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

(upbeat music) >> From theCube Studios in Palo Alto in Boston, bringing you data driven insights from theCube and ETR. This is Breaking Analysis with Dave Vellante. >> While by no means a safe haven, the cybersecurity sector has outpaced the broader tech market by a meaningful margin, that is up until very recently. Cybersecurity remains the number one technology priority for the C-suite, but as we've previously reported the CISO's budget has constraints just like other technology investments. Recent trends show that economic headwinds have elongated sales cycles, pushed deals into future quarters, and just like other tech initiatives, are pacing cybersecurity investments and breaking them into smaller chunks. Hello and welcome to this week's Wikibon Cube Insights powered by ETR. In this Breaking Analysis we explain how cybersecurity trends are reverting to the mean and tracking more closely with other technology investments. We'll make a couple of valuation comparisons to show the magnitude of the challenge and which cyber firms are feeling the heat, which aren't. There are some exceptions. We'll then show the latest survey data from ETR to quantify the contraction in spending momentum and close with a glimpse of the landscape of emerging cybersecurity companies, the private companies that could be ripe for acquisition, consolidation, or disruptive to the broader market. First, let's take a look at the recent patterns for cyber stocks relative to the broader tech market as a benchmark, as an indicator. Here's a year to date comparison of the bug ETF, which comprises a basket of cyber security names, and we compare that with the tech heavy NASDAQ composite. Notice that on April 13th of this year the cyber ETF was actually in positive territory while the NAS was down nearly 14%. Now by August 16th, the green turned red for cyber stocks but they still meaningfully outpaced the broader tech market by more than 950 basis points as of December 2nd that Delta had contracted. As you can see, the cyber ETF is now down nearly 25%, year to date, while the NASDAQ is down 27% and change. Now take a look at just how far a few of the high profile cybersecurity names have fallen. Here are six security firms that we've been tracking closely since before the pandemic. We've been, you know, tracking dozens but let's just take a look at this data and the subset. We show for comparison the S&P 500 and the NASDAQ, again, just for reference, they're both up since right before the pandemic. They're up relative to right before the pandemic, and then during the pandemic the S&P shot up more than 40%, relative to its pre pandemic level, around February is what we're using for the pre pandemic level, and the NASDAQ peaked at around 65% higher than that February level. They're now down 85% and 71% of their previous. So they're at 85% and 71% respectively from their pandemic highs. You compare that to these six companies, Splunk, which was and still is working through a transition is well below its pre pandemic market value and 44, it's 44% of its pre pandemic high as of last Friday. Palo Alto Networks is the most interesting here, in that it had been facing challenges prior to the pandemic related to a pivot to the Cloud which we reported on at the time. But as we said at that time we believe the company would sort out its Cloud transition, and its go to market challenges, and sales compensation issues, which it did as you can see. And its valuation jumped from 24 billion prior to Covid to 56 billion, and it's holding 93% of its peak value. Its revenue run rate is now over 6 billion with a healthy growth rate of 24% expected for the next quarter. Similarly, Fortinet has done relatively well holding 71% of its peak Covid value, with a healthy 34% revenue guide for the coming quarter. Now, Okta has been the biggest disappointment, a darling of the pandemic Okta's communication snafu, with what was actually a pretty benign hack combined with difficulty absorbing its 7 billion off zero acquisition, knocked the company off track. Its valuation has dropped by 35 billion since its peak during the pandemic, and that's after a nice beat and bounce back quarter just announced by Okta. Now, in our view Okta remains a viable long-term leader in identity. However, its recent fiscal 24 revenue guide was exceedingly conservative at around 16% growth. So either the company is sandbagging, or has such poor visibility that it wants to be like super cautious or maybe it's actually seeing a dramatic slowdown in its business momentum. After all, this is a company that not long ago was putting up 50% plus revenue growth rates. So it's one that bears close watching. CrowdStrike is another big name that we've been talking about on Breaking Analysis for quite some time. It like Okta has led the industry in a key ETR performance indicator that measures customer spending momentum. Just last week, CrowdStrike announced revenue increased more than 50% but new ARR was soft and the company guided conservatively. Not surprisingly, the stock got absolutely crushed as CrowdStrike blamed tepid demand from smaller and midsize firms. Many analysts believe that competition from Microsoft was one factor along with cautious spending amongst those midsize and smaller customers. Notably, large customers remain active. So we'll see if this is a longer term trend or an anomaly. Zscaler is another company in the space that we've reported having great customer spending momentum from the ETR data. But even though the company beat expectations for its recent quarter, like other companies its Outlook was conservative. So other than Palo Alto, and to a lesser extent Fortinet, these companies and others that we're not showing here are feeling the economic pinch and it shows in the compression of value. CrowdStrike, for example, had a 70 billion valuation at one point during the pandemic Zscaler top 50 billion, Okta 45 billion. Now, having said that Palo Alto Networks, Fortinet, CrowdStrike, and Zscaler are all still trading well above their pre pandemic levels that we tracked back in February of 2020. All right, let's go now back to ETR'S January survey and take a look at how much things have changed since the beginning of the year. Remember, this is obviously pre Ukraine, and pre all the concerns about the economic headwinds but here's an X Y graph that shows a net score, or spending momentum on the y-axis, and market presence on the x-axis. The red dotted line at 40% on the vertical indicates a highly elevated net score. Anything above that we think is, you know, super elevated. Now, we filtered the data here to show only those companies with more than 50 responses in the ETR survey. Still really crowded. Note that there were around 20 companies above that red 40% mark, which is a very, you know, high number. It's a, it's a crowded market, but lots of companies with, you know, positive momentum. Now let's jump ahead to the most recent October survey and take a look at what, what's happening. Same graphic plotting, spending momentum, and market presence, and look at the number of companies above that red line and how it's been squashed. It's really compressing, it's still a crowded market, it's still, you know, plenty of green, but the number of companies above 40% that, that key mark has gone from around 20 firms down to about five or six. And it speaks to that compression and IT spending, and of course the elongated sales cycles pushing deals out, taking them in smaller chunks. I can't tell you how many conversations with customers I had, at last week at Reinvent underscoring this exact same trend. The buyers are getting pressure from their CFOs to slow things down, do more with less and, and, and prioritize projects to those that absolutely are critical to driving revenue or cutting costs. And that's rippling through all sectors, including cyber. Now, let's do a bit more playing around with the ETR data and take a look at those companies with more than a hundred citations in the survey this quarter. So N, greater than or equal to a hundred. Now remember the followers of Breaking Analysis know that each quarter we take a look at those, what we call four star security firms. That is, those are the, that are in, that hit the top 10 for both spending momentum, net score, and the N, the mentions in the survey, the presence, the pervasiveness in the survey, and that's what we show here. The left most chart is sorted by spending momentum or net score, and the right hand chart by shared N, or the number of mentions in the survey, that pervasiveness metric. that solid red line denotes the cutoff point at the top 10. And you'll note we've actually cut it off at 11 to account for Auth 0, which is now part of Okta, and is going through a go to market transition, you know, with the company, they're kind of restructuring sales so they can take advantage of that. So starting on the left with spending momentum, again, net score, Microsoft leads all vendors, typical Microsoft, very prominent, although it hadn't always done so, it, for a while, CrowdStrike and Okta were, were taking the top spot, now it's Microsoft. CrowdStrike, still always near the top, but note that CyberArk and Cloudflare have cracked the top five in Okta, which as I just said was consistently at the top, has dropped well off its previous highs. You'll notice that Palo Alto Network Palo Alto Networks with a 38% net score, just below that magic 40% number, is healthy, especially as you look over to the right hand chart. Take a look at Palo Alto with an N of 395. It is the largest of the independent pure play security firms, and has a very healthy net score, although one caution is that net score has dropped considerably since the beginning of the year, which is the case for most of the top 10 names. The only exception is Fortinet, they're the only ones that saw an increase since January in spending momentum as ETR measures it. Now this brings us to the four star security firms, that is those that hit the top 10 in both net score on the left hand side and market presence on the right hand side. So it's Microsoft, Palo Alto, CrowdStrike, Okta, still there even not accounting for a Auth 0, just Okta on its own. If you put in Auth 0, it's, it's even stronger. Adding then in Fortinet and Zscaler. So Microsoft, Palo Alto, CrowdStrike, Okta, Fortinet, and Zscaler. And as we've mentioned since January, only Fortinet has shown an increase in net score since, since that time, again, since the January survey. Now again, this talks to the compression in spending. Now one of the big themes we hear constantly in cybersecurity is the market is overcrowded. Everybody talks about that, me included. The implication there, is there's a lot of room for consolidation and that consolidation can come in the form of M&A, or it can come in the form of people consolidating onto a single platform, and retiring some other vendors, and getting rid of duplicate vendors. We're hearing that as a big theme as well. Now, as we saw in the previous, previous chart, this is a very crowded market and we've seen lots of consolidation in 2022, in the form of M&A. Literally hundreds of M&A deals, with some of the largest companies going private. SailPoint, KnowBe4, Barracuda, Mandiant, Fedora, these are multi billion dollar acquisitions, or at least billion dollars and up, and many of them multi-billion, for these companies, and hundreds more acquisitions in the cyberspace, now less you think the pond is overfished, here's a chart from ETR of emerging tech companies in the cyber security industry. This data comes from ETR's Emerging Technologies Survey, ETS, which is this diamond in a rough that I found a couple quarters ago, and it's ripe with companies that are candidates for M&A. Many would've liked, many of these companies would've liked to, gotten to the public markets during the pandemic, but they, you know, couldn't get there. They weren't ready. So the graph, you know, similar to the previous one, but different, it shows net sentiment on the vertical axis and that's a measurement of, of, of intent to adopt against a mind share on the X axis, which measures, measures the awareness of the vendor in the community. So this is specifically a survey that ETR goes out and, and, and fields only to track those emerging tech companies that are private companies. Now, some of the standouts in Mindshare, are OneTrust, BeyondTrust, Tanium and Endpoint, Net Scope, which we've talked about in previous Breaking Analysis. 1Password, which has been acquisitive on its own. In identity, the managed security service provider, Arctic Wolf Network, a company we've also covered, we've had their CEO on. We've talked about MSSPs as a real trend, particularly in small and medium sized business, we'll come back to that, Sneek, you know, kind of high flyer in both app security and containers, and you can just see the number of companies in the space this huge and it just keeps growing. Now, just to make it a bit easier on the eyes we filtered the data on these companies with with those, and isolated on those with more than a hundred responses only within the survey. And that's what we show here. Some of the names that we just mentioned are a bit easier to see, but these are the ones that really stand out in ERT, ETS, survey of private companies, OneTrust, BeyondTrust, Taniam, Netscope, which is in Cloud, 1Password, Arctic Wolf, Sneek, BitSight, SecurityScorecard, HackerOne, Code42, and Exabeam, and Sim. All of these hit the ETS survey with more than a hundred responses by, by the IT practitioners. Okay, so these firms, you know, maybe they do some M&A on their own. We've seen that with Sneek, as I said, with 1Password has been inquisitive, as have others. Now these companies with the larger footprint, these private companies, will likely be candidate for both buying companies and eventually going public when the markets settle down a bit. So again, no shortage of players to affect consolidation, both buyers and sellers. Okay, so let's finish with some key questions that we're watching. CrowdStrike in particular on its earnings calls cited softness from smaller buyers. Is that because these smaller buyers have stopped adopting? If so, are they more at risk, or are they tactically moving toward the easy button, aka, Microsoft's good enough approach. What does that mean for the market if smaller company cohorts continue to soften? How about MSSPs? Will companies continue to outsource, or pause on on that, as well as try to free up, to try to free up some budget? Adam Celiski at Reinvent last week said, "If you want to save money the Cloud's the best place to do it." Is the cloud the best place to save money in cyber? Well, it would seem that way from the standpoint of controlling budgets with lots of, lots of optionality. You could dial up and dial down services, you know, or does the Cloud add another layer of complexity that has to be understood and managed by Devs, for example? Now, consolidation should favor the likes of Palo Alto and CrowdStrike, cause they're platform players, and some of the larger players as well, like Cisco, how about IBM and of course Microsoft. Will that happen? And how will economic uncertainty impact the risk equation, a particular concern is increase of tax on vulnerable sectors of the population, like the elderly. How will companies and governments protect them from scams? And finally, how many cybersecurity companies can actually remain independent in the slingshot economy? In so many ways the market is still strong, it's just that expectations got ahead of themselves, and now as earnings forecast come, come, come down and come down to earth, it's going to basically come down to who can execute, generate cash, and keep enough runway to get through the knothole. And the one certainty is nobody really knows how tight that knothole really is. All right, let's call it a wrap. Next week we dive deeper into Palo Alto Networks, and take a look at how and why that company has held up so well and what to expect at Ignite, Palo Alto's big user conference coming up later this month in Las Vegas. We'll be there with theCube. Okay, many thanks to Alex Myerson on production and manages the podcast, Ken Schiffman as well, as our newest edition to our Boston studio. Great to have you Ken. Kristin Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our EIC over at Silicon Angle. He does some great editing for us. Thank you to all. Remember these episodes are all available as podcasts. Wherever you listen, just search Breaking Analysis podcast. I publish each week on wikibond.com and siliconangle.com, or you can email me directly David.vellante@siliconangle.com or DM me @DVellante, or comment on our LinkedIn posts. Please do checkout etr.ai, they got the best survey data in the enterprise tech business. This is Dave Vellante for theCube Insights powered by ETR. Thanks for watching, and we'll see you next time on Breaking Analysis. (upbeat music)

>> From theCUBE Studios in Palo Alto and Boston, bringing you data driven insights from theCUBE in ETR, this is Breaking Analysis with Dave Vellante. >> The introduction in socialization of data mesh has caused practitioners, business technology executives, and technologists to pause, and ask some probing questions about the organization of their data teams, their data strategies, future investments, and their current architectural approaches. Some in the technology community have embraced the concept, others have twisted the definition, while still others remain oblivious to the momentum building around data mesh. Here we are in the early days of data mesh adoption. Organizations that have taken the plunge will tell you that aligning stakeholders is a non-trivial effort, but necessary to break through the limitations that monolithic data architectures and highly specialized teams have imposed over frustrated business and domain leaders. However, practical data mesh examples often lie in the eyes of the implementer, and may not strictly adhere to the principles of data mesh. Now, part of the problem is lack of open technologies and standards that can accelerate adoption and reduce friction, and that's what we're going to talk about today. Some of the key technology and architecture questions around data mesh. Hello, and welcome to this week's Wikibon CUBE Insights powered by ETR, and in this Breaking Analysis, we welcome back the founder of data mesh and director of Emerging Technologies at Thoughtworks, Zhamak Dehghani. Hello, Zhamak. Thanks for being here today. >> Hi Dave, thank you for having me back. It's always a delight to connect and have a conversation. Thank you. >> Great, looking forward to it. Okay, so before we get into it in the technology details, I just want to quickly share some data from our friends at ETR. You know, despite the importance of data initiative since the pandemic, CIOs and IT organizations have had to juggle of course, a few other priorities, this is why in the survey data, cyber and cloud computing are rated as two most important priorities. Analytics and machine learning, and AI, which are kind of data topics, still make the top of the list, well ahead of many other categories. And look, a sound data architecture and strategy is fundamental to digital transformations, and much of the past two years, as we've often said, has been like a forced march into digital. So while organizations are moving forward, they really have to think hard about the data architecture decisions that they make, because it's going to impact them, Zhamak, for years to come, isn't it? >> Yes, absolutely. I mean, we are moving really from, slowly moving from reason based logical algorithmic to model based computation and decision making, where we exploit the patterns and signals within the data. So data becomes a very important ingredient, of not only decision making, and analytics and discovering trends, but also the features and applications that we build for the future. So we can't really ignore it, and as we see, some of the existing challenges around getting value from data is not necessarily that no longer is access to computation, is actually access to trustworthy, reliable data at scale. >> Yeah, and you see these domains coming together with the cloud and obviously it has to be secure and trusted, and that's why we're here today talking about data mesh. So let's get into it. Zhamak, first, your new book is out, 'Data Mesh: Delivering Data-Driven Value at Scale' just recently published, so congratulations on getting that done, awesome. Now in a recent presentation, you pulled excerpts from the book and we're going to talk through some of the technology and architectural considerations. Just quickly for the audience, four principles of data mesh. Domain driven ownership, data as product, self-served data platform and federated computational governance. So I want to start with self-serve platform and some of the data that you shared recently. You say that, "Data mesh serves autonomous domain oriented teams versus existing platforms, which serve a centralized team." Can you elaborate? >> Sure. I mean the role of the platform is to lower the cognitive load for domain teams, for people who are focusing on the business outcomes, the technologies that are building the applications, to really lower the cognitive load for them, to be able to work with data. Whether they are building analytics, automated decision making, intelligent modeling. They need to be able to get access to data and use it. So the role of the platform, I guess, just stepping back for a moment is to empower and enable these teams. Data mesh by definition is a scale out model. It's a decentralized model that wants to give autonomy to cross-functional teams. So it is core requires a set of tools that work really well in that decentralized model. When we look at the existing platforms, they try to achieve this similar outcome, right? Lower the cognitive load, give the tools to data practitioners, to manage data at scale because today centralized teams, really their job, the centralized data teams, their job isn't really directly aligned with a one or two or different, you know, business units and business outcomes in terms of getting value from data. Their job is manage the data and make the data available for then those cross-functional teams or business units to use the data. So the platforms they've been given are really centralized around or tuned to work with this structure as a team, structure of centralized team. Although on the surface, it seems that why not? Why can't I use my, you know, cloud storage or computation or data warehouse in a decentralized way? You should be able to, but some changes need to happen to those online platforms. As an example, some cloud providers simply have hard limits on the number of like account storage, storage accounts that you can have. Because they never envisaged you have hundreds of lakes. They envisage one or two, maybe 10 lakes, right. They envisage really centralizing data, not decentralizing data. So I think we see a shift in thinking about enabling autonomous independent teams versus a centralized team. >> So just a follow up if I may, we could be here for a while. But so this assumes that you've sorted out the organizational considerations? That you've defined all the, what a data product is and a sub product. And people will say, of course we use the term monolithic as a pejorative, let's face it. But the data warehouse crowd will say, "Well, that's what data march did. So we got that covered." But Europe... The primest of data mesh, if I understand it is whether it's a data march or a data mart or a data warehouse, or a data lake or whatever, a snowflake warehouse, it's a node on the mesh. Okay. So don't build your organization around the technology, let the technology serve the organization is that-- >> That's a perfect way of putting it, exactly. I mean, for a very long time, when we look at decomposition of complexity, we've looked at decomposition of complexity around technology, right? So we have technology and that's maybe a good segue to actually the next item on that list that we looked at. Oh, I need to decompose based on whether I want to have access to raw data and put it on the lake. Whether I want to have access to model data and put it on the warehouse. You know I need to have a team in the middle to move the data around. And then try to figure organization into that model. So data mesh really inverses that, and as you said, is look at the organizational structure first. Then scale boundaries around which your organization and operation can scale. And then the second layer look at the technology and how you decompose it. >> Okay. So let's go to that next point and talk about how you serve and manage autonomous interoperable data products. Where code, data policy you say is treated as one unit. Whereas your contention is existing platforms of course have independent management and dashboards for catalogs or storage, et cetera. Maybe we double click on that a bit. >> Yeah. So if you think about that functional, or technical decomposition, right? Of concerns, that's one way, that's a very valid way of decomposing, complexity and concerns. And then build solutions, independent solutions to address them. That's what we see in the technology landscape today. We will see technologies that are taking care of your management of data, bring your data under some sort of a control and modeling. You'll see technology that moves that data around, will perform various transformations and computations on it. And then you see technology that tries to overlay some level of meaning. Metadata, understandability, discovery was the end policy, right? So that's where your data processing kind of pipeline technologies versus data warehouse, storage, lake technologies, and then the governance come to play. And over time, we decomposed and we compose, right? Deconstruct and reconstruct back this together. But, right now that's where we stand. I think for data mesh really to become a reality, as in independent sources of data and teams can responsibly share data in a way that can be understood right then and there can impose policies, right then when the data gets accessed in that source and in a resilient manner, like in a way that data changes structure of the data or changes to the scheme of the data, doesn't have those downstream down times. We've got to think about this new nucleus or new units of data sharing. And we need to really bring back transformation and governing data and the data itself together around these decentralized nodes on the mesh. So that's another, I guess, deconstruction and reconstruction that needs to happen around the technology to formulate ourselves around the domains. And again the data and the logic of the data itself, the meaning of the data itself. >> Great. Got it. And we're going to talk more about the importance of data sharing and the implications. But the third point deals with how operational, analytical technologies are constructed. You've got an app DevStack, you've got a data stack. You've made the point many times actually that we've contextualized our operational systems, but not our data systems, they remain separate. Maybe you could elaborate on this point. >> Yes. I think this is, again, has a historical background and beginning. For a really long time, applications have dealt with features and the logic of running the business and encapsulating the data and the state that they need to run that feature or run that business function. And then we had for anything analytical driven, which required access data across these applications and across the longer dimension of time around different subjects within the organization. This analytical data, we had made a decision that, "Okay, let's leave those applications aside. Let's leave those databases aside. We'll extract the data out and we'll load it, or we'll transform it and put it under the analytical kind of a data stack and then downstream from it, we will have analytical data users, the data analysts, the data sciences and the, you know, the portfolio of users that are growing use that data stack. And that led to this really separation of dual stack with point to point integration. So applications went down the path of transactional databases or urban document store, but using APIs for communicating and then we've gone to, you know, lake storage or data warehouse on the other side. If we are moving and that again, enforces the silo of data versus app, right? So if we are moving to the world that our missions that are ambitions around making applications, more intelligent. Making them data driven. These two worlds need to come closer. As in ML Analytics gets embedded into those app applications themselves. And the data sharing, as a very essential ingredient of that, gets embedded and gets closer, becomes closer to those applications. So, if you are looking at this now cross-functional, app data, based team, right? Business team, then the technology stacks can't be so segregated, right? There has to be a continuum of experience from app delivery, to sharing of the data, to using that data, to embed models back into those applications. And that continuum of experience requires well integrated technologies. I'll give you an example, which actually in some sense, we are somewhat moving to that direction. But if we are talking about data sharing or data modeling and applications use one set of APIs, you know, HTTP compliant, GraQL or RAC APIs. And on the other hand, you have proprietary SQL, like connect to my database and run SQL. Like those are very two different models of representing and accessing data. So we kind of have to harmonize or integrate those two worlds a bit more closely to achieve that domain oriented cross-functional teams. >> Yeah. We are going to talk about some of the gaps later and actually you look at them as opportunities, more than barriers. But they are barriers, but they're opportunities for more innovation. Let's go on to the fourth one. The next point, it deals with the roles that the platform serves. Data mesh proposes that domain experts own the data and take responsibility for it end to end and are served by the technology. Kind of, we referenced that before. Whereas your contention is that today, data systems are really designed for specialists. I think you use the term hyper specialists a lot. I love that term. And the generalist are kind of passive bystanders waiting in line for the technical teams to serve them. >> Yes. I mean, if you think about the, again, the intention behind data mesh was creating a responsible data sharing model that scales out. And I challenge any organization that has a scaled ambitions around data or usage of data that relies on small pockets of very expensive specialists resources, right? So we have no choice, but upscaling cross-scaling. The majority population of our technologists, we often call them generalists, right? That's a short hand for people that can really move from one technology to another technology. Sometimes we call them pandric people sometimes we call them T-shaped people. But regardless, like we need to have ability to really mobilize our generalists. And we had to do that at Thoughtworks. We serve a lot of our clients and like many other organizations, we are also challenged with hiring specialists. So we have tested the model of having a few specialists, really conveying and translating the knowledge to generalists and bring them forward. And of course, platform is a big enabler of that. Like what is the language of using the technology? What are the APIs that delight that generalist experience? This doesn't mean no code, low code. We have to throw away in to good engineering practices. And I think good software engineering practices remain to exist. Of course, they get adopted to the world of data to build resilient you know, sustainable solutions, but specialty, especially around kind of proprietary technology is going to be a hard one to scale. >> Okay. I'm definitely going to come back and pick your brain on that one. And, you know, your point about scale out in the examples, the practical examples of companies that have implemented data mesh that I've talked to. I think in all cases, you know, there's only a handful that I've really gone deep with, but it was their hadoop instances, their clusters wouldn't scale, they couldn't scale the business and around it. So that's really a key point of a common pattern that we've seen now. I think in all cases, they went to like the data lake model and AWS. And so that maybe has some violation of the principles, but we'll come back to that. But so let me go on to the next one. Of course, data mesh leans heavily, toward this concept of decentralization, to support domain ownership over the centralized approaches. And we certainly see this, the public cloud players, database companies as key actors here with very large install bases, pushing a centralized approach. So I guess my question is, how realistic is this next point where you have decentralized technologies ruling the roost? >> I think if you look at the history of places, in our industry where decentralization has succeeded, they heavily relied on standardization of connectivity with, you know, across different components of technology. And I think right now you are right. The way we get value from data relies on collection. At the end of the day, collection of data. Whether you have a deep learning machinery model that you're training, or you have, you know, reports to generate. Regardless, the model is bring your data to a place that you can collect it, so that we can use it. And that leads to a naturally set of technologies that try to operate as a full stack integrated proprietary with no intention of, you know, opening, data for sharing. Now, conversely, if you think about internet itself, web itself, microservices, even at the enterprise level, not at the planetary level, they succeeded as decentralized technologies to a large degree because of their emphasis on open net and openness and sharing, right. API sharing. We don't talk about, in the API worlds, like we don't say, you know, "I will build a platform to manage your logical applications." Maybe to a degree but we actually moved away from that. We say, "I'll build a platform that opens around applications to manage your APIs, manage your interfaces." Right? Give you access to API. So I think the shift needs to... That definition of decentralized there means really composable, open pieces of the technology that can play nicely with each other, rather than a full stack, all have control of your data yet being somewhat decentralized within the boundary of my platform. That's just simply not going to scale if data needs to come from different platforms, different locations, different geographical locations, it needs to rethink. >> Okay, thank you. And then the final point is, is data mesh favors technologies that are domain agnostic versus those that are domain aware. And I wonder if you could help me square the circle cause it's nuanced and I'm kind of a 100 level student of your work. But you have said for example, that the data teams lack context of the domain and so help us understand what you mean here in this case. >> Sure. Absolutely. So as you said, we want to take... Data mesh tries to give autonomy and decision making power and responsibility to people that have the context of those domains, right? The people that are really familiar with different business domains and naturally the data that that domain needs, or that naturally the data that domains shares. So if the intention of the platform is really to give the power to people with most relevant and timely context, the platform itself naturally becomes as a shared component, becomes domain agnostic to a large degree. Of course those domains can still... The platform is a (chuckles) fairly overloaded world. As in, if you think about it as a set of technology that abstracts complexity and allows building the next level solutions on top, those domains may have their own set of platforms that are very much doing agnostic. But as a generalized shareable set of technologies or tools that allows us share data. So that piece of technology needs to relinquish the knowledge of the context to the domain teams and actually becomes domain agnostic. >> Got it. Okay. Makes sense. All right. Let's shift gears here. Talk about some of the gaps and some of the standards that are needed. You and I have talked about this a little bit before, but this digs deeper. What types of standards are needed? Maybe you could walk us through this graphic, please. >> Sure. So what I'm trying to depict here is that if we imagine a world that data can be shared from many different locations, for a variety of analytical use cases, naturally the boundary of what we call a node on the mesh will encapsulates internally a fair few pieces. It's not just the boundary of that, not on the mesh, is the data itself that it's controlling and updating and maintaining. It's of course a computation and the code that's responsible for that data. And then the policies that continue to govern that data as long as that data exists. So if that's the boundary, then if we shift that focus from implementation details, that we can leave that for later, what becomes really important is the scene or the APIs and interfaces that this node exposes. And I think that's where the work that needs to be done and the standards that are missing. And we want the scene and those interfaces be open because that allows, you know, different organizations with different boundaries of trust to share data. Not only to share data to kind of move that data to yes, another location, to share the data in a way that distributed workloads, distributed analytics, distributed machine learning model can happen on the data where it is. So if you follow that line of thinking around the centralization and connection of data versus collection of data, I think the very, very important piece of it that needs really deep thinking, and I don't claim that I have done that, is how do we share data responsibly and sustainably, right? That is not brittle. If you think about it today, the ways we share data, one of the very common ways is around, I'll give you a JDC endpoint, or I give you an endpoint to your, you know, database of choice. And now as technology, whereas a user actually, you can now have access to the schema of the underlying data and then run various queries or SQL queries on it. That's very simple and easy to get started with. That's why SQL is an evergreen, you know, standard or semi standard, pseudo standard that we all use. But it's also very brittle, because we are dependent on a underlying schema and formatting of the data that's been designed to tell the computer how to store and manage the data. So I think that the data sharing APIs of the future really need to think about removing this brittle dependencies, think about sharing, not only the data, but what we call metadata, I suppose. Additional set of characteristics that is always shared along with data to make the data usage, I suppose ethical and also friendly for the users and also, I think we have to... That data sharing API, the other element of it, is to allow kind of computation to run where the data exists. So if you think about SQL again, as a simple primitive example of computation, when we select and when we filter and when we join, the computation is happening on that data. So maybe there is a next level of articulating, distributed computational data that simply trains models, right? Your language primitives change in a way to allow sophisticated analytical workloads run on the data more responsibly with policies and access control and force. So I think that output port that I mentioned simply is about next generation data sharing, responsible data sharing APIs. Suitable for decentralized analytical workloads. >> So I'm not trying to bait you here, but I have a follow up as well. So you schema, for all its good creates constraints. No schema on right, that didn't work, cause it was just a free for all and it created the data swamps. But now you have technology companies trying to solve that problem. Take Snowflake for example, you know, enabling, data sharing. But it is within its proprietary environment. Certainly Databricks doing something, you know, trying to come at it from its angle, bringing some of the best to data warehouse, with the data science. Is your contention that those remain sort of proprietary and defacto standards? And then what we need is more open standards? Maybe you could comment. >> Sure. I think the two points one is, as you mentioned. Open standards that allow... Actually make the underlying platform invisible. I mean my litmus test for a technology provider to say, "I'm a data mesh," (laughs) kind of compliant is, "Is your platform invisible?" As in, can I replace it with another and yet get the similar data sharing experience that I need? So part of it is that. Part of it is open standards, they're not really proprietary. The other angle for kind of sharing data across different platforms so that you know, we don't get stuck with one technology or another is around APIs. It is around code that is protecting that internal schema. So where we are on the curve of evolution of technology, right now we are exposing the internal structure of the data. That is designed to optimize certain modes of access. We're exposing that to the end client and application APIs, right? So the APIs that use the data today are very much aware that this database was optimized for machine learning workloads. Hence you will deal with a columnar storage of the file versus this other API is optimized for a very different, report type access, relational access and is optimized around roles. I think that should become irrelevant in the API sharing of the future. Because as a user, I shouldn't care how this data is internally optimized, right? The language primitive that I'm using should be really agnostic to the machine optimization underneath that. And if we did that, perhaps this war between warehouse or lake or the other will become actually irrelevant. So we're optimizing for that human best human experience, as opposed to the best machine experience. We still have to do that but we have to make that invisible. Make that an implementation concern. So that's another angle of what should... If we daydream together, the best experience and resilient experience in terms of data usage than these APIs with diagnostics to the internal storage structure. >> Great, thank you for that. We've wrapped our ankles now on the controversy, so we might as well wade all the way in, I can't let you go without addressing some of this. Which you've catalyzed, which I, by the way, I see as a sign of progress. So this gentleman, Paul Andrew is an architect and he gave a presentation I think last night. And he teased it as quote, "The theory from Zhamak Dehghani versus the practical experience of a technical architect, AKA me," meaning him. And Zhamak, you were quick to shoot back that data mesh is not theory, it's based on practice. And some practices are experimental. Some are more baked and data mesh really avoids by design, the specificity of vendor or technology. Perhaps you intend to frame your post as a technology or vendor specific, specific implementation. So touche, that was excellent. (Zhamak laughs) Now you don't need me to defend you, but I will anyway. You spent 14 plus years as a software engineer and the better part of a decade consulting with some of the most technically advanced companies in the world. But I'm going to push you a little bit here and say, some of this tension is of your own making because you purposefully don't talk about technologies and vendors. Sometimes doing so it's instructive for us neophytes. So, why don't you ever like use specific examples of technology for frames of reference? >> Yes. My role is pushes to the next level. So, you know everybody picks their fights, pick their battles. My role in this battle is to push us to think beyond what's available today. Of course, that's my public persona. On a day to day basis, actually I work with clients and existing technology and I think at Thoughtworks we have given the talk we gave a case study talk with a colleague of mine and I intentionally got him to talk about (indistinct) I want to talk about the technology that we use to implement data mesh. And the reason I haven't really embraced, in my conversations, the specific technology. One is, I feel the technology solutions we're using today are still not ready for the vision. I mean, we have to be in this transitional step, no matter what we have to be pragmatic, of course, and practical, I suppose. And use the existing vendors that exist and I wholeheartedly embrace that, but that's just not my role, to show that. I've gone through this transformation once before in my life. When microservices happened, we were building microservices like architectures with technology that wasn't ready for it. Big application, web application servers that were designed to run these giant monolithic applications. And now we're trying to run little microservices onto them. And the tail was riding the dock, the environmental complexity of running these services was consuming so much of our effort that we couldn't really pay attention to that business logic, the business value. And that's where we are today. The complexity of integrating existing technologies is really overwhelmingly, capturing a lot of our attention and cost and effort, money and effort as opposed to really focusing on the data product themselves. So it's just that's the role I have, but it doesn't mean that, you know, we have to rebuild the world. We've got to do with what we have in this transitional phase until the new generation, I guess, technologies come around and reshape our landscape of tools. >> Well, impressive public discipline. Your point about microservice is interesting because a lot of those early microservices, weren't so micro and for the naysayers look past this, not prologue, but Thoughtworks was really early on in the whole concept of microservices. So be very excited to see how this plays out. But now there was some other good comments. There was one from a gentleman who said the most interesting aspects of data mesh are organizational. And that's how my colleague Sanji Mohan frames data mesh versus data fabric. You know, I'm not sure, I think we've sort of scratched the surface today that data today, data mesh is more. And I still think data fabric is what NetApp defined as software defined storage infrastructure that can serve on-prem and public cloud workloads back whatever, 2016. But the point you make in the thread that we're showing you here is that you're warning, and you referenced this earlier, that the segregating different modes of access will lead to fragmentation. And we don't want to repeat the mistakes of the past. >> Yes, there are comments around. Again going back to that original conversation that we have got this at a macro level. We've got this tendency to decompose complexity based on technical solutions. And, you know, the conversation could be, "Oh, I do batch or you do a stream and we are different."' They create these bifurcations in our decisions based on the technology where I do events and you do tables, right? So that sort of segregation of modes of access causes accidental complexity that we keep dealing with. Because every time in this tree, you create a new branch, you create new kind of new set of tools and then somehow need to be point to point integrated. You create new specialization around that. So the least number of branches that we have, and think about really about the continuum of experiences that we need to create and technologies that simplify, that continuum experience. So one of the things, for example, give you a past experience. I was really excited around the papers and the work that came around on Apache Beam, and generally flow based programming and stream processing. Because basically they were saying whether you are doing batch or whether you're doing streaming, it's all one stream. And sometimes the window of time, narrows and sometimes the window of time over which you're computing, widens and at the end of today, is you are just getting... Doing the stream processing. So it is those sort of notions that simplify and create continuum of experience. I think resonate with me personally, more than creating these tribal fights of this type versus that mode of access. So that's why data mesh naturally selects kind of this multimodal access to support end users, right? The persona of end users. >> Okay. So the last topic I want to hit, this whole discussion, the topic of data mesh it's highly nuanced, it's new, and people are going to shoehorn data mesh into their respective views of the world. And we talked about lake houses and there's three buckets. And of course, the gentleman from LinkedIn with Azure, Microsoft has a data mesh community. See you're going to have to enlist some serious army of enforcers to adjudicate. And I wrote some of the stuff down. I mean, it's interesting. Monte Carlo has a data mesh calculator. Starburst is leaning in, chaos. Search sees themselves as an enabler. Oracle and Snowflake both use the term data mesh. And then of course you've got big practitioners J-P-M-C, we've talked to Intuit, Orlando, HelloFresh has been on, Netflix has this event based sort of streaming implementation. So my question is, how realistic is it that the clarity of your vision can be implemented and not polluted by really rich technology companies and others? (Zhamak laughs) >> Is it even possible, right? Is it even possible? That's a yes. That's why I practice then. This is why I should practice things. Cause I think, it's going to be hard. What I'm hopeful, is that the socio-technical, Leveling Data mentioned that this is a socio-technical concern or solution, not just a technology solution. Hopefully always brings us back to, you know, the reality that vendors try to sell you safe oil that solves all of your problems. (chuckles) All of your data mesh problems. It's just going to cause more problem down the track. So we'll see, time will tell Dave and I count on you as one of those members of, (laughs) you know, folks that will continue to share their platform. To go back to the roots, as why in the first place? I mean, I dedicated a whole part of the book to 'Why?' Because we get, as you said, we get carried away with vendors and technology solution try to ride a wave. And in that story, we forget the reason for which we even making this change and we are going to spend all of this resources. So hopefully we can always come back to that. >> Yeah. And I think we can. I think you have really given this some deep thought and as we pointed out, this was based on practical knowledge and experience. And look, we've been trying to solve this data problem for a long, long time. You've not only articulated it well, but you've come up with solutions. So Zhamak, thank you so much. We're going to leave it there and I'd love to have you back. >> Thank you for the conversation. I really enjoyed it. And thank you for sharing your platform to talk about data mesh. >> Yeah, you bet. All right. And I want to thank my colleague, Stephanie Chan, who helps research topics for us. Alex Myerson is on production and Kristen Martin, Cheryl Knight and Rob Hoff on editorial. Remember all these episodes are available as podcasts, wherever you listen. And all you got to do is search Breaking Analysis Podcast. Check out ETR's website at etr.ai for all the data. And we publish a full report every week on wikibon.com, siliconangle.com. You can reach me by email david.vellante@siliconangle.com or DM me @dvellante. Hit us up on our LinkedIn post. This is Dave Vellante for theCUBE Insights powered by ETR. Have a great week, stay safe, be well. And we'll see you next time. (bright music)

>> Narrator: From around the globe, it's theCUBE. With digital coverage of AWS Reinvent Executive Summit 2020. Sponsored by Accenture and AWS. >> Welcome everyone to theCUBE virtual coverage of the Accenture Executive Summit part of AWS Re-invent 2020. I'm your host, Rebecca Knight. We are talking today about reinventing the energy data platform. We have two guests joining us. First, we have Johan Krebbers. He is the GM Digital Emerging Technologies and VP of IT Innovation at Shell. Thank you so much for coming on the show, Johan. >> You're welcome. >> Rebecca: And next we have Liz Dennett. She is the Lead Solution Architect for OSDU on AWS. Thank you so much Liz. >> Happy to be here. >> So I want to start our conversation by talking about OSDU. Like so many great innovations, it started with a problem. Johann, what was the problem you were trying to solve at Shell? >> Yeah, let's go back a couple of the years. We started summer 2017, where we had a meeting with the guys from exploration in Shell. And the main problem they had of course they got lots and lots of data, but aren't unable to find the right data they need to work from. Well the data was scattered and is scattered, it was scattered it's all over the place. And so the real problem trying to solve is how that person working in exploration could find their proper data, not just the data also the data really needed. That's what we probably talked about in summer 2017. And we said, "Okay, the only way we see this moving forward is to start pulling that data into a single data platform." And that was at the time that we called it OSDU, the Open Subsurface Data Universe, and that was what the Shell name was. So, in January 2018, we start a project with Amazon to start creating and confronting the building that OSDU environment, that subservient the universe. So that single data platform to put all your exploration and wealth data into a single environment that was the intent. And then we said, already in March of that same year, we said, 'Well, from a Shell point of view, we would be far better off if we could make this an industry solution and not just a Shell solution." Because Shell will be, if you can make this an industry solution, but people start developing applications for it also, it's far better than for Shell to say, we have it Shell special solution. Because we don't make money out of how we store the data we can make money out of we have access to the data, we can exploit the data. So storing the data, we should do as efficiently possibly can. So in March we reached out to about eight or nine other large oil and gas operators, like the ECONOS, like the Totals, like the Chevrons of this world they said, "Hey, we in Shell are doing this, do you want to join this effort?" And to our surprise, they all said yes. And then in September 2018 we had our kick-off meeting with the open group, where we said, "Okay, if you want to work together with lots of other companies, we also need to look a bit at how we organize that." Because if you start working with lots of large companies you need to have some legal framework around it. So that's why, we went to the open group and said," Okay, let's form the OSDU forum." As we call it at the time. So in September, 2018 where I had a Galleria in Houston we had a kick off meeting for the OSDU forum with about 10 members at the time. So there's, just over two years ago, we started to exercise formally we called it OSDU, we kicked it off. And so that's really where we coming from and how we got there also. >> The origin story. >> Yes. >> What, so what, digging a little deeper there, what were some of the things you were trying to achieve with the OSDU? >> Well, a couple of things we've tried to achieve with OSDU. First is really separating data from applications. But what is the biggest problem we have in the subsurface space that the data and applications are all interlinked. They are all tied together and if you have then a new company coming along and say, "I have this new application, and needs access to the data." That is not possible because the data often interlinked with the application. The first thing we did is, really breaking the link between the application and the data. So that was the first thing we did. Secondly, put all the data to a single data platform, take the silos out because what was happening in the subsurface space I mean, they got all the data in what we call silos, in small little islands out there. So we try to do is, first, break the link. Two, create, put the data in a single data platform. And then third part, put a standard layer on top of that the same API layer on top of the created platform so we could create an ecosystem out of companies to start developing software applications on top of that data platform. Because you might have a data platform, but you aren't successful if you have a rich ecosystem of people start developing applications on top of that. And then you can exploit the data like small companies, large companies, universities, you name it. But you have to create an ecosystem out of there. So the three things was, first break the link between the application data, just break it and put data at the center. And also make sure that data, this data structure would not be managed by one company. But it would be managed the data structures, by the OSDU forum. Secondly then, put the data, single data platform. Thirdly then, have an API layer on top and then create an ecosystem, really go for people, say, "Please start developing applications." Because now you have access to the data, because the data is no longer linked to somebody's application was all freely available for an API layer. That was all September, 2018, more or less. >> Liz I want to bring you, in here a little bit. >> Yeah. >> Can you talk a little bit some of the imperatives from the AWS standpoint in terms of what you were trying to achieve with this? >> Yeah, absolutely. And this whole thing is Johan said, started with a challenge that was really brought out at Shell. The challenges that geoscientists spend up to 70% of their time looking for data. I'm a geologist I've spent more than 70% of my time trying to find data in these silos. And from there, instead of just figuring out, how we could address that one problem, we worked together to really understand the root cause of these challenges. And working backwards from that use case, OSDU and OSDU on AWS has really enabled customers to create solutions that span not just this in particular problem. But can really scale to be inclusive of the entire energy value chain and deliver value from these used cases to the energy industry and beyond. >> Thank you. Johan, so talk a little bit about Accenture's Cloud First approach and how it has helped Shell work faster and better with speed. >> Well, of course Accenture Cloud First approach, really works together with Amazon environment, AWS environment. So we really look at Accenture and Amazon together, helping Shell in this space. Now the combination of the two is what we're really looking at where access of course can bring business knowledge to that environment, operate support knowledge to an environment and of course Amazon will be bring that to this environment, that underpinning services, et cetera. So we would expect of that combination, a lot of goods when we started rolling out in production, the other two or three environment. And probably our aim is, when a release fee comes to the market, in Q1 next year of OSDU have already started going out in production inside Shell. But as the first OSDU release which is ready for prime time production across an enterprise. Well we have released our one just before Christmas, last year, released two in May of this year. But release three is the first release we want to use for full scale production and deployment inside Shell and also all the operators around the world. And there is what Amazon, sorry and there when Accenture can play a role in the ongoing, in the deployment building up, but also support environment. >> So one of the other things that we talk a lot about here on theCUBE is sustainability and this is a big imperative at so many organizations around the world in particular energy companies. How does this move to OSDU, help organizations become how is this a greener solution for companies? >> Well, first we make, it's a great solution because you start making a much more efficient use of your resources, which is a really important one. The second thing we're doing is also we started with OSDU in very much in the oil and gas space, within the export development space. We've grown OSDU but in our strategy, we've grown OSDU now also to an alternative energy source. So obviously we'll all start supporting next year things like solar farms, wind farms, the geothermal environment, hydrogen. So it becomes an open energy data platform not just for the oil and gas industry, but for any type of industry, any type of energy industry. So our focus is to create, bring the data of all those various energy data sources together into a single data platform. You're going to use AI and other technology on top of that, to exploit the data to be together into a single data platform. >> Liz, I want to ask you about security, because security is such a big concern when it comes to data. How secure is the data on OSDU? >> Actually, can I talk, can I do a follow-up on the sustainability talking? >> Absolutely by all means. >> I mean, I want to interject, though security is absolutely our top priority I don't mean to move away from that but with sustainability, in addition to the benefits of the OSDU data platform. When a company moves from on-prem to the cloud they're also able to leverage the benefits of scale. Now, AWS is committed to running our business in the most environmentally friendly way possible. And our scale allows us to achieve higher resource utilization and energy efficiency than a typical on-prem data center. Now, a recent study by 451 research found that, AWS's infrastructure, is 3.6 times more energy efficient than the median of surveyed enterprise data centers. Two thirds of that advantage is due to a higher server utilization and a more energy efficient server population. But when you factor in the carbon intensity of consumed electricity and renewable energy purchases 451 found that, AWS performs the same task with an 88% lower carbon footprint. Now that's just another way that AWS and OSDU are working to support our customers as they seek to better understand their workflows and make their legacy businesses less carbon intensive. >> That's, those statistics are incredible. Do you want to talk a little bit now about security? >> Absolutely yeah. Security will always be AWS's top priority. In fact, AWS has been architected to be the most flexible and secure cloud computing environment available today. Our core infrastructure is built to satisfy the security requirements for the military, global banks and other high sensitivity organizations. And in fact, AWS uses the same secure hardware and software to build and operate each of our regions. So that customers benefit from the only commercial cloud that's had hit service offerings and associated supply chain vetted and deemed secure enough for top secret workloads. That's backed by a deep set of cloud security tools with more than 200 security compliance and governmental service and key features. As well as an ecosystem of partners like Accenture, that can really help our customers to make sure that their environments for their data meet and or exceed their security requirements. >> Johann, I want you to talk a little bit about how OSDU you can be used today. Does it only handle subsurface data? >> Today is 100 subsets of wells data we go to add that to that production around the middle of next year. That means that the whole upstream business we got included every piece goes from exploration all the way to production, you bring it together into a single data platform. So production will be added around Q3 of next year. Then in principle, we have a typical elder data, a single environment and we're going to extend them to other data sources or energy sources like solar farms, wind farms, hydrogen, hydro, et cetera. So we're going to add a whole list of other day energy source to that and bring all the data together into a single data platform. So we move from an oil and gas data platform to an energy data platform. That's really what our objective is because the whole industry if you look at our companies all moving in that same direction of course are very strong in oil and gas but also increasingly go into other energy sources like solar, like wind, like hydrogen et cetera. So we move exactly with the same method, that the whole OSDU, can really support that whole energy spectrum of energy sources, of course. >> And Liz and Johan, I want you to close us out here by just giving us a look into your crystal balls and talking about the five and 10 year plan for OSDU. We'll start with you, Liz. What do you see as the future holding for this platform? >> Honestly, the incredibly cool thing about working at AWS is you never know where the innovation and the journey is going to take you. I personally am looking forward to work with our customers wherever their OSDU journeys, take them whether it's enabling new energy solutions or continuing to expand, to support use cases throughout the energy value chain and beyond but really looking forward to continuing to partner as we innovate to slay tomorrow's challenges. >> Johan. >> Yeah, first nobody can look that far ahead anymore nowadays, especially 10 years. I mean, who knows what happens in 10 years? But if you look what our objective is that really in the next five years, OSDU will become the key backbone for energy companies for storing your data, new artificial intelligence and optimize the whole supply, the energy supply chain in this world out here. >> Johan Krebbers, Liz Dennett thank you so much for coming on theCUBE virtual. >> Thank you. >> Thank you. >> I'm Rebecca Knight stay tuned for more of our coverage of the Accenture Executive Summit. (tranquil music).

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation as we've been reporting the Koba 19 pandemic has created a bifurcated IT spending picture and over the last several weeks we've reported both on the macro and even some come at it from from a vendor and a sector view I mean for example we've reported on some of the companies that have really continued to thrive we look at the NASDAQ and its you know near at all-time highs companies like oh and in CrowdStrike we've reported on snowflake uipath the sectors are PA some of the analytic databases around AI maybe even to a lesser extent cloud but still has a lot of tailwind relative to some of those on-prem infrastructure plays even companies like Cisco bifurcated in and of themselves where you see this Meraki side of the house you know doing quite well the work from home stuff but maybe some of the traditional networking not as much well now what if you flip that to really try to understand what's going on with the shape of the recovery which is the main narrative right now is it a v-shape does it a u-shape what is what's that what do people expect and now you understand that you really have to look at different industries because different industries are going to come back at a different pace with me again is Sagar khadiyah who's the director of research at EGR Sagar you guys are all over this as usual timely information it's great to see you again hope all is well in New York City thanks so much David it's a pleasure to be back on again yeah so where are we in the cycle we give dividend a great job and very timely ETR was the first to really put out data on the koban impact with the survey that ran from mid-march to to mid-april and now everybody's attention sagar is focused on okay we're starting to come back stores are starting to open people are beginning to to go out again and everybody wants to know what the shape of the recovery looks like so where are we actually in that research cycle for you guys yeah no problem so like you said you know in that kind of march/april timeframe we really want to go out there and get an idea of what we're doing the budget impacts you know as it relates to IT because of kovat 19 right so we kind of ended off there around a decline of 5% and coming into the year the consensus was of growth of 4 or 5% right so we saw about a 900,000 basis points wing you know to the negative side and the public covered in March and April were you know which sectors and vendors were going to benefit as a result of work from home and so now as we kind of fast forward to the research cycle as we kind of go more into May and into the summer rather than asking those exact same question to get again because it's just been you know maybe 40 or 50 days we really want Singh on the recovery type as well as kind of more emerging private vendors right we want to understand what's gonna be the impact on on these vendors that typically rely on you know larger conferences more in-person meetings because these are younger technologies there's not a lot of information about them and so last Thursday we launched our biannual emerging technology study it covers roughly 300 private emerging technologies across maybe 60 sectors of technology and in tandem we've launched a co-ed flash poll right what we wanted to do was kind of twofold one really understand from CIOs the recovery type they had in mind as well as if they were seeing any any kind of permanent changes in their IT stacks IT spend because of koban 19 and so if we kind of look at the first chart here and kind of get more into that first question around recovery type what we asked CIOs and this kind of COBIT flash poll again we did it last Thursday was what type of recovery are you expecting is it v-shaped so kind of a brief decline you know maybe one quarter and then you're gonna start seeing growth in 2 to H 20 is it you shaped so two to three quarters of a decline or deceleration revenue and you're kind of forecasting that growth in revenue as an organization to come back in 2021 is it l-shaped right so maybe three four five quarters of a decline or deceleration and then you know very minimal to moderate growth or none of the above you know your organization is actually benefiting from from from koban 19 as you know we've seen some many reports so those are kind of the options that we gave CIOs and you kind of see it on that first chart here interesting and this is a survey a flash service 700 CIOs or approximately and the interesting thing I really want to point out here is this you know the koban pandemic was it didn't suppress you know all companies you know and in the return it's not going to be a rising tide lifts all ships you really got to do your research you have to understand the different sectors really try to peel back the onion skin and understand why there's certain momentum how certain organizations are accommodating the work from home we heard you know several weeks ago how there's a major change in in networking mindsets we're talking about how security is changing we're going to talk about some of the permanence but it's really really important to try to understand these different trends by different industries which you're going to talk about in a minute but if you take a look at this slide I mean obviously most people expect this u-shaped decline I mean a you know a u-shaped recovery rather so it's two or three quarters followed by some growth next year but as we'll see some of these industries are gonna really go deeper with an l-shape recovery and then it's really interesting that a pretty large and substantial portion see this as a tailwind presumably those with you know strong SAS models some annual recurring revenue models your thoughts if we kind of star on this kind of aggregate chart you know you're looking at about forty four percent of CIOs anticipated u-shaped recovery right that's the largest bucket and then you can see another 15 percent and to say an l-shape recovery 14 on the v-shaped and then 16 percent to your point that are kind of seeing this this tailwind but if we kind of focus on that largest bucket that you shaped you know one of the thing to remember and again when we asked is two CIOs within the within this kind of coded flash poll we also asked can you give us some commentary and so one of the things that or one of the themes that are kind of coming along with this u-shaped recovery is you know CIOs are cautiously optimistic about this u-shaped recovery you know they believe that they can get back on to a growth cycle into 2021 as long as there's a vaccine available we don't go into a second wave of lockdowns economic activity picks up a lot of the government actions you know become effective so there are some kind of let's call it qualifiers with this bucket of CIOs that are anticipating a u-shape recovery what they're saying is that look we are expecting these things to happen we're not expecting that our lock down we are expecting a vaccine and if that takes place then we do expect an uptick in growth or going back to kind of pre coded levels in in 2021 but you know I think it's fair to assume that if one or more of these are apps and and things do get worse as all these states are opening up maybe the recovery cycle gets pushed along so kind of at the aggregate this is where we are right now yeah so as I was saying and you really have to understand the different not only different sectors and all the different vendors but you got to look into the industries and then even within industries so if we pull up the next chart we have the industry to the breakdown and sort of the responses by the industries v-shape you shape or shape I had a conversation with a CIO of a major resort just the other day and even he was saying what was actually I'll tell you it was Windham Resorts public company I mean and obviously that business got a good crush they had their earnings call the other day they talked about how they cut their capex in half but the stock sagar since the March lows is more than doubled yeah and you know that's amazing and now but even there within that sector they're peeling that on you're saying well certain parts are going to come back sooner or certain parts are going to longer depending on you know what type of resort what type of hotel so it really is a complicated situation so take us through what you're seeing by industry sure so let's start with kind of the IT telco retail consumer space Dave to your point there's gonna be a tremendous amount of bifurcation within both of those verticals look if we start on the IT telco side you know you're seeing a very large bucket of individuals right over twenty percent that indicated they're seeing a tail with our additional revenue because of covin 19 and you know Dave we spoke about this all the way back in March right all these work from home vendors you know CIOs were doubling down on cloud and SAS and we've seen how some of these events have reported in April you know with this very good reports all the major cloud vendors right select security vendors and so that's why you're seeing on the kind of telco side definitely more positivity right as it relates to recovery type right some of them are not even going through recovery they're they're seeing an acceleration same thing on the retail consumer side you're seeing another large bucket of people who are indicating what we've benefited and again there's going to be a lot of bifurcation here there's been a lot of retail consumers you just mentioned with the hotel lines that are definitely hurting but you know if you have a good online presence as a retailer and you know you had essential goods or groceries you benefited and and those are the organizations that we're seeing you know really indicate that they saw an acceleration due to Koga 19 so I thought those two those two verticals between kind of the IT and retail side there was a big bucket or you know of people who indicated positivity so I thought that was kind of the first kind of you know I was talking about kind of peeling this onion back you know that was really interesting you know tech continues to power on and I think you know a lot of people try I think that somebody was saying that the record of the time in which we've developed a fit of vaccine previously was like mumps or something and it was I mean it was just like years but now today 2020 we've got a I we've got all this data you've got these great companies all working on this and so you know wow if we can compress that that's going to change the equation a couple other things sagar that jump out at me here in this chart I want to ask you about I mean the education you know colleges are really you know kind of freaking out right now some are coming back I know like for instance my daughter University Arizona they're coming back in the fall evidently others are saying and no you can clearly see the airlines and transportation as the biggest sort of l-shape which is the most negative I'm sure restaurants and hospitality are kind of similar and then you see energy you know which got crushed we had you know oil you know negative people paying it big barrels of oil but now look at that you know expectation of a pretty strong you know you shape recovery as people start driving again and the economy picks up so maybe you could give us some thoughts on on some of those sort of outliers yeah so I kind of bucket you know the the next two outliers as from an l-shaped in a u-shaped so on the l-shaped side like like you said education airlines transportation and probably to a little bit lesser extent industrials materials manufacturing services consulting these verticals are indicating the highest percentages from an l-shaped recovery right so three plus orders of revenue declines and deceleration followed by kind of you know minimal to moderate growth and look there's no surprise here those are the verticals that have been impacted the most by less demand from consumers and and businesses and then as you mentioned on the energy utility side and then I would probably bucket maybe healthcare Pharma those have some of the largest percentages of u-shaped recovery and it's funny like I read a lot of commentary from some of the energy in the healthcare CIOs and they were said they were very optimistic about a u-shaped type of recovery and so it kind of you know maybe with those two issues then you could even kind of lump them into you know probably to a lesser extent but you could probably open into the prior one with the airlines and the education and services consulting and IMM where you know these are definitely the verticals that are going to see the longest longest recoveries it's probably a little bit more uniform versus what we've kind of talked about a few minutes ago with you know IT and and retail consumer where it's definitely very bifurcated you know there's definitely winners and losers there yeah and again it's a very complicated situation a lot of people that I've talked to are saying look you know we really don't have a clear picture that's why all these companies have are not giving guidance many people however are optimistic not only for a vet a vaccine but but but also they're thinking as young people with disposable income they're gonna kind of say dorm damn the torpedoes I'm not really going to be exposed and you know they can come back much stronger you know there seems to be pent up demand for some of the things like elective surgery or even the weather is sort of more important health care needs so that obviously could be a snap back so you know obviously we're really closely looking at this one thing though is is certain is that people are expecting a permanent change and you've got data that really shows that on the on the next chart that's right so one of the one of the last questions that we asked on this you know quick coded flash poll was do you anticipate permanent changes to your kind of IT stack IT spend based on the last few months you know as everyone has been working remotely and you know rarely do you see results point this much in one direction but 92% of CIOs and and kind of IT you know high level ITN users indicated yes there are going to be permanent changes and you know one of the things we talked about in March and look we were really the first ones you know you know in our discussion where we were talking about work from home spend kind of negating or balancing out all these declines right we were saying look yes we are seeing a lot of budgets come down but surprisingly we're seeing 2030 percent of organizations accelerate spent and even the ones that are spending less they even then you know some of their some of their budgets are kind of being negated by this work from home spend right when you think about collaboration tool is an additional VPN and networking bandwidth in laptops and then security all that stuff CIOs now continue to spend on because what what CIO is now understand as productivity has remained at very high levels right in March CIOs were very with the catastrophe and productivity that has not come true so on the margin CIOs and organizations are probably much more positive on that front and so now because there is no vaccine where you know CIOs and just in general the population we don't know when one is coming and so remote work seems to be the new norm moving forward especially that productivity you know levels are are pretty good with people working from home so from that perspective everything that looked like it was maybe going to be temporary just for the next few months as people work from home that's how organizations are now moving forward well and we saw Twitter basically said we're gonna make work from home permanent that's probably cuz their CEO wants to you know live in Africa Google I think is going to the end of the year I think many companies are going to look at a hybrid and give employees a choice say look if you want to work from home and you can be productive you get your stuff done you know we're cool with that I think the other point is you know everybody talks about these digital transformations you know leading into Kovan and I got to tell you I think a lot of companies were sort of complacent they talked the talk but they weren't walking the walk meaning they really weren't becoming digital businesses they really weren't putting data at the core and I think now it's really becoming an imperative there's no question that that what we've been talking about and forecasting has been pulled forward and you you're either going to have to step up your digital game or you're going to be in big trouble and the other thing that's I'm really interested in is will companies sub optimize profitability in the near term in order to put better business resiliency in place and better flexibility will they make those investments and I think if they do you know longer term they're going to be in better shape you know if they don't they could maybe be okay in the near term but I'm gonna put a caution sign a little longer term no look I think everything that's been done in the last few months you know in terms of having those continuation plans because you know do two pandemics all that stuff that is now it look you got to have that in your playbook right and so to your point you know this is where CIOs are going and if you're not transforming yourself or you didn't or you know lesson learned because now you're probably having to move twice as fast to support all your employees so I think you know this pandemic really kind of sped up you know digital transformation initiatives which is why you know you're seeing some companies desks and cloud related companies with very good earnings reports that are guiding well and then you're seeing other companies that are pulling their guidance because of uncertainty but it's it's likely more on the side of they're just not seeing the same levels of spend because if they haven't oriented themselves on that digital transformation side so I think you know events like this they typically you know Showcase winners and losers then you know when when things are going well and you know everything is kind of going up well I think that - there's a big you know discussion around is the ESPY overvalued right now I won't make that call but I will say this then there's a lot of data out there there's data and earnings reports there's data about this pandemic which change continues to change maybe not so much daily but you're getting new information multiple times a week so you got to look to that data you got to make your call pick your spot so you talk about a stock pickers market I think it's very much true here there are some some gonna be really strong companies emerging out of this you know don't gamble but do your research and I think you'll you'll find some you know some Dems out there you know maybe Warren Buffett can't find them okay but the guys at Main Street I think you know the I am I'm optimistic I wonder how you feel about about the recovery I I think we may be tainted by tech you know I'm very much concerned about certain industries but I think the tech industry which is our business is gonna come out of this pretty strong yeah we look at the one thing we we should we should have stated this earlier the majority of organizations are not expecting a v-shaped recovery and yet I still think there's part of the consensus is expecting a v-shaped recovery you can see as we demonstrate in some of the earlier charts the you know almost the majority of organizations are expecting a u-shaped recovery and even then as we mentioned right that you shape there is some cautious up around there and I have it you probably have it where yes if everything goes well it looks like 2021 we can really get back on track but there's so much unknown and so yes that does give I think everyone pause when it comes from an investment perspective and even just bringing on technologies and into your organization right which ones are gonna work which ones are it so I'm definitely on the boat of this is a more u-shaped in a v-shaped recovery I think the data backs that up I think you know when it comes to cloud and SAS players those areas and I think you've seen this on the investment side a lot of money has come out of all these other sectors that we mentioned that are having these l-shaped recoveries a lot of it has gone into the tech space I imagine that will continue and so that might be kind of you know it's tough to sometimes balance what's going on on the investor in the stock market side with you know how organizations are recovering I think people are really looking out in two to three quarters and saying look you know to your point where you set up earlier is there a lot of that pent up demand are things gonna get right back to normal because I think you know a lot of people are anticipating that and if we don't see that I think you know the next time we do some of these kind of coded flash bolts you know I'm interested to see whether or not you know maybe towards the end of the summer these recovery cycles are actually longer because maybe we didn't see some of that stuff so there's still a lot of unknowns but what we do know right now is it's not a v-shaped recovery agree especially on the unknowns there's monetary policy there's fiscal policy there's an election coming up there's a third there's escalating tensions with China there's your thoughts on the efficacy of the vaccine what about therapeutics you know do people who have this yet immunity how many people actually have it what about testing so the point I'm making here is it's very very important that you update your forecast regularly that's why it's so great that I have this partnership with you guys because we you know you're constantly updating the numbers it's not just a one-shot deal so suck it you know thanks so much for coming on looking forward to having you on in in the coming weeks really appreciate it absolutely yeah well I will really start kind of digging into how a lot of these emerging technologies are faring because of kovat 19 so that's I'm actually interested to start thinking through the data myself so yeah well we'll do some reporting in the coming weeks about that as well well thanks everybody for watching this episode of the cube insights powered by ETR I'm Dave Volante for sauger kuraki check out ETR dot plus that's where all the ETR data lives i published weekly on wiki bon calm and silicon angle calm and reach me at evil on Tay we'll see you next time [Music]

(upbeat music) >> From theCUBE Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a Cube Conversation. >> As we've been reporting, the COVID-19 pandemic has created a bifurcated IT spending picture. And over the last several weeks, we've reported both in the macro and even some come at it from a vendor and a sector view. I mean, for example, we've reported on some of the companies that have really continued to thrive, we look at the NASDAQ and its near a toll-time hard. Companies like Okta and CrowdStrike, we've reported on Snowflake, UiPath. The sectors, RPA, some of the analytic databases around AI, maybe even to a lesser extent Cloud but still has a lot tailwinds relative to some of those on-prem infrastructure plays. Even companies like Cisco, bifurcated in and of themselves, where you see this more rocky side of the house doing quite well. The work-from-home stuff but maybe some of the traditional networking not as much. Well, now what if you flip that to really try to understand what's going on with the shape of the recovery which is the main narrative right now. Is it a V shape? Is it a U shape? What do people expect? And now to understand that, you really have to look at different industries because different industries are going to come back at a different pace. With me again is Sagar Kadakia, who's the Director of Research at ETR. Sagar, you guys are all over this, as usual timely information, it's great to see you again. Hope all is well in New York City. >> Thanks so much David, it's a pleasure to be back on again. >> Yeah, so where are we in the cycle? You've done a great job and very timely, ETR was the first to really put out data on the Covid impact with the server that ran from mid March to mid April. And now everybody's attention Sagar, is focused on, okay, we've started to come back, stores are starting to open, people are beginning to go out again and everybody wants to know what the shape of the recovery looks like. So, where are we actually in that research cycle for you guys? >> Yeah, no problem. So, like you said, in that kind of March, April timeframe, we really want to go out there and get an idea of what are going to be the budget impacts as it relates to IT because of COVID-19, right? So, we kind of ended off there around a decline of 5%. And coming into the year, the consensus was a growth of 4% or 5%, right? So, we saw about a 900 or 1000 base point swing, to the negative side. And then (murmurs) topic we covered in March and April were which sectors of vendors were going to benefit as a result of work-from-home. And so, now as we kind of fast forward to the research cycle as we kind of go more into May and into the summer, rather than asking those exact same question again, because it's just been maybe 40 or 50 days. We really want to (murmurs) on the recovery type as well as well as kind of more emerging private vendors, right? We want it to understand what's going to be the impact on these vendors that typically rely on larger conferences, more in person meetings, because these are younger technologies. There's not a lot of information about them. And so, last Thursday we launched our biannual emerging technology study. It covers roughly 300 private emerging technologies across maybe 60 sectors of technology. And in tandem, we've launched a COVID Flash Poll, right? What we want to do was kind of twofold. One really understand from CIOs the recovery type they had in mind, as well as if they were seeing any kind of permanent changes in their IT, stacks IT spend because of COVID-19. And so, if we kind of look at the first chart here, and kind of get more into that first question around recovery type, what we asked CIOs in this kind of COVID Flash Poll, again, we did it last Thursday was, what type of recovery are you expecting? Is it V-shaped so kind of of a brief decline, maybe 1/4, and then you're going to start seeing growth into 2 each 20. Is it U-shaped? So two to 3/4 of a decline or deceleration revenue, and you're kind of forecasting that growth in revenue as an organization to come back in 2021. Is it L-shaped, right? So, maybe three, four or 5/4 of a decline or deceleration. And very minimal to moderate growth or none of the above, your organization is actually benefiting from COVID-19, as we've seen some many reports. So, those are kind of the options that we gave CIOs and you kind of see them at first chart here. >> Well, interesting. And this is a survey, a flash of survey, 700 CIOs or approximately. And the interesting thing I really want to point out here is, the COVID pandemic, it didn't suppress all companies, and the return is it's not going to be a rising tide that lifts all ships. You really got to do your research. You have to understand the different sectors, really try to peel back the onion skin and understand why there are certain momentum, how certain organizations are accommodating the work from home. We heard several weeks ago, how there's a major change in networking mindsets we're talking about how security is changing. We're going to talk about some of the permanents, but it's really, really important to try to understand these different trends by different industries, which we're going to talk about in a minute. But if you take a look at this slide, I mean, obviously most people expect this U-shape decline. I mean, U-shape recovery rather. So it's two or 3/4 followed by some growth next year. But as we'll see, some of these industries are going to really go deeper with an L-shape recovery. And then it's really interesting that a pretty large and substantial portion see this as a tailwind, presumably those with strong SAS models, annual recurring revenue models, your thoughts? >> If we kind of start on this kind of aggregate chart, you're looking at about 44% of CEO's anticipate a U-shaped recovery, right? That's the largest bucket. Then you can see another 15% anticipate an L-shape recovery 14 on the V-shaped, and then 16% to your point that are kind of seeing this tailwind. But if we kind of focus on that largest bucket that U-shaped, one of the things to remember and again, when we asked this to CIOs within this kind of COVID Flash Poll, we also asked, can you give us some commentary? And so, one of the things that, or one of the themes that are kind of coming along with this U-shape recovery is CIOs are cautiously optimistic about this U-shape recovery. They believe that they can get back onto a growth cycle, into 2021, as long as there's a vaccine available. We don't go into a second wave of lockdowns. Economic activity picks up, a lot of the government actions become effective. So there are some kind of let's call it qualifiers, with this bucket of CIOs that are anticipating a U-shape recovery. What they're saying is that, "look, we are expecting these things to happen, "we're not expecting a lockdown, "we are expecting a vaccine. "And if that takes place, "then we do expect an uptake in growth, "or going back to kind of pre COVID levels in 2021." But I think it's fair to assume that if one or more of these are ups and things do get worse as all these States are opening up, maybe the recovery cycle gets pushed along. So kind of at the aggregate, this is where we are right now. >> Yeah. So as I was saying, you really have to understand the different, not only different sectors not only the different vendors, but you can really get to look into the industries, and then even within industries. So if we pull up the next chart, we have the industry sort of break down, and sort of the responses by the industry's V-shape, U-shape or L-shape. I had a conversation with a CIO of a major resort, just the other day. And even he was saying, well, it was actually, I'll tell you it was Wyndham Resorts, public company. I mean, and obviously that business got crushed. They had their earnings call the other day. They talked about how they cut their capex in half. But the stock, Sagar, since the March loss is more than doubled. >> Yeah. >> It was just amazing. And now, but even there, within that sector, they're appealing that on you are doing well, certain parts are going to come back sooner, certain parts are going to take longer, depending on, what type of resort, what type of hotel. So, it really is a complicated situation. So, take us through what you're seeing by industry. >> Yeah, sure. So let's start with kind of the IT-Telco, retail, consumer space. Dave to your point, there's going to be a tremendous amount of bifurcation within both of those verticals. Look, if we start on the IT-Telco side, you're seeing a very large bucket of individuals, right over 20%? That indicated they're seeing a tailwind or additional revenue because of COVID-19 and Dave, we spoke about this all the way back in March, right? All these work from home vendors. CIOs were doubling down on Cloud and SAS and we've seen how some of these vendors have reported in April, with very good reports, all the major Cloud vendors, right? Like Select Security vendors. And so, that's why you're seeing on the kind of Telco side, definitely more positivity, right? As you relates to recovery type, right? Some of them are not even going through recovery. They're seeing an acceleration, same thing on the retail consumer side. You're seeing another large bucket of people who are indicating, "look, we've benefited." And again, there's going to be a lot of bifurcation, there's been a lot of retail consumers. You just mentioned with the hotel lines, that are definitely hurting. But if you have a good online presence as a retailer, and you had essential goods or groceries, you benefited. And those are the organizations that we're seeing really indicate that they saw an acceleration due to COVID-19. So, I thought those two verticals between kind of the IT and retail side, there was a big bucket of people who indicated positivity. So I thought that was kind of the first kind of as we talked about kind of feeling this onion back. That was really interesting. >> Tech continues to power on, and I think a lot of people try, I think somebody was saying that the record time in which we've developed a vaccine previously was like mumps or something. I mean, it was just like years. But now today, 2020, we've got AI, we've got all this data, you've got these great companies all working on this. And so, wow, if we can compress that, that's going to change the equation. A couple of other things Sagar that jump out at me here in this chart that I want to ask you about. I mean, the education, the colleges, are really kind of freaking out right now, some are coming back. I know, like for instance, my daughter at University of Arizona, they're coming back in the fall indefinitely, others are saying, no. You can clearly see the airlines and transportation, has the biggest sort of L-shape, which is the most negative. I'm sure restaurants and hospitality are kind of similar. And then you see energy which got crushed. We had oil (laughs) negative people paying it, big barrels of oil. But now look at that, expectation of a pretty strong, U-shape recovery as people start driving again, and the economy picks up. So, maybe you could give us some thoughts on some of those sort of outliers. >> Yeah. So I kind of bucket the next two outliers as from an L-shaped and a U-shaped. So on the L-shaped side, like you said, education airlines, transportation, and probably to a little bit lesser extent, industrials materials, manufacturing services consulting. These verticals are indicating the highest percentages from an L-shaped recovery, right? So, three plus 1/4 of revenue declines in deceleration, followed by kind of minimal to moderate growth. And look, there's no surprise here. Those are the verticals that have been impacted the most, by less demand from consumers and businesses. And then as you mentioned on the energy utility side, and then I would probably bucket maybe healthcare, pharma, those have some of the largest, percentages of U-shaped recovery. And it's funny, like I read a lot of commentary from some of the energy and the healthcare CIOs, and they were saying they were very optimistic (laughs) about a U-shaped type of recovery. And so it kind of, maybe with those two issues that we could even kind of lump them into, probably to a lesser extent, but you could probably lump it into the prior one with the airlines and the education and services consulting, and IMM, where these are definitely the verticals that are going to see the longest, longest recoveries. And it's probably a little bit more uniform, versus what we've kind of talked about a few minutes ago with IT and retail consumer where it's definitely very bifurcated. There's definitely winners and losers there. >> Yeah. And again, it's a very complicated situation. A lot of people that I've talked to are saying, "look, we really don't have a clear picture, "that's why all these companies are not giving guidance." Many people, however, are optimistic only for a vaccine, but also their thinking is young people with disposable income, they're going to kind of say,"Damn the torpedoes, "I'm not really going to be exposed." >> And they could come back much stronger, there seems to be pent up demand for some of the things like elective surgery, or even some other sort of more important, healthcare needs. So, that obviously could be a snapback. So, obviously we're really closely looking at this, one thing though is certain, is that people are expecting a permanent change, and you've got data that really shows that on the next chart. >> That's right. So, one of the last questions that we ask kind of this quick COVID Flash Poll was, do you anticipate permanent changes to your kind of IT stack, IT spend, based on the last few months? As everyone has been working remotely, and rarely do you see results point this much in one direction, but 92% of CIOs and kind of high level IT end users indicated yes, there are all going to be permanent changes. And one of the things we talked about in March, and look, we were really the first ones, in our discussion, where we were talking about work from home spend, kind of negating or bouncing out all these declines, right? We were saying, look, yes, we are seeing a lot of budgets come down, but surprisingly, we're seeing 20,30% of organizations accelerate spend. And even the ones that are spending less, even them, some of their budgets are kind of being negated by this work from home spend, right? When you think about collaboration tools and additional VPN and networking bandwidth, and laptops and then security, all that stuff. CIOs now continue to spend on, because what CIOs now understand is productivity has remained at very high levels, right? In March CIOs were very concerned with the catastrophe and productivity that has not come true. So on the margin CIOs and organizations are probably much more positive on that front. And so now, because there is no vaccine, where we know CIOs and just in general, the population, we don't know when one is coming. And so remote work seems to be the new norm moving forward, especially that productivity levels are pretty good with people working from home. So, from that perspective, everything that looked like it was maybe going to be temporary, just for the next few months, as people work from home, that's how organizations are now moving forward. >> Well, and we saw Twitter, basically said, "we're going to make work from home permanent." That's probably because their CEO wants to live in Africa. Google, I think, is going to the end of the year. >> I think many companies are going to look at a hybrid, and give employees a choice, say, "look, if you want to work from home "and you can be productive, you get your stuff done, we're cool with that." I think the other point is, everybody talks about these digital transformations leading into COVID. I got to tell you, I think a lot of companies were sort of complacent. They talk the talk, but they weren't walking the walk, meaning they really weren't becoming digital businesses. They really weren't putting data at the core. And I think now it's really becoming an imperative. And there's no question that what we've been talking about and forecasting has been pulled forward, and you're either going to have to step up your digital game or you're going to be in big trouble. And the other thing I'm really interested in is will companies sub-optimize profitability in the near term, in order to put better business resiliency in place, and better flexibility, will they make those investments? And I think if they do, longer term, they're going to be in better shape. If they don't, they could maybe be okay in the near term, but I'm going to put up a caution sign, although the longer term. >> Now look, I think everything that's been done in the last few months, in terms of having those continuation plans, due to pandemics and all that stuff, look, you got to have that in your playbook, right? And so to your point, this is where CIOs are going and if you're not transforming yourself or you didn't before, lesson learned, because now you're probably having to move twice as fast to support all your employees. So I think this pandemic really kind of sped up digital transformation initiatives, which is why, you're seeing some companies, SAS and Cloud related companies, with very good earnings reports that are guiding well. And then you're seeing other companies that are pulling their guidance because of uncertainty, but it's likely more on the side if they're just not seeing the same levels of spend, because if they haven't oriented themselves, on that digital transformation side. So I think events like this, they typically showcase winners and losers than when things are going well. and everything's kind of going up. >> Well, I think that too, there's a big discussion around is the S&P over valued right now. I won't make that call, but I will say this, that there's a lot of data out there. There's data in earnings reports, there's data about this pandemic, which it continues to change. Maybe not so much daily, but we're getting new information, multiple times a week. So you got to look to that data. You got to make your call, pick your spots, earlier you talk about a stock pickers market. I think it's very much true here. There are some going to be really strong companies. emerging out of this, don't gamble but do your research. And I think you'll find some gems out there, maybe Warren buffet can't find them okay. (laughs) But the guys at main street. I'm optimistic, I wonder how you feel about the recovery. I think I maybe tainted by tech. (laughs). I'm very much concerned about certain industries, but I think the tech industry, which is our business's, going to come out of this pretty strong? >> Yeah. Look, the one thing we should have stated this earlier, the majority of organizations are not expecting a V-shaped recovery. And yet I still think there's part of the consensus is expecting a V-shaped recovery. You can see as we demonstrate in some of the earlier charts, That U-shaped, there is some cautious optimism around there, almost the majority of organizations are expecting a U-shape recovery. And even then, as we mentioned, right? That U-shape, there is some cautious optimism around there, and I have it, you probably have it where. Yes, if everything goes well, it looks like 2021 we can really get back on track. But there's so much unknown. And so yes, that does give I think everyone pause when it comes from an investment perspective, and even just bringing on technologies. into your organization, right? Which ones are going to work, which ones aren't? So, I'm definitely on the boat of, this is a more U-shaped in a V-shape recovery. I think the data backs that up. I think when it comes to Cloud and SAS players, those areas, and I think you've seen this on the investment side, a lot of money has come out of all these other sectors that we mentioned that are having these L-shaped recoveries. A lot of it has gone into the text-based. I imagine that will continue. And so that might be kind of, it's tough to sometimes balance what's going on, on the investment that stock market side, with how organizations are recovering. I think people are really looking out into two, 3/4 and saying, look to your point where you said that earlier, is there a lot of that pent up demand, are things going to get right back to normal? Because I think a lot of people are anticipating that. And if we don't see that, I think the next time we do some of these kind of COVID Flash Polls I'm interested to see whether or not, maybe towards the end of the summer, these recovery cycles are actually longer because maybe we didn't see some of that stuff. So there's still a lot of unknowns. But what we do know right now is it's not a V-shaped recovery. >> I agree, especially on the unknowns, there's monetary policy, there's fiscal policy, there's an election coming up. >> That's fine. >> There's escalating tensions with China. There's your thoughts on the efficacy of the vaccine? what about therapeutics? Do people who've had this get immunity? How many people actually have it? What about testing? So the point I'm making here is it's very, very important that you update your forecast regularly That's why it's so great to have this partnership with you guys, because you're constantly updating the numbers. It's not just a one shot deal. So Sagar, thanks so much for coming on. I'm looking forward to having you on in the coming weeks. Really appreciate it. >> Absolutely. Yeah, we'll really start kind of digging into how a lot of these emerging technologies are fairing because of COVID-19. So, I'm actually interested to start digging through the data myself. So yeah, we'll do some reporting in the coming weeks about that as well. >> Well, thanks everybody for watching this episode of theCUBE Insights powered by ETR. I'm Dave Vellante for Sagar Kadakia, check out etr.plus, that's where all the ETR data lives, I publish weekly on wikibond.com and siliconangle.com. And you can reach me @dvellante. We'll see you next time. (gentle music).

>> Narrator: Live from Denver, Colorado, it's theCUBE. Covering Commvault Go 2019. Brought to you by Commvault. (upbeat electronic music) >> Hey, welcome back to theCUBE. Lisa Martin with Stu Miniman. We are covering Commvault Go '19 from Colorado and Stu and I are pleased to welcome a couple of guys back to theCUBE. We've got David Wigglesworth, a VP, now VP of Global Sales and Emerging Technologies at Commvault for what, a couple weeks now David? >> About a month and five days. >> About a month, and look who's back, it's Don Foster, VP of Storage Solutions, >> Great to be back. from the Keynote stage, welcome back Don. >> Thank you very much. >> Don, and we appreciate you bringing your own personal makeup artist, Sanjay Merchandandi, >> Yeah. >> A man of many skills. >> Indeed. (laughing) >> He really is. So if this whole, like, CEO thing doesn't work, he's clearly got a career in, you know, touch-up makeup. >> In makeup. >> Yeah, all right, so Wigs we'll start with you, you've got a cool nickname, so I got to use it. You've been here for about a month or so. This is a new Commvault. We've heard a lot in the last two days. A lot of news, a lot of leadership changes, obviously, go-to-market changes, new partner offerings, lots of stuff. Tell us first, before we dig in, what attracted you to Commvault? >> That's a pretty easy question to answer, it's the leadership. So, obviously I'm very familiar with Commvault. I've competed with them in my past career. Always been a very formidable competitor. When you walked into an account in my previous life and they said they had Commvault, you usually kind-of wiped your brow, and thought 'Oh okay, I've got to find something else here to talk about' but in all seriousness, for me it was, you know, when I first noticed in the News that Sanjay had come onboard. That peaked my interest, because obviously I knew Sanjay in my previous life at EMC and at VMware. And then when I watched Ricardo join the company, I was like, okay, this is something I really need to dig into. And so when I had the opportunity to meet with them and understand the direction of where they want to take the company, which was also already just a phenomenal IT organization, just a pillar in the IT community, with what the founders were able to do in relatively short amount of time. I was really excited to be able to come over and be a part of it. >> Wigs, you've got a emerging tech under your purview, tell us a little bit about what that's going to mean in your role. >> Right now it means I'm head big, right? So, by now, everyone's heard of the acquisition that was made. That was the other thing also that really interested me, was that technology because I really think that's where the market is going and I just felt like it was a great addition to the Commvault family of products. But it's a different technology. It's calling on a different set of folks with inside of an account and it's primarily an enterprise play. It can be a go-down-market a little bit, and enterprise's is kind of where I spent the last several years of my career, the last 20 or so (laughs) and so what we've decided to do is, because it's so different, we've decided for the time being, that we were going to create a special aid organization globally to go sell that solution so that our existing core sellers can focus on our existing set of products, right? That we can be a specialist organization that can help them with their customers, selling all of the additional emerging tech, right? And so, here at the show, we've obviously spent time talking about Hedvig. Metallic is another new technology for us. Now Metallic is going to handled differently, but as we continue to grow our emerging technologies from the traditional core Commvault family of products, that's what I'm going to be focused on. So it'll begin with Hedvig. >> So for the role that you're in now, you said about a month or so, are you bringing in a brand-new sales overlay team? Are you guys hiring like crazy or are some of the Commvault OG sales-guys-or-girls shifting up, we'll say? >> For the most part, we're bringing in new talent. We're looking for people that have a broad spectrum of the experience, right. Obviously someone with strong storage background, but also people that know virtualization code, people that understand containers. Those skillsets are really important to us. And so we're busy building out both an America sales team and also building out a Nemea sales team. And then my partner, I call him my partner-in-crime, Ediz. Ediz is building out our SE organization for the same two theaters. We'll start in those two theaters and then once we get the product fully integrated, which is part of what this guy is doing, once we get the product fully integrated, then I think you'll see us start to move into some other theaters. But right now we're going to focus on those. So yes, we're hiring. Right now my LinkedIn says, "David Wigglesworth, we're hiring." >> I think I saw that actually (laughing). >> So Don, we got to dig into some of the technology with you and Avinash yesterday. >> Absolutely. >> So we're now getting most of the way through the conference, bring us inside some of the conversations you're having. I know it was one of the biggest question, we had coming in was: 'All right The Hedvig that we knew, what's going to change, how does that fit?' Blurring the lines between primary and secondary and all those discussions we had with Sanjay. So take us to how people, are they kind of getting it at this point? And we know it's a journey for the integration and where it will ultimately end. >> Here's the real interesting thing, is probably in the first, I don't know, maybe 24 hours of having conversations with people from partner exchange all the way through to basically day one of actual Commvault Go, I probably had about four, maybe five if you count one of the service providers from Customers' Partners, come up and say, "Okay look, we looked at this tech about 18, 12 months ago and it was top of our list for what we wanted to do for building out this initiative, but there was a little bit too much risk." Going okay, do we really want to invest that much on a company that is maybe not the largest, most, I wouldn't want to say, reputable, but substantial in the marketplace. Will they be there in the future? And they're like, "Now that we know you've legitimized that business "and you want to keep that technology going forward, "this is fantastic. "We totally want to go and take a re-look back at this "and see how we can apply "that back into our infrastructure." So that's a great feedback to hear, and only serves as validation that when we look at the tech and say "This is good stuff," that we know it's good stuff and then of course the next piece is always, "All right, so now when can I start using this for Commvault and?" >> Right. >> That's when we start getting into the conversations of all right, we've got some integration work to do, the partners are asking when they can start to get access to sell it and again, we've got some work to do just to industrialize what we're doing and make the experience similar and then we'll start to roll it out in a considered fashion. >> I'm curious about the education piece. One of the customers that was onstage this morning, Sonic Healthcare, one of the things he said, on main stage and when he stopped by theCUBE a couple of hours ago, was, he said: "I wouldn't be in my job," and he runs disaster recovery and business continuity for Sonic Healthcare, "I wouldn't be in my job without Commvault's support." And I really appreciated and respected how he talked about some of the failures that they had. I always think failure is a good F-word if you leverage it in that way, (agreement) failure can mean success, if you learn from it. But the support organization and the training he talked about have been instrumental. Talk to us, guys, about how you're going to be partnering together to not just enable the big partners for those large enterprise accounts but maybe even the new sales-guys-and-girls that are coming, David, to your team to help everybody really understand how best to delivery a really stellar customer experience with something as exciting now as Hedvig is. >> You want to start, since you've been working on the integration. >> Yes, absolutely. First and foremost, I've been working with Avinash and his brother, Srinivas, and a lot of their engineering team. You really start to lock in things that are repeatable and scalable in nature, right? So that if we are going to open this up to more people, we do need to have repeatable nature of the building blocks for different use cases. So there's some core work we're doing on outlining, positioning, criteria, success, what the outcome needs to be, how that ties back in to hardware. Making sure as well that we understand how the messaging really does resonate and make sure that we're following and being focused on what our core targets are. Because a solution like what Hedvig offers, you can quickly start talking about a lot of different things that could be all things to many people, and we know that that's probably the worst decision to make, because you go super wide and don't go very deep at all and you end up losing the value prop. So identifying what the real core use cases are, getting deep in how it works, one with what the structure of it looks like, making it repeatable, that's the first and foremost thing, I think, for how we can help both Ediz and Wigs' sales team, and on the support side, doing very similar things but also doing some of the programmatic work of the integration and the experience. I talk about experience, like the sending of logs, the things that Matthew Magby from Sonic Healthcare was talking about how we really helped him. We want that same level of experience tied into where the software storage platform works as well. So there's some work to be done there. But as we get it done, the enablement on the support side, as you know, we deal with storage everyday anyway, so it's not like it's a big leap, but we do have to bring them into the mix of how the actual technology works, where it breaks, why it breaks, and those are all the things that we're really focused on in the next 90 days. >> Yeah, I think the real key for me as we talk to customers and also employees is I want them all to have the same experience with the new Hedvig solution that they experience with Commvault, right? And that goes from training our employees, really getting our SEs up to speed, so they can have a meaningful conversation to be able to get a customer to say, "Yeah, I think I'd like to speak with the Special Aid team. "Please have them give me a call." And also on the enablement for the clients, and having the customer understand that you can dial to 1-800 number for support, you can talk to somebody that can lead you down a path and give you the same quality of support you've been used to whether you're calling about a Hedvig solution or whether you're calling about a Commvault solution. >> Yeah, we talked about it a little yesterday, but the scale of the offering is a little bit different. >> It is. >> And therefore, that has some challenges on the support. And something that I'm sure Commvault is going to work on making that, it's not identical for every customer but a little bit more repeatable to be able to scale out that offering. >> I would agree, I would agree. The hardest thing to do is when you have a product that has so much functionality as Hedvig is to not lose focus and try to talk way too broad. What you've really got to do is, you've got to drill down with the client try to understand where their pinpoints are and because, quite frankly, the Hedvig product can do a lot of things. >> Don: Yeah, it can. >> Who's the ideal target customers, we talked about the theaters in which you're going to be launching first. Enterprise, we talked about that. Commvault has a significant presence in the Fortune 500, I think I read about three quarters of Commvault's revenue today comes from the Fortune 500, and Stu was saying yesterday about 80% of the revenue comes from the channel. So we look at Hedvig and the enterprise for a second, customers that are new to Commvault, those existing enterprise customers, GTM both? >> Yeah, I would say, the primary focus is going to be calling on the existent customer set. It's much easier to have a conversation with someone who knows who you are, even though you may be selling a new solution, at least they know who you are and they have a positive experience with us. So that, number one, we're going to focus on our probably our top 300 global accounts to start, as well as our top enterprise accounts. So there's probably, I would say, in the two theaters I mentioned earlier, there's probably about 35 hundred accounts that we're really going to focus on, and really try to make sure that we get in front of as many as we can and tell the story. I think that's where we have to start. Now, will there be greenfield opportunities? Yeah, I think quite frankly, that the Hedvig offering is different enough that it will enable us to go call on some of accounts that aren't doing business with Commvault today, maybe doing business with some of our competitors. So hopefully we can use that to actually win more traditional Commvault business. That's the plan. >> And the reason the enterprise really makes sense, the global accounts, is most larger companies have figured out how try solve the CapEx problem, right? >> David: Yeah. >> They've figured out just the economies of scale and how they grow and move, they can kind of handle that. What really still becomes a challenging piece is the operational efficiency. So, can I get the right solution at the right cost, but do it in a way that I'm actually making things more simplified? I'm not actually exploding more complexity into my environment. That's really where the Commvault data management platform and the Hedvig solution together really make a really solid story. >> All right, so Wigs, Don's team's really got their work cut out for them with all the integration work and know they've got a cadence and a roadmap. For you, obviously, new logos, there's got to be revenue goals. What are some of the key KPIs to measure how this becomes a successful acquisition? >> Well if my CEO is standing close by, he may be in earshot of this, right now it's trying to drive as much revenue as we can. But we also have to realize that we also have to build a pipeline, right? So right now my main focus here is I got to get a team in place that can go articulate the value of this solution to a client, right, number one, both technically and then working with Ediz to get the SE team in place, so that's number one. Number two, while we're doing that, we need to build a pipeline, right? When you make an investment, as you guys know, you're expected to start getting a return on that pretty quickly. And, it's nice, we inherited some nice pipeline with the acquisition. But with opportunity comes responsibility and so we've got to build that pipeline up and really get out in front of customers and find some opportunities that we can not only try to finish for this second half so we can hit all of our financial metrics, but really build pipeline for FY21, for us which starts in April. >> So the voice of the customer is, really can be really powerful. We've heard from a number of Commvault customers on our program yesterday, today on main stage. Is there a plan, Wigs, from your perspective, to get customers into some sort of data so that you have proof in the pudding to show those large enterprises and those theaters to help build that pipeline. Look at someone who's been an existing Commvault customer for five, 10 years or so, here's the, I don't want to say migration path, but maybe upgrade path to expand footprint in there. Here's how we did it, here's why this was ideal for this customer. Plans to get those early adopters to help you dial up the pipeline? >> So have you been reading my 'Go to market strategy' (laughing) 'cause you kind of you basically just read it. So yes, listen we are inheriting some nice accounts with Hedvig. They have some nice logos out there which is really good. And it's a good foundation for us to build upon. But we're very fortunate in that our core sellers have some really good relationships with some pretty large customers really in all different industries. And so, what we're doing right now is we're trying to identify probably about 10 accounts that make sense. That are really strong partners. They don't have to necessarily be really big customers, but just really strong partners that want to work together with us. And exactly what you just said, let's get in front of them, let's give them an opportunity to play with the technology and have them help us figure out, we think we have a pretty good idea what the go-to-marketing messaging should be for our existing customer base but certainly don't assume that we know everything. So have them help us build that strategy. So that is absolutely the plan. >> We've been hearing a lot about the last couple of days, of just, the openness of Commvault. Whether it's, I really thought it was cool with Metallic that the telemetry that partners can get to help customers, maybe even before a customer knows of an issue or an opportunity, but this telemetry, this 'let's learn from our customers,' couldn't agree as a marketer with you more about, we might think we have a great tagline, great messaging, but it's the users who need to validate that. What I'm hearing a lot over the last day and a half is how receptive Commvault is. We're listening to our customers, whether it's existing and comeback customers that Sanjay's team are dealing with, or even through partners. That message is loud and clear, and that's pretty important. >> Yeah, I couldn't agree more. And I'll be honest with you, what's it's also been able to give us an opportunity to do is where we've had some relationships, quite frankly, that maybe we need to work a little harder on. Hedvig has given us that opportunity to kind of start those conversations as well. I think there's a lot of value, both on the existing opportunities as well as growing the business overall. >> Guys, nothing short of a lot of work ahead. But, pretty exciting stuff. We thank you both. Wigs, welcome again to Commvault. >> Thank you. >> Can't wait for next year. Going to bring some cool customers on the program. >> Yeah, absolutely. >> Looking forward. The buzz is so amazing this year. So many customers have said, "I know you weren't here last year, but wow," and that's what they've said. I can't wait to see what this is going to be like next year. Thank you for having us on here. >> You've got to come back. >> Absolutely we will. >> Yeah? >> Yeah. >> All right, guys, thank you for joining Stu and I. >> Thank you both very much. >> Thank you. >> For Stu Miniman, I am Lisa Martin, and you're watching theCUBE from Commvault Go '19. (upbeat electronic music)

(upbeat music) >> Announcer: Live from Austin, Texas, it's the Cube, covering Kubecon and CloudNativeCon 2017. Brought to you by Red Hat, the Linux Foundation, and the Cube's Ecosystem partners. >> Hello and welcome back to the Cube's exclusive coverage live in Austin, Texas here for the three day CloudNative and now two days of KubeCon, Kubernetes conference. We had the second annual conference celebrating the evolution and growth of Kubernetes. I'm John Furrier, my cohost Stu Miniman and next guest Steve Watt, Chief Architect of Emerging Technologies at Red Hat, welcome back to the Cube. Good to see you. >> Thanks for having me, always a pleasure. >> So Red Hat making some good bets, some Kubernetes, not a bad call. >> No, Kubernetes has done wonders for our openship business, absolutely. (laughter) >> So how is this all playing out? We were just talking before we came on camera here about the just the pace of change. You been at Red Hat five years. We interviewed you when you were at HB during the big day to days, boy the world has certainly grown and changed. What has changed in your mind the most the people need to understand? >> I think Kubernetes has been a single biggest driving force to shift all enterprising architecture from scale up to scale out and I think that has just created a whole number of ripple effects across how applications are designed within the enterprise. >> I think that's the big one. >> Yeah. >> So Steve, that whole shift from scale up to scale out has affected lots of parts of the stack, but storage is something you've been working on, something we've been keeping a close eye on and was one of the top items we wanted to kind of dig into this week. Maybe, bring us inside a little bit, what's happening, what's Red Hat's role? >> Sure. >> Help explain. >> Absolutely, one of my favorite topics. It's kind of counterintuitive. I work in a CT office, I run the emerging technologies team, which is sort of the team that does the experiments that help shape and inform our long term strategy. And so you might think, well storage is kind of old news, how does that fit into this CloudNative world? Why does Red Hat care about it so much for their platform? And I think if you look at the CloudNative stack today, you have GKE, the new Amazon Kubernetes service, Azure, et cetera, these are all places where you can run your Kubernetes app, but just in that one place. Red Hat's platform perspective's a little different. We want you to be able to run your platform in an open hybrid cloud, whether that's in Google, in Azure or on premise, on OpenStack or on Bare Metal So you want to be able to run everywhere, but what's the biggest problem to achieving that application portability? It's data locking, so storage becomes cool again. (laughter) We got to solve this problem. >> Because you got to store the data somewhere. >> Steve: Right. >> And that's in the storage devices. >> Right, exactly. >> In the new way, the architecture. >> The new architecture, right? So the problem is, you've got to be very careful that if you want to move, ever you should think upfront about your persistence platform, so that it gives you the freedom to be able to move around. So Red Hat is investing heavily in trying to solve this problem. We've got a few exploratory prototypes that we're actually showing at this conference. And we work in both Kubernetes, building out the storage sub-system there, but also sort of in our products for like container native storage. >> Steve walk us through a little bit because we've been talking about this in the Docker Ecosystem for a bunch of years, where are we, what's being worked on? What still needs to be kind of sorted out? >> So, yeah that's interesting, I think we're finally over the hump where everybody's asking, Who's solving the persistence problem for containers? It used to drive me crazy, that went on for about three years. I think people finally realize, there are solutions. Kubernetes has always had them actually. And so, we've got past sort of the day one, like being able to, dynamically provision. Kind of like you'd see with Cinder in OpenStack. We've got a great storage. we've got a vibrant huge storage ecosystem and at our Kubernetes face to face meetings we have 50 people, they're like a mini conference. So we've got broad engagement from the entire storage ecosystem and that's doing everything that you need sort of on the file level, but there is recent (mumbles) work that we've done in Kubernetes for Service Broker is now the pattern to sort of provision object storage if you need it and most importantly, we've just enabled lock storage in Kubernetes in the 1.9 release that ships this week. And that is really interesting because it opens up the potential to run virtualization with loads on Kubernetes. >> Where's the action for the projects with storage? I heard some hallway rumbles just when I was, the Rook project. >> Steve: Yeah. >> Is that something, what projects, if I'm interested in storage, where do I dive in? Where's the most action for moving the needle for tuning the innovation around storage. >> I think it's if you're a storage vendor it's different if you're a storage consumer so Rook is a project that's focused on providing a sort of an abstraction for software defined storage platforms to run inside Kubernetes. Cluster doesn't take that approach, we've used sort of more of the pure Kubernetes approach. Sort of get to the same place. But Rook is definitely an interesting project in that, it's sort of an inception level project phase. Then for people that are wanting to consume storage, I think Kubernetes is the king of the pack. I obviously have a strong opinion on it, amongst the other container orchestrators, but the amount of investment in allowing people to do more continually more sophisticated features, you know snapshot's in, you know cloning, things like that. And obviously, I'm sure you've heard a little bit about container storage interface. >> Yes. >> CSI, and that makes it a lot easier for storage vendors to build one adapter that works across, Decos, Cloud foundry, Kubernetes, et cetera. >> What's the biggest surprise here for you, because we've been looking trying to read the tea leaves. Obviously Kubernetes, clear the runway, good standardization seeing some commoditization, great adoption, although people can tailor it. A lot of different versions, still early. >> Steve: Yeah. >> We're only two years old conference. >> I know. >> Three years it's been around. What's surprising you right now? What's jumping out at you? >> I think Amazon's announcement yesterday was very interesting. I think the fact that it's heartening to see that there's pure Kubernetes as a service being offered in Azure, Google and Amazon. And I think that quite interesting for affordability standpoint, right. And so I think to me that was a big surprise. Amazon doesn't usually go the pure vanilla open source approach and also the statements they're going to contribute back to Kubernetes, I think is quite interesting as well. So to me that's the one thing that stood out. >> What's going on for the future too? You mentioned you've got to set the roadmap. You guys have an agenda there obviously of installed base. >> Steve: Yeah. >> Now you've got OpenShift doing really well. What are you guys looking at? What's on your radar, how do you see this thing unfolding? What's in your mind? >> Yeah, I think there's a couple of really interesting things. Container orchestration is a legitimate disruption to virtualization. And that it solves the same problem opportunity space but in a fundamentally different manner that reshapes the market. I think the Kubert project is something that we're working on at Red Hat. It's another one of our sort of emerging technology focus areas. And when we enable block storage and it enables virtualization, what it gives us the opportunity to do in Kubernetes is have a single deployed platform that can serve both later adopters and early adopters. So the early adopters with pure container orchestration, but if you're wanting to have the same platform and do virtualization too on it, you can have sort of one investment, one shared experience to be able to do all of those. I think that's pretty cool. (laughter) >> Steve, talk about the customers that are watching or will be hearing over the next few months and a year around how to architectually package this and think about it in their mind. Whether it's a mental model or specifics. 'Cause there's always going to be that time tested trade off between performance, security and so you have, obviously people have VM's, not going away, but containerization where Google say, hey, we don't really care about VM's, we're a container company. There's always still going to be trade offs. >> Steve: Yeah. >> Speed, security. >> Steve: Security. >> So security factors in there. How should a practitioner think about getting their arms around this? >> I think this is the tact that OpenShift takes which is that Kubernetes is a decent project. Despite the huge amount of interest and contributions that we have and its maturity curve as far as, there are different things at attention, like enterprise use cases, versus public cloud use cases. And so we're very focused on our enterprise use cases and sort of enabling that inside OpenShift and bringing OpenShift up as a platform back to sort of enterprise level that our customers would expect. Virtualization platforms are much further down the maturity curve, and so I think that's sort of our approach is that, where that tries to meet our customers where they are. Some organizations have teams that are more advanced. Some that are less advanced. And so we try to offer, you know if you want to go virtualization we've got OpenStack, we've got Rev. If you want you could use this new school Kubernetes based container orchestration and you got teams understand it. (laughter) And you corrupt microservices then we've got a solution for that. >> Well you know that whole theme here is infrastructures is boring storage. It used to be called snorage back in the day. >> Steve: Yeah. >> It's pretty boring but relevant. Most people look at like Lambda from Amazon and some other serverless trends and certainly see them here with ServiceMesh and what not, the abstraction way of infrastructure, it's almost eliminating storage in the mind of the developer, yet it's changing, how are you guys specifically riding that wave? Because one, it's good for developers. >> Steve: Right. >> The velocity of developers increases, but the role of storage is changing. You mention block, people are like, oh block-- >> Yeah. >> It's dead. I mean storage has been dead for like 20 years now? >> Steve: Yeah. >> It keeps growing and growing, but now the role changes to the developer, abstracted away and also more important for automation and some of the dev ops things. What specifically are you guys doing? >> So, I think you said the word role. That's really important right? Like to an application developer what you said is absolutely true, they want to use persistence platforms for storing their data in a cloud native way, okay. However, the maturity code is also important. Not every application developer team is fully microservice based and understands all these architectural patterns. It's a journey, right? So we want to basically give them multiple options along their journey. So that's the one around the application persistence. So if they used to like file storage or object storage, et cetera, like we have our container native storage platform provides that for them from the application persistence level, but from an OpenShift standpoint, an OpenShift is our new platform. It's based on real but it's our new platform, our new service area to build applications and most notably, infrastructure services on. So just like with (mumbles) where we have, we created the opportunity to have a fertile ecosystem around it, we're doing the same with OpenShift, which means that we've got to enable the companies that are providing those persistence platforms. Those message cues, those NoSQL databases, to run on OpenShift. You want to run Cassandra on OpenShift on premise? What do you need underneath the Cassandra? Block storage, direct attached block storage, which we're building in Kubernetes 1.10. >> Steve, any patterns you're seeing between the customers that are being able to embrace really the kind of this new cloud data world versus those that are having challenges? Any advice you can give based on customer interactions and what you're seeing. >> That's a good question. I think, I just have to fall back on the fact that culture is a hard thing to change. It takes a long time. Institutions are persistent and so I think that for what we sort of say to our customers, our guidance on these topics is that, what we try and give you is choice. Depending on where you are on the journey, slowly move our customers through that journey and try to give them a variety of different choices on that. I think personally like with any new disruption, it usually has like 10 x value. Like the one benefit of containers over to machines is you don't have to bring the operating system along every time you create a new container, right? You can much more densely pack a server with containers with virtual machines. Get more resource utilization, but it takes a long time for an application development team to like fully get there. And so, that's the thing I think, is you just got to be judicious about like the right tool at the right time. >> Yeah, the other thing related to that is the pace of change. >> Steve: Yeah. >> I've talked to some of the people that created Kubernetes, the people who are running all this and they're like, I can't keep up with all these projects. What are you finding internally in Red Hat, as well as from your customers? >> Yeah, I think that it's absolutely true. I was just remarking on that a minute ago it's, you know I'm walking around. I hear this great quote, like why do you come to conferences? Do you come to conferences to learn or do you come to conferences to learn about what you need to learn? (laughter) >> Yeah. >> And it's the latter for me, right. And the ecosystem, the CloudNative ecosystem is exploding. And so I think what we try to do at Red Hat is, especially our team. Our goal in Emerging Technologies is to look 18 months down the road and pick the winners. Like community vitality standpoint, but also like the right technology. And there's this plethora of choices that we need to wave through and what we tend to do is distill that down into our platform that's something our customers can rely on. And that's reliable and we've picked the right project, but it's a big challenge. Like there's so much happening and even in storage it's becoming challenging. >> Steve Watt the Chief Architect of Emerging Engineering at Red Hat thanks for coming on the Cube, appreciate your perspective. It's an architectural game right now. A lot of people putting these new architectures together. It's cultural change. Congratulations on your success with OpenShift and everything else. >> Steve: Yeah, thank you very much. >> Alright, and more coverage here on the Cube after this short break. >> Steve: Thanks. (upbeat music)

>> Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're at Node Summit 2017 in downtown San Francisco at the Mission Bay Convention Center. About 800 hardcore developers talkin' about Node and really the crazy growth and acceleration in this community as well as the applications. We're excited to have our next quest. He's Nick O'Leary, Developer Advocate from IBM for Watson IoT, and you're workin' on somethin' kind of cool called Node-REDS. First off, welcome. >> Thank you, thank you very much for havin' me. >> Absolutely, so what is Node-RED? >> So, Node-RED is an open source project we started working on about four years ago now in the Emerging Technologies group in the UK parts of IBM, and it's a Node.js application that gives you a visual programming tool for Internet of Things-type applications. So when you run it, you point your web browser at it, and it gives you this visual workspace to start dragging in nodes into your canvas that represent some sort of functionality, like connect to Twitter and get some tweets or save something to a database or read some sensor data, whatever it might be, and you start drawing wires between those nodes to express how you want your application to flow, how you want data to flow through your application. So it's quite a lightweight tool and really accessible to a wide range of developers whether sort of seasoned, experienced Node developers or your kids just learning how to program because it hides complexity. And, yeah, it's Node.js-based, so it runs down on a Raspberry Pi, it runs up in the cloud like IBM Bluemix, wherever you want to run it. So really flexible developer platform. >> Pretty interesting 'cause we just had Monica on from Intel, and she was talking about one of the interesting things in this development world of Node.js is so much of the code was written by somebody else. I think she said in a lot of projects the actual original code may be 2% because you're using all these other stuff, and libraries have already been created. And it sounds like you're really kind of leveraging that infrastructure to be able to do something like this. >> Absolutely, so, one of the key things we enabled very early on was to, 'cause we recognized the power of our tool, is those nodes in our palette that you drag on. So we built the system so that people could write their own nodes and extend the palette, and we used the same node packaging as the standard MPM ecosystem. And as of a couple weeks ago, we have over a thousand third party nodes people have written, so there's probably already a module for most hardware devices, online APIs, databases, whatever you want. People are creating and extending the platform in all sorts of ways just building on top of that incredible ecosystem that Node.js has. >> And then how does that tie back to Watson? You said you're involved in Watson. So Watson people don't think of necessarily a simple, simple interface but not necessarily a simple application. So what's the tie between Watson and Node.js and Node-RED? >> So, Node-RED is a development tool. I say it all hinges on those nodes and what they connect to, so we have got nodes for the Watson IoT platform, so that's great for getting, if you're running node-RED on a Raspberry Pi, connected up to our IoT platform, connect to applications in the Bluemix space. But we also have nodes for the Watson cognitive services, like the machine learning things, visual recognition, text to speech, all of those services we have nodes for. So, again, it allows people to start playing with the rich capabilities of the Watson platform without having to dive straight into understanding lines of code and you can start being productive and create real meaningful solutions without having to understand whether it's Node.js or Java, whatever language you would normally write to access low-level APIs. >> And can the visual tool connect to things that are not necessarily Node specific? >> So, anything that provides some sort of API. If it's got a programmatic API, then it's easier to do with Node 'cause we are in a Node ecosystem. But we've got established patterns for talking to other languages but also things often provides like a rest API, HTTP, MQTT, many other protocols, and we have all of that support built straight into the platform. >> Right, and so what was the motivation to build this, just to have an easier development interface? >> Yeah, it was twofold really. One was in the Emerging Technologies where I was, we do proof of concepts for clients we have to turn around really quickly, so whereas we're more than capable of writing individual lines of code, having that tool that lets us experiment much quicker and solve real client problems much quicker was a great value to us. But then we also saw the advantage for the developers who don't understand individual lines of code for educational purposes, whatever it might be. Those great motivators there in the various communities we're involved with, in IoT home hobbyists, all that sort of space as well, it's found a real incredible user community across the board. >> And when it started, was it designed to be an open source project or that kind of realization, if you will, kind of came along the way? >> I think on day one it wasn't the first thing to mind. You know, we were just experimenting with technology, which is kind of how we operated. But we very quickly got to the point where we realized we didn't have the time and resource to write all the nodes that could be written, and there was a much broader audience than just us doing our day job that this tool could tap into. So, maybe not on day one but maybe on a month in we thought this has to be open source. So, it was about six months after we started it we moved to an open source project, and that was September 2013. And then in October last year, IBM contributed the project to be a founding project of the JavaScript Foundation. Whereas it's a project that has come from IBM, it's now a project that is independently governed. It's not owned by IBM, it's part of the foundation. So, look at the wide range of other companies getting involved, making use of it, contributing back, and really good to see that ecosystem build. >> Oh, that's great, so I'm just curious, you said you deal with a lot of customer prototyping. Obviously you're involved in Watson, which is kind of the pointy end of the spear right now with IBM, with the cognitive and the IoT. As you kind of look at the landscape and stuff you're workin' on over the next, I would never say multiple years 'cause that's way too long, six months, nine months, what are some of your priorities, what are some of the things you're seeing, kind of that customers are doing today that they couldn't do before that gets you excited to get up out of bed and go to work every day? >> From my perspective, with our focus on Node-RED, which is kind of where my focus is right now, it's really that developer experience. We've gone so far with our really intuitive to use tooling, but we recognize there's more to do. So, how can we enable better collaboration, better basic workflows within our particular tooling, because there are people using Node-RED, in particular happily in production today, but it's funny 'cause we don't have a 1.0 version number because, for us, that wasn't interesting to us because we are delivering meaningful function. But in the project, we have just published our road map to a one point zero to really give that firm statement to people who are unsure about it as a technology that this is good for production. And we've got a wealth of use cases of companies who are using it today, so, that's very much our focus, my focus within Node-RED, and all of it does then tie back to yes, it's a JS foundation project, but then with my developer advocate hat on, making sure that draw from Node-RED into the Watson platform is as seamless and intuitive as possible because that helps everyone. >> Right, right, okay, so before I let you go, two things: One begs the question what version are you on, and where can people go to find more information so they can see when that 1.0 and obviously contribute? >> So as a Node project, we've stuck to Symantec versioning, so we are currently version naught dot 17. So we've done 17 major releases over the last about three and a bit years, and that's where we're moving forward. We've got this road map to get to 1.0 first quarter of next year. And if you want to find out more, nodered.org is where we're based, or you can find us through links by the JS Foundation as well. >> Alright, well, Nick, thanks for takin' a little bit of your time and safe travels home at the end of the show. >> Thank you very much. >> Alright, he's Nick O'Leary from IBM. I'm Jeff Frick, you're watchin' theCUBE. Thanks for watchin', see ya next time. (bubbly electronic music)

>> Narrator: Live from Las Vegas, it's theCUBE. Covering Dell EMC World 2017, brought to you by Dell EMC. >> Okay, welcome back everyone, we are live in Las Vegas for Dell EMC World 2017. This is theCUBE's eighth year of coverage of what was once EMC World, now it's Dell EMC World 2017. I'm John Furrier at SiliconANGLE, and also my cohost from SiliconANGLE, Paul Gillin. Our next guest is Vikram Bhambri, who is the Vice President of Product Management at Dell EMC. Formally with Microsoft Azure, knows cloud, knows VIPRE, knows the management, knows storage up and down, the Emerging Technologies Group, formerly of EMC. Good to see you on theCUBE again. >> Good to see you guys again. >> Okay, so Elastic Compute, this is going to be the game changer. We're so excited about one of our favorite interviews was your colleague we had on earlier. Unstructured data, object store, is becoming super valuable. And it was once the throwaway, "Yeah, store, later late ". Now with absent data driven enterprises having access to data is the value proposition that they're all driving towards. >> Absolutely. >> Where are you guys with making that happen and bringing that data to life? >> So, when I think about object storage in general, people talk about it's the S3 protocol, or it's the object protocol versus the file protocol. I think the conversation is not about that. The conversation is about data of the universe is increasing and it's increasing tremendously. We're talking about 44 zettabytes of data by 2020. You need an easier way to consume, store, that data in a meaningful way, and not only just that but being able to derive meaningful insights out of that either when the data is coming in or when the data is stored on a periodic basis being able to drive value. So having access to the data at any point of time, anywhere, is the most important aspect of it. And with ECS we've been able to actually attack the market from both sides. Whether it's talking about moving data from higher cost storage arrays or higher performance tiers down to a more accessible, more cheap storage that is available geographically, that's one market. And then also you have tons of data that's available on the tape drive but that data is so difficult to access, so not available. And if you want to go put that tape back on a actual active system the turnaround time is so long. So being able to turn all of that storage into an active storage system that's accessible all the time is the real value proposition that we have to talk about. >> Well now help me understand this because we have all these different ways to make sense of unstructured data now. We have NoSQL databases, we have JSON, we have HDFS, and we've got object storage. Where does it fit into the hierarchy of making sense of unstructured data? >> The simplest way to think about it is we talk about a data ocean, with the amount of data that's growing. Having the capability to store data that is in a global content repository. That is accessible-- >> Meaning one massive repository. >> One massive repository. And not necessarily in one data center, right? It's spread across multiple data centers, it's accessible, available with a single, global namespace, regardless of whether you're trying to access data from location A or location B. But having that data be available through a single global namespace is the key value proposition that object storage brings to bear. The other part is the economics that we're able to provide consistently better than what the public clouds are able to offer. You're talking about anywhere between 30 to 48% cheaper TCO than what public clouds are able to offer, in your own data center with all the constraints that you want to like upload to it, whether it's regular environments. Whether you're talking about country specific clouds and such, that's where it fits well together. But, exposing that same data out whether through HDFS or a file is where ECS differentiated itself from other cloud platforms. Yes, you can go to a Hadoop cluster and do a separate data processing but then you're creating more copies of the same data that you have in your primary storage. So things like that essentially help position object as the global content repository where you can just dump and forget about, about the storage needs. >> Vikram I want to ask you about the elastic cloud storage, as you mentioned, ECS, it's been around for a couple of years. You just announced a ECS lesser cloud storage, dedicated cloud. Can you tell me what that is and more about that because some people think of elastic they think Amazon, "I'll just throw it in object storage in the cloud." What are you guys doing specifically 'cause you have this hybrid offering. >> Absolutely. >> What is this about, can you explain that? >> Yeah, so if you look at, there are two extremes, or two paradigms that people are attracted by. On one side you have public clouds which give you the ease of use, you just swipe your credit card and you're in business. You don't have to worry about the infrastructure, you don't have to worry about, like, "Where my data is going to be stored?" It's just there. And then on the other side you have regular environments or you just have environments where you cannot move to public clouds so customers end up put in ECS, or other object storage for that matter, though ECS is the best. >> John: Biased, but that's okay. >> Yeah, now we are starting to see customers they're saying, "Can I have the best of both worlds? "Can I have a situation where I like the ease of use "of the public cloud but I don't want to "be in a shared bathtub environment. "I don't want to be in a public cloud environment. "I like the privacy that you are able to provide me "with this ECS in my own data center "but I don't want to take on the infrastructure management." So for those customers we have launched ECS dedicated cloud service. And this is specifically targeted for scenarios where customers have maybe one data center, two data centers, but they want to use the full strength and the capabilities of ECS. So what we're telling them we will actually put their bought ECS in our data centers, ECS team will operate and manage that environment for the customer but they're the only dedicated customer on that cloud. So that means they have their own environment-- >> It's completely secure for their data. >> Vikram: Exactly. >> No multi tenant issues at all. >> No, and you can have either partial capabilities in our data center, or you can fully host in our data center. So you can do various permutation and combinations thus giving customers a lot of flexibility of starting with one point and moving to the other. Let's them start with a private cloud, they want to move to a hybrid version they can move that, or if they start from the hybrid and they want to go back to their own data centers they can do that as well. >> Let's change gears and talk about IoT. You guys had launched Project Nautilus, we also heard that from your boss earlier, two days ago. What is that about? Explain, specifically, what is Project Nautilus? >> So as I was mentioning earlier there is a whole universe of data that is now being generated by these IoT devices. Whether you're talking about connected cars, you're talking about wind sensors, you're talking about anything that collects a piece of data that needs to be not only stored but people want to do realtime analysis on that dataset. And today people end up using a combination of 10 different things. They're using Kafka, Speak, HDFS, Cassandra, DASH storage to build together a makeshift solution, that sort of works but doesn't really. Or you end up, like, if you're in the public cloud you'll end up using some implementation of Lambda Architecture. But the challenge there is you're storing same amount of data in a few different places, and not only that there is no consistent way of managing data, processing data that effectively. So what Project Nautilus is our attempt to essentially streamline all of that. Allow stream of data that's coming from these IoT devices to be processed realtime, or for batch, in the same solution. And then once you've done that processing you essentially push that data down to a tier, whether it's Isilon or ECS, depending on the use case that you are trying to do. So it simplifies the whole story on realtime analytics and you don't want to do it in a closed source way. What we've done is we've created this new paradigm, or new primitive called streaming storage, and we are open sourcing it, we are Project Pravega, which is in the Apache Foundation. We want the whole community, just like there is a common sense of awareness for object file we want to that same thing for streaming storage-- >> So you guys are active in open source. Explain quickly, many might not know that. Talk about that. >> So, yeah, as I mentioned Project Prevega is something we announced at Flink Forward Conference. It's a streaming storage layer which is completely open source in the Apache Foundation and we just open sourced it today. And giving customers the capability to contribute code to it, take their version, or they can do whatever they want to do, like build additional innovation on top. And the goal is to make streaming storage just like a common paradigm like everything else. And in addition we're partnering with another open source component. There is a company called data Artisans based out of Berlin, Germany, and they have a project called Flink, and we're working with them pretty closely to bring Nautilus to fruition. >> theCUBE was there by the way, we covered Flink Forward again, one of the-- >> Paul: True streaming engine. >> Very good, very big open source project. >> Yeah, we we're talking with Jeff Woodrow earlier about software defined storage, self driving storage as he calls it. >> Where does ECS fit in the self driving storage? Is this an important part of what you're doing right now or is it a different use? >> Yeah, our vision right from the beginning itself was when we built this next generation of object storage system it has to be software first. Not only software first where a customer can choose their commodity hardware to bring to bear or we an supply the commodity hardware but over time build intelligence in that layer of software so that you can pull data off smartly to other, from SSDs to more SATA based drives. Or you can bring in smarts around metadata search capabilities that we've introduced recently. Because you have now billions of billions of records that are being stored on ECS. You want ease of search of what specifically you're looking for, so we introduced metadata search capability. So making the storage system and all of the data services that were usually outside of the platform, making them be part of the code platform itself. >> Are you working with Elasticsearch? >> Yes, we are using Elasticsearch more to enable customers who want to get insights about ECS itself. And Nautilus, of course, is also going to integrate with Elasticsearch as well. >> Vikram let's wrap this up. Thank you for coming on theCUBE. Bottom line, what's the bottom line message, quickly, summarize the value proposition, why customers should be using ECS, what's the big aha moment, what's the proposition? >> I would say the value proposition is very simple. Sometimes it can be like, people talk about lots of complex terms, it's very simple. Sustainably, low cost storage, for storing a wide variety of content in a global content repository is the key value proposition. >> And used for application developers to tap into? The whole dev ops, data as code, infrastructure as code movement. >> Yeah, you start, what we have seen in the majority of the used cases customers start with one used case of archiving. And then they very quickly realize that there's, it's like a Swiss Army knife. You start with archiving then you move on to application development, more modern applications, or in the cloud native applications development. And now with IoT and Nautilus being able to leverage data from these IoT devices onto these-- >> As I said two days ago, I think this is a huge, important area for agile developers. Having access to data in less than a hundred milliseconds, from any place in the world, is going to be table steaks. >> ECS has to be, or in general, object storage, has to be part of every important conversation that is happening about digital IT transformation. >> It sounds like eventually most of the data's going to end up there. >> Absolutely. >> Okay, so I'll put ya on the spot. When are we going to be seeing data in less than a hundred milliseconds from any database anywhere in the fabric of a company for a developer to call a data ocean and give me data back from any database, from any transaction in less than a hundred milliseconds? Can we do that today? >> We can do that today, it's available today. The challenge is how quickly enterprises are adopting the technology. >> John: So they got to architect it? >> Yeah. >> They have to architect it. >> Paul: If it's all of Isilon. >> They can pull it, they can cloud pull it down from Isilon to ECS. >> True. >> Yeah. >> Speed, low latency, is the key to success. Congratulations. >> Thank you so much. >> And I love this new object store, love this tier two value proposition. It's so much more compelling for developers, certainly in cloud native. >> Vikram: Absolutely. >> Vikram, here on theCUBE, bringing you more action from Las Vegas. We'll be right back as day three coverage continues here at Dell EMC World 2017. I'm John Furrier with Paul Gillan, we'll be right back.