they don't like change, and they're going to resist everything I try to do. But the fact is, from their perspective, they know how to be successful, and they know when risk is going to introduce something that they don't want to go there. It's unjustifiable risk. So the missing link is that no one's made that light bulb go off, to say, there is actually a good reason to change the way you've done things, right? And it's like, maybe it's in your best interest to do things differently, and to care more about something that sounds like IT stuff, like data governance, and data quality. So, that's why I think the chief data officer role, whether it's that title or chief analytics officer, or there's actually a chief artificial intelligence officer at the conference this time around, someone has to be the evangelist who can tell really meaningful stories. I mean, you know, 20 years ago, when IT was trying to convince the business that they should care more about data, data architects and DBAs could talk till they're blue in the face about why data was important. No one wanted to hear it. People get turned off even faster now than they did before, because they have a shorter attention span now than they did before. The fact is that somebody with a lot of credibility on the business side, people who kind of really believe it's capable of driving the business forward, hasta have a very meaningful message, not a half-hour wrap on why data is good for you, but what, specifically, can change in your business that you should want to change. I mean, basically, if you can't put it in terms of what's in it for me, why should they listen to you, right? And so yeah, you know, we've got this thing goin' on, it's really important, and everybody's behind it, and I can give you a list of people whose job title begins with C who really thinks that this is a really important idea, get right down to it, if it's not going to make their area of the business work better, or more efficiently, or, especially with, you know, top line growth sort of issues, they're not going to be that interested. And so it's the job of the person who's trying to evangelize these things to put it in those terms. And it might take some research, it certainly would take some in-depth business knowledge about what happens in that area of the business, you can't give an example from another industry or even another company. You've got to go around and find out what's broken, and talk about what can be fixed, you have to have some really good ideas about what can be innovative in very material terms. One of the breakout sessions I had earlier today, well, they're all around how you define new data products, and get innovative, and very interesting to hear some of the techniques by the folks who'd been successful there, down to, you know, it was somebody's job to go around, and when I say somebody, I don't mean a flunky, I need a chief analytics officer sort of person, talking to people about, you know, what did they hate about their job. Finding, collecting all the things that are broken, and thinking about what could be my best path forward to fix something that's going to get a lot of attention, that I can actually build a marketing message here about why everybody should care about this. And so, the missing link is really not seeing the value in changing behaviors. >> So one of the things that I've always respected about George Colony is he brings people into Forrester that care about social, cultural, organizational issues, not just technology. One of your counterparts, Doug Laney, just wrote a book called Infonomics. You mighta seen it on Twitter, there's a little bit of noise going around it. Premise of the book is essentially that organization shouldn't wait for the accounting industry to tell them how to value data. They should take it upon themselves, and he went into a lot of very detailed, you know, kind of mind-numbing calculations and ways to apply it. But there's a real cultural issue there. First of all, do you buy the premise, and what are you seeing in your client base in terms of the culture of data first, data value, and understanding data value? >> Really good question, really good question. And I do follow what Doug Laney does. Actually, Peter Burris, who you folks know, a long time ago, when he was at Forrester, said, "You know what Doug Laney is doing? "We better be doing that sort of thing." So he brought my attention to it a long time ago. I'm really glad he's working on that area, and I've been in conversations with him at other conferences, where people get into those mind-numbing discussions about the details and how to measure the value of data and stuff, and it's a really good thing that that is going on, and those discussions have to happen. To link my answer to that to answer to your second part of your question about what am I seeing in our client base, is that I'm not seeing a technical answer about how to value data in the books, in a spreadsheet, in some counting rules, going to be the differentiator. The missing link has not been that we haven't had the right rules in place to take X terabytes of data and turn it into X dollars of assets on the books. To me, the problem with that point of view is just that there is data that will bring you gold, and there's data that'll sit there, and it's valuable, but it's not really all that valuable. You know, it's a matter of what do you do with it. You know, I can have a hunk of wood on this table, and it's a hunk of wood, and how much it is, you know, what kind of wood is it and how much does it cost. If I make something out of it that's really valuable to somebody else, it'll cost something completely different based on what its function is, or its value as an art piece or whatever it might be. So, it's so much the product end of it. It's like, what do you do with it, and whether there's an asset value in terms of how it supports the business, in terms of got some regular reporting, but where all the interest is at these days, and why there's a lot of interest in it is like, okay, what are we missing about our business model that can be different, because now that everything's digitized, there are products people aren't thinking of. There are, you know, things that we can sell that may be related to our business, and somehow it's not even related our business, it's just that we now have this data, and it's unique to us, and there's something we can do with it. So the value is very much in terms of who would care about this, and what can I do with it to make it into an analytics product, or, you know, at very least I've got valuable data, I think this is how people tend to think of monetizing data, I've got valuable data, maybe I can put it somewhere people will download it and pay me for it. It's more that I can take this, and then from there do something really interesting with it and create a product, or a service, it's really it's on an app, it's on a phone, or it's on a website, or it's something that you deliver in person, but is giving somebody something they didn't have before. >> So what would you say, from your perspective, what are the companies that are being the most innovative at creating new data products, monetizing, creating new analytics products? What are they doing? What are the best practices of those companies from your perspective? >> You know, I think the best practice of those companies are they've got people who are actively trying to answer the question of, what can I do with this that's new, and interesting, and innovative. I'd say, in the examples I've seen, there been more small to medium companies doing interesting things than really, really huge companies. Or if they're huge companies, they're pockets of huge companies. It's kind of very hard to kind of institutionalize at the enterprise level. It's when you have somebody who gets it about the value of data, working to understand the business at a detailed level enough to understand what might be valuable to somebody in that business if I have a product, is when the magic can potentially happen. And what I've heard people doing are things like that hackathons, where in order to kind of surface these ideas, you get a bunch of folks who kind of get technology and data together with folks who get the business. And they play around with stuff, and they're matching the data to the business problem, comin' up with really kind of cool ideas. Those kind of things tend to happen on a smaller scale. You don't have a hackathon, as far as I can tell, with a couple thousand people in a room. It's usually a smaller sort of operation, where people are digging this up. So, it's folks who kind of get it, because they've been kind of working to find the value in analytics, and it's where there's pockets of people who're kind of working together with the business to make it happen. The profile is such that it's organizations that tend to be more mature about data. They're not complaining that data is something IT should take care of for me. They've kind of been there 10 years ago, or five years ago even, and they've gotten at a point where they actually wanted to move forward from defense and do some offensive playing. They're looking for those kind of cool things to do. So, they're more mature, certainly, than folks who aren't doing it. They're more agile and nimble, I think, than your typical organization in the sense of they can build cross disciplinary teams to make this happen, and that's really where the magic happens. You don't get a genius in the room to come up with this, you get this combination of technical skills, and data knowledge, and data engineering skills, and business smarts all in the same room, and that might be four or five different people to kind of brainstorm until they kind of come up with this. And so the folks who recognize that problem, make that happen, regardless of the industry, regardless of the size of the company, are where it's actually happening. >> I know we have to go, but I wanted to ask you, what about the IBM scorecard in terms of how they're doing in that regard? >> You know, I want to talk to them more. From what they said, you know, in a day, you hear a lot of talk, it's been a long day of hearing people talk about this. It sounds pretty amazing, you know, and I think, actually, we had a half hour session with Inderpal after his keynote, I'm going to get together with him more, and hear more about what's going on under the covers, 'cause it sounds like they're being very effective in kind of making this happen at the enterprise level. And I think that's the unusual thing. I mean, IBM is a huge, huge place. So the notion that you can take these cool ideas and make them work in pockets is one thing. Trying to make it enterprise class, scalable, cognitive-driven organization, with all the right wheels in motion to the data, and analytics, and process, and business change, and operating model change, is kind of amazing. From what I've heard so far, they're actually making it happen. And if it's really, really true, it's really amazing. So it makes me want to hear more, certainly, I have no reason to doubt that what they're saying is happening is happening, I just would love to hear just some more of the story. >> Yeah, you're making us all want to hear more. Well, thanks so much, Gene. It's been a pleasure-- >> Not a problem. >> having you on the show. >> A pleasure. >> Thanks. >> Thank you. >> I'm Rebecca Knight, for Dave Vellante, we will have more from the CDO Summit just after this. (upbeat music)

>> Narrator: Live from Boston, Massachusetts, this is the Cube, covering Spark Summit East 2017, brought to you by Databricks. Now, here are your hosts, Dave Vellante and George Gilbert. >> Welcome back to Boston, everybody, where the town is still euphoric. Mike Gualtieri is here, he's the principal analyst at Forrester Research, attended the parade yesterday. How great was that, Mike? >> Yes. Yes. It was awesome. >> Nothing like we've ever seen before. All right, the first question is what was the bigger shocking surprise, upset, greatest win, was it the Red Sox over the Yankees or was it the Superbowl this weekend? >> That's the question, I think it's the Superbowl. >> Yeah, who knows, right? Who knows. It was a lot of fun. So how was the parade yesterday? >> It was magnificent. I mean, it was freezing. No one cared. I mean--but it was, yeah, it was great. Great to see that team in person. >> That's good, wish we could talk, We can, but we'll get into it. So, we're here at Spark Summit, and, you know, the show's getting bigger, you're seeing more sponsors, still heavily a technical audience, but what's your take these days? We were talking off-camera about the whole big data thing. It used to be the hottest thing in the world, and now nobody wants to have big data in their title. What's Forrester's take on that? >> I mean, I think big data-- I think it's just become mainstream, so we're just back to data. You know, because all data is potentially big. So, I don't think it's-- it's not the thing anymore. I mean, what do you do with big data? You analyze it, right? And part of what this whole Spark Summit is about-- look at all the sessions. Data science, machine learning, streaming analytics, so it's all about sort of using that data now, so big data is still important, but the value of big data comes from all this advanced analytics. >> Yeah, and we talked earlier, I mean, a lot of the value of, you know, Hadoop was cutting costs. You know, you've mentioned commodity components and reduction in denominator, and breaking the need for some kind of big storage container. OK, so that-- we got there. Now, shifting to new sources of value, what are you spending your time on these days in terms of research? >> Artificial intelligence, machine learning, so those are really forms of advanced analytics, so that's been-- that's been very hot. We did a survey last year, an AI survey, and we asked a large group of people, we said, oh, you know, what are you doing with AI? 58% said they're researching it. 19% said they're training a model. Right, so that's interesting. 58% are researching it, and far fewer are actually, you know, actually doing something with it. Now, the reality is, if you phrase that a little bit differently, and you said, oh, what are you doing with machine learning? Many more would say yes, we're doing machine learning. So it begs the question, what do enterprises think of AI? And what do they think it is? So, a lot of my inquiries are spent helping enterprises understand what AI is, what they should focus on, and the other part of it is what are the technologies used for AI, and deep learning is the hottest. >> So, you wrote a piece late last year, what's possible today in AI. What's possible today in AI? >> Well, you know, before understanding was possible, it's important to understand what's not possible, right? And so we sort of characterize it as there's pure AI, and there's pragmatic AI. So it's real simple. Pure AI is the sci-fi stuff, we've all seen it, Ex Machina, Star Wars, whatever, right? That's not what we're talking about. That's not what enterprises can do today. We're talking about pragmatic AI, and pragmatic AI is about building predictive models. It's about conversational APIs, to interact in a natural way with humans, it's about image analysis, which is something very hot because of deep learning. So, AI is really about the building blocks that companies have been using, but then using them in combination to create even more intelligent solutions. And they have more options on the market, both from open source, both from cloud services that-- from Google, Microsoft, IBM, and now Amazon, at their re-- Were you guys at their reinvent conference? >> I wasn't, personally, but we were certainly there. >> Yeah, they announced the Amazon AI, which is a set of three services that developers can use without knowing anything about AI or being a data scientist. But, I mean, I think the way to think about AI is that it is data science. It requires the expertise of a data scientist to do AI. >> Following up on that comment, which was really interesting, is we try and-- whereas vendors try and democratize access to machine learning and AI, and I say that with two terms because usually the machine learning is the stuff that's sort of widely accessible and AI is a little further out, but there's a spectrum when you can just access an API, which is like a pre-trained model-- >> Pre-trained model, yep. >> It's developer-accessible, you don't need to be a data scientist, and then at the other end, you know, you need to pick your algorithms, you need to pick your features, you need to find the right data, so how do you see that horizon moving over time? >> Yeah, no, I-- So, these machine learning services, as you say, they're pre-trained models, totally accessible by anyone, anyone who can call an API or a restful service can access these. But their scope is limited, right? So, if, for example, you take the image API, you know, the imaging API that you can get from Google or now Amazon, you can drop an image in there and it will say, oh, there's a wine bottle on a picnic table on the beach. Right? It can identify that. So that's pretty cool, there might be a lot of use cases for that, but think of an enterprise use case. No. You can't do it, and let me give you this example. Say you're an insurance company, and you have a picture of a steel roof that's caved in. If you give that to one of these APIs, it might say steel roof, it may say damage, but what it's not going to do is it's not going to be able to estimate the damage, it's not going to be able to create a bill of materials on how to repair it, because Google hasn't trained it at that level. OK, so, enterprises are going to have to do this themselves, or an ISV is going to have to do it, because think about it, you've got 10 years worth of all these pictures taken of damage. And with all of those pictures, you've got tons of write-ups from an adjuster. Whoa, if you could shove that into a deep learning algorithm, you could potentially have consumers take pictures, or someone untrained, and have this thing say here's what the estimate damage is, this is the situation. >> And I've read about like insurance use cases like that, where the customer could, after they sort of have a crack up, take pictures all around the car, and then the insurance company could provide an estimate, tell them where the nearest repair shops are-- >> Yeah, but right now it's like the early days of e-commerce, where you could send an order in and then it would fax it and they'd type it in. So, I think, yes, insurance coverage is taking those pictures, and the question is can we automate it, and-- >> Well, let me actually iterate on that question, which is so who can build a more end-to-end solution, assuming, you know, there's a lot of heavy lifting that's got to go on for each enterprise trying to build a use case like that. Is it internal development and only at big companies that have a few of these data science gurus? Would it be like an IBM Global Services or an EXIN SURE, or would it be like a vertical ISV where it's semi-custom, semi-patent? >> I think it's both, but I also think it's two or three people walking around this conference, right, understanding Spark, maybe understanding how to use TensorFlow in conjunction with Spark that will start to come up with these ideas as well. So I think-- I think we'll see all of those solutions. Certainly, like IBM with their cognitive computing-- oh, and by the way, so we think that cognitive computing equals pragmatic AI, right, because it has similar characteristics. So, we're already seeing the big ISVs and the big application developers, SAP, Oracle, creating AI-infused applications or modules, but yeah, we're going to see small ISVs do it. There's one in Austin, Texas, called InteractiveTel. It's like 10 people. What they do is they use the Google-- so they sell to large car dealerships, like Ernie Boch. And they record every conversation, phone conversation with customers. They use the Google pre-trained model to convert the speech to text, and then they use their own machine learning to analyze that text to find out if there's a customer service problem or if there's a selling opportunity, and then they alert managers or other people in the organization. So, small company, very narrowly focused on something like car buying. >> So, I wonder if we could come back to something you said about pragmatic AI. We love to have someone like you on the Cube, because we like to talk about the horses on the track. So, if Watson is pragmatic AI, and we all-- well, I think you saw the 60 Minutes show, I don't know, whenever it was, three or four months ago, and IBM Watson got all the love. They barely mentioned Amazon and Google and Facebook, and Microsoft didn't get any mention. So, and there seems to be sentiment that, OK, all the real action is in Silicon Valley. But you've got IBM doing pragmatic AI. Do those two worlds come together in your view? How does that whole market shake up? >> I don't think they come together in the way I think you're suggesting. I think what Google, Microsoft, Facebook, what they're doing is they're churning out fundamental technology, like one of the most popular deep learning frameworks, TensorFlow, is a Google thing that they open sourced. And as I pointed out, those image APIs, that Amazon has, that's not going to work for insurance, that's not going to work for radiology. So, I don't think they're in-- >> George Gilbert: Facebook's going to apply it differently-- >> Yeah, I think what they're trying to do is they're trying to apply it to the millions of consumers that use their platforms, and then I think they throw off some of the technology for the rest of the world to use, fundamentally. >> And then the rest of the world has to apply those. >> Yeah, but I don't think they're in the business of building insurance solutions or building logistical solutions. >> Right. >> But you said something that was really, really potentially intriguing, which was you could take the horizontal Google speech to text API, and then-- >> Mike Gualtieri: And recombine it. >> --put your own model on top of that. And that's, techies call that like ensemble modeling, but essentially you're taking, almost like an OS level service, and you're putting in a more vertical application on top of it, to relate it to our old ways of looking at software, and that's interesting. >> Yeah, because what we're talking about right now, but this conversation is now about applications. Right, we're talking about applications, which need lots of different services recombined, whereas mostly the data science conversation has been narrowly about building one customer lifetime value model or one churn model. Now the conversation, when we talk about AI, is becoming about combining many different services and many different models. >> Dave Vellante: And the platform for building applications is really-- >> Yeah, yeah. >> And that platform, the richest platform, or the platform that is, that is most attractive has the most building blocks to work with, or the broadest ones? >> The best ones, I would say, right now. The reason why I say it that way is because this technology is still moving very rapidly. So for an image analysis, deep learning, very good for image, nothing's better than deep learning for image analysis. But if you're doing business process models or like churn models, well, deep learning hasn't played out there yet. So, right now I think there's some fragmentation. There's so much innovation. Ultimately it may come together. What we're seeing is, many of these companies are saying, OK, look, we're going to bring in the open source. It's pretty difficult to create a deep learning library. And so, you know, a lot of the vendors in the machine learning space, instead of creating their own, they're just bringing in MXNet or TensorFlow. >> I might be thinking of something from a different angle, which is not what underlying implementation they're using, whether it's deep learning or whether it's just random forest, or whatever the terminology is, you know, the traditional statistical stuff. The idea, though, is you want a platform-- like way, way back, Windows, with the Win32 API had essentially more widgets for helping you build graphical applications than any other platform >> Mike Gualtieri: Yeah, I see where you're going. >> And I guess I'm thinking it doesn't matter what the underlying implementation is, but how many widgets can you string together? >> I'm totally with you there, yeah. And so I think what you're saying is look, a platform that has the most capabilities, but abstracts, the implementations, and can, you know, can be somewhat pluggable-- right, good, to keep up with the innovation, yeah. And there's a lot of new companies out there, too, that are tackling this. One of them's called Bonsai AI, you know, small startup, they're trying to abstract deep learning, because deep learning right now, like TensorFlow and MXNet, that's a little bit of a challenge to learn, so they're abstracting it. But so are a lot of the-- so is SAS, IBM, et cetera. >> So, Mike, we're out of time, but I want to talk about your talk tomorrow. So, AI meets Spark, give us a little preview. >> AI meets Spark. Basically, the prerequisite to AI is a very sophisticated and fast data pipeline, because just because we're talking about AI doesn't mean we don't need data to build these models. So, I think Spark gives you the best of both worlds, right? It's designed for these sort of complex data pipelines that you need to prep data, but now, with MLlib for more traditional machine learning, and now with their announcement of TensorFrames, which is going to be an interface for TensorFlow, now you've got deep learning, too. And you've got it in a cluster architecture, so it can scale. So, pretty cool. >> All right, Mike, thanks very much for coming on the Cube. You know, way to go Pats, awesome. Really a pleasure having you back. >> Thanks. >> All right, keep right there, buddy. We'll be back with our next guest right after this short break. This is the Cube. (peppy music)

>> Announcer: From Miami Beach, Florida, it's theCUBE. Covering Acronis Global Cyber Summit 2019. Brought to you by Acronis. >> Hello everyone, welcome back to theCUBE's coverage here in Miami Beach, Florida at the Fontainebleau Hotel for the Acronis Global Cyber Summit 2019, where cyber protection is becoming an emerging trend. And we see these once in a while, when you have these big waves, you know, some unique trends. Observability and cloud computing, automation and cloud computing came out of nowhere from these white spaces. Now you're seeing the confluence of data protection and cyber security coming together to the platform. That's what they're talking about here. And my next guest, to break it all down, is an analyst from Forrester Research, Naveen Chhabra. Thanks for joining us today. >> Thank you for having me here. >> So Miami Beach, not a bad venue is it? >> Oh yeah, absolutely. (laughing) >> Get a dip in the ocean there, the water's warm. I got to ask you this, break down this market. Acronis is on here earlier. They've got a story to tell, and their story is not something that's obvious. It's kind of a new category, I guess, emerging, not really a traditional category from a research standpoint. But cyber protection by combining traditional thinking about data protection and cyber security software, bringing them together into one thinking, wholistic data model, with a platform that can enable services. I mean, this is a classic platform. This is what these guys have. What's your take on the industry? Is the industry ready for this? Is this a real trend? >> The industry certainly needs the technology, and I'll give you some examples as to why. So if you think upon the ransomware attacks that have happened in the past, the ransomware attacks would cripple any organization, right? And the best defense that an organization has to recover from, backups. Now, what that means is, okay, I can certainly recover from a backup which was taken last hour, last yesterday or a few days back, a few weeks back. But the most important question is how do I find out that the last copy or the last snapshot is a clean, uninfected copy? Because that's important, right? So if you recover from an infected copy, you're going to be hit again. And you don't want that, right? So, the million dollar question there is how do I get back to the copy which is clean and uninfected? Right? And you cannot do that traditionally the way organizations have been structured. You have infrastructure and operations guys, those who are responsible for operations, you know, keeping copies in their place, wherever required, and then you have the second group, which is security and risk, which is responsible for identifying all things security, right? But, ransomware is one thing in the industry which is pulling these two teams together. But the organizations are not ready yet. In one of the surveys that I did, I asked the respondents, "Do you have these two teams working together "to solve this problem?" And the answer was abysmally low. You know, no they don't work with each other. >> You point at a great point. I think one of the things you highlight there that I think is really critical is backup and recovery was because of some operational disruption. >> Naveen: Yes. >> Outage, flood, so rollback. The disruption wasn't a hack, so to that point, all those mechanisms around, generations of backup and recovery didn't actually take into account security. >> Exactly. >> Meaning the malware or the infection, the disruption is coming from a secure breach, not some electrical outage or some sort of other disruption. And they used to call that non-disruptive operations. I remember all the stories when we just talked about that. >> Right. >> Now it's not that anymore. The disruption is coming from security, so how do you bake security in from day one? That's the million dollar question that I always hear. What's your answer to that? What's the industry doing to get security baked in? What are some of the mechanisms you've seen successful for a large enterprise to adopt a plan that way? >> So I, specifically from a technology standpoint, I see very little efforts. The technology vendors are doing their own efforts, but you know, my guidance to clients is to be proactive in terms of your using the right storage for that matter. Let's say, if you have a WORM storage which can not be encrypted. Written once, cannot be changed, right? Use that model which will ensure that whatever you backed up yesterday, one, the backup is not infected, right? Or even from your core business application standpoint, you know, you want to schedule the data to be kept at a particular point in time to that WORM storage, for example, right? I don't see much of an effort from the organizations because, again, inner security is a domain which is handled by security, backup has not looked at using WORM as a potential storage target. >> WORM being "Write Once Read Many" for the folks-- >> Yes. >> at home tracking this. >> Right, and not that they do not know the technology. They know the technology. It's also about thinking out of the box and applying what's available to another-- >> To a known problem, right? >> Yes. >> And ransomware is so bad, it's such a hard problem to solve. I've heard (mumbles) has been in solution, WORM's a good one. That's the first time I heard that. That's awesome. It makes sense. >> Absolutely. >> But how do you deploy that to scale throughout the enterprise where you had these traditional work stream workflows that-- >> That becomes a problem. >> A people problem. You've been doin' a lot of work around the people equation. People process technology, everyone says it's digital transformation. But the people equation is a hard nut to crack. What's your take on the people situation? >> It certainly is a hard nut to crack because security would not trust more infrastructure in place that our guys would be doing. They've been told to operate in that model and now comes a situation, ransomware situation, where they're asked to trust each other and work with each other. Boy, that's not happening, is it? (chuckles) >> Yeah, they hate each other before, now they have to like each other. I mean, that's been a 20 year, 10 year, 5 year you've seen it evolve over time. Dev Ops is certainly with the cloud enforced a lot of that. That's kind of what brought people together under the Dev Ops infrastructure's code. But we're talkin' about application development that's growing like crazy. (mumbles) C.s want to build in-house stacks and communicate via A.P.I.s and, or some data-sharing with vendors. So this idea of a lot of this there's a restructuring that's going on at least from a architectural, technically, and staffing. What's some of the best practices that you've seen? What is some of the customer environments out there that you can talk to to show and point at a success story? >> I think some of the examples I've seen organizationally addressing this problem, wholistically, is to start from the top. I came out with this report a couple of years back titled Ransomware is a Business Continuity Issue. So don't approach it with a technology solution. Eventually, you will end up in adopting that same technology but I didn't define why do you need to use that technology so that it ties up your business requirements. So start from identifying that as a business risk which I see very little organizations do that today. Cyber risks are not identified as vulnerable as important a risk as they should be. So start off from that and trickle down into the next sub-steps that you must be taking, going eventually to the same technology. >> You know, one of the things I want to get your thoughts on is that obviously the digital threats are the industrialization of automating attacks. You're seeing Zero-day, you're seeing all this malware out there. You got surface errors with I.O.T.s increasing. So, the threats are coming. They're not going away. In fact, they're going to be increasing over time. Maybe get, you might not see it like D-DOS kind of been distracted away. But now the complexity is a huge issue because the costs will kick off of the complexity, this is something that Acronis is talking about and this is what I want to get your thoughts on. Complexity is one of those things that if you don't solve it and you look the other way, it gets more expensive to solve over time. So as complexity piles up, it's like climate change or cleaning up the Boston harbor. The longer you wait the more expensive it's going to be. >> Exactly. >> So that's startin' be be realized in some people's minds. They call it re-platforming, digital transmission, there's just buzz words for that. But I think this is a reality that people like, "Oh, I got to get... "I got to take care of business." I got I.O.T., I got complete industrial I.O.T., N.I.O.C. I got all those data centers movin' to the cloud. I got to clean up the complexity problem. What's the answer? How do you, What's the research tell you? >> Unfortunately, there's no easy answer because all the tools, technologies the organizations are using, they're using it for a purpose. So silos is a challenge, increasing silos is a challenge. So, I would highly recommend organizations start to think about reducing the silos, not be reducing the tools, but by potentially looking at cross-liberating by integrating, right? And one of the examples here is very important around recovery from ransomware attacks. So, going back to the point that, "Okay, how do you identify where is the right, "clean copy of the backup?" So these two teams would have to work together. Now the teams would work right out of their heads. They got to depend on technology, right? So that's where the requirement of the tools, themselves, working with each other, security to identifying, "Okay, when to do the forensics tracing "you know where the ransomware part "would identify when did the ransomware get in? "When did the malware get in? "Which systems did it infect?" And then, the backup tools correspondingly acting on those backup instances which have been identified as clean and uninfected. Easier said than done, but that's a part forward. >> And the other thing to make that more complex is that you said business continuity before, that's a people issue, as well. Not just technical process. >> Absolutely. >> Okay, so the two has to have a plan. Like, "What's the plan?" Do they actually huddle and do dry runs? Do they have fire drills? I mean, these are the things that most cyber groups do. They tend to have, you know, very structured approaches to either incidents, response,... So as these worlds come together, what does your research tell you around (chuckles) the questions of working together, proactively, show you? >> Interestingly, enough. I, a couple of years back, I did a survey asking those organizations who have been hit by ransomware attack and have lost data. I asked them, "How many of you have these two teams "working together?" Apparently, you know, some thirty-odd percent responded and said, "Yes, we have these two teams working together." But among, you know, asking final questions, qualifying questions about, "Yes, these two teams "work together," but do they effectively and eventually get to where they should be. Like, have a common plan, right? I think three, four, five percent of the respondents would say, "Yes, we do have a common operating, "understood plan "between the two teams." But largely, all I can say almost all organizations do not have that plan, unfortunately. >> You're, I think, one of the first ransomware experts I've had on theCUBE that's done a lot of research in the area, directly. So I got to ask you on ransomware, first of all it's really bad news and it comes from multiple actors. People lookin' for cash and also state-sponsored, which I believe is goin' on a lot, but no one's reporting on it, but, you know, still that's not proofed yet, but I still get a feeling it's done. On ransomware, do you have any data or insights around if the people clean up their act and get fixed, because I see a lot of ransomware coming back to the same places where they hit once, solve it, pay some bit-coin or whatever their extortion currency is, and then they get hit again. And hit again. Because (clap) there's cash there. Do you see that as a trend? What's the data? Is there any anecdotal insight or are people gettin' hit twice? Three times? >> There are incidents, and I was speaking on, you know, on a panel like half an hour back, and I gave this example. There was a hotel chain in central Europe which was attacked. And the key management system, like if you're one of the guests of that you would not be able to get in, into our rooms. And while they paid a ransom for to release that key management application, they didn't secure that infrastructure and applications further, which was required. And three months later, they were attacked once again. So such incidents are happening. And that's where, you know, guidance from Forrester where we have published a paper about when to consider to pay ransom. Because, you would not be sure that you get the keys. You get the keys for all the data? You don't get any traces of malware left behind or a new malware coming in. You never know, right? >> Of course, yes. >> While this is an untrusted world, but you got to trust if you're paying. (chuckles) >> Yeah, well I think I would bet that the criminals would come back for, you know, new shoes, new coat, new car... They need new things. They need cash (clap), they're going to come back to the bank. >> Absolutely! And they're coming back to basic prey. >> Naveen, thanks for comin' on. I want to get your thoughts on the industry as we wrap up this segment on the trends around cyber protection, data protection, platform. You know, really we're living in a cyber data-driven world. And data is a key part of it. What's the most important trend or story that you think needs to be told or is being told today, in terms of customers to pay attention to? What's, is it ransomware? What's, in your mind, are the top three things that are the most important stories that must be covered or need to be covered, or aren't covered? >> So I think it's not just my story, it's about the state of affairs at an industrial level, globally. I was referring to the World Economic Forum where all the global risks that economies face. It could be famine. It could be a country going bankrupt, right? It could be any other risk that the industry faces. We have seen that, to that starting the World Economic Forum did, in the last 10 years, cyber risk has started to appear on the list of top four, top five risks for the last three years. >> In the world? >> Globally. >> Global issues? >> Global issues, yes. And one of our research also tells us that the number of ransomware incidents have grown 500% in the preceding last 12 months. And the impact, intensity, and frequency of a ransomware attack is simply great. Many organizations are actually shutting down operations. Medical practice in mid-west, called upon the practice and said, "Oh, they are closing operations?" And in fact, it's in public domain. "We're closing operations, you can come back to us "for whatever data we currently have on you." But, I mean I think that from a regulation standpoint, people (mumbles) so that you have to keep control of the data and also be able to provide. But guess what? In this case, the medical practice doesn't have data. If you were their client, if you were a patient they don't have any data on you. Guess what? If it was there for years, you've lost years of your medical data. >> So global issues, ransomware's real and cyber attacks are happening at high frequency. >> Absolutely. >> Naveen, thanks for comin' on. Naveen Chhabra, senior analyst at Forrester Research here inside theCUBE. We are at the Acronis Global Cyber Summit 2019. I'm John Furrier. Back with more coverage for two days here, in Miami Beach, after this short break. Stay with us. (techno music)

>> Narrator: Live, from Miami Beach, Florida it's theCUBE covering UiPathForward Americas. Brought to you by UiPath. >> Welcome back to Miami everybody. You're watching theCUBE, the leader in live tech coverage. I'm Dave Vellante with my cohost Stu Miniman. We got all the action going on behind us. We are seeing the ascendancy of Robotic Process Automation, software robots. one of the leader's in that industry, one of the innovators, Daniel Dines is here, he's the founder and CEO of UiPath. Hot off the keynote, Daniel, thanks for coming on theCUBE. >> Daniel: Thank you for inviting me. >> Dave: You're very welcome, so, the great setup here, the Fontainebleau in Miami's an awesome venue for a conference this size; about 1500 people. In your keynote, you talked about your vision and we want to get into that but, go back to why you started UiPath. >> Daniel: I started UiPath to have joy at work, to do what I like, and to build something big. >> Dave: And you're a Developer, right? I mean you code-- >> Daniel: I am a Software Engineer. >> Dave: I mean, I can tell by the way you're dressed. (laughter) Developer CEO. >> Daniel: Yeah. >> Dave: Yeah, okay, so but you have a vision. You talked about a robot for every person. You mentioned Bill Gates, the PC for every person. I said a chicken for every pot, Harry Truman. What is that vision? Tell us about it. >> Daniel: Well, in our old day they work, we do a lot of menial stuff, repetitive, boring stuff. It's-- that is not human-- it's not human-like. Why not having this robot that we can talk to, we can command and just do the boring stuff for us? I think it's no-brainer. >> Dave: Right. >> Daniel: We just didn't think it's possible. We showed with our technology this is possible, actually. This is an angle of automation that people didn't think it was possible before. >> Dave: Well, so I neglected to congratulate you on your early success, I mean, you said one of your tenants is you're humble. So you got a lot of work to do, we understand that. But you've raised over $400 million to date, you just had a giant raise, we had Carl Eschenbach on in our Palo Alto studios. He was-- he was one of the guys in the round. So that's confirmation that this is a big market, we've pegged it at around a billion dollars today, 10x growth by 2023, so very impressive growth potential. What's driving that growth? >> Daniel: It's all from the customers. When they see it working, it's a "wow," it's different, they won't go back to the same way of delivering work. It's changing how people really work. You see people becoming joyful when we show them the robot, and they say, "I don't need to do this stuff anymore? Wow." Imagine people doing the same reports every day, going through hundreds of page and clicking the same-- this is, this is nirvana. >> Dave: And we saw customers, UnitedHealth was on stage today, Mr. Yamamoto has a thousand robots, Wells Fargo's up there, you had some partners. So you're doing that hard integration work as well. Stu, you noted that the global presence of this company was impressing you. You're thoughts on that. >> Stu: Yeah, absolutely, I mean first of all, company started in Romania, we had-- you know you don't see too many American keynotes where there's a video up there in a foreign language. It's Japanese with English subtitles, you've got customers already starting with a global footprint. What's it like being a founder in a start-up from Europe playing in a global marketplace? >> Daniel: Well, actually it help us to become-- we've been born global. We are one of the first start-ups born global from day one. We've been this company, with Japanese talent, Indian talent, Romanian talent, American talent. And being from this remote part of Europe help us... think big, because really are-- we cannot build this start-up only with Romanians. That's clear, we don't have the pool of talent. So why not just go in global, get the best talent we can and spread global? And we are one of the few companies in the world that has their revenue split equally across the three big continents. >> Stu: Yeah, Daniel, the other thing that struck me-- you're growing the company very fast. We talked about the money, but you said you're going to have over 4,000 employees by 2019. You know, I play a lot in the open source world, it's often small-team, you've got to go marketplace, how come you need so many employees for a software company? Maybe explain a little bit that relationship with a customer, how much you, you're technical people, what they need to do to interact and help them to grow these; is it verticals, you know, what's that dynamic? >> Daniel: Well, first of all, we hire more than 1,000 people in last year alone. We started from 200 and now we are 1,400. We need all these people because this technology is at the intersection of software and services. We need to help our customers scale, and we need to inject a lot of customer success people making our customer successful. My, my way of building a company is customer first. We want to offer this boutique type of approach to our customers, and they are happy. And they-- and we build this trust relationship. This is why we need so many-- We have 2,000 customers. Next year, we have 5,000 customers. We need our people to help them grow. >> Dave: We're going to have Craig Le Clair on a little later. He's the Vice President of Forrester Research. They've done a deep dive in this marketplace in the last couple years now. UiPath has jumped from number three to number one in the Forrester wave, and when you look at that report, really, the feature and function analysis shows you guys lead in a number of places. In listening to your keynote, I discerned several things that I wonder if you could explain for our audience. It sounds like computer vision is a key linchpin to your architecture, and there seems to be an orchestrator and then maybe a studio to enable simple low code, or even no code automations to be developed. Can you describe, so a layperson-- your architecture, and why you've been able to jump into the lead. >> Daniel: Well, we've done everything wrong as a start-up. We spent like seven years building a computer vision technology that-- it was of little use, back then. We did it just because we liked it. And now, this is our powerful weapon, because, what's important for this robot is to be accurate, and to be able to work in any situations. Why our technology works better, is that we do way better the extra mile of automation. 80% of the job anyone can do, even with free software. But the last 20% is where the real issues is. And with the last 20% there is no automation. And we are doing way faster. So all our signal sources-- the fact that we've done something against Lean, against every principal in start-up, we had the lecture in building so many years technology, without even envisioning the use. But when we found the market, and it was a great product market, then we scale the company. >> Dave: There are a couple key statistics that I want to bring up and get your thoughts on. We know that there are now more jobs than there are people to fill those jobs. We also know that the productivity hasn't been increasing, so your vision is to really close that gap through RPA and automation. So your narrative is really that you're not replacing humans, you're augmenting humans, but at the same time, there's got to be some training involved. You guys are making a huge commitment in training. You're going to train a million people, that's the goal, within three years. We have Tom Clancy on next. We're going to ask him how he's going to do that. But talk about that skills gap and how you're embracing re-training. >> Daniel: Well, we realize that at some point that change management, it's kind of the key-- it's the cornerstone of delivering this technology. Because there is inertia, there is fear, and-- if we bring, at the same time, automation and training, it solves this-- that solve this issue. And we have to think big; this is why: one million is a big goal, but we will achieve it because we-- I love my way to think big. I was thinking small for so many years, and thinking big it's like, it's like liberty. You sat down and realize, "Yes, you can." >> Stu: Daniel, we talk a lot about digital transformation. The automation often doesn't get talked, but in big companies; Microsoft, Oracle, SAP, seems a natural fit, I saw some of them are your partners, you came from Microsoft, maybe talk about that dynamic about how some of the, you know, big players that, you know, have the business process applications, how your solution fits with them, you know, are they going to be paying attention to this space? >> Daniel: Well, digital transformation, it's a big initiative for everybody. And RPA, it's actually right now, recognizes the first step in digital transformation. And obviously that if was RPA, AI, big business applications, it's not one single angle, but we covered the last mile of automation. We've covered the impossible, before, before this. And our automation first view of the world is beyond digital transformation because companies will exist after they build for digital transformation. But automation first is a, is a mindset. It's rethinking your operations by applying automation first. >> Dave: You have an open mindset, which is interesting. You even said on stage that, "Look, our competitors are beginning to mimic "some of our features and functions and our approach." And you said, "That's okay." I was surprised by that, especially given your Microsoft background, which was like, grind competitors into the ground. What's changed? Why the open mindset and why do you believe that's the right approach? >> Daniel: Look at Microsoft, Microsoft has changed. This is the-- it's much better, it's-- you feel better as a human. When you can offer something, "This is up, take it, give me feedback." We've been able to build way faster than them, having our open and free community. Open the software-- It gives you more joy as a developer seeing thousands of people than just guarding my little secret just for fear someone will copy it. It's way better. >> Dave: Now, you said on stage that a lot of people laughed at you when you were starting this company, you dream big. Somebody once said, Stu, that, "If you believe you can do it, "or you don't believe you can do it, you're right." "So you got to believe," was one of the things that you said. >> Daniel: That's the first thing. >> Dave: Yeah, so share with the young people out here who are dreaming big, everybody in their early 20's, they're dreaming big. Tell us about your story, your dreams, people who laughed at you, what were they laughing about and how did you power through that? Where did you get your conviction? >> Daniel: Well, first of all, they don't dream big enough. It's very difficult to big dream enough because you have your, you know-- it's the common sense that comes into the picture and it's the fear of other people laughing at you. And we haven't dreamt big enough. For 10-- for the first 10 years, we just wanted to make a good technology, the best technology that we can but that's not big enough. Big enough is change the world, big enough is bring something that makes people life better. This is big enough. If they think making people lives better, that's big enough. Nothing else is big enough. >> Dave: Well I love the fact, Daniel, that your mission-driven; that's clear. You're having some fun. You know this-- these apps are really a lot of fun. Do you still code? >> Daniel: No but I do a lot of software design and review. >> Dave: Okay, so you help, so the coders, they-- how do-- what's that dynamic like? You have-- obviously experienced developer. Do you sort of, tell them which path to go down or which path not to go down? Do you challenge them? What's your style, as a leader? >> Daniel: I challenge them to do things faster, always. They-- I ask them, let's do this feature and they say, "Two month." "No, two days." Why not? And then we go and break that one and it's a lot of conversation but usually we will deliver. Fast-- fast is also a way of being. Fastest company wins, and fast is a-- it's not easy to change the mind. Because you want-- maybe you want to be very organized, very sophisticated. If you are fast, you have to be ready to make mistakes, reverse your decision going, but you will go fast in the end. >> Dave: So that is kind of Steve Jobs-like, set a really challenging goal, and people somehow will figure it out, but culturally, you seem friendlier, nicer. It's not grinding people anymore, it's inspiring them. Is that a fair assessment? >> Daniel: My goal is to have the happiest team employees everywhere. Hap-- I like to be happy. I started this company for the joy of doing what I like, why not, this is, this is what I want for everyone. And we are-- we recently scored in comparably as one of the best company in terms of people happiness. >> Dave: Well congratulations, thanks so much for coming on theCUBE. >> Daniel: Thank you very much for inviting me. >> Dave: Really a pleasure having you. Alright, Stu and I will be back with our next guest. Right after this short break, we're live from UiPath... in Miami, you're watching theCUBE. Stay right there. (electronic music)

>> Narrator: Live, from downtown San Francisco. It's theCUBE. Covering IBM Chief Data Officer Strategy Summit 2018. Brought to you by IBM. (techno music) >> Welcome back to San Francisco everybody. You're watching theCUBE, the leader in live tech coverage. My name is Dave Vellante. And we are here at the IBM CDO Strategy Summit hashtag IBMCDO. Caitlin Halferty is here. She's a client engagement executive for the chief data officer at IBM. Caitlin great to see you again. >> Great to be here, thank you. >> And she's joined by Brandon Purcell, who's principal analyst at Forrester Research. Good to have you on. >> Thanks very much, thanks for having me. >> First time on theCUBE. >> Yeah. >> You're very welcome. >> I'm a newbie. >> Caitlin... that's right, you're a newbie. You'll be a Cube alum in no time, I promise you. So Caitlin let's start with you. This is, you've done a number of these CDO events. You do some in Boston, you do some in San Francisco. And it's really great to see the practitioners here. You guys are bringing guys like Inderpal to the table. You've announced your blueprint in it. The audience seems to be lapping up the knowledge transfer. So what's the purpose of these events? How has it evolved? And just set the table for us. >> Sure, so we started back in 2014 with our first Chief Data Officer Summit and we held that here in San Francisco. Small group, probably only had about 30 or 40 attendees. And we said let's make this community focused, peer to peer networking. We're all trying to, ya know, build the role of either the Chief Data Officer or whomever is responsible for enterprise wide data strategy for their company, a variety of different titles. And we've grown that event over, since 2014. We do Spring, in San Francisco, which tends to be a bit more on the technical side, given where we are here in San Francisco in Silicon Valley. And then we do our business focused sessions in Fall in Boston. And I have to say, it's been really nice to see the community grow from a small set of attendees. And now was are at about 130 that join us on each coast. So we've built a community in total of about 500 CDOs and data executives, >> Nice. that are with us on this journey, so they're great. >> And Brandon, your focus at Forrester, part of it is AI, I know you did some other things in analytics, the ethics of AI, which we're going to talk about. I have to ask you from Forrester's perspective, we're enter... it feels like we're entering this new era of there's digital, there's data, there's AI. They seem to all overlap. What's your point of view on all this? >> So, I'm extremely optimistic about the future of AI. I realize that the term artificial intelligence is incredibly hyped right now. But I think it will ultimately fulfill it's promise. If you think about the life cycle of analytics, analytics start their lives as customer data. As customers interact and transact with you, that creates a foot print that you then have to analyze to unleash some sort of insight. This customer's likely to buy, or churn, or belongs to a specific segment. Then you have to take action. The buzzwords of the past have really focused on one piece of that life cycle. Big data, the data piece. Not much value unless you analyze that. So then predictive analytics, machine learning. What AI promises to do is to synthesize all of those pieces, from data, to insights, to action. And continuously learn and optimize. >> It's interesting you talk about that in terms of customer churn. I mean, with the internet, there was like a shift in the balance of power to the consumer. There used to be that the brand had all the knowledge about the buyer. And then with the internet, we shop around, we walk into a store and, look at them. Then we go buy it on the internet right? Now that AI maybe brings back more balance, symmetry. I mean, what are your thoughts on that? Are the clients that you work with, trying to sort of regain that advantage? So they can better understand the customer. >> Yeah, well that's a great question. I mean, if there's one kind of central ethos to Forrester's research it's that we live in the age of the customer and understanding and anticipating customer needs is paramount to be able to compete, right? And so it's the businesses in the age of AI and the age of the customer that have the data on the customer and enable the ability to distill that into insights that will ultimately succeed. And so the companies that have been able to identify the right value exchange with consumers, to give us a sense of convenience, so that we're willing to give up enough personal data to satisfy that convenience are the ones that I think are doing well. And certainly Netflix and Amazon come to mind there. >> Well for sure, and of course that gets into the privacy and the ethics of AI. I mean everyone's making a big deal out of this. You own your data. >> Yeah. >> You're not trying to monetize, ya know, figure out which ad to click on. Maybe give us your perspective, Caitlin, on IBMs point of view there? >> Sure, so we lead with this thought around trusting your data. You're data's your data. Insights derive from that data, your insights. We spend a lot of time with our Watson Legal folks. And one of the things, pieces of material we've released today is the real detail at every level how you engage the traceability of where your data is. So you have a sense of confidence that you know how it's treated, how it's curated. If it's used in some third party fashion. The ability to know that, have visibility into it. The opt-out, opt-in opt-out set of choices. Making sure that we're not exploiting the network effect, where perhaps party C benefits from data exchange between A and B. That A and B do not, or do not have an opportunity to influence. And so what we wanted to do, here at the summit over the next couple of days is really share that in detail and our thoughts around it. And it comes back to trust and being able to have that viability and traceability of your data through the value chain. >> So of course Brandon, as a customer I'm paying IBM so I would expect that IBM would look out for my privacy and make that promise. I don't really pay Facebook right? But I get some value out of it. So what are the ethics of that? Is it a pay or no pay? Or is it a value or no value? Is it everybody really needs to play by the same rules? How to you parse all that? >> Ya know, I hate to use a vague term. But it's a reasonable expectation. Like I think that when a person interacts with Facebook, there is a reasonable expectation that they're not going to take that data and sell it or monetize it to some third party, like Cambridge Analytica. And that's where they dropped the ball in that case. But, that's just in the actual data collection itself. There's also, there are also inherent ethical issues in how the data is actually transformed and analyzed. So just because you don't have like specific characteristics or attributes in data, like race and gender and age and socioeconomic status, in a multidimensional data set there are proxies for those through something called redundant encoding. So even if you don't want to use those factors to make decisions, you have to be very careful because they're probably in there anyway. And so you need to really think about what are your values as a brand? And when can you actually differentiate treatment, based on different attributes. >> Because you can make accurate inferences from that. >> Brandon: Yeah you're absolutely (mumbles). >> And is it the case of actually acting on that data? Or actually the ability to act on that data? If that makes sense to you. In other words, if an organization has that data and could, in theory, make the inference, but doesn't. Is that crossing the line? Is it the responsibility of the organization to identify those exposures and make sure that they can not be inferred? >> Yeah, I think it is. I think that that is incumbent upon our organizations today. Eventually regulators are going to get around to writing rules around this. And there's already some going into effect of course in Europe, with GDPR at the end of this month. But regulators are usually slow to catch up. So for now it's going to have to be organizations that think about this. And think about, okay, when is it okay to treat different customers differently? Because if we, if we break that promise, customers are going to ultimately leave us. >> That's a hard problem. >> Right, right. >> You guys have a lot of these discussions internally? >> We do. >> And can you share those with us? >> Yeah, absolutely, we do. And we get a lot of questions. We often engage at the data strategy perspective. And it starts with, hey we've got great activity occurring in our business units, in our functional areas, but we don't really have a handle on the enterprise wide data strategy. And at that point we start talking about trust, and privacy, and security, and what is your what does your data flows look like. So it starts at that initial data strategy discussion. And one other thing I mentioned in my opening remarks this morning is, we released this blueprint and it's intended, as you said, to put a framework in process and reflect a lot of the lessons learned that we're all going through. I know you mentioned that many companies are looking at AI adoption, perhaps more so than we realized. And so the framework was intended to help accelerate that process. And then our big announcement today has been around the showcases, in particular our platform showcase. So it's really the platform we've built, within our organization. The components, the products, the capabilities that drives for us. And then with the intent of hopefully being, illustrative and helpful to clients that are looking to build similar capabilities. >> So let's talk about adoption. >> Brandon: Yeah, sure. >> Ya know, we... you often hear this bromide that we live in a world where, that pace of change is so fast. And things are changing so quickly it's hard to deny that. But then when you look at adoption of some of the big themes in our time. Whether it's big data or AI, digital, block chains, there are some major barriers to adoption. So you see them adopted in pockets. What's your perspective, and Forrester's perspective on adoption of, let's call it machine intelligence? >> Yeah, sure, so I mean, every year Forrester does a global survey of business and technology decision leaders called Business Technographics. And we ask folks about adoptions rates of certain technologies. And so when it comes to AI, globally, 52% of companies have adopted AI in some way. And another 20% plan to in the next 12 months. What's interesting to me, actually, is when you break that down geographically, the highest adoption rate, 60 plus percent, is in APAC, followed by North America, followed by Europe. And when you think about the privacy regulations in each of those geographies, well there are far fewer in APAC than there are, and will be, in Europe. And that's, I think kind of hamstringing adoption in that geography. Now is that a problem for Europe? I don't think so actually. I think AI, the way AI is going to be adopted in Europe is going to be more refined and respectful of customers' intrinsic right to privacy. >> Dave: Ya know I want... Go ahead. >> I've got to, I have to say Dave, I have to put a plug in. I've been a huge fan of Brandon's, for a long time. I've actually, ya know, a few years now of his research. And some of the research that you're mentioning, I hope people are reading it. Because we find these reports to be really helpful to understand, as you said, the specifics of adoptions, the trends. So I've got to put a plug in there. >> Thanks Caitlin. >> Because, the quality of the work and the insights are incredible. So that is why I was quite excited when Brandon accepted our offer to join us here in this session. >> Awesome. Yeah, so, let's dig into that a little bit. >> Brandon: Sure. >> So it seems like, so 52%, I'm wondering, what the other 48 are doing? They probably are, and they just don't know it. So it's possible that the study looks at, a strategy to adopt, presumably. I mean actively adopting. But it seems, I wonder if I could run this by you, get your comment. It seems that people will, organizations will more likely be buying AI as embedded in applications or systems or just kind of invisible. Then they won't necessarily be building it. I know many are trying to probably build it today. And what's your thought on that? In terms of just AI infused everywhere? >> So the first foray for most enterprises into this world of AI is chat bots for customer service. >> Dave: Sure. >> I mean we get a ton of inquires at Forrester about that. And there are a number of solutions. Ya know, IBM certainly has one for, that fulfill that need. And that's a very narrow use case, right? And it's also a value added of use case. If you can take more of those call center agents out of the loop, or at least accelerate or make them better at their jobs, then you're going to see efficiency gains. But this isn't this company wide AI transformation. It's just one very narrow use case. And usually that's, most elements of that are pre-built. We talked this morning, or the speakers this morning talked about commoditization of certain aspects of machine learning and AI. And it's very true. I mean, machine learning algorithms, many of them have been around for a long time, and you can access them for multiple different platforms. Even natural language processing, which a few years ago was highly inaccurate, is getting really, really accurate. So when, in a world where all of these things are commoditized, it's going to end up being how you implement them that's going to drive differentiation. And so, I don't think there's any problem with buying solutions that have been pre-built. You just have to be very thoughtful about how you use them to ultimately make decisions that impact the customer experience. >> I want to, in the time we have remaining, I want to get into the tech radar, the sort of taxonomy of AI or machine intelligence. You've done some work here. How do you describe, can you paint a picture, for what that taxonomy looks like? >> So I think most people watching realize AI is not one specific thing right? It's a bunch of components, technologies that stitched together lead to something that can emulate certain things that humans do, like sense the world around us, see, read, hear, that can think or reason. That's the machine learning piece. And that can then take action. And that's the kind of automation piece. And there are different core technologies that make up each of those faculties. The kind of emerging ones are deep learning. Of course you hear about it all the time. Deep learning is inherently the use of artificial neural networks, usually to take some unstructured data, let's say pictures of cats, and identify this is actually a cat right? >> Who would have thought? That we're led to this boom right? >> Right exactly. That was something you couldn't do five or six years ago, right? You couldn't actually analyze picture data like you analyze row and column data. So that's leading to a transformation. The problem there is that not a lot of people have this massive number of pictures of cats that are consistently and accurately labeled cat, not cat, cat, not cat. And that's what you need to make that viable. So a lot of vendors, and Watson has an API for this have already trained a deep neural network to do that so the enterprises aren't starting from scratch. And I think we'll see more and more of these kind of pre-trained solutions and companies gravitating towards the pre-trained solutions. And looking for differentiation, not in the solutions themselves, but again how they actually implement it to impact the customer experience. >> Hmmm, well that's interesting, just hearing you sense, see, read, hear, reason, act. These are words that describe not the past era. This is a new era that we're entering. We're in the cloud era now. We can sort of all agree with that. But these, the cloud doesn't do these things. We are clearly entering a new wave. Maybe it's driven by Watson's Law, or whatever holds out. Caitlin I'll give you the last word. Put a bumper sticker on this event, and where we're at here in 2018? >> I'll say, it's interesting to watch the themes evolve over the last few years. Ya know, we started with sort of a defensive posture. Most of our data executives were coming perhaps from an IT type background. We see a lot more with line of business, and chief operations type role. And we've seen the, we still king of the data warehouse, that's sort of how we described at the time. And now, I see our data leaders really driving transformation. They're responsible for both the data as well as the digital transformation. On the data side, it's the AI focus. And trying to really understand the deep learning capabilities, machine learning, that they're bringing to bear. So it's been, for me, it's been really interesting to see the topics evolve, see the role in the strategic piece of it. As well as see these guys elevated, in terms of influence within their organization. And then, our big topic this year was around AI and understanding it. And so, having Brandon to share his expertise was very exciting for me because, he's our lead analyst in the AI space. And that's what our attendees are telling us. They want to better understand, and better understand how to take action to implement and see those business results. So I think we're going to continue to see more of that. And yeah, it's been great to see, great to see it evolve. >> Well congratulations on taking the lead, this is a very important space. Ya know, a lot of people didn't really believe in it early on, thought the Chief Data Officer role would just sort of disappear. But you guys, I think, made the right investment and a good call, so congratulations on that. >> I was laughed out of the room when I proposed, I said hey we're hearing of this, doing a market scan of Chief Data Officer, either by title or something similar, titled responsible for enterprise wide data. I was laughed out of the room. I said let me do a qualitative piece. Let me interview 20 and just show, and then you're right, it was the thought was, role's going to go by the wayside. And I think we've seen the opposite. >> Oh yeah, absolutely. >> Data has grown in importance. The associative capabilities have grown. And I'm seeing these individuals, their scope, their sphere of responsibility really grow quite a bit. >> Yeah Forrester's tracked this. I mean, you guys I think just a few years ago was like eh, yeah 20% of organizations have a Chief Data Officer and now it's much much higher than that. >> Yeah, yeah, it's approaching 50%. >> Yeah, so, good. Alright Brandon, Caitlin, thanks very much for coming on theCUBE. >> Thanks for having us. >> Thank you, it was great. >> Keep it right there everybody. We'll be back, at the IBM Chief Data Officer Strategy Summit. You're watching theCUBE. (techno music) (telephone tones)

>> Narrator: From the Silicon Angle Media office, in Boston, Massachusets. It's The Cube. Now here's you host, Stu Miniman. >> Hi, I'm Stu Miniman and we're coming to you from the Boston area studio here of The Cube. Excited to talk about some of my favorite topics. Talking about the culture, innovation, and really transformation in what's happening in data center. Digital transformation is on everybody's mind. Specifically happy to welcome Mike Grandinetti who is the Chief Marketing and Corporate Strategy Officer with Reduxio. Mike, thanks so much for joining us. >> Stu, thank you so much for having me. Great to be out here with you today. >> Alright, so you're a local guy? >> Mike: Yeah. >> We're glad that you could join us here. Before we jump into the company tells a little about your background, what you worked on, what brought you to Reduxio. >> In a nutshell I guess my background is all about innovation. I've sort of eat, breathe and slept innovation for the last 25 years of my career. So I started off as an engineer in Silicon Valley with HP back when Bill and Dave were still around. At a time when it was America's most admired company. Was a remarkable sort of introduction to what is possible. Went back, got my MBA, did several years at McKinzie doing corporate strategy consulting. Mostly around innovation related projects. And then I moved up here to Boston to be a part of the first of what is now eight consecutive enterprise venture capital backed start ups. And I've been lucky enough that two of those went public on the NASDAQ. The prior seven have all been acquired by companies like AT&T and Oracle. And now Reduxio is my eighth start up. We're really having a great time building this business. >> Great, we're definitely going to big into some of the innovations of Redux I O. >> Yes. >> So the name kind of tells itself. We've seen a few companies with the I O at the end. We've talked so much that when we've talked about kind of 2018 data is at the center of everything. Really what is driving business. So for an audience that hasn't run across Reduxio kind of give us the why and the what. >> Yeah, and so to your point, data's driving everything. Mark Andressen famously said software's eating the world. I think if we were to update that it's data is eating the world. And so I think you and I have had this discussion off camera. Whether it's fair or not, I think it's true. And it needs to be stated that the amount of innovation that has occurred in the storage industry over the last 20 years, has been disappointing at best. The solutions that have evolved have evolved in an extremely fragmented way. They are over, way too complex. They're way too expensive. And because it's a collection of piece parts, you've got to manage multiple screens, multiple learning curves. And a lot of things fall through the cracks. So when you go and look at some of the research data from a wide range of analysts, what you hear from them is there's this extraordinary lack of confidence that even though I've spend a ton of money, invested a lot of staff time and attention to building out this infrastructure, very lacking in confidence that I'm actually going to get that data back when I need it. So it's the old adage, it's time to fix it. So this is exactly what the founders of Reduxio saw. They were looking at this evolutionary path and saying people are just making it worse. So they did what many people would condsider to be radical. They threw out the entire playbook of what storage architecture has been and they took a clean sheet of paper, design centric approach. What are the use cases? Where are we in the world with regard to technology? And how do we design and experience for storage admin or BD admin or a person in the dev center that doesn't require a PhD in storage? And so that's kind of what the premise was. >> Yeah, so many things there that there are to dig into. Absolutely. I live, I worked for one of the storage companies for a decade. Absolutely complexity is how we would describe it. And what companies are looking for today, is they need simplicity. They need to focus on the business. Turing dials and worrying about do I have enough capacity? Do I have enough performance? Do I have enough of those things, is not what drives the business. >> Mike: Exactly. >> They need to focus on their applications. The bit flip we saw in big data, and we can argue whether or not big data was hype or whatever we had there, but it was oh my gosh I'm getting all this data to oh my gosh I have all of this data and therefore I can do more things, I can find more value. >> Mike: Absolutely. >> I worry a little bit when I hear things like oh, the storage admin. >> Yeah. >> The storage admin's job before was how to I triage and kind of deal with those issues? Many solutions now you look at the wave of hyper convergence. Let's push that to a cloud architect or the virtualization layer. How do we start with a clean slate and get out of the storage business and get into the data business? >> Mike: I love it. So I'm going to bring you back ten years to one of the most remarkable product introductions that has ever been conducted on this planet. It was the introduction of the iPhone. And if you recall in those first five minutes that Steve Jobs took the stage in a way that only Steve Jobs could. He went onto tease the audience by saying that we are going to be introducing three products today. And then over the next minute or two became clear that it wasn't three products, it was one very innovative product at the time. The iPhone. What they basically did is they integrated these three previously disparate pieces of technology. Certainly the mobile phone but also a music player and an internet navigator. Behind this gorgeous revolutionary user interface. So what we've tried to do is take a page out of the Job's iPhone innovation. We're integrating. And Forrester Research has written an incredible report about this and others, IDC and others, have consistently supported it. Chris Malore from the Register has written about this at length as well. Reduxio is integrating primary and secondary storage along with built in data protection. So those previously siloed capabilities are now one. We're also, like Jobs did, when you looked at the old style smart phone, the BlackBerry and the Trio and the- ya know all of those things that had all of those keyboards, is we've created a user interface using game designers so when our customers go home at night and they log into Reduxio, their little kids will say, hey dad what game are you playing? And dad will say, I'm not playing a game. I'm actually working on Reduxio. And so what that's done for us I think is it's allowed us to be able to drop a Reduxio system into any number of use cases with someone who may not have the luxury of being deep in storage. And literally get time to value that they put production workloads on the system that day. >> It's interesting, another piece that I'll draw from your analogy is when you talk about how did Apple take all of those pieces. And it's kind of certain technologies moving along. But there's one specific technology that really helped drive that adoption. And it's Flash. >> Mike: Yes. >> And the consumer adoption of Flash ten years ago drove the wave that we've seen in enterprise storage. >> Right. >> So help connect the dots for us, because we look at- I remember a decade ago primary to secondary storage oh I'll give you a big eleven refrigerator size cabinet and you can do both. >> Mike: Right, sure. >> But I put expensive stuff here, I put cheap stuff here. I used the software to put it together. I'm assuming I can consolidate it down and I think Flash has something to do with it. >> Yeah, and so it's a multi tiered system. The array itself. It's an appliance. And obviously most of the value is in the software. There's a management platform that allows us to peer deep into the data. But everything is time stamped and indexed. So we have a global view of the data. And you can tier it, the most hot data very mission critical, business app data, goes to Flash. Secondary data can go to spinning disk or now we can archive to the cloud. Specifically any S3 target, Amazon or any S3 target. But what I think makes it very relevant is we've illuminated the notion of snapshotting. So we've built something that we call the time OS or the time operating system. And it's a time machine for your data. What happens is rather than incur that incredible burden of having to schedule snapshots, that only requires you at another incredible heroic effort to bring the data back, you have continuous data protection. I can go back at any point in time and literally with a very graphical screen point and say I want to bring data back from two seconds ago. And one of our best examples of that is we had a customer who had been attacked, has suffered from a ransomware attack. They went down for a week, they went down hard for a week. And they came and found Reduxio. They got attacked again. And the second time around they lost only two minutes of data. And the recovery time was 20 minutes. So this is what we enable you to do. By being able to give you access to wherever you're data may be, anywhere in the world, you can- we're approaching near zero RPO and RTO. >> Mike, there's been a number of companies that come and said data protection's been broken. We've been hearing that for a while. I think right down the road from us, like Tiffeo, company that looked at data management. Companies like Cohesity and Rubric, have quite a bit of buzz. Give us a little compare, contrast how Redxio looks at it verses some of those other- >> Yeah, and I'd say again, for anybody watching I think the Forrester Research Report outlines Reduxio, Cohesity and Rubric, right? And of course Cohesity and Rubric are doing an extraordinary job. They're scaling rapidly. They've got world class in Silicon Valley money in the company. They've got a world class client base. I think the primary difference is that we are bringing that third component. We're integrating primary storage along with secondary storage in data protection. Both of them are focusing just on the secondary and the data protection. We take issue architecturally with the fact that you've got to make additional copies. We take issue with the fact that the way they're approaching this actually they're in some ways exacerbating the problem because they're creating more data. But at the same time, they're also, for a given amount of capability two to three times the cost. So what we're hearing from a lot of our customers and our vars that sell both is they're walking into a lot of more, let's call them price sensitive accounts. Where they don't believe that the incremental value of what Cohesity or Rubric is offering is easily justifiable. There's going to be some pretty extreme use cases to justify a $300,000 initial investment as you go into the data center. >> Another piece, when I talk to companies today, one of the biggest challenges they have is really figuring out what their strategy is and how that fits. You talked about tiering and how the cloud fits into it, but how does Reduxio fit in that overall cloud strategy for companies today? >> Again, it's very early in our product evolution and so with version three which we announced back in late June, we allow companies to archive to the cloud. But do instantaneous recovery from the cloud. So we have two capabilities. One is called no migrate. So there's no longer a need to migrate data. So you were at the Amazon invent show and you saw the snowmobile get rolled out. And the reason that Amazon rolled that snowmobile and at first I thought it was a joke, is because it takes an incredible amount of time and effort to move data from one data center to the next. Reduxio has this no migrate capability where if I need to move data from that data center, I set that data in motion. And I don't know if you're a Trekkie or not, but you remember the teleporter? In version three we've created a teleporter. You can move that data from the cloud and although it may take a long time for that data to actually get to its target, you can start working on that app as if that data had already been migrated. When we run usability tests, and I remember one of them very specifically. And I know that you speak a little bit of Hebrew. I speak zero Hebrew. But I can remember watching one of our Israeli customers seeing this happen and this visceral reaction, like oh my god, I can't believe they did that. So we're trying to bring that end to end ease of use experience to managing and protecting your data wherever it may be. Bringing it back with almost zero RPOs, zero RTO. >> Mike, one of the questions, I've been talking to a number of CMOs lately, and just you've worked for a number of start ups. Today, digital transformations on the mind, what's the changing role of the CMO today? What have you seen the last five to ten years that's different and exciting? >> It's a great question. And I'd say that, and again, I did my first start up in 1991. So I can't begin to tell you how much high tech marketing has changed. But everything changed with social, digital and inbound marketing. It used to be that the sales team was responsible for filling the funnel. It is very clear that is an incredibly non scalable unproductive effort. And so we now are all about acquiring high quality prospects. We're a hub spot shop. We're a highly automated shop. And we are very biased toward digital and social. Is doesn't mean that we're not going to events and things like that but we feel that the way that we're going to scale this business, especially when we compete against big guys like Dell EMC and HP and others, there's no way that we can go person to person. So I'm not a very big fan of cold calling. I'm not a very big fan of going to trade shows. And collecting business cards in fish bowls and giving away tee shirts. We really believe that our customers are too busy, the know what they need when they need it. They've built a fortress around themselves. They're getting hammered. Just like I'm a CMO. And I must get 150 LinkedIn inmails and emails a day telling me about the next great lead management service. I can't even imagine what our customers are putting up with. So our job is to find relevant personas with highly relevant content at the moment that that is relevant to them. And there's many ways to do that, but this is really what we have to do with the data. >> So, Mike, at the beginning of the conversation we talked a little bit about innovation. >> Mike: Yes. >> Those of us that have been in a while, they're too many peers of mine that I think if you say the word innovation they roll their eyes. You have the great opportunity, you're working with master students around the globe, talk to us the people coming out of those programs. What does innovation mean today? What are they looking for, from a career standpoint? >> It's a great question. I think you and I could probably go for the next three hours on this subject so we'll have to be careful. >> We'll make sure to post on the website the expanded audio. >> Okay, but I mean innovation is such an overused word. And most companies really can't spell it and they can't spell it because their culture doesn't allow for it. So first and foremost, I think any innovative company or any innovative team starts with a culture that is all about trying to manage at the bleeding edge of best practices and really understand what's current. I have the blessing of being both the Chief Marketing and Corporate Strategy Officer of Reduxio and a global professor of innovation entrepreneurship at the Hult International School of Business. I teach between 1,200 and 1,500 students a year. I teach them courses in entrepreneurship, in innovation, in digital marketing. And I run hackathons on campus. We do a lot of events that give me an insight into who's passionate about innovation. And it's one thing to think innovation is interesting, because you can get a good job. It's another thing to actually have the comfort level of living in a world of ambiguity and high velocity. So a lot of it is, I'm looking for students that really want to sort of push the envelope. And they exhibit that in the classroom, they exhibit that in hackathons. They exhibit that in some of the internships that we take. They exhibit it by getting certified on HubSpot. Without me telling them to. Getting certified on Idio without me telling them to. Going to conferences. Learning. And then me learning from them. Because nobody can know everything. It's just so much new stuff going on right now. I've now got a team of 11 people and nine of them were my former students. I had a chance to observe them in action over 18 months and they're world class. And they have that innovation gene in their DNA. We're really at a point where I'm learning from them everyday. It's a very symbiotic relationship. >> Mike, for closing comments, I want to give you the opportunity, people find out more about Reduxio. What should we be looking for in 2018? >> Yeah, and so again, the one thing is will say is we are now at 200 distinct customers. We have in a very short period of time, and you know, when you sell into the data center people don't have a real sense of humor. It's pretty important that the stuff works. So the first thing I would say is we've gotten to that point now where we've got a lot of very significant customer references across websites and a lot of peer review sites. So we're now, so 2018 is building on that foundation. I think what you're going to see from us is couple of very radically innovative new projects. One a software only project. That will allow us to drive an inflection point in growth. By making available some of our core capabilities to anybody. Whether they own a Reduxio system or not. We really want to go big now. We've validated the architecture. We've got some great early indications from the market that this stuff works as advertised. Our customers are telling us we're simplifying their lives, we're making them more productive. And 2018 is about to really kick this thing into high gear. >> Stu: Mike Grandinetti, pleasure chatting with you. Thanks so much for sharing. And thank you for watching The Cube. >> Mike: Great. (upbeat music)