>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> Spark became a top level Apache project in 2014, and then shortly thereafter, burst onto the big data scene. Spark, along with the cloud, transformed and in many ways, disrupted the big data market. Databricks optimized its tech stack for Spark and took advantage of the cloud to really cleverly deliver a managed service that has become a leading AI and data platform among data scientists and data engineers. However, emerging customer data requirements are shifting into a direction that will cause modern data platform players generally and Databricks, specifically, we think, to make some key directional decisions and perhaps even reinvent themselves. Hello and welcome to this week's wikibon theCUBE Insights, powered by ETR. In this Breaking Analysis, we're going to do a deep dive into Databricks. We'll explore its current impressive market momentum. We're going to use some ETR survey data to show that, and then we'll lay out how customer data requirements are changing and what the ideal data platform will look like in the midterm future. We'll then evaluate core elements of the Databricks portfolio against that vision, and then we'll close with some strategic decisions that we think the company faces. And to do so, we welcome in our good friend, George Gilbert, former equities analyst, market analyst, and current Principal at TechAlpha Partners. George, good to see you. Thanks for coming on. >> Good to see you, Dave. >> All right, let me set this up. We're going to start by taking a look at where Databricks sits in the market in terms of how customers perceive the company and what it's momentum looks like. And this chart that we're showing here is data from ETS, the emerging technology survey of private companies. The N is 1,421. What we did is we cut the data on three sectors, analytics, database-data warehouse, and AI/ML. The vertical axis is a measure of customer sentiment, which evaluates an IT decision maker's awareness of the firm and the likelihood of engaging and/or purchase intent. The horizontal axis shows mindshare in the dataset, and we've highlighted Databricks, which has been a consistent high performer in this survey over the last several quarters. And as we, by the way, just as aside as we previously reported, OpenAI, which burst onto the scene this past quarter, leads all names, but Databricks is still prominent. You can see that the ETR shows some open source tools for reference, but as far as firms go, Databricks is very impressively positioned. Now, let's see how they stack up to some mainstream cohorts in the data space, against some bigger companies and sometimes public companies. This chart shows net score on the vertical axis, which is a measure of spending momentum and pervasiveness in the data set is on the horizontal axis. You can see that chart insert in the upper right, that informs how the dots are plotted, and net score against shared N. And that red dotted line at 40% indicates a highly elevated net score, anything above that we think is really, really impressive. And here we're just comparing Databricks with Snowflake, Cloudera, and Oracle. And that squiggly line leading to Databricks shows their path since 2021 by quarter. And you can see it's performing extremely well, maintaining an elevated net score and net range. Now it's comparable in the vertical axis to Snowflake, and it consistently is moving to the right and gaining share. Now, why did we choose to show Cloudera and Oracle? The reason is that Cloudera got the whole big data era started and was disrupted by Spark. And of course the cloud, Spark and Databricks and Oracle in many ways, was the target of early big data players like Cloudera. Take a listen to Cloudera CEO at the time, Mike Olson. This is back in 2010, first year of theCUBE, play the clip. >> Look, back in the day, if you had a data problem, if you needed to run business analytics, you wrote the biggest check you could to Sun Microsystems, and you bought a great big, single box, central server, and any money that was left over, you handed to Oracle for a database licenses and you installed that database on that box, and that was where you went for data. That was your temple of information. >> Okay? So Mike Olson implied that monolithic model was too expensive and inflexible, and Cloudera set out to fix that. But the best laid plans, as they say, George, what do you make of the data that we just shared? >> So where Databricks has really come up out of sort of Cloudera's tailpipe was they took big data processing, made it coherent, made it a managed service so it could run in the cloud. So it relieved customers of the operational burden. Where they're really strong and where their traditional meat and potatoes or bread and butter is the predictive and prescriptive analytics that building and training and serving machine learning models. They've tried to move into traditional business intelligence, the more traditional descriptive and diagnostic analytics, but they're less mature there. So what that means is, the reason you see Databricks and Snowflake kind of side by side is there are many, many accounts that have both Snowflake for business intelligence, Databricks for AI machine learning, where Snowflake, I'm sorry, where Databricks also did really well was in core data engineering, refining the data, the old ETL process, which kind of turned into ELT, where you loaded into the analytic repository in raw form and refine it. And so people have really used both, and each is trying to get into the other. >> Yeah, absolutely. We've reported on this quite a bit. Snowflake, kind of moving into the domain of Databricks and vice versa. And the last bit of ETR evidence that we want to share in terms of the company's momentum comes from ETR's Round Tables. They're run by Erik Bradley, and now former Gartner analyst and George, your colleague back at Gartner, Daren Brabham. And what we're going to show here is some direct quotes of IT pros in those Round Tables. There's a data science head and a CIO as well. Just make a few call outs here, we won't spend too much time on it, but starting at the top, like all of us, we can't talk about Databricks without mentioning Snowflake. Those two get us excited. Second comment zeros in on the flexibility and the robustness of Databricks from a data warehouse perspective. And then the last point is, despite competition from cloud players, Databricks has reinvented itself a couple of times over the year. And George, we're going to lay out today a scenario that perhaps calls for Databricks to do that once again. >> Their big opportunity and their big challenge for every tech company, it's managing a technology transition. The transition that we're talking about is something that's been bubbling up, but it's really epical. First time in 60 years, we're moving from an application-centric view of the world to a data-centric view, because decisions are becoming more important than automating processes. So let me let you sort of develop. >> Yeah, so let's talk about that here. We going to put up some bullets on precisely that point and the changing sort of customer environment. So you got IT stacks are shifting is George just said, from application centric silos to data centric stacks where the priority is shifting from automating processes to automating decision. You know how look at RPA and there's still a lot of automation going on, but from the focus of that application centricity and the data locked into those apps, that's changing. Data has historically been on the outskirts in silos, but organizations, you think of Amazon, think Uber, Airbnb, they're putting data at the core, and logic is increasingly being embedded in the data instead of the reverse. In other words, today, the data's locked inside the app, which is why you need to extract that data is sticking it to a data warehouse. The point, George, is we're putting forth this new vision for how data is going to be used. And you've used this Uber example to underscore the future state. Please explain? >> Okay, so this is hopefully an example everyone can relate to. The idea is first, you're automating things that are happening in the real world and decisions that make those things happen autonomously without humans in the loop all the time. So to use the Uber example on your phone, you call a car, you call a driver. Automatically, the Uber app then looks at what drivers are in the vicinity, what drivers are free, matches one, calculates an ETA to you, calculates a price, calculates an ETA to your destination, and then directs the driver once they're there. The point of this is that that cannot happen in an application-centric world very easily because all these little apps, the drivers, the riders, the routes, the fares, those call on data locked up in many different apps, but they have to sit on a layer that makes it all coherent. >> But George, so if Uber's doing this, doesn't this tech already exist? Isn't there a tech platform that does this already? >> Yes, and the mission of the entire tech industry is to build services that make it possible to compose and operate similar platforms and tools, but with the skills of mainstream developers in mainstream corporations, not the rocket scientists at Uber and Amazon. >> Okay, so we're talking about horizontally scaling across the industry, and actually giving a lot more organizations access to this technology. So by way of review, let's summarize the trend that's going on today in terms of the modern data stack that is propelling the likes of Databricks and Snowflake, which we just showed you in the ETR data and is really is a tailwind form. So the trend is toward this common repository for analytic data, that could be multiple virtual data warehouses inside of Snowflake, but you're in that Snowflake environment or Lakehouses from Databricks or multiple data lakes. And we've talked about what JP Morgan Chase is doing with the data mesh and gluing data lakes together, you've got various public clouds playing in this game, and then the data is annotated to have a common meaning. In other words, there's a semantic layer that enables applications to talk to the data elements and know that they have common and coherent meaning. So George, the good news is this approach is more effective than the legacy monolithic models that Mike Olson was talking about, so what's the problem with this in your view? >> So today's data platforms added immense value 'cause they connected the data that was previously locked up in these monolithic apps or on all these different microservices, and that supported traditional BI and AI/ML use cases. But now if we want to build apps like Uber or Amazon.com, where they've got essentially an autonomously running supply chain and e-commerce app where humans only care and feed it. But the thing is figuring out what to buy, when to buy, where to deploy it, when to ship it. We needed a semantic layer on top of the data. So that, as you were saying, the data that's coming from all those apps, the different apps that's integrated, not just connected, but it means the same. And the issue is whenever you add a new layer to a stack to support new applications, there are implications for the already existing layers, like can they support the new layer and its use cases? So for instance, if you add a semantic layer that embeds app logic with the data rather than vice versa, which we been talking about and that's been the case for 60 years, then the new data layer faces challenges that the way you manage that data, the way you analyze that data, is not supported by today's tools. >> Okay, so actually Alex, bring me up that last slide if you would, I mean, you're basically saying at the bottom here, today's repositories don't really do joins at scale. The future is you're talking about hundreds or thousands or millions of data connections, and today's systems, we're talking about, I don't know, 6, 8, 10 joins and that is the fundamental problem you're saying, is a new data error coming and existing systems won't be able to handle it? >> Yeah, one way of thinking about it is that even though we call them relational databases, when we actually want to do lots of joins or when we want to analyze data from lots of different tables, we created a whole new industry for analytic databases where you sort of mung the data together into fewer tables. So you didn't have to do as many joins because the joins are difficult and slow. And when you're going to arbitrarily join thousands, hundreds of thousands or across millions of elements, you need a new type of database. We have them, they're called graph databases, but to query them, you go back to the prerelational era in terms of their usability. >> Okay, so we're going to come back to that and talk about how you get around that problem. But let's first lay out what the ideal data platform of the future we think looks like. And again, we're going to come back to use this Uber example. In this graphic that George put together, awesome. We got three layers. The application layer is where the data products reside. The example here is drivers, rides, maps, routes, ETA, et cetera. The digital version of what we were talking about in the previous slide, people, places and things. The next layer is the data layer, that breaks down the silos and connects the data elements through semantics and everything is coherent. And then the bottom layers, the legacy operational systems feed that data layer. George, explain what's different here, the graph database element, you talk about the relational query capabilities, and why can't I just throw memory at solving this problem? >> Some of the graph databases do throw memory at the problem and maybe without naming names, some of them live entirely in memory. And what you're dealing with is a prerelational in-memory database system where you navigate between elements, and the issue with that is we've had SQL for 50 years, so we don't have to navigate, we can say what we want without how to get it. That's the core of the problem. >> Okay. So if I may, I just want to drill into this a little bit. So you're talking about the expressiveness of a graph. Alex, if you'd bring that back out, the fourth bullet, expressiveness of a graph database with the relational ease of query. Can you explain what you mean by that? >> Yeah, so graphs are great because when you can describe anything with a graph, that's why they're becoming so popular. Expressive means you can represent anything easily. They're conducive to, you might say, in a world where we now want like the metaverse, like with a 3D world, and I don't mean the Facebook metaverse, I mean like the business metaverse when we want to capture data about everything, but we want it in context, we want to build a set of digital twins that represent everything going on in the world. And Uber is a tiny example of that. Uber built a graph to represent all the drivers and riders and maps and routes. But what you need out of a database isn't just a way to store stuff and update stuff. You need to be able to ask questions of it, you need to be able to query it. And if you go back to prerelational days, you had to know how to find your way to the data. It's sort of like when you give directions to someone and they didn't have a GPS system and a mapping system, you had to give them turn by turn directions. Whereas when you have a GPS and a mapping system, which is like the relational thing, you just say where you want to go, and it spits out the turn by turn directions, which let's say, the car might follow or whoever you're directing would follow. But the point is, it's much easier in a relational database to say, "I just want to get these results. You figure out how to get it." The graph database, they have not taken over the world because in some ways, it's taking a 50 year leap backwards. >> Alright, got it. Okay. Let's take a look at how the current Databricks offerings map to that ideal state that we just laid out. So to do that, we put together this chart that looks at the key elements of the Databricks portfolio, the core capability, the weakness, and the threat that may loom. Start with the Delta Lake, that's the storage layer, which is great for files and tables. It's got true separation of compute and storage, I want you to double click on that George, as independent elements, but it's weaker for the type of low latency ingest that we see coming in the future. And some of the threats highlighted here. AWS could add transactional tables to S3, Iceberg adoption is picking up and could accelerate, that could disrupt Databricks. George, add some color here please? >> Okay, so this is the sort of a classic competitive forces where you want to look at, so what are customers demanding? What's competitive pressure? What are substitutes? Even what your suppliers might be pushing. Here, Delta Lake is at its core, a set of transactional tables that sit on an object store. So think of it in a database system, this is the storage engine. So since S3 has been getting stronger for 15 years, you could see a scenario where they add transactional tables. We have an open source alternative in Iceberg, which Snowflake and others support. But at the same time, Databricks has built an ecosystem out of tools, their own and others, that read and write to Delta tables, that's what makes the Delta Lake and ecosystem. So they have a catalog, the whole machine learning tool chain talks directly to the data here. That was their great advantage because in the past with Snowflake, you had to pull all the data out of the database before the machine learning tools could work with it, that was a major shortcoming. They fixed that. But the point here is that even before we get to the semantic layer, the core foundation is under threat. >> Yep. Got it. Okay. We got a lot of ground to cover. So we're going to take a look at the Spark Execution Engine next. Think of that as the refinery that runs really efficient batch processing. That's kind of what disrupted the DOOp in a large way, but it's not Python friendly and that's an issue because the data science and the data engineering crowd are moving in that direction, and/or they're using DBT. George, we had Tristan Handy on at Supercloud, really interesting discussion that you and I did. Explain why this is an issue for Databricks? >> So once the data lake was in place, what people did was they refined their data batch, and Spark has always had streaming support and it's gotten better. The underlying storage as we've talked about is an issue. But basically they took raw data, then they refined it into tables that were like customers and products and partners. And then they refined that again into what was like gold artifacts, which might be business intelligence metrics or dashboards, which were collections of metrics. But they were running it on the Spark Execution Engine, which it's a Java-based engine or it's running on a Java-based virtual machine, which means all the data scientists and the data engineers who want to work with Python are really working in sort of oil and water. Like if you get an error in Python, you can't tell whether the problems in Python or where it's in Spark. There's just an impedance mismatch between the two. And then at the same time, the whole world is now gravitating towards DBT because it's a very nice and simple way to compose these data processing pipelines, and people are using either SQL in DBT or Python in DBT, and that kind of is a substitute for doing it all in Spark. So it's under threat even before we get to that semantic layer, it so happens that DBT itself is becoming the authoring environment for the semantic layer with business intelligent metrics. But that's again, this is the second element that's under direct substitution and competitive threat. >> Okay, let's now move down to the third element, which is the Photon. Photon is Databricks' BI Lakehouse, which has integration with the Databricks tooling, which is very rich, it's newer. And it's also not well suited for high concurrency and low latency use cases, which we think are going to increasingly become the norm over time. George, the call out threat here is customers want to connect everything to a semantic layer. Explain your thinking here and why this is a potential threat to Databricks? >> Okay, so two issues here. What you were touching on, which is the high concurrency, low latency, when people are running like thousands of dashboards and data is streaming in, that's a problem because SQL data warehouse, the query engine, something like that matures over five to 10 years. It's one of these things, the joke that Andy Jassy makes just in general, he's really talking about Azure, but there's no compression algorithm for experience. The Snowflake guy started more than five years earlier, and for a bunch of reasons, that lead is not something that Databricks can shrink. They'll always be behind. So that's why Snowflake has transactional tables now and we can get into that in another show. But the key point is, so near term, it's struggling to keep up with the use cases that are core to business intelligence, which is highly concurrent, lots of users doing interactive query. But then when you get to a semantic layer, that's when you need to be able to query data that might have thousands or tens of thousands or hundreds of thousands of joins. And that's a SQL query engine, traditional SQL query engine is just not built for that. That's the core problem of traditional relational databases. >> Now this is a quick aside. We always talk about Snowflake and Databricks in sort of the same context. We're not necessarily saying that Snowflake is in a position to tackle all these problems. We'll deal with that separately. So we don't mean to imply that, but we're just sort of laying out some of the things that Snowflake or rather Databricks customers we think, need to be thinking about and having conversations with Databricks about and we hope to have them as well. We'll come back to that in terms of sort of strategic options. But finally, when come back to the table, we have Databricks' AI/ML Tool Chain, which has been an awesome capability for the data science crowd. It's comprehensive, it's a one-stop shop solution, but the kicker here is that it's optimized for supervised model building. And the concern is that foundational models like GPT could cannibalize the current Databricks tooling, but George, can't Databricks, like other software companies, integrate foundation model capabilities into its platform? >> Okay, so the sound bite answer to that is sure, IBM 3270 terminals could call out to a graphical user interface when they're running on the XT terminal, but they're not exactly good citizens in that world. The core issue is Databricks has this wonderful end-to-end tool chain for training, deploying, monitoring, running inference on supervised models. But the paradigm there is the customer builds and trains and deploys each model for each feature or application. In a world of foundation models which are pre-trained and unsupervised, the entire tool chain is different. So it's not like Databricks can junk everything they've done and start over with all their engineers. They have to keep maintaining what they've done in the old world, but they have to build something new that's optimized for the new world. It's a classic technology transition and their mentality appears to be, "Oh, we'll support the new stuff from our old stuff." Which is suboptimal, and as we'll talk about, their biggest patron and the company that put them on the map, Microsoft, really stopped working on their old stuff three years ago so that they could build a new tool chain optimized for this new world. >> Yeah, and so let's sort of close with what we think the options are and decisions that Databricks has for its future architecture. They're smart people. I mean we've had Ali Ghodsi on many times, super impressive. I think they've got to be keenly aware of the limitations, what's going on with foundation models. But at any rate, here in this chart, we lay out sort of three scenarios. One is re-architect the platform by incrementally adopting new technologies. And example might be to layer a graph query engine on top of its stack. They could license key technologies like graph database, they could get aggressive on M&A and buy-in, relational knowledge graphs, semantic technologies, vector database technologies. George, as David Floyer always says, "A lot of ways to skin a cat." We've seen companies like, even think about EMC maintained its relevance through M&A for many, many years. George, give us your thought on each of these strategic options? >> Okay, I find this question the most challenging 'cause remember, I used to be an equity research analyst. I worked for Frank Quattrone, we were one of the top tech shops in the banking industry, although this is 20 years ago. But the M&A team was the top team in the industry and everyone wanted them on their side. And I remember going to meetings with these CEOs, where Frank and the bankers would say, "You want us for your M&A work because we can do better." And they really could do better. But in software, it's not like with EMC in hardware because with hardware, it's easier to connect different boxes. With software, the whole point of a software company is to integrate and architect the components so they fit together and reinforce each other, and that makes M&A harder. You can do it, but it takes a long time to fit the pieces together. Let me give you examples. If they put a graph query engine, let's say something like TinkerPop, on top of, I don't even know if it's possible, but let's say they put it on top of Delta Lake, then you have this graph query engine talking to their storage layer, Delta Lake. But if you want to do analysis, you got to put the data in Photon, which is not really ideal for highly connected data. If you license a graph database, then most of your data is in the Delta Lake and how do you sync it with the graph database? If you do sync it, you've got data in two places, which kind of defeats the purpose of having a unified repository. I find this semantic layer option in number three actually more promising, because that's something that you can layer on top of the storage layer that you have already. You just have to figure out then how to have your query engines talk to that. What I'm trying to highlight is, it's easy as an analyst to say, "You can buy this company or license that technology." But the really hard work is making it all work together and that is where the challenge is. >> Yeah, and well look, I thank you for laying that out. We've seen it, certainly Microsoft and Oracle. I guess you might argue that well, Microsoft had a monopoly in its desktop software and was able to throw off cash for a decade plus while it's stock was going sideways. Oracle had won the database wars and had amazing margins and cash flow to be able to do that. Databricks isn't even gone public yet, but I want to close with some of the players to watch. Alex, if you'd bring that back up, number four here. AWS, we talked about some of their options with S3 and it's not just AWS, it's blob storage, object storage. Microsoft, as you sort of alluded to, was an early go-to market channel for Databricks. We didn't address that really. So maybe in the closing comments we can. Google obviously, Snowflake of course, we're going to dissect their options in future Breaking Analysis. Dbt labs, where do they fit? Bob Muglia's company, Relational.ai, why are these players to watch George, in your opinion? >> So everyone is trying to assemble and integrate the pieces that would make building data applications, data products easy. And the critical part isn't just assembling a bunch of pieces, which is traditionally what AWS did. It's a Unix ethos, which is we give you the tools, you put 'em together, 'cause you then have the maximum choice and maximum power. So what the hyperscalers are doing is they're taking their key value stores, in the case of ASW it's DynamoDB, in the case of Azure it's Cosmos DB, and each are putting a graph query engine on top of those. So they have a unified storage and graph database engine, like all the data would be collected in the key value store. Then you have a graph database, that's how they're going to be presenting a foundation for building these data apps. Dbt labs is putting a semantic layer on top of data lakes and data warehouses and as we'll talk about, I'm sure in the future, that makes it easier to swap out the underlying data platform or swap in new ones for specialized use cases. Snowflake, what they're doing, they're so strong in data management and with their transactional tables, what they're trying to do is take in the operational data that used to be in the province of many state stores like MongoDB and say, "If you manage that data with us, it'll be connected to your analytic data without having to send it through a pipeline." And that's hugely valuable. Relational.ai is the wildcard, 'cause what they're trying to do, it's almost like a holy grail where you're trying to take the expressiveness of connecting all your data in a graph but making it as easy to query as you've always had it in a SQL database or I should say, in a relational database. And if they do that, it's sort of like, it'll be as easy to program these data apps as a spreadsheet was compared to procedural languages, like BASIC or Pascal. That's the implications of Relational.ai. >> Yeah, and again, we talked before, why can't you just throw this all in memory? We're talking in that example of really getting down to differences in how you lay the data out on disk in really, new database architecture, correct? >> Yes. And that's why it's not clear that you could take a data lake or even a Snowflake and why you can't put a relational knowledge graph on those. You could potentially put a graph database, but it'll be compromised because to really do what Relational.ai has done, which is the ease of Relational on top of the power of graph, you actually need to change how you're storing your data on disk or even in memory. So you can't, in other words, it's not like, oh we can add graph support to Snowflake, 'cause if you did that, you'd have to change, or in your data lake, you'd have to change how the data is physically laid out. And then that would break all the tools that talk to that currently. >> What in your estimation, is the timeframe where this becomes critical for a Databricks and potentially Snowflake and others? I mentioned earlier midterm, are we talking three to five years here? Are we talking end of decade? What's your radar say? >> I think something surprising is going on that's going to sort of come up the tailpipe and take everyone by storm. All the hype around business intelligence metrics, which is what we used to put in our dashboards where bookings, billings, revenue, customer, those things, those were the key artifacts that used to live in definitions in your BI tools, and DBT has basically created a standard for defining those so they live in your data pipeline or they're defined in their data pipeline and executed in the data warehouse or data lake in a shared way, so that all tools can use them. This sounds like a digression, it's not. All this stuff about data mesh, data fabric, all that's going on is we need a semantic layer and the business intelligence metrics are defining common semantics for your data. And I think we're going to find by the end of this year, that metrics are how we annotate all our analytic data to start adding common semantics to it. And we're going to find this semantic layer, it's not three to five years off, it's going to be staring us in the face by the end of this year. >> Interesting. And of course SVB today was shut down. We're seeing serious tech headwinds, and oftentimes in these sort of downturns or flat turns, which feels like this could be going on for a while, we emerge with a lot of new players and a lot of new technology. George, we got to leave it there. Thank you to George Gilbert for excellent insights and input for today's episode. I want to thank Alex Myerson who's on production and manages the podcast, of course Ken Schiffman as well. 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 Siliconangle.com, he does some great editing. Remember all these episodes, they're available as podcasts. Wherever you listen, all you got to do is search Breaking Analysis Podcast, we publish each week on wikibon.com and siliconangle.com, or you can email me at David.Vellante@siliconangle.com, or DM me @DVellante. Comment on our LinkedIn post, and please do check out ETR.ai, great survey data, enterprise tech focus, phenomenal. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, and we'll see you next time on Breaking Analysis.

>>From the Cube Studios in Palo Alto in Boston, bringing you data-driven insights from the cube and E T R. This is breaking analysis with Dave Valante. >>Making technology predictions in 2022 was tricky business, especially if you were projecting the performance of markets or identifying I P O prospects and making binary forecast on data AI and the macro spending climate and other related topics in enterprise tech 2022, of course was characterized by a seesaw economy where central banks were restructuring their balance sheets. The war on Ukraine fueled inflation supply chains were a mess. And the unintended consequences of of forced march to digital and the acceleration still being sorted out. Hello and welcome to this week's weekly on Cube Insights powered by E T R. In this breaking analysis, we continue our annual tradition of transparently grading last year's enterprise tech predictions. And you may or may not agree with our self grading system, but look, we're gonna give you the data and you can draw your own conclusions and tell you what, tell us what you think. >>All right, let's get right to it. So our first prediction was tech spending increases by 8% in 2022. And as we exited 2021 CIOs, they were optimistic about their digital transformation plans. You know, they rushed to make changes to their business and were eager to sharpen their focus and continue to iterate on their digital business models and plug the holes that they, the, in the learnings that they had. And so we predicted that 8% rise in enterprise tech spending, which looked pretty good until Ukraine and the Fed decided that, you know, had to rush and make up for lost time. We kind of nailed the momentum in the energy sector, but we can't give ourselves too much credit for that layup. And as of October, Gartner had it spending growing at just over 5%. I think it was 5.1%. So we're gonna take a C plus on this one and, and move on. >>Our next prediction was basically kind of a slow ground ball. The second base, if I have to be honest, but we felt it was important to highlight that security would remain front and center as the number one priority for organizations in 2022. As is our tradition, you know, we try to up the degree of difficulty by specifically identifying companies that are gonna benefit from these trends. So we highlighted some possible I P O candidates, which of course didn't pan out. S NQ was on our radar. The company had just had to do another raise and they recently took a valuation hit and it was a down round. They raised 196 million. So good chunk of cash, but, but not the i p O that we had predicted Aqua Securities focus on containers and cloud native. That was a trendy call and we thought maybe an M SS P or multiple managed security service providers like Arctic Wolf would I p o, but no way that was happening in the crummy market. >>Nonetheless, we think these types of companies, they're still faring well as the talent shortage in security remains really acute, particularly in the sort of mid-size and small businesses that often don't have a sock Lacework laid off 20% of its workforce in 2022. And CO C e o Dave Hatfield left the company. So that I p o didn't, didn't happen. It was probably too early for Lacework. Anyway, meanwhile you got Netscope, which we've cited as strong in the E T R data as particularly in the emerging technology survey. And then, you know, I lumia holding its own, you know, we never liked that 7 billion price tag that Okta paid for auth zero, but we loved the TAM expansion strategy to target developers beyond sort of Okta's enterprise strength. But we gotta take some points off of the failure thus far of, of Okta to really nail the integration and the go to market model with azero and build, you know, bring that into the, the, the core Okta. >>So the focus on endpoint security that was a winner in 2022 is CrowdStrike led that charge with others holding their own, not the least of which was Palo Alto Networks as it continued to expand beyond its core network security and firewall business, you know, through acquisition. So overall we're gonna give ourselves an A minus for this relatively easy call, but again, we had some specifics associated with it to make it a little tougher. And of course we're watching ve very closely this this coming year in 2023. The vendor consolidation trend. You know, according to a recent Palo Alto network survey with 1300 SecOps pros on average organizations have more than 30 tools to manage security tools. So this is a logical way to optimize cost consolidating vendors and consolidating redundant vendors. The E T R data shows that's clearly a trend that's on the upswing. >>Now moving on, a big theme of 2020 and 2021 of course was remote work and hybrid work and new ways to work and return to work. So we predicted in 2022 that hybrid work models would become the dominant protocol, which clearly is the case. We predicted that about 33% of the workforce would come back to the office in 2022 in September. The E T R data showed that figure was at 29%, but organizations expected that 32% would be in the office, you know, pretty much full-time by year end. That hasn't quite happened, but we were pretty close with the projection, so we're gonna take an A minus on this one. Now, supply chain disruption was another big theme that we felt would carry through 2022. And sure that sounds like another easy one, but as is our tradition, again we try to put some binary metrics around our predictions to put some meat in the bone, so to speak, and and allow us than you to say, okay, did it come true or not? >>So we had some data that we presented last year and supply chain issues impacting hardware spend. We said at the time, you can see this on the left hand side of this chart, the PC laptop demand would remain above pre covid levels, which would reverse a decade of year on year declines, which I think started in around 2011, 2012. Now, while demand is down this year pretty substantially relative to 2021, I D C has worldwide unit shipments for PCs at just over 300 million for 22. If you go back to 2019 and you're looking at around let's say 260 million units shipped globally, you know, roughly, so, you know, pretty good call there. Definitely much higher than pre covid levels. But so what you might be asking why the B, well, we projected that 30% of customers would replace security appliances with cloud-based services and that more than a third would replace their internal data center server and storage hardware with cloud services like 30 and 40% respectively. >>And we don't have explicit survey data on exactly these metrics, but anecdotally we see this happening in earnest. And we do have some data that we're showing here on cloud adoption from ET R'S October survey where the midpoint of workloads running in the cloud is around 34% and forecast, as you can see, to grow steadily over the next three years. So this, well look, this is not, we understand it's not a one-to-one correlation with our prediction, but it's a pretty good bet that we were right, but we gotta take some points off, we think for the lack of unequivocal proof. Cause again, we always strive to make our predictions in ways that can be measured as accurate or not. Is it binary? Did it happen, did it not? Kind of like an O K R and you know, we strive to provide data as proof and in this case it's a bit fuzzy. >>We have to admit that although we're pretty comfortable that the prediction was accurate. And look, when you make an hard forecast, sometimes you gotta pay the price. All right, next, we said in 2022 that the big four cloud players would generate 167 billion in IS and PaaS revenue combining for 38% market growth. And our current forecasts are shown here with a comparison to our January, 2022 figures. So coming into this year now where we are today, so currently we expect 162 billion in total revenue and a 33% growth rate. Still very healthy, but not on our mark. So we think a w s is gonna miss our predictions by about a billion dollars, not, you know, not bad for an 80 billion company. So they're not gonna hit that expectation though of getting really close to a hundred billion run rate. We thought they'd exit the year, you know, closer to, you know, 25 billion a quarter and we don't think they're gonna get there. >>Look, we pretty much nailed Azure even though our prediction W was was correct about g Google Cloud platform surpassing Alibaba, Alibaba, we way overestimated the performance of both of those companies. So we're gonna give ourselves a C plus here and we think, yeah, you might think it's a little bit harsh, we could argue for a B minus to the professor, but the misses on GCP and Alibaba we think warrant a a self penalty on this one. All right, let's move on to our prediction about Supercloud. We said it becomes a thing in 2022 and we think by many accounts it has, despite the naysayers, we're seeing clear evidence that the concept of a layer of value add that sits above and across clouds is taking shape. And on this slide we showed just some of the pickup in the industry. I mean one of the most interesting is CloudFlare, the biggest supercloud antagonist. >>Charles Fitzgerald even predicted that no vendor would ever use the term in their marketing. And that would be proof if that happened that Supercloud was a thing and he said it would never happen. Well CloudFlare has, and they launched their version of Supercloud at their developer week. Chris Miller of the register put out a Supercloud block diagram, something else that Charles Fitzgerald was, it was was pushing us for, which is rightly so, it was a good call on his part. And Chris Miller actually came up with one that's pretty good at David Linthicum also has produced a a a A block diagram, kind of similar, David uses the term metacloud and he uses the term supercloud kind of interchangeably to describe that trend. And so we we're aligned on that front. Brian Gracely has covered the concept on the popular cloud podcast. Berkeley launched the Sky computing initiative. >>You read through that white paper and many of the concepts highlighted in the Supercloud 3.0 community developed definition align with that. Walmart launched a platform with many of the supercloud salient attributes. So did Goldman Sachs, so did Capital One, so did nasdaq. So you know, sorry you can hate the term, but very clearly the evidence is gathering for the super cloud storm. We're gonna take an a plus on this one. Sorry, haters. Alright, let's talk about data mesh in our 21 predictions posts. We said that in the 2020s, 75% of large organizations are gonna re-architect their big data platforms. So kind of a decade long prediction. We don't like to do that always, but sometimes it's warranted. And because it was a longer term prediction, we, at the time in, in coming into 22 when we were evaluating our 21 predictions, we took a grade of incomplete because the sort of decade long or majority of the decade better part of the decade prediction. >>So last year, earlier this year, we said our number seven prediction was data mesh gains momentum in 22. But it's largely confined and narrow data problems with limited scope as you can see here with some of the key bullets. So there's a lot of discussion in the data community about data mesh and while there are an increasing number of examples, JP Morgan Chase, Intuit, H S P C, HelloFresh, and others that are completely rearchitecting parts of their data platform completely rearchitecting entire data platforms is non-trivial. There are organizational challenges, there're data, data ownership, debates, technical considerations, and in particular two of the four fundamental data mesh principles that the, the need for a self-service infrastructure and federated computational governance are challenging. Look, democratizing data and facilitating data sharing creates conflicts with regulatory requirements around data privacy. As such many organizations are being really selective with their data mesh implementations and hence our prediction of narrowing the scope of data mesh initiatives. >>I think that was right on J P M C is a good example of this, where you got a single group within a, within a division narrowly implementing the data mesh architecture. They're using a w s, they're using data lakes, they're using Amazon Glue, creating a catalog and a variety of other techniques to meet their objectives. They kind of automating data quality and it was pretty well thought out and interesting approach and I think it's gonna be made easier by some of the announcements that Amazon made at the recent, you know, reinvent, particularly trying to eliminate ET t l, better connections between Aurora and Redshift and, and, and better data sharing the data clean room. So a lot of that is gonna help. Of course, snowflake has been on this for a while now. Many other companies are facing, you know, limitations as we said here and this slide with their Hadoop data platforms. They need to do new, some new thinking around that to scale. HelloFresh is a really good example of this. Look, the bottom line is that organizations want to get more value from data and having a centralized, highly specialized teams that own the data problem, it's been a barrier and a blocker to success. The data mesh starts with organizational considerations as described in great detail by Ash Nair of Warner Brothers. So take a listen to this clip. >>Yeah, so when people think of Warner Brothers, you always think of like the movie studio, but we're more than that, right? I mean, you think of H B O, you think of t n t, you think of C N N. We have 30 plus brands in our portfolio and each have their own needs. So the, the idea of a data mesh really helps us because what we can do is we can federate access across the company so that, you know, CNN can work at their own pace. You know, when there's election season, they can ingest their own data and they don't have to, you know, bump up against, as an example, HBO if Game of Thrones is going on. >>So it's often the case that data mesh is in the eyes of the implementer. And while a company's implementation may not strictly adhere to Jamma Dani's vision of data mesh, and that's okay, the goal is to use data more effectively. And despite Gartner's attempts to deposition data mesh in favor of the somewhat confusing or frankly far more confusing data fabric concept that they stole from NetApp data mesh is taking hold in organizations globally today. So we're gonna take a B on this one. The prediction is shaping up the way we envision, but as we previously reported, it's gonna take some time. The better part of a decade in our view, new standards have to emerge to make this vision become reality and they'll come in the form of both open and de facto approaches. Okay, our eighth prediction last year focused on the face off between Snowflake and Databricks. >>And we realized this popular topic, and maybe one that's getting a little overplayed, but these are two companies that initially, you know, looked like they were shaping up as partners and they, by the way, they are still partnering in the field. But you go back a couple years ago, the idea of using an AW w s infrastructure, Databricks machine intelligence and applying that on top of Snowflake as a facile data warehouse, still very viable. But both of these companies, they have much larger ambitions. They got big total available markets to chase and large valuations that they have to justify. So what's happening is, as we've previously reported, each of these companies is moving toward the other firm's core domain and they're building out an ecosystem that'll be critical for their future. So as part of that effort, we said each is gonna become aggressive investors and maybe start doing some m and a and they have in various companies. >>And on this chart that we produced last year, we studied some of the companies that were targets and we've added some recent investments of both Snowflake and Databricks. As you can see, they've both, for example, invested in elation snowflake's, put money into Lacework, the Secur security firm, ThoughtSpot, which is trying to democratize data with ai. Collibra is a governance platform and you can see Databricks investments in data transformation with D B T labs, Matillion doing simplified business intelligence hunters. So that's, you know, they're security investment and so forth. So other than our thought that we'd see Databricks I p o last year, this prediction been pretty spot on. So we'll give ourselves an A on that one. Now observability has been a hot topic and we've been covering it for a while with our friends at E T R, particularly Eric Bradley. Our number nine prediction last year was basically that if you're not cloud native and observability, you are gonna be in big trouble. >>So everything guys gotta go cloud native. And that's clearly been the case. Splunk, the big player in the space has been transitioning to the cloud, hasn't always been pretty, as we reported, Datadog real momentum, the elk stack, that's open source model. You got new entrants that we've cited before, like observe, honeycomb, chaos search and others that we've, we've reported on, they're all born in the cloud. So we're gonna take another a on this one, admittedly, yeah, it's a re reasonably easy call, but you gotta have a few of those in the mix. Okay, our last prediction, our number 10 was around events. Something the cube knows a little bit about. We said that a new category of events would emerge as hybrid and that for the most part is happened. So that's gonna be the mainstay is what we said. That pure play virtual events are gonna give way to hi hybrid. >>And the narrative is that virtual only events are, you know, they're good for quick hits, but lousy replacements for in-person events. And you know that said, organizations of all shapes and sizes, they learn how to create better virtual content and support remote audiences during the pandemic. So when we set at pure play is gonna give way to hybrid, we said we, we i we implied or specific or specified that the physical event that v i p experience is going defined. That overall experience and those v i p events would create a little fomo, fear of, of missing out in a virtual component would overlay that serves an audience 10 x the size of the physical. We saw that really two really good examples. Red Hat Summit in Boston, small event, couple thousand people served tens of thousands, you know, online. Second was Google Cloud next v i p event in, in New York City. >>Everything else was, was, was, was virtual. You know, even examples of our prediction of metaverse like immersion have popped up and, and and, and you know, other companies are doing roadshow as we predicted like a lot of companies are doing it. You're seeing that as a major trend where organizations are going with their sales teams out into the regions and doing a little belly to belly action as opposed to the big giant event. That's a definitely a, a trend that we're seeing. So in reviewing this prediction, the grade we gave ourselves is, you know, maybe a bit unfair, it should be, you could argue for a higher grade, but the, but the organization still haven't figured it out. They have hybrid experiences but they generally do a really poor job of leveraging the afterglow and of event of an event. It still tends to be one and done, let's move on to the next event or the next city. >>Let the sales team pick up the pieces if they were paying attention. So because of that, we're only taking a B plus on this one. Okay, so that's the review of last year's predictions. You know, overall if you average out our grade on the 10 predictions that come out to a b plus, I dunno why we can't seem to get that elusive a, but we're gonna keep trying our friends at E T R and we are starting to look at the data for 2023 from the surveys and all the work that we've done on the cube and our, our analysis and we're gonna put together our predictions. We've had literally hundreds of inbounds from PR pros pitching us. We've got this huge thick folder that we've started to review with our yellow highlighter. And our plan is to review it this month, take a look at all the data, get some ideas from the inbounds and then the e t R of January surveys in the field. >>It's probably got a little over a thousand responses right now. You know, they'll get up to, you know, 1400 or so. And once we've digested all that, we're gonna go back and publish our predictions for 2023 sometime in January. So stay tuned for that. All right, we're gonna leave it there for today. You wanna thank Alex Myerson who's on production and he manages the podcast, Ken Schiffman as well out of our, our Boston studio. I gotta really heartfelt thank you to Kristen Martin and Cheryl Knight and their team. They helped get the word out on social and in our newsletters. Rob Ho is our editor in chief over at Silicon Angle who does some great editing for us. Thank you all. Remember all these podcasts are available or all these episodes are available is podcasts. Wherever you listen, just all you do Search Breaking analysis podcast, really getting some great traction there. Appreciate you guys subscribing. I published each week on wikibon.com, silicon angle.com or you can email me directly at david dot valante silicon angle.com or dm me Dante, or you can comment on my LinkedIn post. And please check out ETR AI for the very best survey data in the enterprise tech business. Some awesome stuff in there. This is Dante for the Cube Insights powered by etr. Thanks for watching and we'll see you next time on breaking analysis.

(bright music) >> Welcome back to Vegas, guys and girls. We're pleased that you're watching theCUBE. We know you've been with us. This is our fourth day. We know you've been with us since day one. Why wouldn't you be? Lisa Martin, here. As I mentioned, day four of theCUBE's coverage of AWS re:Invent. There are north of 55,000 people that have been at this event this week. We're hearing hundreds of thousands online. It really feels like old times, which is awesome. We're pleased to welcome back a gentleman from Dataiku who's actually new to theCUBE but Dataiku is not. Jed Dougherty is here, the VP of Platform Strategy. Thanks to joining me today, Jed. >> Oh, I'm so happy to be here. >> Talk a little bit, for anybody that isn't familiar with Dataiku, tell the audience a little bit about the technology, what you guys do. >> Dataiku is an end-to-end data science machine learning platform. We take everything from data ingestion, piplining of that data, bringing it all together, something that's useful for building models, deploying those models and then managing your ML ops workflow. So, really all the way across. And we sit on top of, basically, tons of different AWS stack as well as lots of the partners that are here today. >> Okay, got it. >> Snowflake, Databricks, all that. >> Got it, so one of the things that, it was funny, I think it was Adam's keynote Tuesday morning. I didn't time it, I watched it, but one of my guests said to me earlier this week that Adam spent exactly 52 minutes talking about data. >> Yeah. >> 52 minutes. Obviously, we can't come to an event like this without talking about data. Every company these days has to be a data company. Whether it's my grocery store or a retailer, a hospital, and so- >> Jed: It is the lifeblood of every modern company. >> It is, but you have to be able to access it. You have to be able to harness it, access it, derive insights from it, and be able to act on that faster than the competitors that are waiting, like, right back here. One of the things Adam Selipsky talked about with our boss, John Furrier, who's the co-CEO of theCUBE, they had a sit-down about a week before re:Invent. John always gets a preview of the show and Adam said, you know, he thinks the role of data analyst is going to go away. Or at least the term, because with data democratization that needs to happen. Putting data in the hands of all the business users, that every business user, whether you're in technology or marketing or ops or finance, it's going to have to analyze data to do their jobs. >> Could not agree more. >> Are you hearing that from customers? >> 100% >> Yeah. >> I was just at the CTO Summit of Bank of America two weeks ago out in California, and they told, their CTO had a statistic, 60,000 technologists in Bank of America, all asking data-type questions. You can have the best team of data scientists in the world, and they do. They have some of the best data scientists in the world there. And this team of data scientists could answer any one of the questions that those 60,000 people might have but they can't answer all of them, right? You need those people to be able to answer their own questions. I don't know if the term data analysts are going away. I think, yeah, everybody's just going to have to become a bit more of one. Just like how Excel taught everybody how to use the spreadsheet, in the future, in the next five, 10 years, the democratization of AI means that tools like Dataiku and other data science tools are going to teach everybody how to analyze data. >> Talk about Dataiku as a facilitator of that, of that democratization. Giving, like the citizen technologist who might be in finance, the ability to do that. >> So, a lot of data science tools are aimed at your hardcore coder, right? Somebody who wants to be sitting at a notebook writing (indistinct) or something like that and running models on some big fancy Spark server. Dataiku is still going to be running models on some big fancy Spark server but we're really obfuscating the challenge of writing code away from the user. So we target low code, no code, and high code users all working together in a collaborative platform. So we really do, we believe that there is always going to be a place for data scientists. That role is not going away. You will always need hardcore coders to take on those moonshot very challenging topics. But for every day AI, anybody should be able to do this and it should be open to anybody. >> Right. >> Jed: Really aim to facilitate that. >> I would love to hear some feedback, you know, this is day four of the show as I was saying, and day four is packed. I mean, this is energy-level-wise, guys, it is the same as it was when we started here on Friday night. But I'd love to hear, Jed, from your perspective some of the customer conversations that you've had, what are some of the challenges? They're coming to you saying, "Jed, Dataiku, help us eradicate these challenges so we can transform our business." >> What I'm hearing from customers and partners and AWS here is, over and over, we don't want to buy tools anymore. We want to buy solutions. We want a vertical solution that's pre-built for our industry. And we want it to be, not necessarily click and run out of the box, but we want a template that we can build off of quickly. And I've heard that customers are also looking to understand how tools can be packaged together. You got how many booths are here? 1000 booths? >> Yes, easily. >> You have 1000 different products being talked about, right behind us. Customers need to know which of these products are friends with each other and how they fit together so that they are making sure that when they purchase a set, a suite of tools to do their jobs, it's all going to work naturally together. So, being able, I think this is a really vital concept for GSIs as well. GSIs needs to understand how to package sets of tools together to deliver a full solution to clients. People don't want to be, you know, I think 10 years ago, five years ago, AWS was in the business of selling servers in the cloud. But basically what you do is, you would buy an EC two instance and you install whatever software you wanted on it. I don't know that they're in that business still but customers don't want to buy servers from AWS anymore. They want to buy solutions. >> Right. >> Rent, whatever. >> Yeah. (chuckles) >> That is the big repeated message that I've heard here. >> So you brought up a good point that there are probably 1000 booths here. You could be here every day and not get to see everything that's going on. Plus this show was going on across the strip. We're only getting a fraction of the people that are here. But with that said, to your point, there are so many tools out there. Customers are looking for solutions. One of the things that we say about theCUBE is, we extract the signal from the noise. How does Dataiku get past the noise? How do you get up the stack to really impact customers so they understand the value that you're delivering? >> I think that Data science and ML sound like a very complicated topic but our value prop is relatively simple. And we appeal both to your end users who are excited to learn about how data science works and how they can leverage these tools in their day-to-day jobs, as well as appealing to IT. IT, right now, at major organizations they want to be able to build a full stack that makes sense. And the big choices they're making right now are around infrastructure. Where am I going to run my compute? So, they're choosing between Snowflake or Databricks or a native AWS compute solution, right? And so they make this big choice around compute and then they realize, "Oh, how many of our users across our organization are actually able to leverage this big compute choice?" Oh, maybe 100, maybe 200. That's not incredibly useful for what we've just decided to completely stand behind. Dataiku, all of a sudden, opens that up to 1000s of users across your organization. So it makes IT feel empowered by being able to help more people. And it makes users feel empowered by being able to use a great tool and start answering their own questions. >> And where are your customer conversations these days? As we look at AI and ML, emerging technologies, so many customers and companies, knowing we have to go in this direction. We have to have AI to speed the business. Are you seeing more of the conversations are still in IT or are they actually going up the stack? >> (chuckles) It's a great question. When you're going into large organizations, there's two sales motions, right? There's convincing the business users that this is a great thing and then convincing IT that it's not going to be too painful. You always have to go to both places. IT doesn't want to take on a boondoggler, or there's an albatross, I don't remember the word, but, something that they're going to have to deal with for the next 10 years and then eventually dismantle and pull apart. I think a lot of IT got very scared about big data platforms and solutions because of Hadoop. To be honest, Hadoop was incredibly powerful but maybe not as mature of technology as IT would've liked it to be. From a maintenance and administration standpoint. So yes, you will always have to sell to IT and help IT feel comfortable with the platform. But no, the conversations that I want to have are the use case conversations with a Chief Data Officer, Chief Revenue Officer, Chief Marketing Officer. That's who I really want to convince that this is going to be a worthwhile opportunity. >> And what are some of the key, sorry. What are some of the key use cases that Dataiku is tackling in the market these days? >> So we work a lot. Two of the biggest organizations, or verticals, that I work with personally are finance and pharmaceuticals. In finance, we are closely embedded with wealth management organizations. So, a lot of that is around customer entertainment, churn, relatively obvious, simple concepts but ones where it's worth a lot of money. In pharma, we work both on the supply side. So, doing supply chain optimization, ensuring the right drugs get to the right places at the right time. As well as on the business and marketing side. So, ensuring that your ad spend is correctly distributed across different advertising platforms. >> So if you're working with a financial organization, I want to understand from a consumer, from the end user's perspective, although obviously this technology impacts the end user who's trying to do a transaction. What's in it for me? And I don't know as the end user that Dataiku is under the hood. >> You'd never know. >> Which is good. I shouldn't have to worry about the technology. >> Jed: You shouldn't have to worry about that at all. >> What's in it for the end user customer? What are they gaining from this? >> So, from a very end user perspective, if you think about when you logged onto maybe your Bank of America, your Chase app, five or 10 years ago, maybe you didn't even have it on your phone five years ago. Or when you logged into your account online. We do 95% of our banking online right now, right? I go into a physical location, what? I don't know, once every six months or something? Get a cashier's check? I don't know. The experience that you're getting and the amount of information you're getting back about your spending habits, where your money is going, what your credit score is, all of these things are being driven by these big data organizations inside the banks. Also, any type, this is a little creepier, but any type of promotional emails or the types of things that you get feedback on when you use your credit card and the offers that you get through that, are all being personalized to you through the information that these banks are collecting about your spending habits. >> Yeah, but we want that as a consumer, we want the personalized. >> Yeah, of course. We want it to be magic slash not creepy. (laughs) >> Right, I want them to recommend the best card for me. >> Right. >> The next best thing. >> It's good for me, it's good for them. >> Don't serve me up something that I've already bought. That always bugs me when I'm like, I already bought that. >> I get that all the time. I'm like, yeah, I have that card already. It's in my wallet. Why are you telling me? >> We only have a couple of minutes left Jed, but talk to me about from a platform strategy perspective, what's next for Dataiku and AWS? >> So we are making a matrix transition right now and it's core to our platform. For a long time, the way that we've installed Dataiku is, we help our customers install it on their AWS account so it runs inside their tenant. This is very comfortable for, for example, large banking clients, pharma clients that have personally identifiable information, all that kind of thing. They own everything. However, as we were talking about before, we're really moving from providing a tool to providing solutions. And part of that is obviously a move to SaaS. So two years ago we released a SaaS offering. We've been expanding it more and more to, this year, we want to be pushing SaaS first. So Dataiku online should be the first option when new customers move on. And that is a huge platform shift. It means making sure that we have the right security in place. It means making sure that we have the right scaling in place, that we have 24-7 support. All this has been a big challenge. A big fascinating challenge, actually, to put together. >> Awesome. Last question for you. Say you get a brand new DeLorean, I hear they're coming back, and you want to put, you really, really want to put a bumper sticker on it, 'cause why not? And it's about Dataiku and it's like a sizzle reel kind of thing. >> A sizzle real, alright. >> Yeah. What does it say? >> Extraordinary people, everyday AI. >> Wow. Drop the mic, Jed. That was awesome. Thank you so much for coming on the program. We really appreciate the update on Dataiku. What you guys are doing for customers, your specialization and solutions for verticals. Awesome stuff, we'll have to have you back. >> Thank you so much. >> Alright, my pleasure. >> Bye-Bye. >> For my guest, I'm Lisa Martin. You're watching theCUBE, the leader in live enterprise and emerging tech coverage. (bright music)

>>Hey everyone. Welcome to the cubes presentation of the AWS startup showcase. This is season two, episode four of our ongoing series that features exciting startups within the AWS ecosystem. This episode's theme, cybersecurity protect and detect against threats. I'm your host, Lisa Martin, and I'm pleased to welcome back. One of our alumni chase joins me the principal strategist at jump cloud chase. It's great to have you back on the >>Perfect Michael, thank you so much for having me again, >>Tell the audience just a little quick refresher on jump cloud, open directory platform. We just give them that little bit of context. >>You bet. So jump cloud provides an open directory platform and what we mean by that is we help manage all of your employees, identities, the devices that they operate on, and then all the access that they need in order to get their work done in a modern it environment. >>So from a target, a market segment perspective, this is really targeted at small medium enterprise SMEs managed security providers. MSPs, talk to me a little bit about that and some of the what's in it for me, for those folks. >>Yeah, absolutely. And when we are thinking about specifically within that market, so small, medium enterprises and the it, or the managed service providers that help support those organizations, there's a lot of different technologies that you use in order to make sure that you have a secure organization. And within that group specifically, there's a lot less of a luxury right of an enterprise budget or kind of all these different personnel that you might have available to you. And it's really kind of down to maybe one team or just a couple folks or just one person wearing a lot of different hats. And so we've designed the open directory platform to help accommodate for a lot of those different pieces where we're bringing in multiple different types of technologies from identity access management, device management and MDM, MFA access through single sign on all of those different pieces and more that help kind of come into one platform. >>So not only do you have all the technology there at your disposable, but also all the visibility and analytics of folks that are getting in and just trying to get their job done. But now all of those pieces are, are consolidated into one platform and it really helps support a lot of those organizations, right? And keep in mind, you know, small, medium businesses are the most common businesses, not everyone's coming in from an enterprise. And so here we're able to layer on levels of security and making sure that you have best practices, no matter what size you're operating in. >>So consolidating it management, securing employees, access to a variety of it. Resources is really kind of in a nutshell. >>Absolutely. And just making sure that you're combining that combination of securely accessing all the things that you need, but also making sure that from an end user perspective, it's really easy and you have all those things kind of built in from the get go. >>So how are SSEs and MSPs leveraging jump cloud right now? What are some of the outcomes that you are helping them to achieve? Anything stand out to you? >>I think there's a couple different areas that we help support organizations. One is you can think about just the whole employee life cycle. So when, when someone joins an organization from onboarding, you know, where does that identity come from? How can we make sure that they're productive, you know, effective human beings as they come into it, but then the whole life cycle, as they're accessing or changing resources within their role, all the way to the end, where they might be leaving the organization and we can securely off board that person. And so that whole flow that you might have from an organization standpoint is one aspect. Another area is as companies continue to grow, they might be going after, you know, maybe audits, level compliance, other pieces that might help them grow. And there's a lot of layers that you need to think about or different types of technologies and processes to have those certifications and credentials. >>And so we help support those organizations again, by consolidating all those different technologies into one spot. It makes it a lot easier for people to get up to par in how they think that their security standards should be set within an organization. And finally too, I'd say just ease of mind. There's a lot of pieces when you're thinking about, you know, where people might be coming in from how do I get visibility into all those different aspects? And when you have all that under one roof, it adds a lot of, I'd say, you know, less mental stress in terms of one, how all those technologies should be working together effectively, also securely, but then also making sure that you have time in the day to tackle big projects and let some of the, let's say, run rate security out of the way. >>Yeah. That's really important to be able to assign resources that are able to make the biggest impact across the organization, moving things off the plate that are not necessary or more mundane twice a year. I understand jump cloud does a survey with SMEs where you really are aimed at understanding kind of where they are in the market today, their concerns, trends, challenges, budgets. Then I saw you just published results from a survey in June of 2022. Talk to me a little bit about the demographics of the survey, who, who are you talking to within SMEs? And then we can kind of crack open some of those really interesting findings that came out this year. >>Yeah. So we love to get a pulse check of what's happening within the industry, but specifically within that small, medium size, if you will. And so for that survey that we ran, we talked to 400 different roles, kind of that touch it from security. So from vice president of the CCSO all the way down to it, admins and anyone else in between, and we're really looking at organizations that had about 500 employees or less, cuz there's a lot of information out there, especially from the enterprise of, you know, Hey, here's best practices. Here's all the things that you can do. But for smaller organizations, it's not as clear cut or you have less of an understanding of what your peers might be going through or kind of what their concerns are. And so when we're running that survey, that's one thing that we like to keep in mind is it's really meant for organizations at that size because there's, there's some commonalities that you start to see in suss out. >>And it's not to say that those aren't the same concerns that the enterprise folks have as well, because a lot of the things that will come out, you know, they are security based say, Hey, what's top of mind, or what's kind of keeping you up at night. There were some clear indicators and especially well from kind of, as we do this survey, you know, every six months or kind of even year over year, you start to see some trends that are emerging. And so a, a lot of the big ones are, you know, ransomware software, vulnerability and network security. Those are kind of the top three aspects when we're looking at, Hey, what are specifics that are keeping you up? And those are easy to say because ransomware is obviously in the news. Even this week, there are three different organizations just kind of pick out. >>So brussel who does dental manufacturing, they had ransomware in trust, which is another cybersecurity organization. They were breached. But then also Fremont county here in Colorado as a government organization, all three of those were hit by ransomware. And you might not say, Hey, there's, you know, they're all kind of random and they're not put together, but under the hood really it's a lot of the same different technologies that are powering, how people get access into things. Do they have the right levels of credentials? Are there conditions set within that type of access, especially if it's privilege. And so you start to consolidate and bubble down all those different things that can lead up to those concerns. And then even on the software vulnerability side, Mac release, two different vulnerabilities this week. And so now it quickly becomes, okay, great. How can I make sure that my employees are using not only a secure device, but a secure device, that's up to date because it's a dynamic field as all of these things coming through. >>And these are a lot of the gotchas that can keep, you know, small, medium enterprises up at night because if something happens a security event like that, it could be a, you know, a career ending event, but also a company ending event. When you think about that. And so that becomes a really high level of importance because no one wants to see their name in the news, but it also takes a lot of different steps in order to create the layers that are necessary in order to achieve, you know, really solid round stand on for organization to do that. And so that's where we like to come in and help and making sure that a lot of those layers are actually easier to implement than you thought. And it's not this huge project, but you're doing it in a way that's conscious and also not really getting the way of kind of battling users or making sure that their experience is a nightmare as well in order to achieve these goals that you have as an organization, >>You bring up ransomware, it's become a household term that I think probably every generation alive right now in some form or fashion understands what it is to a, to some degree it's now security threats in general. Now no longer if we get hit, it's a matter of one. You gave three great examples of SMEs that were hit recently and organizations. We wouldn't think really them everybody's vulnerable. You talked about the different, you know, some of the, the concerns, software, vulnerable vulnerability, exploits, the use of unsecured networks, people, and this is so common using the same password across applications that SSEs and enterprises too are dealing with. They have to be able to lean on MSPs, for example, in the SME space to say, help us with these obvious vulnerabilities, we need to make sure that our employees are productive. They're working together. We can onboard and offboard people in a secure way. How did this survey uncover how SMEs are leaning more on MSPs to help solve some of those risks that you've talked about? >>I think one of the more interesting trends that we've seen is just the ability and the ramp for organizations to lean on managed service providers. You saw a lot of this during kind of the, the beginning of the pandemic or kind of this really shift to remote work where people kind of have this mentality of, okay, it might be a cost center and, and will have, but it it's always felt this importance to making sure that people are on site. They understand their culture. They understand the, the ways that the organization works. However, now, a lot more organizations are stepping back and saying, well, if I can't see anyone in the office or if there's only half or maybe 10% that are showing up, you know, are there other economies of scale almost that I can get from leveraging a managed service provider bringing in other expertise, right? >>And so it might be valuable to say, Hey, it's not only just managing my organization, but five others. And so now you can start to see and kind of lean on best practices that they've evolved over time. And I think one of the more interesting stats is we see that, you know, almost nine out of 10 organizations that we surveyed are either leveraging an MSP or have considered it. And one of those things that's actually pulling them back or some organizations say, Hey, I've looked at it, but I'm not quite ready to commit to outsourcing this section of my organization that, or kind of bringing in someone to manage it fully alongside with me almost in a co-managed type of environment is a third of 'em say, Hey, I, I don't know how secure the MSPs are themselves. How do they think about their own internal practices? >>And what does that look like? Because again, you, you're thinking about handing over the crown jewels over to someone and say, Hey, here's some of our, our most vulnerable or critical assets that we need to have secured and, and making sure that that's part of the organization. And so it's a, it's an honest conversation that a lot of owners have with MSPs and say, look, are, are you up to snuff, right? Because if something happens, sure, I might have one person to go after, or you might have SLAs that I can, I can go. But it still means me as an organization has been targeted. What does that look like in our types of relationship? And so a lot of the partners that we have on the jump outside, it's a very common conversation that they have with our clients and saying, walking them through and say, Hey, here's our, our security plan. >>Here's how we approach that. Here's all the different tools that we have at, at our disposal that are working alongside jump cloud in order to make sure that not only do you have good posture, I'd say good areas where the organization is set up for success, where you're thinking about not sharing passwords or there's password complexity, or there's other technologies like single sign on that, help reduce that. But in addition to what type of network scanning do you have available? What type of antivirus do you leverage? What are all the other pieces that create that holistic security structure? And so sometimes it's a lot easier for MSPs to deliver that and package it up instead of having, you know, an overburdened it, admin said, great, this is another project that I have to go through and think about and look at pricing and kind of other those components, because it helps speed up. I'd say your time to being more secure. And that's a really real conversation for organizations as they think about planning, as they think about budgets and what impact that might have on organization, making sure that employees can get work done. But we're also thinking about in a very secure mindset within the organization. >>That's so critical as we talked about every or every organization of every size in every industry is vulnerable. There's just no weight getting around it. These days. You talked about an interesting stat, about 90% of the SME surveyed some written we're yes, we're relying on MSV, but we still worry about security. Talk to me from the jump cloud, AWS perspective. How do you help though? That's cause that's a big number, the 90% of SMEs that are still concerned about security, how do you help them dial that down? >>I think it's really understanding, you know, you mentioned AWS, so what are the critical access and what are those points that look like that we need to get a handle on? And how can we make that easier? Cause I think one of the pieces that will often come at and say, Hey, we really wanna make this approach work. We really wanna make sure that when you, when you wake up and you need to get into Q and a environments or, or production or whatever, that might be, that it's a seamless experience, but we as an organization have visibility into what's going on and Hey, if you're getting promoted or your role is changing, we wanna make sure that those attributes or kind of those pieces that are associated to you and your identity are changing with it. And so making sure that there's this dynamic motion available to folks, as they start thinking about, you know, where a majority of their IP lives, it's no longer in some server closet and yes, it might still be on a, on a manufacturing floor, but it's those components that become the most critical for organizations you've heard, I'd say, you know, certainly within the last five years and probably even goes further back where a lot of traditional organizations say, Hey, we're a software company now we're, you know, kind of insert for innovation, making sure we can do that. >>And I think a lot of organizations are still going through that transition, but right behind it and what's coming next. And certainly a lot of organizations start to say, not only are we a software company, but we're a security company. And with that, that comes the mindset. Not only of here's how we tactically get into the things that we need to do our job, but the why behind it. And I think that's one of the elements that might be missing or is certainly one of, I know that we have a lie attainment kind of take that approach of, yes, we're gonna be implementing, we need to have your device passion updated because there's vulnerabilities. But for everyone else kind of on the end user side, it's like, well, okay, well why, why do we need to do that? And so by having that security first type of mentality, that allows everyone to be on the same page, play on the same team and making sure that when, you know, those requests are coming in both back and forth between end users and its security team, anyone else that might be involved within that process, you all understand that say, Hey, it's not, you know, it, it's not my job. >>It's everyone's job, right? We're all in this together because that's some of the parts where it can start to fall down too. You might have a team that has the best practices and in, you know, in intentions, but if the implementation and the follow through isn't bought in from everyone, then you're also playing against the speed of the organization to adopt it. And that's really the timeline that you're battling, especially when you're thinking about ransomware or someone who already might be in it is how can we help mitigate a lot of those different pieces. So by combining all those different elements into a thought process, into a mentality of being a security first organization, that's really kind of helps within the ripple effect all the way down into, you know, the critical resources like AWS. >>It has to be a holistic view. There's really no other choice these days. And it also has to be done in a timely fashion. What did, as we wrap up kind of talking about the survey here, what were some of the trends, the future trends it uncovered as we are still in a remote and distributed work environment. It probably always will be. We've seen challenges and everyone's mental health in terms of, of strapped resources. What did the survey uncover as to what these folks saw as future trends? >>So I'd say there's a, there's a couple, there there's a lot, but we'll break it down and say, I'd say three core trends that you saw across every organization that we talked to, including our own base of over 180,000 organizations that rely on gem cloud is, Hey, security is number one, right? And we we've talked to that about at length device management is another extension of that. I'm sorry, making sure that, Hey, this is the only piece of hardware I have from the company in front of me. I wanna make sure that I can manage secure it, make sure it's patched as well as we kind of operate in this dynamic and environment, making sure that we're resilient as an organization. And then I'd say finally, as those pieces start to evolve, there's still some organizations that are how trying to understand kind of truly manage what does hybrid and remote and kind of what does that look like for me as an organization? >>Cause I think we're now out of this panic mode and now organizations are now setting up. Okay, what are some of the long term structures as I think about that, and you hear a lot about too, from other organizations that are mandating folks to come back or okay. Maybe it's just a couple days a week or all of those decisions have impacts on the it organization. So that is very alive and well, I'd say one of the other pieces you mentioned mental health is that we are starting to understand a little bit more, you know, kind of who's behind the computer. Who's, who's behind the keyboard. What does the impact have for them? Because in this type of work environment as well, you know, it's still challenging to find really good talent. And so you might be strapped for resources. You might be the only person that's trying to implement these processes or the security protocol, or trying to help get us up into a good compliance posture, all of those different pieces kind of on it. >>And so you can start to think about man, how do I, how do I make progress? And I think that's one of the other pieces that is really important for folks kind of from that perspective is, you know, always understand that you're making progress, even though the, the tickets might be coming at you and you, there's never ending in sight. All those steps that you take for an organization are critically important. And so, and it's not always just a people answer cuz you might, might not be in the position to say, Hey, we need an extra five hands on this in order to make it done. It might have to be more of a conversation of, Hey, here are the pieces that we need to automate. Here are the business processes that we really need to think about in order to have a fundamental impact on what we can do. >>And then you can come back and say, great. And if we have this, it might actually look like one and a half people. You can't really hire a half person, but you come into those types of mentality with a really solid argument of here's what we need to have in order to make this happen. And I think too, getting that type of buy-in again, making sure, Hey, we are a security company after all, we're all in this together that allows everyone to kind of help pitch in because if you don't have that piece, then you know, everything can feel much more burdensome, right? And the level of burnout increases the, the level of mental health in general, across the teams that are acting as supporting functions for an organization, start to get burnout. And it might not always be as Hey, as important as, as revenue or Hey, we're getting this marketing campaign out, but it's this underwriting thing in terms of really, truly important infrastructure that the company needs to think about. >>And when you can involve all of those different pieces, then people feel like they can make a positive impact. They feel more empowered. They have, you know, emojis attached to tickets and say, Hey, it was so great to help you out today. And a lot of those I'd say interpersonal connections that you might be missing in a remote only type of world in organization. And so bringing all those little tidbits back into, you know, how to, how to be a good person, how to be a good human and how to make sure that there's some personality involved with it. And it's not just this ongoing process. I think there's a little bit of give and take, but that's one other thing that we've surfaced is really just understanding a better picture of who's implementing all these amazing things around the world. >>That's so important. There's so many different levers to the pull here where becoming a security company is concerned. Where can folks go to one chase, get the surveying two, some final thoughts. What, where can folks go to actually test out jump drive? >>Yeah, absolutely >>Jump out. Excuse me. >>So within everything that we talked about, some from various different technologies from identity management, device management, SSO, MFA, and many, many more. So you can go to jumpcloud.com, create a free organization. It's free up to 10 users, 10 devices. So even for really small organizations, even if you're a startup, we can help leverage enterprise grade security technology for you to implement as well as more detailed on the reports. And so if you wanna get a better sense of kind of how we look at the world types of information that we can bring back and making sure that you're learning from your peers and how to implement and put your best foot forward within the organization, we always have a ton of amazing resources and content that really looks at, you know, who's doing the work. Why are they doing the work? And how is that work impactful within multiple different organizations and not only just the organizations themselves, but those that are supporting it like managed service providers of the world. >>Got it. Awesome. Chase. Thank you so much for joining me on this episode of the AWS startup showcase, talking to us about what jump cloud is uncovered with respect to the concerns that SMEs have, how MSPs are helping, how jump cloud is also a facilitator of really helping to organizations to become security organizations. We appreciate your time. >>Absolutely. Thank you so much for having me again. >>Our pleasure. We wanna you for watching. Keep it right here on the, for more action. The, is your leader in live coverage?

(upbeat music) >> Hey, everyone. Welcome to this CUBE Conversation that's part of the AWS startup showcase Season Two, Episode Four. I'm your host Lisa Martin. Chase Doelling joins me, the principles strategist at JumpCloud. Chase, welcome to theCUBE. It's great to have you. >> Chase: Perfect. Well, thank you so much, Lisa. I really appreciate the opportunity to come and hang out. >> Let's talk about JumpCloud. First of all, love the name. This is an open directory platform. Talk to the audience about what the platform is, obviously, the evolution of the domain controller. But give us that backstory? >> Yeah, absolutely. And so, company was started, and I think, from serial entrepreneurs, and after kind of last exit, taking a look around and saying, "Why is this piece of hardware still the dominant force when you're thinking about identities, especially when the world is moving to cloud, and all the different pieces that have been around it?" And so, over the years, we've evolved JumpCloud into an open directory platform. And what that is, is we're managing your identities, the devices that are associated to that, all the access points that employees need just to get their job done. And the best part is, is we're able to do that no matter where they are within the world. >> It seems like kind of a reinvention of how modern IT teams are getting worked done, especially in these days of remote work. Talk to me a little bit about the last couple of years particularly as remote work exploded, and here we are still probably, permanently, in that situation? >> Yeah, absolutely. And I think it's probably going to be one of those situations where we stick with it for quite a while. We had a very abrupt force in making sure that essentially every IT and security team could grapple with the fact of their users are no longer coming into the office. You know, how do we VPN into all of our different resources? Those are very common and unfortunate pain points that we've had over the last couple years. And so, now, people have starting to kind of get into the motion of it, working from home, having background and setups and other pieces. But one of the main areas of concern, especially as you're thinking about that, is how does it relate to my security infrastructure, or kind of my approach to my organization. And making sure that too, on the tail end, that a user's access and making sure that they can get into everything that they need to do in order to get work done, is still happening? And so, what we've done, is we've really taken, evolving and really kind of ripping apart this notion of what a directory was. 'Cause originally, it was just like, great, almost like a phone directory. It's where people lived they're going into all those different pieces. But it wasn't set up for the modern world, and kind of how we're approaching it, and how organizations now are started with a credit card and have all of their infrastructure. And essentially, all of their IP, is now hosted somewhere else. And so, we wanted to take a different approach where we're thinking about, not only managing that identity, but taking an open approach. So, matter where the identity's coming from, we can integrate that into the platform but then we're also managing and securing those devices, which is often the most important piece that we have sitting right in front of us in order to get into that. But then, also that final question, of when you're accessing networks applications, can you create the conditions for trust, right? And so, if you're looking at zero trust, or kind of going after different levels of compliance, ISO, SOC2, whatever that might be, making sure that you have all that put in place no matter where your employees are. So, in that way, as we kind of moved into this remote, now hybrid world, it wasn't the office as the gating point anymore, right? So, key cards, as much as we love 'em, final part, whereas the new perimeter, the kind of the new barrier for organizations especially how they're thinking about security, is the people's identities behind that. And so, that's the approach that we really wanted to take as we continue to evolve and really open up what a directory platform can do. >> Yeah. Zero trust security, remote work. Two things that have exploded in the last couple of years. But as employees, we expected to be able to still have the access that we needed to apps, to the network, to WiFi, et cetera. And, of course, on the security side, we saw massive changes in the threat landscape that really, obviously, security elevates to a board level conversation. So, I imagine zero trust security, remote work, probably compliance, you mentioned SOC2, are some of the the key use cases that you're helping organizations with? >> Those are a lot of the drivers. And what we do, is we're able to combine a lot of different aspects that you need for each one of those. And so, now you're thinking about essentially, the use case of someone joins an organization, they need access to all these different things. But behind the scenes, it's a combination of identity access management, device management, applications, networks, everything else, and creating those conditions for them to do their roles. But the other piece of that, is you also don't want to be overly cumbersome. I think a lot of us think about security as like great biometrics, so I'm going to add in these keys, I'm going to do everything else to kind of get into these secured resources. But the reality of it now, is those secure resources might be AWS infrastructure. It might be other Salesforce reporting tools. It might be other pieces, or kind of IP within the organization. And those are now your crown jewel. And so, if you're not thinking about the identities behind them and the security that you have in order to facilitate that transaction, it becomes a board level conversation very quickly. But you want to do it in a way that people can move forward with their lives, and they're not spending a ton of time battling the systems and procedures you put in place to protect it, but that it's working together seamlessly. And so, that's where, kind of this notion for us of bringing all these different technologies into one platform. You're able to consolidate a lot of those and remove a lot of the friction while maintaining the visibility, and answering the question, of who has access to what? And when did they do that? Those are the most critical pieces that IT and security teams are asking themselves when something happens. And hopefully, on the preventative side and not so much on the redacted side. >> Have you seen the escalation up the C-Suite change of the board in terms of really focusing on how do we do identity management? How do we do single sign on? How do we do device management and network access? Is that all the way up to the C-Suite board level as well? >> It certainly can be. And we've seen it in a lot of different conversations, because now you are thinking about all different portions of the organization. And then, two, as we're thinking about times we're currently in, there's also a cost associated to that. And so, when you start to consolidate all of those technologies into one area, now it becomes much more of total cost optimization types of story while you're still maintaining a lot of the security and basic blocking and tackling that you need for most organizations. So, everything you just mentioned, those are now table stakes for a lot of small, medium, startups to be at the table. So, how do you have access to enterprise level, essentially technology, without the cost that's associated to it. And that's a lot of the trade offs that organizations are facing and having those types of conversations as it relates to business preparedness and how we're making sure that we are putting our best foot forward, and we're able to be resilient in no matter what type, of either economic or security threat that the organization might be looking at. >> So, let's talk about the go-to market, the strategy from a sales and marketing perspective. Where are the customer conversations happening? Are they at the IT level? Are they higher up the stack? >> It's really at, I'd say the IT level. And so, by that, I mean the builders, the implementers, everyone that's responsible for putting devices in people's hands, and making sure that they can do their job effectively. And so, those are their, I'd say the IT admins the world as well as the managed service providers who support those organizations, making sure that we can enable them to making sure that their organizations or their client organizations have all the tools that their disposable to make sure that they have the security or the policies, and the technology behind them to enable all those different practices. >> Let's unpack the benefits from an IT perspective? Obviously, they're getting one console that they can manage at all. One user identity for email, and devices, and apps, and things. You mentioned regardless of location, but this is also regardless of operating system, correct? >> That's correct. And so, part of taking an open approach, is also the devices that you're running on. And so, we take a cross OS approach. So, Mac, Windows, Linux, iPhone, whatever it might be, we can make sure that, that device is secure. And so, it does a couple different things. So, one, is the employees have device choice, right? So, I'm a Mac person coming in. If forced into a Windows, it'd be an interesting experience. But then, also too, from the back end, now you have essentially one platform to manage your entire fleet. And also give visibility and data behind what's happening behind those. And then, from the end user perspective as well, everything's tied together. And so, instead of having, what we'll call user ID schizophrenia, it might be one employee, but hundreds of different identities and logins just to get their work done. We can now centralize that into one person, making sure you have one password to get into your advice, get into the network, to get into your single sign on. We also have push MFA associated with that. So, you can actually create the conditions for your most secured access, or you understand, say, "Hey, I'm actually in the office. I'm going to be a hybrid employee. Maybe I can actually relax some of those security concerns I might have for people outside of the network." And all we do, is making sure that we give all that optionality to our IT admins, manage service providers of the world to enable that type of work for their employees to happen. >> So, they have the ability to toggle that, is critically important in this day and age of the hybrid work model, that's probably here to stay? >> It is, yeah. And it's something that organizations change, right? Our own organizations, they grow, they change different. New threats might emerge, or same old existing threats continue to come back. And we need to just have better processes and automations put within that. And it's when you start to consolidate all of those technologies, not only are you thinking about the visibility behind that, but then you're automating a lot of those different pieces that are already tightly coupled together. And that actually is truly powerful for a lot of the IT admins of the world, because that's where they spend a lot of time, and they're able to spend more time helping users tackling big projects instead of run rate security, and blocking, and tackling. That should be enabled from the organization from the get go. >> You mentioned automation. And I think that there's got to be a TCO reduction aspect here with respect to security and IT practices. Can you talk about that a little bit? >> Yeah, absolutely. Let's think about the opposite of that. Let's say we have a laundry list of technology that we need to go out and source. One is, great, where the identity is, so we have an identity provider. Now, we need to make sure that we have application access that might look like single sign on. Now, we need to make sure, you are who you are no matter where you are in the world. Well, now we need multifactor authentication and that might involve either a push button, or biometrics. And then, well, great the device's in front of us, that's a huge component, making sure that I can understand, not only who's on the device, but that the device is secure, that there's certificates there, that there's policies that ensure the proper use of that wherever it might be. Especially, if I'm an employee, either, it used to be on the the jet center going between flying anywhere you need. Now, it's kind of cross country, cross domain, all those different areas. And when you start to have that, it really unlocks, essentially IT sprawl. You have a lot of different pieces, a lot of different contracts, trying to figure out one technology works, but the other might not. And you're now you're creating workarounds for all these different pieces. So, the opposite of that, is essentially, let's take all those technologies and consolidate that into one platform. So, not only is it cheaper essentially, looking after that and understanding all the different technologies, but now it's all the other soft costs around it that many people don't think about. It's all the other automations. It's all the workarounds that you didn't have to do in the first place. It's all the other pieces that you'd spend a lot of time trying to wire it together. Into the hopes of that, it creates some security model. But then again, you lose a lot of the visibility. So, you might have an incident happen over here, or a trigger, or alert, but it's not tied to the rest of the stack. And so, now you're spending a lot of time, especially, either trying to understand. And worse timing, is if you have an incident and you're trying to understand what's happening? Unraveling all of that as it happens, becomes impossible, especially if it's not consolidated with one platform. So, there's not only the hard cost aspect of bringing all that together, but also the soft costs of thinking about how your business can perform, or at least optimize for a lot of those different standard processes, including onboarding, offboarding, and everything else in between. >> Yeah. On the soft cost side, I can imagine. I can see huge benefits for HR onboarding, offboarding. I can see benefits for the employee experience period, which directly relates to the customer experience. So, in terms of the business impact that JumpCloud can make, it seems to be pretty horizontal across any type of organization? >> It is, and especially as you mentioned HR. Because when you think about, where does the origin of someone's identity start? Well, typically, it starts with a resume and that might be in applicant tracking software. Now, we're going to get hired, so we're going to move into HR, because, well, everyone likes payroll, and we need that in our lives, right? But now you get into the second phase, of great, now I've joined the organization. Now, I need access to all of these different pieces. But when you look at it, essentially horizontally, from HR, all the way into the employee experience, and their whole life cycle within the organization, now you're touching multiple different teams And that's one of the other, I'd say benefits of that, is now you're actually bringing in HR, and IT, and security, and everyone else that might be related within these kind of larger use cases of making work happen all coming under. And when they're tightly integrated, it's also a lot more secure, right? So, you're not passing notes along. You're not having a checklist of other stuff, especially when it relates to something as important as someone's identity, which is more often than not, the most common attack vector for people to go after. Because they know it's the keys to the kingdom. There's going to be a lot of different attempts, maybe malware and other pieces, but a lot of it comes back into, can I impersonate, or become the person that I want within the organization, because it's the identity allows you to access all those different pieces. And so, if it's coming from a disjointed process or something that's not as tightly as it could be, that's where it really opens up a lot of different vectors that organizations don't think about. >> Right, and those vectors are only growing and multiplying as we know, and here to stay. When you're in customer conversations what do you describe as maybe the top three differentiators of JumpCloud compared to the competition? >> Well, I think a lot of it is we take an open approach. And so, by that, I mean, it's one we're not locking into, I'd say different vendors or other areas. We're really looking into making sure that we can work within your environment as it stands today, or where you want to migrate in the future. And so, this could be a combination of on-prem resources, cloud resources, or nothing if you're starting a company from today. And the second, is again, coming back into how we're looking at devices. So, we take a cross OS approach that way, no matter what you're operating on, it all comes back from the same dashboard. But then, finally, we leverage a ton of different protocols to make sure it works with everything within your current technology stack, as well as it continues to elevate and evolve over time. So, it could be LD app and Radius, and Sam, and skim, and open ID Connect, and open APIs. And whatever that might be, we are able to tie in all those different pieces. So, now, all of a sudden, it's not just one platform, but you have your whole business tied into as that gives you some flexibility too, to evolve. Because even during the pandemic and the shift for remote, there's a lot of technology choices that shifted. A lot of people are like, "Okay, now's the time to go to the cloud." There might be other events that organizations change. There's other things that might happen. So, creating that flexibility for organizations to move and make those calls, is essentially how we're differentiating ourselves. And we're not locking you into this, walled garden of technology that's just our own. We really want to make sure that we can operate, and be that glue, so that way, no matter what you're trying to do and making sure that your work is being done, we can help facilitate that. >> Nice. No matter what happens. Because boy, at this day, anything's possible. One more question for you about your AWS partnership. Talk to me a little bit about that? >> Yeah, absolutely. So, we are preferred ADP identity provider and SSO provider for AWS. And so, now rebranded under their identity center. But it's crucial for a lot of our organizations and joint customers because again, when we think about a lot of organization IP and how they operate as a business, is tied into AWS. And so, really understanding, who has the right level of access? Who should be in there or not? And when too, you should challenge in making sure that actually there's something fishy there. Like let's make sure that they're not just traveling to Europe on a sabbatical, and it's really who they are instead of a threat actor. Those are some of the pieces when we're thinking about creating that authentication, but then also, the right authorization into those AWS resources. And so, that's actually something that we've been very close to, especially, I'd say that the origins of a company. Because a lot of startups, that's where they go. That's where they begin their journey. And so, we meet them where they are, and making sure that we're protecting not only everything else within their organization, but also what they're trying to get into, which is typically AWS >> Meeting customers where they are. It's all about that. Chase, thank you so much for joining me on the program talking about JumpCloud, it's open directory platform. The benefits, the capabilities, what's in it for IT, HR, security, et cetera. We appreciate all of your insights and time. Where do you want to point folks to go to learn more? >> Well, absolutely. Well, thank you so much for having us. And I'd say, if you're curious about any and all these different technologies, the best part is everything I talked about is free up to 10 users, 10 devices. So, just go to jumpcloud.com. You can create an organization, and it's great for startups, people at home. Any size company that you're at, we can help support all of those different facets in bringing in those different types of technologies all into one roof. >> Awesome. Chase, thank you so much. This is awesome, go to jumpcloud.com. For Chase Doelling, I'm Lisa Martin. We want to thank you so much for giving us some of your time and watching this CUBE Conversation. (upbeat music)

(calm electronic music) >> Okay, welcome back everyone to theCUBE's coverage here live in New York City for AWS Summit 2022. I'm John Furrier, host of theCUBE, but a great conversation here as the day winds down. First of all, 10,000 plus people, this is a big event, just New York City. So sign of the times that some headwinds are happening? I don't think so, not in the cloud enterprise innovation game. Lot going on, this innovation conversation we're going to have now is about the confluence of cloud scale integration data and the future of how FinTech and other markets are going to change with technology. We got Chris Thomas, the CTO of Slalom, and Rob Krugman, chief digital officer at Broadridge. Gentlemen, thanks for coming on theCUBE. >> Thanks for having us. >> So we had a talk before we came on camera about your firm, what you guys do, take a quick minute to just give the scope and size of your firm and what you guys work on. >> Yeah, so Broadridge is a global financial FinTech company. We work on, part of our business is capital markets and wealth, and that's about a third of our business, about $7 trillion a day clearing through our platforms. And then the other side of our business is communications where we help all different types of organizations communicate with their shareholders, communicate with their customers across a variety of different digital channels and capabilities. >> Yeah, and Slalom, give a quick one minute on Slalom. I know you guys, but for the folks that don't know you. >> Yeah, no problem. So Slalom is a modern consulting firm focused on strategy, technology, and business transformation. And me personally, I'm part of the element lab, which is focused on forward thinking technology and disruptive technology in the next five to 10 years. >> Awesome, and that's the scope of this conversation. The next five to 10 years, you guys are working on a project together, you're kind of customer partners. You're building something. What are you guys working on? I can't wait to jump into it, explain. >> Sure, so similar to Chris, at Broadridge, we've created innovation capability, innovation incubation capability, and one of the first areas we're experimenting in is digital assets. So what we're looking to do is we're looking at a variety of different areas where we think consolidation network effects that we could bring can add a significant amount of value. And so the area we're working on is this concept of a wallet of wallets. How do we actually consolidate assets that are held across a variety of different wallets, maybe traditional locations- >> Digital wallets. >> Digital wallets, but maybe even traditional accounts, bring that together and then give control back to the consumer of who they want to share that information with, how they want their transactions to be able to control. So the idea of, people talk about Web 3 being the internet of value. I often think about it as the internet of control. How do you return control back to the individual so that they can make decisions about how and who has access to their information and assets? >> It's interesting, I totally like the value angle, but your point is what's the chicken and the egg here, the cart before the horse, you can look at it both ways and say, okay, control is going to drive the value. This is an interesting nuance, right? >> Yes, absolutely. >> So in this architectural world, they thought about the data plane and the control plane. Everyone's trying to go old school, middleware thinking. Let's own the data plane, we'll win everything. Not going to happen if it goes decentralized, right, Chris? >> Yeah, yeah. I mean, we're building a decentralized application, but it really is built on top of AWS. We have a serverless architecture that scales as our business scales built on top of things like S3, Lambda, DynamoDB, and of course using those security principles like Cognito and AWS Gateway, API Gateway. So we're really building an architecture of Web 3 on top of the Web 2 basics in the cloud. >> I mean, all evolutions are abstractions on top of each other, IG, DNS, Key, it goes the whole nine yards. In digital, at least, that's the way. Question about serverless real quick. I saw that Redshift just launched general availability of serverless in Redshift? >> Yes. >> You're starting to see the serverless now part of almost all the services in AWS. Is that enabling that abstraction, because most people don't see it that way. They go, oh, well, Amazon's not Web 3. They got databases, you could use that stuff. So how do you connect the dots and cross the bridge to the future with the idea that I might not think Web 2 or cloud is Web 3? >> I'll jump in quick. I mean, I think it's the decentralize. If you think about decentralization. serverless and decentralization, you could argue are the same way of, they're saying the same thing in different ways. One is thinking about it from a technology perspective. One is thinking about it from an ecosystem perspective and how things come together. You need serverless components that can talk to each other and communicate with each other to actually really reach the promise of what Web 3 is supposed to be. >> So digital bits or digital assets, I call it digital bits, 'cause I think zero ones. If you digitize everything and everything has value or now control drives the value. I could be a soccer team. I have apparel, I have value in my logos, I have photos, I have CUBE videos. I mean some say that this should be an NFT. Yeah, right, maybe, but digital assets have to be protected, but owned. So ownership drives it too, right? >> Absolutely. >> So how does that fit in, how do you explain that? 'Cause I'm trying to tie the dots here, connect the dots and tie it together. What do I get if I go down this road that you guys are building? >> So I think one of the challenges of digital assets right now is that it's a closed community. And I think the people that play in it, they're really into it. And so you look at things like NFTs and you look at some of the other activities that are happening and there are certain naysayers that look at it and say, this stuff is not based upon value. It's a bunch of artwork, it can't be worth this. Well, how about we do a time out there and we actually look at the underlying technology that's supporting this, the blockchain, and the potential ramifications of that across the entire financial ecosystem, and frankly, all different types of ecosystems of having this immutable record, where information gets stored and gets sent and the ability to go back to it at all times, that's where the real power is. So I think we're starting to see. We've hit a bit of a hiccup, if you will, in the cryptocurrencies. They're going to continue to be there. They won't all be there. A lot of them will probably disappear, but they'll be a finite number. >> What percentage of stuff do you think is vapor BS? If you had to pick an order of magnitude number. >> (laughs) I would say at least 75% of it. (John laughs) >> I mean, there's quite a few projects that are failing right now, but it's interesting in that in the crypto markets, they're failing gracefully. Because it's on the blockchain and it's all very transparent. Things are checked, you know immediately which companies are insolvent and which opportunities are still working. So it's very, very interesting in my opinion. >> Well, and I think the ones that don't have valid premises are the ones that are failing. Like Terra and some of these other ones, if you actually really looked at it, the entire industry knew these things were no good. But then you look at stable coins. And you look at what's going on with CBDCs. These are backed by real underlying assets that people can be comfortable with. And there's not a question of, is this going to happen? The question is, how quickly is it going to happen and how quickly are we going to be using digital currencies? >> It's interesting, we always talk about software, software as money now, money is software and gold and oil's moving over to that crypto. How do you guys see software? 'Cause we were just arguing in the queue, Dave Vellante and I, before you guys came on that the software industry pretty much does not exist anymore, it's open source. So everything's open source as an industry, but the value is integration, innovation. So it's not just software, it's the free. So you got to, it's integration. So how do you guys see this software driving crypto? Because it is software defined money at the end of the day. It's a token. >> No, I think that's absolutely one of the strengths of the crypto markets and the Web 3 market is it's governed by software. And because of that, you can build a trust framework. Everybody knows it's on the public blockchain. Everybody's aware of the software that's driving the rules and the rules of engagement in this blockchain. And it creates that trust network that says, hey, I can transact with you even though I don't know anything about you and I don't need a middleman to tell me I can trust you. Because this software drives that trust framework. >> Lot of disruption, lot of companies go out of business as a middleman in these markets. >> Listen, the intermediaries either have to disrupt themselves or they will be disrupted. I think that's what we're going to learn here. And it's going to start in financial services, but it's going to go to a lot of different places. I think the interesting thing that's happening now is for the first time, you're starting to see the regulators start to get involved. Which is actually a really good thing for the market. Because to Chris's point, transparency is here, how do you actually present that transparency and that trust back to consumers so they feel comfortable once that problem is solved. And I think everyone in the industry welcomes it. All of a sudden you have this ecosystem that people can play in, they can build and they can start to actually create real value. >> Every structural change that I've been involved in my 30 plus year career has been around inflection points. There was always some sort of underbelly. So I'm not going to judge crypto. It's been in the market for a while, but it's a good sign there's innovation happening. So as now, clarity comes into what's real. I think you guys are talking a conversation I think is refreshing because you're saying, okay, cloud is real, Lambda, serverless, all these tools. So Web 3 is certainly real because it's a future architecture, but it's attracting the young, it's a cultural shift. And it's also cooler than boring Web 2 and cloud. So I think the cultural shift, the fact that it's got data involved, there's some disruption around middleman and intermediaries, makes it very attractive to tech geeks. You look at, I read a stat, I heard a stat from a friend in the Bay Area that 30% of Cal computer science students are dropping out and jumping into crypto. So it's attracting the technical nerds, alpha geeks. It's a cultural revolution and there's some cool stuff going on from a business model standpoint. >> There's one thing missing. The thing that's missing, it's what we're trying to work on, I think is experience. I think if you're being honest about the entire marketplace, what you would agree is that this stuff is not easy to use today, and that's got to be satisfied. You need to do something that if it's the 85 year old grandma that wants to actually participate in these markets that not only can they feel comfortable, but they actually know how to do it. You can't use these crazy tools where you use these terms. And I think the industry, as it grows up, will satisfy a lot of those issues. >> And I think this is why I want to tie back and get your reaction to this. I think that's why you guys talking about building on top of AWS is refreshing, 'cause it's not dogmatic. Well, we can't use Amazon, it's not really Web 3. Well, a database could be used when you need it. You don't need to write everything through the blockchain. Databases are a very valuable capability, you get serverless. So all these things now can work together. So what do you guys see for companies that want to be Web 3 for all the good reasons and how do they leverage cloud specifically to get there? What are some things that you guys have learned that you can point to and share, you want to start? >> Well, I think not everything has to be open and public to everybody. You're going to want to have some things that are secret. You're going to want to encrypt some things. You're going to want to put some things within your own walls. And that's where AWS really excels. I think you can have the best of both worlds. So that's my perspective on it. >> The only thing I would add to it, so my view is it's 2022. I actually was joking earlier. I think I was at the first re:Invent. And I remember walking in and this was a new industry. >> It was tiny. >> This is foundational. Like cloud is not a, I don't view like, we shouldn't be having that conversation anymore. Of course you should build this stuff on top of the cloud. Of course you should build it on top of AWS. It just makes sense. And we should, instead of worrying about those challenges, what we should be worrying about are how do we make these applications easier to use? How do we actually- >> Energy efficient. >> How do we enable the promise of what these things are going to bring, and actually make it real, because if it happens, think about traditional assets. There's projects going on globally that are looking at how do you take equity securities and actually move them to the blockchain. When that stuff happens, boom. >> And I like what you guys are doing, I saw the news out through this crypto winter, some major wallet exchanges that have been advertising are hurting. Take me through what you guys are thinking, what the vision is around the wallet of wallets. Is it to provide an experience for the user or the market industry itself? What's the target, is it both? Share the design goals for the wallet of wallets. >> My favorite thing about innovation and innovation labs is that we can experiment. So I'll go in saying we don't know what the final answer is going to be, but this is the premise that we have. In this disparate decentralized ecosystem, you need some mechanism to be able to control what's actually happening at the consumer level. So I think the key target is how do you create an experience where the consumer feels like they're in control of that value? How do they actually control the underlying assets? And then how does it actually get delivered to them? Is it something that comes from their bank, from their broker? Is it coming from an independent organization? How do they manage all of that information? And I think the last part of it are the assets. It's easy to think about cryptos and NFTs, but thinking about traditional assets, thinking about identity information and healthcare records, all of that stuff is going to become part of this ecosystem. And imagine being able to go someplace and saying, oh, you need my information. Well, I'm going to give it to you off my phone and I'm going to give it to you for the next 24 hours so you can use it, but after that you have no access to it. Or you're my financial advisor, here's a view of what I actually have, my underlying assets. What do you recommend I do? So I think we're going to see an evolution in the market. >> Like a data clean room. >> Yeah, but that you control. >> Yes! (laughs) >> Yes! >> I think about it very similarly as well. As my journey into the crypto market has gone through different pathways, different avenues. And I've come to a place where I'm really managing eight different wallets and it's difficult to figure exactly where all my assets are and having a tool like this will allow me to visualize and aggregate those assets and maybe even recombine them in unique ways, I think is hugely valuable. >> My biggest fear is losing my key. >> Well, and that's an experience problem that has to be solved, but let me give you, my favorite use case in this space is, 'cause NFTs, right? People are like, what does NFTs really mean? Title insurance, right? Anyone buy a house or refinance your mortgage? You go through this crazy process that costs seven or eight thousand dollars every single time you close on something to get title insurance so they could validate it. What if that title was actually sitting on the chain, you got an NFT that you put in your wallet and when it goes time to sell your house or to refinance, everything's there. Okay, I'm the owner of the house. I don't know, JP Morgan Chase has the actual mortgage. There's another lien, there's some taxes. >> It's like a link tree in the wallet. (laughs) >> Yeah, think about it, you got a smart contract. Boom, closing happens immediately. >> I think that's one of the most important things. I think people look at NFTs and they think, oh, this is art. And that's sort of how it started in the art and collectable space, but it's actually quickly moving towards utilities and tokenization and passes. And that's where I think the value is. >> And ownership and the token. >> Identity and ownership, especially. >> And the digital rights ownership and the economics behind it really have a lot of scale 'cause I appreciate the FinTech angle you are coming from because I can now see what's going on here with you. It's like, okay, we got to start somewhere. Let's start with the experience. The wallet's a tough nut to crack, 'cause that requires defacto participation in the industry as a defacto standard. So how are you guys doing there? Can you give an update and then how can people get, what's the project called and how do people get involved? >> Yeah, so we're still in the innovation, incubation stages. So we're not launching it yet. But what I will tell you is what a lot of our focus is, how do we make these transactional things that you do? How do we make it easy to pull all your assets together? How do we make it easy to move things from one location to the other location in ways that you're not using a weird cryptographic numeric value for your wallet, but you actually can use real nomenclature that you can renumber and it's easy to understand. Our expectation is that sometime in the fall, we'll actually be in a position to launch this. What we're going to do over the summer is we're going to start allowing people to play with it, get their feedback, and we're going to iterate. >> So sandbox in when, November? >> I think launch in the fall, sometime in the fall. >> Oh, this fall. >> But over the summer, what we're expecting is some type of friends and family type release where we can start to realize what people are doing and then fix the challenges, see if we're on the right track and make the appropriate corrections. >> So right now you guys are just together on this? >> Yep. >> The opening up friends and family or community is going to be controlled. >> It is, yeah. >> Yeah, as a group, I think one thing that's really important to highlight is that we're an innovation lab. We're working with Broadridge's innovation lab, that partnership across innovation labs has allowed us to move very, very quickly to build this. Actually, if you think about it, we were talking about this not too long ago and we're almost close to having an internal launch. So I think it's very rapid development. We follow a lot of the- >> There's buy-in across the board. >> Exactly, exactly, and we saw lot of very- >> So who's going to run this? A Dow, or your companies, is it going to be a separate company? >> So to be honest, we're not entirely sure yet. It's a new product that we're going to be creating. What we actually do with it. Our thought is within an innovation environment, there's three things you could do with something. You can make it a product within the existing infrastructure, you can create a new business unit or you can spin it off as something new. I do think this becomes a product within the organization based upon it's so aligned to what we do today, but we'll see. >> But you guys are financing it? >> Yes. >> As collective companies? >> Yeah, right. >> Got it, okay, cool. Well, let us know how we can help. If you guys want to do a remote in to theCUBE. I would love the mission you guys are on. I think this is the kind of work that every company should be doing in the new R and D. You got to jump in the deep end and swim as fast as possible. But I think you can do it. I think that is refreshing and that's smart. >> And you have to do it quick because this market, I think the one thing we would probably agree on is that it's moving faster than we could, every week there's something else that happens. >> Okay, so now you guys were at Consensus down in Austin when the winter hit and you've been in the business for a long time, you got to know the industries. You see where it's going. What was the big thing you guys learned, any scar tissue from the early data coming in from the collaboration? Was there some aha moments, was there some oh shoot moments? Oh, wow, I didn't think that was going to happen. Share some anecdotal stories from the experience. Good, bad, and if you want to be bold say ugly, too. >> Well, I think the first thing I want to say about the timing, it is the crypto winter, but I actually think now's a really great time to build something because everybody's continuing to build. Folks are focused on the future and that's what we are as well. In terms of some of the challenges, well, the Web 3 space is so new. And there's not a way to just go online and copy somebody else's work and rinse and repeat. We had to figure a lot of things on our own. We had to try different technologies, see which worked better and make sure that it was functioning the way we wanted it to function. Really, so it was not easy. >> They oversold that product out, that's good, like this team. >> But think about it, so the joke is that when winter is when real work happens. If you look at the companies that have not been affected by this it's the infrastructure companies and what it reminds me of, it's a little bit different, but 2001, we had the dot com bust. The entire industry blew up, but what came out of that? >> Everything that exists. >> Amazon, lots of companies grew up out of that environment. >> Everything that was promoted actually happened. >> Yes, but you know what didn't happen- >> Food delivery. >> But you know what's interesting that didn't happen- >> (laughs) Pet food, the soccer never happened. >> The whole Super Bowl, yes. (John laughs) In financial services we built on top of legacy. I think what Web 3 is doing, it's getting rid of that legacy infrastructure. And the banks are going to be involved. There's going to be new players and stuff. But what I'm seeing now is a doubling down of the infrastructure investment of saying okay, how do we actually make this stuff real so we can actually show the promise? >> One of the things I just shared, Rob, you'd appreciate this, is that the digital advertising market's changing because now banner ads and the old techniques are based on Web 2 infrastructure, basically DNS as we know it. And token problems are everywhere. Sites and silos are built because LinkedIn doesn't share information. And the sites want first party data. It's a hoarding exercise, so those practices are going to get decimated. So in comes token economics, that's going to get decimated. So you're already seeing the decline of media. And advertising, cookies are going away. >> I think it's going to change, it's going to be a flip, because I think right now you're not in control. Other people are in control. And I think with tokenomics and some of the other things that are going to happen, it gives back control to the individual. Think about it, right now you get advertising. Now you didn't say I wanted this advertising. Imagine the value of advertising when you say, you know what, I am interested in getting information about this particular type of product. The lead generation, the value of that advertising is significantly higher. >> Organic notifications. >> Yeah. >> Well, gentlemen, I'd love to follow up with you. I'm definitely going to ping in. Now I'm going to put CUBE coin back on the table. For our audience CUBE coin's coming. Really appreciate it, thanks for sharing your insights. Great conversation. >> Excellent, thank you for having us. >> Excellent, thank you so much. >> theCUBE's coverage here from New York City. I'm John Furrier, we'll be back with more live coverage to close out the day. Stay with us, we'll be right back. >> Excellent. (calm electronic music)

from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante predictions are all the rage at this time of year now on december 29th 2020 in collaboration with eric porter bradley of enterprise technology research etr we put forth our predictions for 2021 and the focus of our prognostications included tech spending remote work productivity apps cyber security ipos specs m a data architecture cloud hybrid cloud multi-cloud ai containers automation and semiconductors we covered a lot of ground now over the past several weeks we've been inundated with literally thousands of inbound emails pitching us on various predictions and trends in these and other areas here's my predictions folder and this is only a portion of the documents that i've received by email obviously printed them out killed a few trees sorry hello and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we're going to review briefly each of our predictions for this past year 2021 and suggest a grade as to how we did we're going to do this as a little warm up for our 2022 predictions which we'll be doing in the next over the next couple of weeks now before we dig in i want to make an observation many of the predictions that we received they were observations of trends and sometimes not really predictions or you know or not surprising we got a lot of self-serving marketing statements you know predictions in our view they should be measurable so you can look back and say okay did they get it right now granted there are gray areas so that's why we'll use a grading system today now there are also many really well done and thought-provoking predictions and there's an example of one that we received that is strong it's from equinix cio milan waglay who said within the decade data centers will be powered by a hundred percent renewable energy okay so you know that's clear and we can measure that but anyway thanks to all the pr folks who sent along like i said literally thousands of predictions we tried to read them all but the volume over the past week or so was just so overwhelming and we'll try to scan them before we do our 2022 predictions but today we want to do that warm up by evaluating how we did in 2021 so let's get started our first prediction was that tech spending would increase by four percent this year coming off of what we had thought was a contraction in 2020 and depending on which data you look at you know best case maybe was flat we definitely correctly called the continuation into 2022 of the remote work trend and the positive impact it would have on pcs and the like but we underestimated the shape of that rebound that that spend back curve idc has tech spending wrote this year at five and a half percent so we feel like while we called the bounce back it was more pronounced than we had thought in fact you know we think that idc number is probably going to go up even higher and we'll address that in our 2022 predictions so so we'll give ourselves a b minus here okay next prediction was remote worker trends become fossilized settling in at an average of 34 percent by year end 2021. so on average 34 of the workers would be remote by the end of this year now you know we made the call but we missed delta no we missed omacrom we said 34 remote which would be 2x the historical norms now the etr data suggests it was 52 in september and it's probably going to be somewhere in the 40 to 45 range by by the end of this month into december and the thing is 75 of the workforce is probably still working either fully remote or in a hybrid model and hybrid work is probably going to be the dominant trend and we're going to have to revisit that framework or how we think about this whole structure and we'll do that again in our 2022 predictions so we'll give ourselves a c on that one we'll take some credit for the permanence of the trend but the percentage was well off the mark you know thanks to the variance as well as some cultural shifts that whole hybrid notion okay so hey not really a great start for eric and me but we rebound with the next one the productivity increases we said seen in 2020 will lead organizations to double down on the successes and certain productivity apps will benefit so to measure this we said let's take a look at the most recent quarterly earnings and gauge the revenue growth year on year as an indicator docusign was up 42 smartsheet who we also called up was up 46 in revenue twilio up 65 zoom growth was 35 down from 325 confirming our layup call the zoom growth would moderate it had nowhere to go but down and microsoft teams has never been more ubiquitous has never seen greater adoption with hundreds of companies having a hundred thousand or more users and thousands of companies with ten thousand users or more so we really feel like we nailed this one so we're gonna give us give ourselves an a plus okay so now on to cyber it's an area that we've been making calls in for a couple of years now and we're really pleased looking back here we said permanent shifts in cso strategies are going to lead to share shifts in network security now we said to give you more detail maybe that sounds like an easy one but we said specifically identity cloud security and endpoint security would continue to benefit and we specifically named crowdstrike octa zscaler and a few others that are targeting their growth rates now gartner has the security market growing at 11 percent octa and zscaler revenues last quarter grew at 62 percent year over year crowdstrike 63 illumia we also called out they raised 225 million dollars on a 2.75 billion valuation on the strength of its growth that was in september now akamai acquired guardiocor for 600 million dollars another company we called out that they would do it they did that as a ransomware protection play and they paid a huge revenue multiple for the company and it seems the guys listed on the last line are all talking about subscriptions sas arr remaining performance obligations or rpo so we feel very good about this look back we'll take an a on this one no it's not an a plus because we're too conservative on the growth of octa crowdstrike and zscaler topping at 50 they they blew that away by another 10 points or so 10 to 15. but look pretty good call nonetheless okay again the next one you might feel like is a layup but not really so we said the increased tech spend would drive even more ipos spax and m a according to spac analytics ipos were up 109 this year the spac attack continued up 109 percent in 2021 on top of a record 2020 and according to kpmg m a dollar volume was up 19 okay you might say uh that was easy call but there was much more underneath this prediction we called out uipass ipo which was a lock but also said automation anywhere would go public uipath did aa didn't we did correctly call the hashicorp ipo we said they'd either get go ipo or get acquired and cloud flare grew revenue 219 percent last quarter but akamai was not acquired so the degree of difficulty on the overall prediction wasn't high but the automation anywhere in akamai events we made those calls that didn't happen and those were you know obviously tougher calls so we think this still deserves a b grade all right as you know data is one of our favorite subjects and we've reported extensively in the successes and failures of so-called big data we said next in the next prediction that in the 2020s 75 percent of large organizations will re-architect their big data platforms and we said this would occur you know in earnest over the next four to five years now again you may say duh dave but you have to evaluate the prediction based on the underlying comments here the jury is still out on things like snowflakes data cloud but we absolutely believe that it's the right direction but then you have then you have data bricks coming in taking a different approach they're coming at the problem from a data science angle trying to take on traditional bi and then you get snowflake coming from the analytics space and moving into ai and data science and you know we asked at aws aws re invent we asked benoit dejaville on the cube if there needs to be a semantic layer to bring these two worlds together and he said yes and that's what he claims snowflake is building meanwhile you got the big whales like oracle they continue to invest in their capabilities to try to eliminate data movement and then there's aws taking a totally different approach to data where it gives customers maximum optionality of offerings and database and other services and you can't forget microsoft and google so many customers might not take the steps that we predicted because they're comfortable where they are specifically we're talking about here a shift toward domain ownership and data product thinking and the reorganization of hyper-specialized technical teams many of the principles put forth by data mesh and we've said this change is going to take a number of years to play out four to five years so we start noticing in 2021 that that's clearly been the case as we reported on parts of jpmorgan chase uh rethinking its data architecture hellofresh and many others so this is still an incomplete the professor we'll give ourselves an incomplete on this one but we think it's trending in the right direction okay the next one is always fun discussion that's the battle to define hybrid and multi-cloud we said that's going to escalate in 2021 and we'll create bifurcated cio strategies now here we go aws sees the world as bringing its apis and primitives and model to the edge and the data center to aws is just another edge node and the company says that in still believes in the fullness of time that all data will be in the cloud however that's defined and aws awareness would say all this talk about hybrid of connecting on-prem to a cloud they would flat out say adam silipsky told us this that's not cloud is what he said then on the other side of the table you have the likes of cisco dell hpe etc saying hold on cloud is an operating model it's not a place and aws might say yeah and aws along with its customers is defining that operating model and these other guys would say no actually you're not we are with our customers and this battle 100 percent escalated in 2021 with the launch of apex by dell hp e double down on green lake cisco's as the service models and then of course oracle which actually announced a true same same public to on-prem hybrid capability two years before aws announced outpost and of course oracle's executing on that strategy in earnest in 2021 and the other nuance here is a concept that we introduced called super cloud which refers to the notion that look something like for example multi-cloud is not about running within a respective cloud it's not about cloud compatibility rather it's about abstracting the complexity of the underlying cloud primitives and building value on top of those cloud services on top of the investments in capex that the hyperscalers have made now some people didn't like the term super cloud maybe uber cloud would be a better term we're going to continue to use it to describe this capability we think it has meaning and we're seeing new examples like goldman sachs's financial cloud running on top of aws so a super cloud is not as an application or a suite of applications running on a single cloud now if those applications span multiple clouds like like snowflake is trying to do okay that's a service that could span multiple clouds or in the case of goldman sachs it's a portfolio of data tools and software that's made accessible as a service that floats on top of a single or even multiple clouds regardless we feel that this was a correct call given the evidence and we'll give ourselves an a minus taking points off for the somewhat anecdotal and observational measurement system that we apply to look back at this prediction okay the next prediction was we made was cloud containers ai and ml automation uh are gonna power that those big four are gonna power 2021 spending here's a graphic we use to predict that it plots survey data for the various technologies within the etr taxonomy net score or spending momentum on the vertical axis and market share or presence in the data set it's a pervasive measurement on the horizontal axis the one that matters here is the vertical that dotted line of 40 percent anything above that is considered highly elevated and these four areas have held served this year based on recent etr survey data that we're not showing here we'll we'll bring that into our 2022 prediction so this prediction came in correctly for the most recent survey data and that's our measurement system on this one so we're going to take an a for this one too now on the penelope ultimate prediction here we came back to automation saying that the automation mandate accelerates in 2021 uipath and automation anywhere we said would go public but microsoft remains a threat to these pure play rpa vendors well we gave ourselves a b on this one doubling down on automation anywhere going public you know that was wrong but we definitely saw this year companies leaning hard into automation and microsoft despite the fact that it doesn't have as feature rich a product and offering as uipath and automation anywhere microsoft remains a very large presence you know we spoke to a lot of customers at the uipath forward four event in october in las vegas physical event and they confirmed you know this is true but at the same time so they're using power automate from microsoft but also using in this case uipath so they've kind of confirmed that yeah it's not the same we use that for some of our productivity we're an azure customer it's easy for us but they're still leaning heavily and investing heavily into uipath and i think the same can be said for automation anywhere but autom but power automate shows up as a big time leader in the magic gartner magic quadrant so it can't be ignored but clearly the two leaders in rpa have a sizable product advantage relative to the legacy software players now if you look at the comment on pega systems they cooled off a bit as measured by their stock price their revenue grew 13 percent last quarter on a year-on-year basis but perhaps we overestimated the tailwind effect and the company's momentum so we'll take a b on this prediction correct call on the automation trend and the big software vendors piling in ibm et cetera but the chance we took on automation anywhere again was a miss so we'll dig ourselves on that and our last prediction for 2021 was 5g rollouts push new edge iot workloads and necessitate new system architectures now much of this prediction you can see in the underlying bullets here really related to the observation that arm was dominating at the edge it would find its way into the mainstream enterprise workloads and we've been asking a lot of the mainstream you know companies the oems you know what do you what do you see with with arm in the enterprise and they say yeah we don't see it yet but very clearly this came into focus in 2021 is aws announced graviton 3 now and new inference and new training silicon these are different types of workloads that are emerging in the enterprise these are all based on arm microsoft google alibaba oracle and others are now shipping or readying arm-based systems for the enterprise when you look at new storage network and security appliances and other systems they're very offering and often including arm-based processors to assist with the offloads and look intel is definitely under product under pressure as we've predicted many times not just in our predictions post even pat gelsinger has admitted this is a turnaround it's going to take at least five years that's kind of new and recent data that he's made public so we're going to take an a minus on this one we're going to take off some points for the fact that you know 5g rollouts in edge are evolving and this is a longer term trend but the underlying points that we made on this slide are still pretty solid now if we use the following scale where a plus is a hundred out of a hundred a minus is a 90 a b is an 85 a b minus is an 80 and a c is a 75 out of 100 and we exclude that incomplete prediction on data architectures we average out to an 87.8 so that's a solid b plus and so the professor in us said hey little yellow sticky good effort as most of the predictions could be quantified and or you know we tried to object objectively score them there were some layups in there so yeah maybe we'll try to take more risks uh you know or not you know we we we'll see we like winning and so you know you always have to couch some of these things with some obvious ones but but really try to give some detail underneath that's maybe non-obvious um and we'll try to keep it down in the legs we did this year to one or two multi-year predictions so what's next well eric bradley and i were working on our 2022 predictions we're going to release those in the next couple of weeks so stay tuned for that you know what do you think how did we do you know we're grading ourselves here love to know you know for we're off base on base we're too hard on ourselves too easy give us your feedback don't forget these episodes are all available as podcasts wherever you listen all you do is search breaking analysis podcast check out etr's website at etr dot plus remember we also publish a full report every week on wikibon.com and siliconangle.com you can always get in touch with email david.velante at siliconangle.com you can dm me at divalante or comment on our linkedin posts this is dave vellante for the cube insights powered by etr have a great week everybody stay safe be well we'll see you next time [Music] you

>>Hello, and welcome to the cubes. Continuous coverage of AWS reinvent 2021. I'm Dave Nicholson, and we're running an incredible event this year. One of the most important technology events. It's a hybrid event with two live stages. Two sets here in Las Vegas. Two studios we've interviewed more than a hundred guests and two distinguished guests that I have here from emphasis today have joined us. Thank you very much. Uh, Mr., who's the executive vice president of Infosys cobalt. And we'll talk about what that is exactly in a moment along with David Wilson, Wilson, I'm sorry, senior vice president and head of global alliances in the partner ecosystem for Infosys gentlemen. Welcome. Thank you. Thank you very much. So let's cut right to the chase cobalt. And when you tell your family that you're executive vice-president of cobalt, do they just smile and immediately nod? Like they know what it is? Absolutely. >>In fact, uh, in fact it is so exciting for us, uh, what we did at Infosys, just to define cobalt in one sentence, it is a set of services, solutions, and products that we are bringing together to solve, you know, accelerate our customer's journey or what we call as the customer's digital journey. So in slough, everybody talking about Kala cloud in a different way, with different narratives, different value proposition we had in forces. And by the way, we were the first ones in the world to combine all of this and the one brand called cobalt. So that's essentially what cobalt is. So anything and everything that we do in cloud, it's all under this brand called cobalt and that's Infosys cobalt. >>So does, does Infosys cobalt include a combination of bespoke solutions, cheering for people as well as packaged standardized things? How do you, how do you strike a balance because you can't have a one size fits all? Uh, what does that look like? How do you segregate those? >>Yes. Great question. So, so essentially what you are done with a cobalt is a delicate cobalt. In two ways. One is there are customers who want a solutions to solve technology problems. It could be getting out of data centers, it could be migrating workloads to cloud. It could be analytics on cloud ERP on cloud daddy's mainframe modernization, and, you know, getting off mainframes. And at the same time, there are industry verticals like financial services, retail manufacturing, and of course, life sciences, and many more who want to understand what are the business solutions and what are the solutions that we have for solving their business problems. So essentially cobalt is a bespoke solutions. It has products, it has platforms, and we have brought all of this together and we take it to our customers. So essentially these are industry blueprints. These are reference architectures. So we have 250 industry blueprints and around 25,000, that's it that we can actually take to our customers to help their digital journey. >>So, David, I imagine that key to the success of cobalt is, uh, uh, the idea of partnerships, talk about the alliances, uh, uh, that, uh, that you're involved with specifically the way that cobalt interacts with the AWS yes. >>Universe. Absolutely. So the, you know, as we designed our cobalt strategy, the partners are a major component of this. They contribute to it. They're part of the design. And ultimately when we go to the clients with these solutions, these assets, uh, our partners, components are baked right into the solution. In the case of AWS, we've been so successful with it that we recognize this week, uh, as their industry solutions partner of the year. Congratulations. Yeah. So I was joking. We should bring our trophy and put it in between, but we emphasis is invested heavily in developing the, the partner ecosystem, you know, gone are the days where our clients are, uh, putting out an RFP and purchasing individual piece parts, and then, you know, searching at NSI. They're looking for a business outcomes and, uh, uh, emphasis along with our cobalt strategy is able to work with partners like AWS and go there and sell an outcome and accelerate the whole. >>Well, you mentioned RFPs. Uh, what is your, uh, what is your go-to market strategy look like in terms of engaging with those end user clients? Um, is it in partnership with AWS? Is it led by Infosys bringing in AWS where appropriate some mix of the two? What does that look like in this world of cooperation and petition that we're in, >>It's actually a mix of two. So essentially the way we go to market is that there are solutions that Infosys has built on AWS that we will take to our customers. There are solutions that you have built, which are cloud neutral, and those are some things that we take to the customers. And the third one, which is very important is co-creating solutions for our customers along with AWS. So our go to market is a combination of all of them, and that's what makes it exciting. >>So a non-test running cobalt, you're, you're responsible for alliances. You guys are probably in contact a lot with one another. All of these crazy new things are announced at AWS. I'm sure you get a little bit of a preview of it. It's not a complete, it's not a complete surprise when you arrive, but you've gotta be screaming for teams and solutions to leverage some of the coolest stuff within cobalt. How does that, how does that conversation go? >>Yeah, so David David and I work very closely, right? In fact, uh, uh, the way, the way we do things is our go to market cannot be complete without partners. So similarly my strategy and our strategy and global cannot be complete without David. So we actually worked together to identify, in fact, we have been visiting a lot of boots. We got to create, we've gone to a lot of great ideas. We want to see how we can bring them into the cobalt framework and bundle some of that as part of our solutions. So we keep looking at those, we'll look at the announcements that were made and we'll solve, you know, identify many more sales motions that we can take to the, >>So David talk about some of the things you've seen here at re-invent this week that are specifically relevant for cobalt and emphasis customer. >>Well, what's some of the most exciting discussions we've been having is with, uh, not only, uh, AWS themselves about the, the announcements and the way in which we can leverage them, leverage them and go to market. But, uh, AWS has built out their own partner ecosystem, uh, that we then interact with. So we've had some exciting conversations with AWS's ISV partners, their, uh, their other solution providers about how we can bring this together and go to market together. You know, when, when an example, we had a lot of discussions this week was about, uh, how we're doing it, right? The mainframe services, uh, that were announced and how we can support them in building out our industry specific assets. So, you know, taking a kernel of what AWS provides and then wrapping our secret sauce around it, in partnership with other companies and then take into our clients, you know, that's what we're, I, it, the good part is we can quickly go from a discussion to a, go to market, a dialogue with our direct clients who are also here, which have been in real-time having those discussions. >>So emphasis a non has been a trusted advisor for clients predating the Dawn of cloud, if you will. Uh, and I'm sure that certain slices of your revenue don't wanna make this too uncomfortable. A question certain slices of your revenue are still dependent upon all of that. 80% of it. That's still on prem. How do you manage that? You're, you're laser focused on cobalt and you've got alliances. Um, everybody's looking towards the cloud. How do you balance that with the very real needs of Infosys as a business? Aren't you in the same boat as your customers, in terms of transformation? >>Well, you know, I, I would, I'm sorry, >>My eyes go back and forth. See, I told you it was gonna be easy for us to have a conversation. Yeah. Jump into >>W when you, when you look at the different partners out there, we have a discussion about being asset heavy asset light emphasis. Um, we, we grew up through application management, uh, and now as we're seeing these transformations go forward, the last thing we wanted to be is a server huggers. Uh, we're ready to accelerate these transformations as fast as possible. And, uh, you know, partners like AWS are recognizing that, uh, a non steam can go in there and be the disruptor to actually accelerate those transformation. >>Absolutely. In fact, involved when we spoke to some of the AWS executives, uh, we want to be the challenger, right? Because we don't carry any baggage. Uh, we clearly believe, as Gartner says that a cloud is going to be the, for business innovation, and we want to drive innovation and transformation for our customers. So essentially we want to make this relationship with AWS much bigger and better. We want to be the partners with our clients to drive business innovation with industry segments, industry clouds, solutions that drive opening, new markets, building better products and solutions, helping get better customer intimacy and those kinds of things. And so that's essentially what our thought processes with, uh, what we want to do. >>It's been mentioned a few times here that, uh, somewhere around 80 to 85% of it spend is still on premises. It's not in the cloud yet. So despite how large, we all think the AWS AWS universe has become so far, we're really just at the beginning stages. But what are you seeing in terms of clients hesitancy towards cloud at this point, has that changed over the last couple of years? Uh, what are the inhibiting factors that you see? What are the accelerants that you see at this stage of the game? >>Well, in fact, in fact, COVID unfortunately Colbert, uh, while it was all a very bad thing, but it actually helped accelerate customer's journey to cloud. Uh, in fact, uh, the, we have several customers who used to say that, you know, everybody has to come to office to work. Nobody can work remotely because there are security constraints that is, there will be impacted the security posture, but to when we hit March, 2020, and everybody had to work remote, it's the same set of customers who decided to go to cloud and started limited him to part of cloud. So I would say COVID in short has accelerated customers knowledge about cloud. They are no longer worried about security. They're no longer worried about, uh, latency and bandwidth. I think I don't see any major hesitancy at this point of time. Uh, but the trend that we're seeing towards cloud is cloud is going to be used more for innovation. And it's not just going to be about, take my data center and moving to cloud, right? So it's not going to be just those tactical reasons. Uh, and that's exactly what we did. We actually came out with a report, which says, moving from cloud chaos to cloud clarity, and it talks about all these facets of what are those strengths that customer should look for. So that's essentially what we use. >>So I imagine cobalt one of the kind of main ideas behind it is to remove friction associated with that move to cloud, to the extent that you can not be reinventing the wheel every single time you're engaging a customer. Is that, is that a fair statement? >>In fact, you know, many customers of ours, in fact, almost all of them are saying, we do not want to reinvent the wheel. So how can you help us? So what we have done as part of cobalt is to bring these reference architectures, right? So for example, if a financial services customer wants to fight fraud, fraud analytics is a reference architecture that we have. Uh, if the telco customer wants to implement 5g, we have a framework and a reference architecture for OSS BSS on cloud. Uh, if there is licenses customer who wants to basically look at drug discovery, we have an architecture for that. So we want to make it more and more in a inference architecture based without reinventing the wheel and bring the best practices from other customers to drive those scenarios. So that's essentially what we do. >>So cobalt underway, you've been recognized for a performance to this point. It's a lot of pressure for 2022. So what are you going to, what do you, what, what, what are you going to slap on the desk in 2022? When we get back together, >>We do plan to up bookends by this time next year, to, to able to pre >>It is perfectly acceptable by the way, to share both the 20, 21 and 2022 award on stage, because we have to make up for 2019 when we weren't here physically. >>But to build off of, with a, knotless saying about the, uh, you know, what's going on in the last 18 to 24 months, you know, we're seeing clients now that we have one that, uh, came to us with a 114 list of products that they bought from various partners, either directly through distributors and such and saying, listen, we no longer want to be in the procurement function. You know, we want to take these hundred and 14 products. We recognize we're going to get it down to 30 or 40 of the key ones, obviously a shifting a lot of that to cloud. And we were able to leverage emphasis cobalt to actually accelerate that and incorporate our partner components to help that shift. So I think next year, I think that will be a major theme that you're seeing clients recognize that the, the way in which they procure and they develop their it platform will be much different. And emphasis with the design we put in place will be in a key position to, to support them at that. Well, >>We recorded this. I'm not sure if you realized we were actually recording this, so we're going to go, we can go back and review this tape next year and we'll see. And I hope to see you then, David, thank you so much for joining us here at the cube and for the cube here in our continuous coverage at AWS reinvent 2021 live in Las Vegas. I'm Dave Nicholson saying stay tuned because there's always more on the cube. And I'd like to remind you that we are your leader in hybrid tech event coverage.

(bright music) >> Well, hi everybody. John Walls here on theCUBE, continuing our coverage of AnsibleFest 2021 with Tom Anderson, the Vice President of Product Management at Red Hat. And Tom, you've been the answer, man, for theCUBE here over the last a week, 10 days or so. Third cube appearance, I hope we haven't worn you out. >> No, you haven't John, I love it, I love doing it. So that's great to have you have you at the event. >> Thank you for letting us be a part of that. It's been a lot of fun. Let's let's go and look at the event now. As far as big picture here, major takeaways that you think that have been talked about, that you think you'd like people, customers to go home with. If you will, though, a lot of this has been virtual obviously, but when I say go home, I made that figuratively, but what, what do you want people to remember and then apply to their businesses? >> Right. So being a product guy, I want to talk about products usually, right? So the big kind of product announcements from this year's event have been the rollout, and really, the next generation of the Ansible automation platform, which is really a rearchitecture turning it into a cloud native application an automation application itself that scales to our customer needs. So a lot of big announcements around that. And so what does that do for customers? That's really bringing them the automation platform that they can scale from the data center, to the cloud, to the Edge and everywhere in between, across a single platform with a single easy to use automation language. And then secondly, on that, as automation starts to shift left, we always talk about technology shifting left towards the developer, as automation is also shifting left towards the developer and other personas in an organization we're really happy about the developer tools and the tooling that we're providing to the customers with the new automation platform too, that brings development of content automation content. So the creation, the testing, the deployment and the management of that content across an enterprise far easier than it's ever been. So it's really kind of, it's a little bit about the democratization of automation. We see that shifting left, if you will. And I know I've said that already, but we see that shifting left of automation into other parts of the organization, beyond the domain experts, the network engineers or the storage experts, et cetera, pushing that automation out into the hands of other personas in the organization has been a big trend that we've seen and a lot of product announcements around that. So really excited about the product announcements in particular, but also the involvement and the engagement of our ecosystem, our upstream community. So important to our product and our success, our ecosystem partners, and obviously last but not least our customers and our users. >> So you hit a lot of big topics there. So let's talk about the Edge. You know, that seems to be a, you know, a fairly significant trend at this point, right? 'Cause trying to get the automation out there where the data besides, and that's where the apps are. Right? So where the data is, that's where things are happening out there on the Edge. So maybe just dive into that a little bit and about how you're trying to facilitate that need. >> Yeah. So a couple of trends around the Edge, obviously it's the architecture itself with lower capacity or lower capability devices and compute infrastructure at the Edge. And whether that's at the far edge with very low capacity devices, or even at near edge scenarios where you don't have, you know, data center, IT people out there to support those environments. So being able to get at those low capability, low capacity environments remotely Ansible is a really good fit for that because of our agentless architecture, the agentless architecture of Ansible itself allows you to drive automation out into the devices and into the environments where there isn't a high capacity infrastructure. And the other thing that the other theme that we've seen is one of the commonalities that no matter where the compute is taking place and the users are, there always has to be network. So we see a lot of network automation use cases out at the Edge and Ansible is, you know, the defacto network automation solution in the market. So we see a lot of our customers driving Ansible use cases out into their Edge devices. >> You know, you talk about development too, and just kind of this changing relationship between Ansible and DevOps and how that has certainly been maturing and seems to be really taking off right now. >> Yeah. So for, you know, what we've seen a lot of, as you know, is becoming frictionless, right? How do we take the friction out of the system that frees developers up to be more productive for organizations to be more agile, to roll out applications faster? How do we do that? We need to get access to the infrastructure and the resources that developers need. We need to get that access into their hands when they need it. And in our frictionless sort of way, right? So, you know, all of the old school, traditional ways of developers having to get infrastructure by opening a help desk ticket to get servers built for them and waiting for IT ops to build the servers and to deploy them and to send them back a message, all that is gone now. These, you know, subsystem owners, whether that's compute or cloud or network or storage, their ability to use Ansible to expose their resources for consumption by other personas, developers in this case, makes developers happy and more efficient because they can just use those automation playbooks, those Ansible playbooks to deploy the infrastructure that they need to develop, test and deploy their applications on. And the actual subsystem owners themselves can be assured that the usage of those environments is compliant with their standards because they've built and shared the automation with those developers to be able to consume when they want. So we're making both sides happy, agile, efficient developers and happy infrastructure owners, because they know that the governance and compliance around that system usage is on point with what they need and what they want. >> Yeah. It's a big win-win and a very good point. I always like it when we kind of get down to the nitty-gritty and talk about what a customer is really doing. Yeah. And because if we could talk about hypotheticals and trends and developing and maturity rates and all those kinds of things, but in terms of actual customers, you know, what people really are doing, what do you think have been a few of the plums that you'd like to make sure people were paying attention to? >> Yeah. I think from this year's event, I was really taken by the JP Morgan Chase presentation. And it really kind of fits into my idea of shifting left in the democratization of automation. They talked about, I think the number was around 7,000 people, associates inside that organization that are across 22 countries. So kind of global consumption of this. Building automation playbooks and sharing those across the organization. I mean, so gone are the days of, you know, very small teams of people doing, just automating the things that they do and it's grown so big. And, so pervasive now, I think JP Morgan Chase really kind of brings that out, tease that out, that kind of cultural impacts that's had on their organization, the efficiencies that have been able to draw off from that their ability to bring the developers and their operations teams together to be working as one. I think their story is really fantastic. And I think this is the second year. I think this is the second year that JP Morgan Chase has been presenting at Fest and this years session was fantastic. I really, really enjoyed that. So I would encourage, I would encourage anybody to go back and look at the recording of that session and there's game six groups, total other end of the spectrum, right? Financial services, JP Morgan Chase, global company to Gamesis, right? These people who are rolling out new games and need to be able to manage capacity really well. When a new game hits, right? Think about a new game hits and the type of demand and consumption there is for that game. And then the underlying infrastructure to support it. And Gamesis did a really great presentation around being able to scale out automation to scale up and down automation, to be able to spin up clusters and deploy infrastructure, to run their games on an as-needed basis. So kind of that business agility and how automation is driving that, or business agility is driving the need for automation in these organizations. So that that's just a couple of examples, but there was a good ones from another financial services that talked about the cultural impacts of automation, their idea of extreme automation. In fact, one of the sessions I interviewed Joe Mills, a gentlemen from this card services, financial services company, and he talked about extreme automation there and how they're using automation guilds in communities of practice in their organization to get over the cultural hurdles of adopting automation and sharing automation across an organization. >> Hm. So a wide array obviously of customer uses and all very effective, I guess, and, you know, and telling their own story. Somewhat related to that, and you, as you put it out there too, if you want to go back and look, these are really great case studies to take a look at. For those who, again, who maybe couldn't attend, or haven't had a chance to look at any of the sessions yet, what are some of the kinds of things that were discussed in terms of sessions to give somebody a flavor of what was discussed and maybe to tease them a little bit for next year, right? And just in case that you weren't able to participate and can't right now, there's always next year. So maybe if you could give us a little bit of flavor of that, too. >> Yeah. So we kind of break down the sessions a little bit into the more kind of technical sessions and then the sort of less technical sessions, let's put it that way. And on the technical session front, certainly a couple of sessions were really about getting started. Those are always popular with people new to Ansible. So there's the session that aired on the 29th, which has been recorded and you can rewatch it. That's getting started Q and A with the technical Ansible experts. That's a really, really great session 'cause you see that the types of questions that are being asked. So you know, you're not alone. If you're new to Ansible, the types of questions are probably the questions that you have as well. And then the, obviously the value of the tech Ansible experts who are answering this question. So that was a great session. And then for a lot of folks who may want to get involved in the community, the upstream community, there's a great session that was also on the 29th. And it was recorded for rewatching, around getting started with participation in the Ansible community and a live Q and A there. So the Ansible community, for those who don't know is a large, robust, vibrant, upstream community of users, of software companies, of all manners of people that are contributing and contributing upstream to the code and making Ansible a better solution for them and for everybody. So that's a great session. And then last but not least, almost always the most popular session is the roadmap sessions and Massimo Ferrari, gentleman on my team did a great session on the Ansible roadmap. So I do a search on roadmap in the session catalog, and you can see the recording of that. So that's always a big deal. >> Yeah, roadmaps were great, right? Because especially for newcomers, they want to know how I'm down here at 0.0. And, I've got a destination in mind, I want to go way out there. So how do I get there? So, to that point for somebody who is beginning their journey, and maybe they have, you know, they're automated with the ability to manually intervene, right? And now you've got to take the hands off the wheel and you're going to allow for full automation. So how, what's the message you want to get across to those people who maybe are going to lose that security blanket they've been hanging on to, you know, for a long time and you take the wheels off and go. >> No John, that's a great question. And that's usually a big apprehension of kind of full automation, which is, you know, that kind of turning over the reins, if you will, right to somebody else. If I'm the person who's responsible for this storage system, if I'm the person responsible for this network elements, these routers, these firewalls, whatever it might be, I'm really kind of freaked out about giving controls or access to those things, from a configuration standpoint, to people outside of my organization, who don't have the same level of expertise that I do, but here's the deal that in a well implemented well architected Ansible automation platform environment, you can control the type of automation that people do. Who does that against what managing that automation as code. So checking in, checking out, version control, deployment access. So there's a lot of controls that can be put in place. So it isn't just a free-for-all automated. Everybody automating everything. Organizations can roll out automation and have access to different kinds of automation, can control and manage what their organizations can use and see and do with Ansible. So there's lots of controls built-in for organizations to put in place and to make those subsystem owners give them confidence that how people are accessing their subsystems using Ansible automation can be controlled in a way that makes them comfortable and assures compliance and governance around those resources. >> Well, Tom, we appreciate the time. Once again, I know you've been a regular here on theCUBE over the course of the event. We'll give you a little bit of time off and let you get back to your day job, but we do appreciate that and I wish you success down the road. >> Thank you very much. And we'll see you again next year. >> You bet. Thank you, Tom Anderson, joining us Vice President of Product Management at Red Hat, talking about AnsibleFest, 2021. I'm John Walls, and you're watching theCUBE. (lively instrumental music)

>>Welcome back to the cubes coverage of AWS public sector summit live in Washington D. C. A face to face event were on the ground here is to keep coverage. I'm john Kerry, your hosts got two great guests. Both cuba alumni Shannon Kellogg VP of public policy for the Americas and john would ceo tell us congratulations on some announcement on stage and congressional john being a public company. Last time I saw you in person, you are private. Now your I. P. O. Congratulations >>totally virtually didn't meet one investor, lawyer, accountant or banker in person. It's all done over zoom. What's amazing. >>We'll go back to that and a great great to see you had great props here earlier. You guys got some good stuff going on in the policy side, a core max on stage talking about this Virginia deal. Give us the update. >>Yeah. Hey thanks john, it's great to be back. I always like to be on the cube. Uh, so we made an announcement today regarding our economic impact study, uh, for the commonwealth of Virginia. And this is around the amazon web services business and our presence in Virginia or a WS as we all, uh, call, uh, amazon web services. And um, basically the data that we released today shows over the last decade the magnitude of investment that we're making and I think reflects just the overall investments that are going into Virginia in the data center industry of which john and I have been very involved with over the years. But the numbers are quite um, uh, >>just clever. This is not part of the whole H. 20. H. Q. Or whatever they call HQ >>To HQ two. It's so Virginia Amazon is investing uh in Virginia as part of our HQ two initiative. And so Arlington Virginia will be the second headquarters in the U. S. In addition to that, AWS has been in Virginia for now many years, investing in both data center infrastructure and also other corporate facilities where we house AWS employees uh in other parts of Virginia, particularly out in what's known as the dullest technology corridor. But our data centers are actually spread throughout three counties in Fairfax County, Loudoun County in Prince William County. >>So this is the maxim now. So it wasn't anything any kind of course this is Virginia impact. What was, what did he what did he announce? What did he say? >>Yeah. So there were a few things that we highlighted in this economic impact study. One is that over the last decade, if you can believe it, we've invested $35 billion 2020 alone. The AWS investment in construction and these data centers. uh it was actually $1.3 billion 2020. And this has created over 13,500 jobs in the Commonwealth of Virginia. So it's a really great story of investment and job creation and many people don't know John in this Sort of came through in your question too about HQ two, But aws itself has over 8000 employees in Virginia today. Uh, and so we've had this very significant presence for a number of years now in Virginia over the last, you know, 15 years has become really the cloud capital of the country, if not the world. Uh, and you see all this data center infrastructure that's going in there, >>John What's your take on this? You've been very active in the county there. Um, you've been a legend in the area and tech, you've seen this many years, you've been doing so I think the longest running company doing cyber my 31st year, 31st year. So you've been on the ground. What does this all mean to you? >>Well, you know, it goes way back to, it was roughly 2005 when I served on the Economic Development Commission, Loudon County as the chairman. And at the time we were the fastest-growing county in America in Loudon County. But our residential real property taxes were going up stratospherically because when you look at it, every dollar real property tax that came into residential, we lose $2 because we had to fund schools and police and fire departments and so forth. And we realized for every dollar of commercial real property tax that came in, We made $97 in profit, but only 13% of the money that was coming into the county was coming in commercially. So a small group got together from within the county to try and figure out what were the assets that we had to offer to companies like Amazon and we realized we had a lot of land, we had water and then we had, you know this enormous amount of dark fiber, unused fibre optic. And so basically the county made it appealing to companies like amazon to come out to Loudon County and other places in northern Virginia and the rest is history. If you look today, we're Loudon County is Loudon County generates a couple $100 million surplus every year. It's real property taxes have come down in in real dollars and the percentage of revenue that comes from commercials like 33 34%. That's really largely driven by the data center ecosystem that my friend over here Shannon was talking. So >>the formula basically is look at the assets resources available that may align with the kind of commercial entities that good. How's their domicile there >>that could benefit. >>So what about power? Because the data centers need power, fiber fiber is great. The main, the main >>power you can build power but the main point is is water for cooling. So I think I think we had an abundance of water which allowed us to build power sources and allowed companies like amazon to build their own power sources. So I think it was really a sort of a uh uh better what do they say? Better lucky than good. So we had a bunch of assets come together that helps. Made us, made us pretty lucky as a, as a region. >>Thanks area too. >>It is nice and >>john, it's really interesting because the vision that john Wood and several of his colleagues had on that economic development board has truly come through and it was reaffirmed in the numbers that we released this week. Um, aws paid $220 million 2020 alone for our data centers in those three counties, including loud >>so amazon's contribution to >>The county. $220 million 2020 alone. And that actually makes up 20% of overall property tax revenues in these counties in 2020. So, you know, the vision that they had 15 years ago, 15, 16 years ago has really come true today. And that's just reaffirmed in these numbers. >>I mean, he's for the amazon. So I'll ask you the question. I mean, there's a lot of like for misinformation going around around corporate reputation. This is clearly an example of the corporation contributing to the, to the society. >>No, no doubt. And you think >>About it like that's some good numbers, 20 million, 30 >>$5 million dollar capital investment. You know, 10, it's, what is it? 8000 9000 >>Jobs. jobs, a W. S. jobs in the Commonwealth alone. >>And then you look at the economic impact on each of those counties financially. It really benefits everybody at the end of the day. >>It's good infrastructure across the board. How do you replicate that? Not everyone's an amazon though. So how do you take the formula? What's your take on best practice? How does this rollout? And that's the amazon will continue to grow, but that, you know, this one company, is there a lesson here for the rest of us? >>I think I think all the data center companies in the cloud companies out there see value in this region. That's why so much of the internet traffic comes through northern Virginia. I mean it's I've heard 70%, I've heard much higher than that too. So I think everybody realizes this is a strategic asset at a national level. But I think the main point to bring out is that every state across America should be thinking about investments from companies like amazon. There are, there are really significant benefits that helps the entire community. So it helps build schools, police departments, fire departments, etcetera, >>jobs opportunities. What's the what's the vision though? Beyond data center gets solar sustainability. >>We do. We have actually a number of renewable energy projects, which I want to talk about. But just one other quick on the data center industry. So I also serve on the data center coalition which is a national organization of data center and cloud providers. And we look at uh states all over this country were very active in multiple states and we work with governors and state governments as they put together different frameworks and policies to incent investment in their states and Virginia is doing it right. Virginia has historically been very forward looking, very forward thinking and how they're trying to attract these data center investments. They have the right uh tax incentives in place. Um and then you know, back to your point about renewable energy over the last several years, Virginia is also really made some statutory changes and other policy changes to drive forward renewable energy in Virginia. Six years ago this week, john I was in a coma at county in Virginia, which is the eastern shore. It's a very rural area where we helped build our first solar farm amazon solar farm in Virginia in 2015 is when we made this announcement with the governor six years ago this week, it was 88 megawatts, which basically at the time quadruple the virginias solar output in one project. So since that first project we at Amazon have gone from building that one facility, quadrupling at the time, the solar output in Virginia to now we're by the end of 2023 going to be 1430 MW of solar power in Virginia with 15 projects which is the equivalent of enough power to actually Enough electricity to power 225,000 households, which is the equivalent of Prince William county Virginia. So just to give you the scale of what we're doing here in Virginia on renewable energy. >>So to me, I mean this comes down to not to put my opinion out there because I never hold back on the cube. It's a posture, we >>count on that. It's a >>posture issue of how people approach business. I mean it's the two schools of thought on the extreme true business. The government pays for everything or business friendly. So this is called, this is a modern story about friendly business kind of collaborative posture. >>Yeah, it's putting money to very specific use which has a very specific return in this case. It's for everybody that lives in the northern Virginia region benefits everybody. >>And these policies have not just attracted companies like amazon and data center building builders and renewable energy investments. These policies are also leading to rapid growth in the cybersecurity industry in Virginia as well. You know john founded his company decades ago and you have all of these cybersecurity companies now located in Virginia. Many of them are partners like >>that. I know john and I both have contributed heavily to a lot of the systems in place in America here. So congratulations on that. But I got to ask you guys, well I got you for the last minute or two cybersecurity has become the big issue. I mean there's a lot of these policies all over the place. But cyber is super critical right now. I mean, where's the red line Shannon? Where's you know, things are happening? You guys bring security to the table, businesses are out there fending for themselves. There's no militia. Where's the, where's the, where's the support for the commercial businesses. People are nervous >>so you want to try it? >>Well, I'm happy to take the first shot because this is and then we'll leave john with the last word because he is the true cyber expert. But I had the privilege of hosting a panel this morning with the director of the cybersecurity and Infrastructure Security agency at the department, Homeland Security, Jenness easterly and the agency is relatively new and she laid out a number of initiatives that the DHS organization that she runs is working on with industry and so they're leaning in their partnering with industry and a number of areas including, you know, making sure that we have the right information sharing framework and tools in place, so the government and, and we in industry can act on information that we get in real time, making sure that we're investing for the future and the workforce development and cyber skills, but also as we enter national cybersecurity month, making sure that we're all doing our part in cyber security awareness and training, for example, one of the things that are amazon ceo Andy Jassy recently announced as he was participating in a White house summit, the president biden hosted in late august was that we were going to at amazon make a tool that we've developed for information and security awareness for our employees free, available to the public. And in addition to that we announced that we were going to provide free uh strong authentication tokens for AWS customers as part of that announcement going into national cybersecurity months. So what I like about what this administration is doing is they're reaching out there looking for ways to work with industry bringing us together in these summits but also looking for actionable things that we can do together to make a difference. >>So my, my perspective echoing on some of Shannon's points are really the following. Uh the key in general is automation and there are three components to automation that are important in today's environment. One is cyber hygiene and education is a piece of that. The second is around mis attribution meaning if the bad guy can't see you, you can't be hacked. And the third one is really more or less around what's called attribution, meaning I can figure out actually who the bad guy is and then report that bad guys actions to the appropriate law enforcement and military types and then they take it from there >>unless he's not attributed either. So >>well over the basic point is we can't as industry hat back, it's illegal, but what we can do is provide the tools and methods necessary to our government counterparts at that point about information sharing, where they can take the actions necessary and try and find those bad guys. >>I just feel like we're not moving fast enough. Businesses should be able to hack back. In my opinion. I'm a hawk on this one item. So like I believe that because if people dropped on our shores with troops, the government will protect us. >>So your your point is directly taken when cyber command was formed uh before that as airlines seeing space physical domains, each of those physical domains have about 100 and $50 billion they spend per year when cyber command was formed, it was spending less than Jpmorgan chase to defend the nation. So, you know, we do have a ways to go. I do agree with you that there needs to be more uh flexibility given the industry to help help with the fight. You know, in this case. Andy Jassy has offered a couple of tools which are, I think really good strong tokens training those >>are all really good. >>We've been working with amazon for a long time, you know, ever since, uh, really, ever since the CIA embrace the cloud, which was sort of the shot heard around the world for cloud computing. We do the security compliance automation for that air gap region for amazon as well as other aspects >>were all needs more. Tell us faster, keep cranking up that software because tell you right now people are getting hit >>and people are getting scared. You know, the colonial pipeline hack that affected everybody started going wait a minute, I can't get gas. >>But again in this area of the line and jenny easterly said this this morning here at the summit is that this truly has to be about industry working with government, making sure that we're working together, you know, government has a role, but so does the private sector and I've been working cyber issues for a long time to and you know, kind of seeing where we are this year in this recent cyber summit that the president held, I really see just a tremendous commitment coming from the private sector to be an effective partner in securing the nation this >>full circle to our original conversation around the Virginia data that you guys are looking at the Loudon County amazon contribution. The success former is really commercial public sector. I mean, the government has to recognize that technology is now lingua franca for all things everything society >>well. And one quick thing here that segues into the fact that Virginia is the cloud center of the nation. Um uh the president issued a cybersecurity executive order earlier this year that really emphasizes the migration of federal systems into cloud in the modernization that jOHN has worked on, johN had a group called the Alliance for Digital Innovation and they're very active in the I. T. Modernization world and we remember as well. Um but you know, the federal government is really emphasizing this, this migration to cloud and that was reiterated in that cybersecurity executive order >>from the, well we'll definitely get you guys back on the show, we're gonna say something. >>Just all I'd say about about the executive order is that I think one of the main reasons why the president thought was important is that the legacy systems that are out there are mainly written on kobol. There aren't a lot of kids graduating with degrees in COBOL. So COBOL was designed in 1955. I think so I think it's very imperative that we move has made these workloads as we can, >>they teach it anymore. >>They don't. So from a security point of view, the amount of threats and vulnerabilities are through the >>roof awesome. Well john I want to get you on the show our next cyber security event. You have you come into a fireside chat and unpack all the awesome stuff that you're doing. But also the challenges. Yes. And there are many, you have to keep up the good work on the policy. I still say we got to remove that red line and identified new rules of engagement relative to what's on our sovereign virtual land. So a whole nother Ballgame, thanks so much for coming. I appreciate it. Thank you appreciate it. Okay, cute coverage here at eight of public sector seven Washington john ferrier. Thanks for watching. Mhm. Mhm.

(smooth upbeat music) >> Hello, everybody, John Walls here. Welcome to "theCUBE," in our continuing coverage of Ansible Fest 2021. We now welcome onto "theCUBE," three representatives from Red Hat. Joining us is Ashesh Badani. Who's the Senior Vice President of Products at Red Hat. Ashesh, thank you for joining us today. >> Thanks for having me, John. >> You bet. Also with us Stefanie Chiras, who is the Senior Vice President of the Platforms Business Group also at Red Hat. And Stefanie, how are you doing? >> Good, thanks, it's great to be here with you, John. >> Excellent, thanks for joining us. And last, but certainly not least, Joe Fitzgerald, who is the Vice President and General Manager of the Ansible Business Unit at Red Hat. Joe, good to see you today, thanks for being with us. >> Good to see you again John, thanks for having us. >> It's like, like the big three at Red Hat. I'm looking forward to this. Stefanie, let's just jump in with you and let's talk about what's going on in terms of automation in the hybrid cloud environment these days. A lot of people making that push, making their way in that direction. Everybody trying to drive more value out of the hybrid cloud environment. How is automation making that happen? How's it making it work? >> We have been focused at Red Hat for a number of years now on the value of open hybrid cloud. We really believe in the value of being able to give your applications flexibility, to use the best technology, where you want it, how you need it, and pulling all of that together. But core to that value proposition is making sure that it is consistent, it is secure and it is able to scale. And that's really where automation has become a core space. So as we continue to work our portfolio and our ecosystems and our partnerships to make sure that that open hybrid cloud has accessibility to everything that's new and relevant in this changing market we're in, the automation space that Ansible drives is really about making sure that it can be done in a way that is predictable. And that is really essential as you start to move your workloads around and start to leverage the diversity that an open hybrid cloud can deliver. >> When you're bringing this to a client, and Joe, perhaps you can weigh in on this as well. I would assume that as you're talking about automation, there's probably a lot of, successful head-nodding this way, but also some kind of this way too. There's a little bit of fear, right? And maybe just, they have these legacy systems, there's maybe a little distrust, I don't want to give away control, all these things. So how do you all answer those kinds of concerns when you're talking to the client about this great value that you can drive, but you got to get them there, right? You have to bring them along a bit. >> It's a great question, John, and look, everybody wants to get the hybrid cloud, as Stefanie mentioned. That journey is a little complicated. And if you had silos and challenges before you went to a hybrid cloud, you're going to have more when you got there. We work with a lot of customers, and what we see is this sort of shift from, I would call it low-level task automation to much more of a strategic focus on automation, but there's also the psychology of automation. One of the analysts recently did some research on that. And imagine just getting in your car and letting the car drive you down the street to work. People are still not quite comfortable with that level of automation, they sort of want to be able to trust, but verify, and maybe have their hands near the wheel. You couldn't take the wheel away from them. We see the same thing with automation. They need automation and a lot automation, or they need to be able to verify what it is doing, what they do, what it's going to do. And once they build that confidence, then they tend to do it at scale. And we're working with a lot of customers in that area. >> Joe, you're talking about a self-driving car, that'll never work, right? (laughs) You us bring an interesting point though. Again, I get that kind of surrendering control a little bit and Ashesh, I would assume in the product development world, that's very much your focus, right? You're looking for products that people, not only can use, but they're also comfortable with. That they can accept and they can integrate, and there's buy-in, not only on the engineering level, but also on the executive level. So maybe walk us through that product development, staging or phases, however you want to put it, that you go through in terms of developing products that you think people, not only need, but they'll also accept. >> I think that's absolutely right. You know, I think both Stefanie and Joe, led us off here. I talked about hybrid cloud and Joe, started talking about moving automation forward and getting people comfortable. I think a lot of this is, meeting customers where they are and then helping them get on the journey, right? So we're seeing that today, right? So traditional configuration management on premise, but at the same time, starting to think about, how do we take them out into the cloud, bringing greater automation to bear there. But so that's true for us across our existing customer base, as well as the new customers that we see out there. So doing that in a way that Joe talked about, right? Ensuring the trust, but verify is in play, is critical. And then there's another area which I'm sure we'll talk a little bit more about, right? Is ensuring that security implications are taken into account as we go through it. >> Well, let's just jump into security, that's one of the many considerations these days. About ensuring that you have the secure operation, you're doing some very complex tasks here, right? And you're blending multi-vendor environments and multi-domain environments. I mean you've got a lot, you're juggling a lot. So I guess to that extent, how much of a consideration is security and those multiple factors today, for you. And again, I don't know which one of the three of you might want to jump on this, but I would assume, this is a high priority, if not the highest priority, because of the headlines that security and those challenges are garnering these days. >> Well, there's the general security question and answer, right? So this is the whole, shift-left DevSecOps, sort of security concerns, but I think specific to this audience, perhaps I can turn over to Joe to talk a little bit about how Ansible has been playing in the security domain. >> Now, it's a great way to start, Ashesh. People are trying to shift left, which means move, sort of security earlier on in the process where people are thinking about it and development process, right? So we've worked with a lot of customers who were trying to do DevSecOps, right? And to provide security, automation capabilities during application build and deployment. Then on the operational side, you have this ongoing issue of some vulnerability gets identified, how fast can I secure my environment, right? There's a whole new area of security, orchestration, automation, or remediation that's involved, and the challenge people have is just like with networking or other areas, they've got dozens in some cases, hundreds of different systems across their enterprise that they have to integrate with, in order to be able to close a vulnerability, whether it's deploying a patch or closing a port, or changing firewall configuration, this is really complicated and they're being measured by, okay, there's this vulnerability, how fast can we get secure? And that comes down to automation, it has to. >> Now, Joe, you mentioned customers, if you would maybe elaborate a little bit about the customers that we've been hearing from on the stage, the virtual stage, if you will, at Ansible Fest this year and maybe summarize for our audience, what you're hearing from those customers, and some of those stories when we're talking about the actual use of the platform. >> Yeah, so Ansible Fest is our annual, automation event, right? For Ansible users. And I think it's really important to hear from the customers. We're vendors, we can tell you anything you want and try and get you to believe it. Customers they're actually doing stuff, right? And so, at Ansible Fest, we've got a great mix of customers that are really pushing the envelope. I'll give you one example, JP Morgan Chase. They're talking about how in their environment with focus over the past couple of years, they've now gotten to a level of maturity with automation, where they have over 50,000 people that are using Ansible automation. They've got a community of practice where they've got people in over twenty-two countries, right? That are sharing over 10,000 playbooks, right? I mean, they've taken automation strategically and embraced it and scaled it out at a level that most other organizations are envious of, right? Another one, and I'm not going to go through the list, but another one I'll mention is Discover, which sort of stepped back and looked at automation strategically and said, we need to elevate this to a strategic area for the company. And they started looking at across all different areas, not just IT automation, business process automation, on their other practices internally. And they're doing a presentation on how to basically analyze where you are today and how to take your automation initiatives forward in a strategic way. Those are usually important to other organizations that maybe aren't as far along or aren't on a scale of that motivation. >> Yeah, so Stefanie, I see you nodding your head and you're talking about, when Joe was just talking about assessment, right? You have to kind of see where are we, how mature are we on our journey right now? So maybe if you could elaborate on that a little bit, and some of the key considerations that you're seeing from businesses, from clients and potential clients, in terms of the kind of thought process they're going through on their journey, on their evolution. >> I think there's a lot of sort of values that customers are looking for when they're on their automation journey. I think efficiency is clearly one. I think one that ties back to the security discussion that we talked about. And I use the term consistency, but it's really about predictability. And I think I have a lot of conversations with customers that if they know that it's consistently deployed, particularly as we move out and are working with customers at the edge, how do they know that it's done the same way every time and that it's predictable? There's a ton of security and confidence built into that. And I think coming back to Joe's point, it is a journey providing transparency and visibility is step one, then taking action on that is then step two. And I think as we look at the customers who are on this automation journey, it's them understanding what's the value they're looking for? Are they looking for consistency in the deployments? Are they looking for efficiency across their deployments? Are they looking for ways to quickly migrate between areas in the open hybrid cloud? What is the value they're looking for? And then they look at how do they start to build in confidence in how they deliver that. And I think it starts with transparency. The next step is starting to move into taking action, and this is a space where Joe and the whole team, along with the community have really focused on pulling together things like collections, right? Playbooks that folks can count on and deploy. We've looked within the portfolio, we're leveraging the capabilities of this type of automation into our products itself with Red Hat enterprise Linux, we've introduced systems roles. And we're seeing a lot of by pulling in that Ansible capability directly into the product, it provides consistency of how it gets deployed and that delivers a ton of confidence to customers. >> So, Ashesh I mean, Stefanie was talking about, the customers and obviously developing, I guess, cultural acceptance and political acceptance, within the ranks there. Where are we headed here, past what know now in terms of the traditional applications and traditional automations and whatever. Kind of where is this going, if you would give me your crystal ball a bit about automation and what's going to happen here in the next 12-18 months. >> So what I'm going to do, John, is try to marry two ideas. So we talked about hybrid cloud, right? Stefanie started talking about joining a hybrid cloud. I'm going to marry automation with containers, right? On this journey of hybrid cloud, right? And give you two examples, both some successful progress we've been making on that front, right? Number one, especially for the group here, right? Check out the Ansible collection for Kubernetes, it's been updated for Python Three, of course, with the end-of-life for Python Two, but more important, right? It's the focus on improving performance for large automation tasks, right? Huge area where Ansible shines, then taking advantage of turbo mode, where instead of the default being a single connection to a Culebra API, for every request that's out there with turbo mode turned on, the API connection gets reused significantly and obviously improving performance. Huge other set of enhancements as well, right? So I think that's an interesting area for the Ansible community to leverage and obviously to grow. And the second one that I wanted to call out was just kind of the, again, back to this sort of your notion of the marriage of automation with containers, right? Is the work that's going on, on the front of the integration, the tight integration between Ansible as well as Red Hat's, advanced cluster management, right? Which is helping to manage Kubernetes clusters at scale. So now Red Hat's ACM technology can help our monthly trigger Ansible playbooks, upon key lifecycle actions that have happened. And so taking advantage of technologies like operators, again, core Kubernetes construct for the hybrid cloud environment. This integration between advanced cluster management and Ansible, allows for much more efficient execution of tasks, right? So I think that's really powerful. So wrapping that up, right? This world of hybrid cloud really can be brought together by just a tighter integration between working Ansible as well as the work that's going on on the container plant. >> Great, well, thank you. Ashesh, Stefanie, Joe, thank you all for sharing the time here. Part of our Ansible Fest coverage here, enjoy the conversation and continuous success at Red Hat. Thank you for the time today. >> Thank you so much John. >> Thank you. >> You bet. I'm joined here by three executives at Red Hat, talking about our Ansible Fest 2021 coverage. I'm John Walls, and you're watching "theCUBE." (bright music)

from the cube studios in palo alto in boston bringing you data-driven insights from the cube and etr this is breaking analysis with dave vellante by our estimates aws will generate around nine billion dollars in storage revenue this year and is now the second largest supplier of enterprise storage behind dell we believe aws storage revenue will hit 11 billion in 2022 and continue to outpace on-prem storage growth by more than a thousand basis points for the next three to four years at its third annual storage day event aws signaled a continued drive to think differently about data storage and transform the way customers migrate manage and add value to their data over the next decade hello and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we'll give you a brief overview of what we learned at aws's storage day share our assessment of the big announcement of the day a deal with netapp to run ontap natively in the cloud as a managed service and we'll share some new data on how we see the market evolving with aws executive perspectives on its strategy how it thinks about hybrid and where it fits into the emerging data mesh conversation let's start with a snapshot of the announcements made at storage day now as with most aws events this one had a number of announcements and introduced them at a pace that was predictably fast and oftentimes hard to follow here's a quick list of most of them with some comments on each the big big news is the announcement with netapp netapp and aws have engineered a solution which ports the rich netapp stack onto aws and will be delivered as a fully managed service this is a big deal because previously customers either had they had to make a trade-off they had a settle for cloud-based file service with less functionality than you could get with netapp on-prem or it had to lose the agility and elasticity of the cloud and the whole pay-by-the-drink model now customers can get access to a fully functional netapp stack with services like data reduction snaps clones the full multi-protocol support replication all the services ontap delivers in the cloud as a managed service through the aws console our estimate is that 80 of the data on-prem is stored in file format and that's not the revenue but that's the data and we all know about s3 object storage but the biggest market from a capacity standpoint is file storage you know this announcement reminds us quite a bit of the vmware cloud on aws deal but applied to storage netapp's aunt anthony lai told me dave this is bigger and we're going to come back to that in a moment aws announced s3 multi-region access points it's a service that optimizes storage performance it takes into account latency network congestion and the location of data copies to deliver data via the best route to ensure our best performance this is something we've talked about for quite some time using metadata to optimize that that access aws also announced improvements to s3 tiering where it will no longer charge for small objects of less than 128k so for example customers won't be charged for most metadata and other smaller objects remember aws years ago hired a bunch of emc engineers and those guys built a lot of tiering functionality into their boxes and we'll come back to that later in this episode aws also announced backup and monitoring tools to ensure backups are in compliance with regulations and corporate edicts this frankly is table stakes and was was overdue in my view aws also made a number of other announcements that have been well covered in the press around block storage and simplified data migration tools so we'll leave that to your perusal through other outlets i want to come back to the big picture on the market dynamics now as we've reported in previous breaking analysis segments aws storage revenue is on a path to 10 billion dollars we reported this last year this chart puts the market in context it shows our estimates for worldwide enterprise storage revenue in the calendar year 2021. this data is meant to include all storage revenue including primary secondary and archival storage and related maintenance services dell is the leader in the 60 billion market with aws now hot on its tail with 15 of the market in terms of the way we've cut it now in the pre-cloud days customers would tell us our storage strategy is the following we buy emc for block and netapp for file keeping it simple while remnants of this past habit continue the market is definitely changing as you can see here the companies highlighted in red represent the growing hyperscaler presence and you can see in the pi on the right they now account for around 25 percent of the market and they're growing much much faster than the on-prem vendors well over that thousand basis points when you combine them all a couple of other things to note in the data we're excluding kindrel from ibm's figures that's ibm spinout but including our estimates of storage software for example spectrums protect that is sold as part of the ibm cloud but not reported in ibm's income statement by the way pre-kindred spin ibm storage business we believe would approach the size of netapp's business now in the yellow we've highlighted the portion of hyper-converged that comprises storage this includes vmware nutanix cisco and others vmware and nutanix are the largest hci players but in total the storage piece of that market is less than two billion okay so the way to look at this market is changing traditional on-prem is vying for budgets with cloud storage services which are rapidly gaining presence in the market and we're seeing the on-prem piece evolve of course into as a service models with hpe's green lake dell's apex and other on-prem cloud-like models now let's come back to the netapp aws deal netapp as we know is the gold standard for file services they've been the market leader for a long long time and other than pure which is considerably smaller netapp is the one company that consistently was able to beat emc in the market emc developed its its nas business and developed on its own nasdaq and it bought isilon to compete with netapp with isilon's excellent global file system but generally netapp remains the best file storage company today now emerging disruptors like cumulo vast weka they would take issue with this statement and rightly so as they have really promising technology but netapp remains the king of the file hill you can't debate that now netapp however has had some serious headwinds as the largest independent storage player as seen in this etr chart the data shows a nine-year view of netapp's presence in the etr survey presence is referred to by etr as market share it's not traditional market share it measures the pervasiveness of responses in the etr survey over a thousand customers each quarter so the percentage of mentions essentially that netapp is getting and you can see well netapp remains a leader it has had a difficult time expanding its tam and it's become frankly less relevant in the eye in the grand scheme and the grand eyes of it buyers the company hit headwinds when it began migrating its base to ontap 8 and was late riding a number of new waves including flash but generally it is recovered from those headwinds and it's really now focused on the cloud opportunity opportunity as evidenced by this deal with aws now as i said earlier netapp evp anthony lai told me that this deal is bigger than vmware cloud on aws like me you may be wondering how can that be vmware is the leader in the data center it has half a million customers its deal with aws has been a tremendous success as seen in this etr chart the data here shows spending momentum or net score from when vmware cloud on aws was picked up in the etr surveys with a meaningful n which today is approaching 100 responses in the survey the yellow line is there for context it's vmware's overall business so repeat it buyers who responded vmware versus specifically vmware cloud on aws so you see vmware overall has a huge presence in the survey more than 600 n the red line is vmware cloud on aws and that red dotted line you see that that's that's my magic 40 mark anything above that line we consider elevated net score or spending velocity and while we saw some deceleration earlier this year in that line that top line for vmware cloud vmware cloud and aws has been consistently showing well in the survey well above that 40 percent line so could this netapp deal be bigger than vmware cloud on aws well probably not in our view but we like the strategy of netapp going cloud native on aws and aws's commitment to deliver this as a managed service now where could get interesting is across clouds in other words if netapp can take a page out of snowflake and build an abstraction layer that hides the underlying complexity of not only the aws cloud but also gcp and azure where you log into the netapp cloud netapp data cloud if you will just go ahead and steal steal it from snowflake and then netapp optimizes your on-prem your aws your azure and or your gcp file storage we see that as a winning strategy that could dramatically expand netapp's tam politically it may not sit well with aws but so what netapp has to go multi-cloud to expand that tam when the vmware deal was announced many people felt it was a one-way street where all the benefit would eventually accrue to aws in reality this has certainly been a near-term winner for aws and vmware and of course importantly vmware and aws join customers now longer term it's going to clearly be a win for aws because it gets access to vmware's customer base but we also think it will serve vmware well because it gives the company a clear and concise cloud strategy especially if it can go across clouds and eventually get to the edge so with this netapp aws deal will it be as big probably not in our view but it is big netapp in our view just leapfrogged the competition because of the deep engineering commitment aws has made this isn't a marketplace deal it's a native managed service and we think that's pretty huge okay we're going to close with a few thoughts on aws storage strategy and some other thoughts on hybrid talk about capturing mission critical workloads and where aws fits in the overall data mesh conversation which is one of our favorite topics first let's talk about aws's storage strategy overall as with other services aws approach is to give builders access to tools at a very granular level that means it does mean a lot of apis and access to primitives that are essentially building blocks while this may require greater developer skills it also allows aws to get to market quickly and add functionality faster than the competition enterprises however where they will pay up for solutions so this leaves some nice white space for partners and also competitors and especially the on-prem folks but let's hear from an aws executive i spoke to milan thompson bucheveck an aws vp on the cube and asked her to describe aws's storage strategy here's what she said play the clip we are dynamically and constantly evolving our aws storage services based on what the application and the customer want that is fundamentally what we do every day we talked a little bit about those deployments that are happening right now dave that is something that idea of constant dynamic evolution just can't be replicated by on-premises where you buy a box and it sits in your data center for three or more years and what's unique about us among the cloud services is again that perspective of the 15 years where we are building applications in ways that are unique because we have more customers and we have more customers doing more things so you know i i've said this before uh it's all about speed of innovation dave time and change wait for no one and if you're a business and you're trying to transform your business and base it on a set of technologies that change rapidly you have to use aws services i mean if you look at some of the launches that we talk about today and you think about s3's multi-region access points that's a fundamental change for customers that want to store copies of their data in any number of different regions and get a 60 performance improvement by leveraging the technology that we've built up over over time the the ability for us to route to intelligently router requests across our network that and fsx for netapp ontap nobody else has these capabilities today and it's because we are at the forefront of talking to different customers and that dynamic evolution of storage that's the core of our strategy so as you hear and can see by milan's statements how these guys think outside the box mentality at the end of the day customers want rock solid storage that's dirt cheap and lightning fast they always have and they always will but what i'm hearing from aws is they think about delivering these capabilities in the broader context of an application or a business think deeper business integration not the traditional suppliers don't think about that as well but the services mentality the cloud services mentality is different than dropping off a box at a loading dock turning it over to a professional services organization and then moving on to the next deal now i also had a chance to speak with wayne dusso he's another aws vp in the storage group wayne do so is a long time tech athlete for years he was responsible for building storage arrays at emc aws as i said hired a bunch of emcs years ago and those guys did a lot of tiered storage so i asked wayne what's the difference in mentality when you're building boxes versus cloud services here's what he said you have physical constraints you have to worry about the physical resources on that device for the life of that device which is years think about what changes in three or five years think about the last two years alone and what's changed can you imagine having being constrained by only uh having boxes available to you during this last two years versus having the cloud and being able to expand or contract based on your business needs that would be really tough right and it has been tough and that's why we've seen customers from every industry accelerate uh their use of the cloud during these last two years so i get that so what's your mindset when you're building storage services and data services so so each of the surfaces that we have in object block file movement services data services each of them provides very specific customer value in each are deeply integrated with the rest of aws so that when you need object services you start using them the integrations come along with you when if you're using traditional block we talked about ebs io2 block express when using file just the example alone today with ontap you know you get to use what you need when you need it and the way that you're used to using it without any concern so so the big difference is no constraints in the box but lots of opportunities to blend in with other services now all that said there are cases where the box is gonna win because of locality and and physics and latency issues you know particularly where latency is king that's where a box is gonna be advantageous and we'll come back to that in a bit okay but what about hybrid how does aws think about hybrid and on-prem here's my take and then let's hear from milan again the cloud is expanding it's moving out to the edge and aws looks at the data center as just another edge node and it's bringing its infrastructure as code mentality to that edge and of course to data centers so if aws is truly customer centric which we believe it is it will naturally have to accommodate on-prem use cases and it is doing just that here's how milan thompson-bucheveck explained how aws is thinking about hybrid roll the clip for us dave it always comes back to what the customer is asking for and we were talking to customers and they were talking about their edge and what they wanted to do with it we said how are we going to help and so if i just take s3 for outposts as an example or ebs and outposts you know we have customers like morningstar and morningstar wants outposts because they are using it as a step in their journey to being on the cloud if you take a customer like first adudabi bank they're using outposts because they need data residency for their compliance requirements and then we have other customers that are using outposts to help like dish networks as an example to place the storage as close as account to the applications for low latency all of those are customer driven requirements for their architecture for us dave we think in the fullness of time every customer and all applications are going to be on the cloud because it makes sense and those businesses need that speed of innovation but when we build things like our announcement today of fxs for netapp ontap we build them because customers asked us to help them with their journey to the cloud just like we built s3 and evs for outposts for the same reason so look this is a case where the box or the appliance wins latency matters as we said and aws gets that this is where matt baker of dell is right it's not a zero-sum game this is especially accurate as it pertains to the cloud versus on-prem discussion but a budget dollar is a budget dollar and the dollar can't go to two places so the battle will come down to who has the best solution the best relationships and who can deliver the most rock solid storage at the lowest cost and highest performance let's take a look at mission critical workloads for a second we're seeing aws go after these it's doing a database it's doing it with block storage we're talking about oracle sap microsoft sql server db2 that kind of stuff high volume oltp transactions mission critical work now there's no doubt that aws is picking up a lot of low hanging fruit with business critical workloads but the really hard to move work isn't going without a fight frankly it's not going that fast aws and mace has made some improvements to block storage to remove some of the challenges related but generally we see this is a very long road ahead for aws and other cloud suppliers oracle is the king of mission critical work along with ibm mainframes and those infrastructures generally it's not easy to move to the cloud it's too risky it's too expensive and the business case oftentimes isn't there because very frequently you have to freeze applications to do so what generally what people are doing is they're building an abstraction layer over that putting that abstraction layer maybe in the cloud building new apps that can connect to the back end and the into the cloud but that back end is largely cemented and fossilized look it's all in the definition no doubt there's plenty of mission critical work that is going to move but just really depends on how you define it even aws struggles to move its most critical transaction systems off of oracle but we'll continue to keep an open mind there it's just that today we define the most mission-critical workloads as we define them we don't see a lot of movement to the hyperscale clouds and we're going to close with some thoughts on data mesh so one of our favorite topics we've written extensively about this and interviewed and are collaborating with jamaa dagani who has coined the term and we've announced a media collaboration with the data mesh community and believe it's a strong direction for the industry so we wanted to understand how aws thinks about data mesh and where it fits in the conversation here's what milan had to say about that play the clip we have customers today that are taking the data mesh architectures and implementing them with aws services and dave i want to go back to the start of amazon when amazon first began we grew because the amazon technologies were built in microservices fundamentally a data match is about separation or abstraction of what individual components do and so if i look at data mesh really you're talking about two things you're talking about separating the data storage and the characteristics of data from the data services that interact and operate on that storage and with data mesh it's all about making sure that the businesses the decentralized business model can work with that data now our aws customers are putting their storage in a centralized place because it's easier to track it's easier to view compliance and it's easier to predict growth and control costs but we started with building blocks and we deliberately built our storage services separate from our data services so we have data services like lake formation and glue we have a number of these data services that our customers are using to build that customized data mesh on top of that centralized storage so really it's about at the end of the day speed it's about innovation it's about making sure that you can decentralize and separate your data services from your storage so businesses can go faster so it's very true that aws has customers that are implementing data mess data mesh data mess data mesh can be a data mess if you don't do it right jpmorgan chase is a firm that is doing that we've we've covered that they've got a great video out there check out the breaking analysis archive you'll see that hellofresh has also initiated a data mesh architecture in the cloud and several others are starting to pop up i think the point is the issues and challenges around data mesh are more organizational and process related and less focused on the technology platform look data by its very nature is decentralized so when mylan talks about customers building on centralized storage that's a logical view of the storage but not necessarily physically centralized it may be in a in a hybrid device it may be a copy that lives outside of that same physical location this is an important point as jpmorgan chase pointed out the data mesh must accommodate data products and services that are in the cloud and also on-prem it's got to be inclusive the data mesh looks at the data store as a node on the data mesh it shouldn't be confined by the technology whether it's a data warehouse a data hub a data mart or an s3 bucket so i would say this while people think of the cloud as a centralized walled garden and in many respects it is that very same cloud is expanding into a massively distributed architecture and that fits with the data mesh architectural model as i say the big challenges of data mesh are less technical and more cultural and we're super excited to see how data mesh plays out over time and we're really excited to be part of part of the the community and a media partner of the data mesh community okay that's it for now remember i publish each week on wikibon.com and siliconangle.com and these episodes they're all available as podcasts all you do is search for breaking analysis podcasts you can always connect on twitter i'm at d vellante or email me at david.velante at siliconangle.com i appreciate the comments you guys make on linkedin and don't forget to check out etr.plus for all the survey action this is dave vellante for the cube insights powered by etr be well and we'll see you next time [Music] you

>> From theCUBE studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR. This is braking analysis with Dave Vellante. >> A new era of data is upon us, and we're in a state of transition. You know, even our language reflects that. We rarely use the phrase big data anymore, rather we talk about digital transformation or digital business, or data-driven companies. Many have come to the realization that data is a not the new oil, because unlike oil, the same data can be used over and over for different purposes. We still use terms like data as an asset. However, that same narrative, when it's put forth by the vendor and practitioner communities, includes further discussions about democratizing and sharing data. Let me ask you this, when was the last time you wanted to share your financial assets with your coworkers or your partners or your customers? Hello everyone, and welcome to this week's Wikibon Cube Insights powered by ETR. In this breaking analysis, we want to share our assessment of the state of the data business. We'll do so by looking at the data mesh concept and how a leading financial institution, JP Morgan Chase is practically applying these relatively new ideas to transform its data architecture. Let's start by looking at what is the data mesh. As we've previously reported many times, data mesh is a concept and set of principles that was introduced in 2018 by Zhamak Deghani who's director of technology at ThoughtWorks, it's a global consultancy and software development company. And she created this movement because her clients, who were some of the leading firms in the world had invested heavily in predominantly monolithic data architectures that had failed to deliver desired outcomes in ROI. So her work went deep into trying to understand that problem. And her main conclusion that came out of this effort was the world of data is distributed and shoving all the data into a single monolithic architecture is an approach that fundamentally limits agility and scale. Now a profound concept of data mesh is the idea that data architectures should be organized around business lines with domain context. That the highly technical and hyper specialized roles of a centralized cross functional team are a key blocker to achieving our data aspirations. This is the first of four high level principles of data mesh. So first again, that the business domain should own the data end-to-end, rather than have it go through a centralized big data technical team. Second, a self-service platform is fundamental to a successful architectural approach where data is discoverable and shareable across an organization and an ecosystem. Third, product thinking is central to the idea of data mesh. In other words, data products will power the next era of data success. And fourth data products must be built with governance and compliance that is automated and federated. Now there's lot more to this concept and there are tons of resources on the web to learn more, including an entire community that is formed around data mesh. But this should give you a basic idea. Now, the other point is that, in observing Zhamak Deghani's work, she is deliberately avoided discussions around specific tooling, which I think has frustrated some folks because we all like to have references that tie to products and tools and companies. So this has been a two-edged sword in that, on the one hand it's good, because data mesh is designed to be tool agnostic and technology agnostic. On the other hand, it's led some folks to take liberties with the term data mesh and claim mission accomplished when their solution, you know, maybe more marketing than reality. So let's look at JP Morgan Chase in their data mesh journey. Is why I got really excited when I saw this past week, a team from JPMC held a meet up to discuss what they called, data lake strategy via data mesh architecture. I saw that title, I thought, well, that's a weird title. And I wondered, are they just taking their legacy data lakes and claiming they're now transformed into a data mesh? But in listening to the presentation, which was over an hour long, the answer is a definitive no, not at all in my opinion. A gentleman named Scott Hollerman organized the session that comprised these three speakers here, James Reid, who's a divisional CIO at JPMC, Arup Nanda who is a technologist and architect and Serita Bakst who is an information architect, again, all from JPMC. This was the most detailed and practical discussion that I've seen to date about implementing a data mesh. And this is JP Morgan's their approach, and we know they're extremely savvy and technically sound. And they've invested, it has to be billions in the past decade on data architecture across their massive company. And rather than dwell on the downsides of their big data past, I was really pleased to see how they're evolving their approach and embracing new thinking around data mesh. So today, we're going to share some of the slides that they use and comment on how it dovetails into the concept of data mesh that Zhamak Deghani has been promoting, and at least as we understand it. And dig a bit into some of the tooling that is being used by JP Morgan, particularly around it's AWS cloud. So the first point is it's all about business value, JPMC, they're in the money business, and in that world, business value is everything. So Jr Reid, the CIO showed this slide and talked about their overall goals, which centered on a cloud first strategy to modernize the JPMC platform. I think it's simple and sensible, but there's three factors on which he focused, cut costs always short, you got to do that. Number two was about unlocking new opportunities, or accelerating time to value. But I was really happy to see number three, data reuse. That's a fundamental value ingredient in the slide that he's presenting here. And his commentary was all about aligning with the domains and maximizing data reuse, i.e. data is not like oil and making sure there's appropriate governance around that. Now don't get caught up in the term data lake, I think it's just how JP Morgan communicates internally. It's invested in the data lake concept, so they use water analogies. They use things like data puddles, for example, which are single project data marts or data ponds, which comprise multiple data puddles. And these can feed in to data lakes. And as we'll see, JPMC doesn't strive to have a single version of the truth from a data standpoint that resides in a monolithic data lake, rather it enables the business lines to create and own their own data lakes that comprise fit for purpose data products. And they do have a single truth of metadata. Okay, we'll get to that. But generally speaking, each of the domains will own end-to-end their own data and be responsible for those data products, we'll talk about that more. Now the genesis of this was sort of a cloud first platform, JPMC is leaning into public cloud, which is ironic since the early days, in the early days of cloud, all the financial institutions were like never. Anyway, JPMC is going hard after it, they're adopting agile methods and microservices architectures, and it sees cloud as a fundamental enabler, but it recognizes that on-prem data must be part of the data mesh equation. Here's a slide that starts to get into some of that generic tooling, and then we'll go deeper. And I want to make a couple of points here that tie back to Zhamak Deghani's original concept. The first is that unlike many data architectures, this puts data as products right in the fat middle of the chart. The data products live in the business domains and are at the heart of the architecture. The databases, the Hadoop clusters, the files and APIs on the left-hand side, they serve the data product builders. The specialized roles on the right hand side, the DBA's, the data engineers, the data scientists, the data analysts, we could have put in quality engineers, et cetera, they serve the data products. Because the data products are owned by the business, they inherently have the context that is the middle of this diagram. And you can see at the bottom of the slide, the key principles include domain thinking, an end-to-end ownership of the data products. They build it, they own it, they run it, they manage it. At the same time, the goal is to democratize data with a self-service as a platform. One of the biggest points of contention of data mesh is governance. And as Serita Bakst said on the Meetup, metadata is your friend, and she kind of made a joke, she said, "This sounds kind of geeky, but it's important to have a metadata catalog to understand where data resides and the data lineage in overall change management. So to me, this really past the data mesh stink test pretty well. Let's look at data as products. CIO Reid said the most difficult thing for JPMC was getting their heads around data product, and they spent a lot of time getting this concept to work. Here's the slide they use to describe their data products as it related to their specific industry. They set a common language and taxonomy is very important, and you can imagine how difficult that was. He said, for example, it took a lot of discussion and debate to define what a transaction was. But you can see at a high level, these three product groups around wholesale, credit risk, party, and trade and position data as products, and each of these can have sub products, like, party, we'll have to know your customer, KYC for example. So a key for JPMC was to start at a high level and iterate to get more granular over time. So lots of decisions had to be made around who owns the products and the sub-products. The product owners interestingly had to defend why that product should even exist, what boundaries should be in place and what data sets do and don't belong in the various products. And this was a collaborative discussion, I'm sure there was contention around that between the lines of business. And which sub products should be part of these circles? They didn't say this, but tying it back to data mesh, each of these products, whether in a data lake or a data hub or a data pond or data warehouse, data puddle, each of these is a node in the global data mesh that is discoverable and governed. And supporting this notion, Serita said that, "This should not be infrastructure-bound, logically, any of these data products, whether on-prem or in the cloud can connect via the data mesh." So again, I felt like this really stayed true to the data mesh concept. Well, let's look at some of the key technical considerations that JPM discussed in quite some detail. This chart here shows a diagram of how JP Morgan thinks about the problem, and some of the challenges they had to consider were how to write to various data stores, can you and how can you move data from one data store to another? How can data be transformed? Where's the data located? Can the data be trusted? How can it be easily accessed? Who has the right to access that data? These are all problems that technology can help solve. And to address these issues, Arup Nanda explained that the heart of this slide is the data in ingestor instead of ETL. All data producers and contributors, they send their data to the ingestor and the ingestor then registers the data so it's in the data catalog. It does a data quality check and it tracks the lineage. Then, data is sent to the router, which persists the data in the data store based on the best destination as informed by the registration. This is designed to be a flexible system. In other words, the data store for a data product is not fixed, it's determined at the point of inventory, and that allows changes to be easily made in one place. The router simply reads that optimal location and sends it to the appropriate data store. Nowadays you see the schema infer there is used when there is no clear schema on right. In this case, the data product is not allowed to be consumed until the schema is inferred, and then the data goes into a raw area, and the inferer determines the schema and then updates the inventory system so that the data can be routed to the proper location and properly tracked. So that's some of the detail of how the sausage factory works in this particular use case, it was very interesting and informative. Now let's take a look at the specific implementation on AWS and dig into some of the tooling. As described in some detail by Arup Nanda, this diagram shows the reference architecture used by this group within JP Morgan, and it shows all the various AWS services and components that support their data mesh approach. So start with the authorization block right there underneath Kinesis. The lake formation is the single point of entitlement and has a number of buckets including, you can see there the raw area that we just talked about, a trusted bucket, a refined bucket, et cetera. Depending on the data characteristics at the data catalog registration block where you see the glue catalog, that determines in which bucket the router puts the data. And you can see the many AWS services in use here, identity, the EMR, the elastic MapReduce cluster from the legacy Hadoop work done over the years, the Redshift Spectrum and Athena, JPMC uses Athena for single threaded workloads and Redshift Spectrum for nested types so they can be queried independent of each other. Now remember very importantly, in this use case, there is not a single lake formation, rather than multiple lines of business will be authorized to create their own lakes, and that creates a challenge. So how can that be done in a flexible and automated manner? And that's where the data mesh comes into play. So JPMC came up with this federated lake formation accounts idea, and each line of business can create as many data producer or consumer accounts as they desire and roll them up into their master line of business lake formation account. And they cross-connect these data products in a federated model. And these all roll up into a master glue catalog so that any authorized user can find out where a specific data element is located. So this is like a super set catalog that comprises multiple sources and syncs up across the data mesh. So again to me, this was a very well thought out and practical application of database. Yes, it includes some notion of centralized management, but much of that responsibility has been passed down to the lines of business. It does roll up to a master catalog, but that's a metadata management effort that seems compulsory to ensure federated and automated governance. As well at JPMC, the office of the chief data officer is responsible for ensuring governance and compliance throughout the federation. All right, so let's take a look at some of the suspects in this world of data mesh and bring in the ETR data. Now, of course, ETR doesn't have a data mesh category, there's no such thing as that data mesh vendor, you build a data mesh, you don't buy it. So, what we did is we use the ETR dataset to select and filter on some of the culprits that we thought might contribute to the data mesh to see how they're performing. This chart depicts a popular view that we often like to share. It's a two dimensional graphic with net score or spending momentum on the vertical axis and market share or pervasiveness in the data set on the horizontal axis. And we filtered the data on sectors such as analytics, data warehouse, and the adjacencies to things that might fit into data mesh. And we think that these pretty well reflect participation that data mesh is certainly not all compassing. And it's a subset obviously, of all the vendors who could play in the space. Let's make a few observations. Now as is often the case, Azure and AWS, they're almost literally off the charts with very high spending velocity and large presence in the market. Oracle you can see also stands out because much of the world's data lives inside of Oracle databases. It doesn't have the spending momentum or growth, but the company remains prominent. And you can see Google Cloud doesn't have nearly the presence in the dataset, but it's momentum is highly elevated. Remember that red dotted line there, that 40% line, anything over that indicates elevated spending momentum. Let's go to Snowflake. Snowflake is consistently shown to be the gold standard in net score in the ETR dataset. It continues to maintain highly elevated spending velocity in the data. And in many ways, Snowflake with its data marketplace and its data cloud vision and data sharing approach, fit nicely into the data mesh concept. Now, a caution, Snowflake has used the term data mesh in it's marketing, but in our view, it lacks clarity, and we feel like they're still trying to figure out how to communicate what that really is. But is really, we think a lot of potential there to that vision. Databricks is also interesting because the firm has momentum and we expect further elevated levels in the vertical axis in upcoming surveys, especially as it readies for its IPO. The firm has a strong product and managed service, and is really one to watch. Now we included a number of other database companies for obvious reasons like Redis and Mongo, MariaDB, Couchbase and Terradata. SAP as well is in there, but that's not all database, but SAP is prominent so we included them. As is IBM more of a database, traditional database player also with the big presence. Cloudera includes Hortonworks and HPE Ezmeral comprises the MapR business that HPE acquired. So these guys got the big data movement started, between Cloudera, Hortonworks which is born out of Yahoo, which was the early big data, sorry early Hadoop innovator, kind of MapR when it's kind of owned course, and now that's all kind of come together in various forms. And of course, we've got Talend and Informatica are there, they are two data integration companies that are worth noting. We also included some of the AI and ML specialists and data science players in the mix like DataRobot who just did a monster $250 million round. Dataiku, H2O.ai and ThoughtSpot, which is all about democratizing data and injecting AI, and I think fits well into the data mesh concept. And you know we put VMware Cloud in there for reference because it really is the predominant on-prem infrastructure platform. All right, let's wrap with some final thoughts here, first, thanks a lot to the JP Morgan team for sharing this data. I really want to encourage practitioners and technologists, go to watch the YouTube of that meetup, we'll include it in the link of this session. And thank you to Zhamak Deghani and the entire data mesh community for the outstanding work that you're doing, challenging the established conventions of monolithic data architectures. The JPM presentation, it gives you real credibility, it takes Data Mesh well beyond concept, it demonstrates how it can be and is being done. And you know, this is not a perfect world, you're going to start somewhere and there's going to be some failures, the key is to recognize that shoving everything into a monolithic data architecture won't support massive scale and agility that you're after. It's maybe fine for smaller use cases in smaller firms, but if you're building a global platform in a data business, it's time to rethink data architecture. Now much of this is enabled by the cloud, but cloud first doesn't mean cloud only, doesn't mean you'll leave your on-prem data behind, on the contrary, you have to include non-public cloud data in your Data Mesh vision just as JPMC has done. You've got to get some quick wins, that's crucial so you can gain credibility within the organization and grow. And one of the key takeaways from the JP Morgan team is, there is a place for dogma, like organizing around data products and domains and getting that right. On the other hand, you have to remain flexible because technologies is going to come, technology is going to go, so you got to be flexible in that regard. And look, if you're going to embrace the metaphor of water like puddles and ponds and lakes, we suggest maybe a little tongue in cheek, but still we believe in this, that you expand your scope to include data ocean, something John Furry and I have talked about and laughed about extensively in theCUBE. Data oceans, it's huge. It's the new data lake, go transcend data lake, think oceans. And think about this, just as we're evolving our language, we should be evolving our metrics. Much the last the decade of big data was around just getting the stuff to work, getting it up and running, standing up infrastructure and managing massive, how much data you got? Massive amounts of data. And there were many KPIs built around, again, standing up that infrastructure, ingesting data, a lot of technical KPIs. This decade is not just about enabling better insights, it's a more than that. Data mesh points us to a new era of data value, and that requires the new metrics around monetizing data products, like how long does it take to go from data product conception to monetization? And how does that compare to what it is today? And what is the time to quality if the business owns the data, and the business has the context? the quality that comes out of them, out of the shoot should be at a basic level, pretty good, and at a higher mark than out of a big data team with no business context. Automation, AI, and very importantly, organizational restructuring of our data teams will heavily contribute to success in the coming years. So we encourage you, learn, lean in and create your data future. Okay, that's it for now, remember these episodes, they're all available as podcasts wherever you listen, all you got to do is search, breaking analysis podcast, and please subscribe. Check out ETR's website at etr.plus for all the data and all the survey information. We publish a full report every week on wikibon.com and siliconangle.com. And you can get in touch with us, email me david.vellante@siliconangle.com, you can DM me @dvellante, or you can comment on my LinkedIn posts. This is Dave Vellante for theCUBE insights powered by ETR. Have a great week everybody, stay safe, be well, and we'll see you next time. (upbeat music)

>>okay. >>Just about a year ago, governments around the world forced shutdowns of their respective economies. We've never seen anything like it. Central banks took immediate action and effective monetary policy like none we've ever seen before. They dropped interest rates to near zero, injected a huge amount of cash into the system, and they fueled this liquidity boom to support those individuals and businesses that were in greatest need. Banks were overwhelmed with the volume of paperwork, for instance, small business P, P P loans and other things. Home buying boomed as mortgage rates hit all time lows for several weeks in the spring, it was complete chaos, but the tech industry stepped up and accommodated work from home. Cloud infrastructure was spun up instantly as access to data centers was really restricted, and Saas companies became a fundamental staple of not only keeping the lights on but helping customers thrive in the face of a pandemic. Automation became a >>mandate >>as humans, they couldn't possibly keep up with the tidal wave of demand, a document overload that was hitting the system. Now, one of the companies that was there to help financial firms in particular, get through the knothole was Oculus, a company that focuses on intelligent automation to deploy the power of machines to allow humans to focus on what they do best. Hello, everyone. And welcome to this cube conversation. My name is Dave Volonte, and we're profiling the most interesting SAS startups that are reimagining how we work. And with me is Sam Bobbly, the co founder and CEO of Oculus. Sam, welcome to the Cube. First time. >>Hey, Dave. Thanks so much for having me excited to have the conversation. >>Yeah, me too. So, listen, I know you've told the story of a zillion times, but I want a community here. How and why did you start the company >>for sure. So when I was in college, I was having a conversation with my dad. Uh, he was telling me about a meeting he just had with his elder law attorney. And the other law attorney was complaining about having to review hundreds or thousands of pages of financial documents for every long term care Medicaid application. When you apply for Medicaid coverage to enter a nursing home, you're required to submit 60 months of financials along with your application. And traditionally the elder law attorney or a nursing home would review those documents literally page by page, line by line to find high value transactions, transfers and other financial trends. And when I heard about this, it just it didn't make sense to me. I said, You know why? In this day and age isn't there? Why isn't there a technology solution that can ingest the documents and spit out a digital report replacing the cumbersome manual page by page review? So it really just started as a research project, trying to learn more about optical character recognition, which is the technology of transforming images into text. And, you know, as we kind of kicked around different products in the market, we we realized that there was an opportunity to build a unique platform that could ingest documents of any format quality and produce perfectly accurate results. And that was the genesis behind what ultimately became Oculus. >>You were a young man at this time. How old were you at that time? >>I was 22 when we started >>so fearless. And, uh, now my friend Eddie Mitchell started a company about 20 years ago. We hacked together a >>Dell >>system and this camera. It was all about the modern operating room in the future, and he showed it to a doctor and and it was just a prototype, she said. How much? He said 10 grand. She wrote a check right there. You have a similar story? How did you see the company? >>So we we we do have a pretty similar experience. You know, Our our concept was we want to get perfect results the customer every time. So if a customer sends us a clean bank statement from Chase or a blurry cell phone image with someone's thumb in the picture from a community bank in Maine, and it's rotated sideways or upside down like we want to give consistent, perfectly active results every single time. And you know, our our view was to completely solve the business problem. So the very first version of the software that we built, we had a rudimentary machine process to extract 60 or 70% of the data, and then we had a little tool built on the back end, where literally, me, myself and some of our early employees would clean up the data output, make sure it's perfect and then return When we couldn't submit, we'd returned to the customer accurate data that could be used at the time for for a Medicaid decision. And what happened is, while we were in our beta period, customers fell in love with the product. They felt it was magical and really just superior from an accuracy standpoint to anything they had ever tested before. And And one of our beta testers said to us, uh, where do I submit credit card information? So at that time, I turned to my colleagues and I said, I think we're ready to I think we're ready. Start charging for this thing and and roll it out for prime time. >>When I was researching the company, I learned that you leveraged. At least some of the idea came from the capture, and I never knew this, But the capture that we all hate came from Google where they write, they had at one point you could maybe you still can. You can go online. You can read books and have to It's just scanned. You can't even read the stuff half the time. So they were putting the capture in front of us, quite brilliant to try to solve for those those those white spaces that they didn't know. So So how did you learn from that? Was there an A P I that you could plug into Google's data set, or did you do your own? What was that? How did that all work? >>The the concept of humans in the loop is super powerful, right? So when we first started, we recognize that OCR and machine data capture couldn't do the job completely. OCR, generally speaking, can process financial documents with roughly 80 to 85% accuracy plus or minus machines, particularly struggled with semi structured and unstructured documents where the format is unpredictable as well as lower quality images. So pretty early on, we recognized that we needed human intervention in the process in order to achieve perfect accuracy every single time, and also to create training data to constantly teach our machine learning models to get smarter and drive additional automation. So, as I mentioned, the very first version was myself and other employees verifying the data on our own. But as we started thinking about how to scale this up and, you know, take on millions and millions of documents, we needed to, uh, you know, learn how to better parallelized task and really build the system for for efficiency and for scanning. So we we we learned about the Google Books Initiative and their ability to leverage capture technology and a distributed workforce to verify pieces of information that their systems couldn't automatically read from books. And we took a lot of those learnings into building our human in the loop infrastructure. And, you know, a way to think about our our product is it's the marriage of machines and humans that makes us unique. As much of the heavy lifting as we can do with machines we do. But whatever we can't do automatically, we slice into smaller tasks and intelligently route those tasks to humans to perform verification. We then layer in algorithmic checks to make sure our humans did the review correctly. The customer gets perfect results, and that same perfect output is used in a feedback loop to train our machine learning models to get smarter and smarter, which dynamically improves the product on an ongoing basis. And, you know, the folks at Google were we're onto this pretty early with the capture technology, and we were following in their footsteps with our own unique take on it, but specifically applying it to financial documents. >>I mean on the Cube. We know a lot about this because we were looking at transcriptions of video all the time, and it just keeps getting better and better and better in our systems. Get smarter and smarter, smarter. So we're sort of closing that gap between what humans can can do and machines can't. And I would expect that you're seeing the same thing. I mean, you think there's always going to be kind of humans in the loop in terms of the quality or is that gap going to be, you know, six nines in the, you know, near near term. >>I think it's gonna take a while to get rid of all the edge cases. You know, you mentioned the PPB program like we've been on the back end processing P p P loans for some of the leading players, like Cross River Bank, blue Vine, Square Capital and others. And you know, what we've seen during the ppb process is just a a wide variety of different documents and inputs, Uh, and a lot of difficult to read documents that are, you know, very challenging to automate. So I think we will, you know, incrementally continue to automate more and more of the process. But the value of having humans plus machines is much more powerful than just having machines alone or just having humans alone. And as it relates to the end customer, our our goal is to do as much of the mundane work as possible to free our customer up to do the more cerebral analysis. So in a lending context and and for the record, you know, our our biggest market opportunity is in the limbic space. Despite the fact that we started with medicating attorneys, we quickly pivoted and realized that our technology was super valuable to to lenders to help them automate the underwriting process. And our our thesis is, if we can take out all of the necessary evils like document review and allow underwriters to focus on the actual analysis of financial health, it's a win win win and creates a really fantastic, complementary relationship between us and our customers. >>Yes, I want to ask you about the pivot to financial services. You said you started well, you have the inspiration from elder law because Jimmy McGill. Okay. Saul Goodman breaking bad. You got started. An elder law. But then you made the pivot to financial services. Really pretty early on. You had really good, great product market fit, but you kind of went for it. I get early twenties. You know, you didn't have a big family at the time. I didn't have a lot of a lot of risks. So you went for it, right? But talk about that pivot because a lot of companies wouldn't do that. They get comfortable and just, you know, stay where they're at. But you made that >>call. It was a big risk, for sure. I mean, look, the product was working. We launched the paid version of our product in 2016. Pretty quickly were onboarding dozens of accountants and attorneys, you know, doing Medicaid work. Um, in mid to late 2016, we got introduced to a large small business lender in New York City called strategic funding Source. They've since renamed them their company Capital as the current name, but we met with the CEO and the head of product and showed them a demo of the technology. And they said, You know, quote unquote, we've been looking for this for years. We've been looking for something exactly like this for years, and we said back to them about how many pages of financial documents to review every single month. They pointed out to a bullpen of dozens of people sitting there tearing through bank statements, page by page, line by line. And they said, You know, it's hundreds of thousands. My eyes almost fell out of my head. I couldn't believe the volume, and it was much bigger than what the, you know, single accountants or attorneys were doing. Uh, so we made the strategic decision to pivot at that time and focus on FINTECH. Lenders continue to tailor the product and build additional features for the fintech lending space. And and, you know, lending in general had the perfect mix of short sale cycle and high average customer value that allowed a company like ours to scale and ramp our revenue quite quite quickly. Um, and then the other thing that happened is kind of as we were getting deeper and deeper into the space, the fintech space as a whole started growing massive. So we we kind of had the perfect storm of product market fit, plus the market growing that allowed us to really ramp significantly grow revenue. And, uh, you know, despite the fact that it was the risk it was, it was totally right. Decision to to focus the business on financial services >>much bigger Tam. And you could subjectively measure it by the size of the stack of papers. Um, how how does this relate to our p A. As you know, the R p. A hot space. You probably get this question a lot, and it sounds like there are some similarities with software bots. What's the similarity? What's the difference? >>Good question. It's It's totally a synergistic offering, right? So rrp a companies like UI path and automation anywhere they typically provide a horizontal toolkit to allow you know, banks and lenders to automate much of the mundane work like, for example, collecting information from emails or doing onboarding for a new employee. Or, you know, different types of tasks that a manual worker would have done but could be automated with relatively simple code. Um, what happens in our p a. Workflows is they get hung up on tasks that can't be completely automated. So, for example, a robot might be, uh, trying to complete an intend lending flow. But when a bank statement is submitted as part of that flow, the robot can't parse it. So what they do instead, is they routed to an underwriter who performs a manual analysis, keys information into a back office system that the bank is using and that information then gets handed back to a robot and continues the automation flow. What we do is we plug the gaps that used to be manual so a robot can pass US documents like bank statements or pay stubs or tax stops. We run our unique human in the loop process. We return structure Jason output directly to a robot, and it continues into the, you know, to the next step of the flow. And, you know, in in summary, the combination of robotic process automation and human in the loop, which is what we're doing, creates true and and automated flows rather than R P. A mite by itself might get you 80% of the way there. >>So do you have, like, software integrations or partnerships with those companies. How are you integrating with them? >>We do. We have software integrations with both UI path and automation anywhere in our core fintech lending business. R P A isn't as prevalent, but we are now expanding beyond fintech lenders into mortgage lending and traditional banks. And we're also expanding use cases, right? Like historically small business lending was the core of our business. More recently, we've moved into consumer auto mortgage, additional asset classes. And as we've gotten deeper with financial institutions, we've seen even more opportunity to partner and coexist with broader r p a player's >>Yeah, great. I mean, I was just staring at their s one. I guess it was came up Monday. Half over half a billion dollars in a are are they're actually cash flow positive as you iPad. So we're going to see we're going to see them hit the public market shortly. Um, hang on, folks. Uh, now it's okay. So this is you sell a sas, right? A SAS service. Even though there's that human in the loop, that's all part of the service. How do you How do you price? >>So usage based model. So we we kind of try to model are themselves nerve. A massive company is super powerful. We apply that same concept document processing, so it's a usage based model. Customers will pay us either per application per document or per page, and if they want to subscribe for, you know, one document per month or millions of documents per month, it's up to them. And we're able to flex up and flex down to meet the supply and demand. Um and that that concept that scalability and flexibility was particularly powerful in the P P P program, right? P P. P. Was kind of a very unique situation in the sense that lenders weren't able to predict the amount of loans they needed to process in normal lending. A small business lender can tell you Hey, we expect to get roughly 10,000 applications in the month of April with P P p. They could tell us, Hey, we're going to send out 200,000 marketing emails and we expect 30% of people might reply, but we really don't have any idea, right? So what happened is the big banks ended up hiring without exaggeration. Thousands of temporary employees to come in and review documents and kind of scrambled to do this in a work from home setting during the pandemic. Whereas Cross River, they took a technology first approach. They implemented our A P I in the back end, and it enabled them to instantly scale up their resources. And the result of that is Cross River ended up becoming a top three pp, a top three p p. P lender nationally, outperforming many of the big banks with a super efficient and fast document review process. Because we were able to help them on the back end with the automation. >>That's awesome. I love the pricing model you mentioned. You mentioned Amazon. Is that the cloud you use or >>we do Our Our product is hosted in AWS and we, you know, take a lot of learnings from them from a business model and and positioning point of view. >>Yeah, and and I'm thrilled to hear you say I mean, I think a lot of forward thinking startups are doing the consumption model. I mean, you certainly see that with companies like snowflake and data dog and stripe. I mean, I think that that SAS model of okay, we're gonna lock you into a one year, two year, three year term. Sorry if if you get acquired, you're stuck with some, you know, stranded licenses. That's your problem. I think that, you know, you really thought that out. Well, um, you mentioned you're sort of expanding your your your total available market now, looking at at new markets, what are some of the big trends that you want to ride over the coming decade as you scale your company? >>The biggest one for us is mortgage automation. You know, the kind of the one of fintech small business and consumer loans were optimized, and we went from a place where, uh, you know, you would deal with a loan officer and have an in person transaction to modern day. You can get a loan from small business. If you're a small business, you can get a loan from PayPal online effectively instantly. If you're a consumer, you can get a loan from Sofia or lending club super smooth digital experience and really revolutionized the way that you know, the market thinks about financial products. I think the next wave of that is mortgage, and that's what we're focused on. Uh, you know, mortgage is a massive market in the sense of thousands of lenders. The average application contains a couple 100 pages worth of financial documents, and the pain points of the back end of the mortgage process were really accentuated. During covid, right refi Valium went way up and mortgage lenders were forced to process that volume in a work from home setting. So what happened is mortgage lenders were struggling with the concept of sending personally identifiable financial information to underwriters who aren't working in an office there, working at home and, you know, kids running around a million things going on. And it's just more difficult to manage than ever before. Um, and you know, as as the the volume kind of normalized debate and mortgage lenders thought about their own future of automation, I think there was just clear recognition across the board that these these mortgage lenders needed to learn from some of the fintech and really focus on automating the back office peace and you know, to your point earlier about business model, what we think about is translating cost that used to be a fixed cost and turning them into a variable costs So now, instead of worrying about having to match supply and demand and hire or fire people, depending on the volume that's coming in on any given month, a mortgage lender can instantly flex up, reflects down and have a super fast, accurate process to handle the darks. Um, and you know, we're seeing just awesome traction in the market with that with the mortgage space and we're excited to push >>forward there. That's great. Thank you. I mean you, Sam. You describe the chaos that work from home. The financial industry is very overly officious. If you know it's very security conscious. How do you handle security? Maybe you could comment on that. How you think about that? >>Sure. I mean, we we take a compliance first approach. We built the product from the ground up with compliance in mind, knowing that we were selling into financial institutions. We have a sock to type one and type two certification, which is, you know, an industry standard. All of our our verification happens with the Oculus employees. So there's no third parties involved in our process whatsoever. Um and then lastly, But perhaps most importantly, our product in and of itself is innately, um, you know, innately drives compliance. So every data point that we process from a financial document, we not only return the data, we return an exact bounding box coordinates of where that data field appeared on the original source so that that audit trail lives with the loan throughout its life cycle. What we saw prior to Oculus is a mortgage would go through an underwriting process. They make a decision, and then that loan might be sold downstream and a diligence firm as to come in. And they don't have the resources to review all the loans. So they review 15% of the loan tape and then they say, you know, they give a rating and what we do is we proactively tackle that by creating a a perfect audit trail upon origination that can live with the loan throughout its life cycle and that that process and that traceability has been super valuable to our mortgage and banking partners. >>So you can ensure the providence there. So let me end just by talking about the company a little bit. So you incubated you nailed the product market fit the and you pivoted and re nailed the product market fit. And like a lot of companies in your position, I would imagine you saw your growth come from just having a great product. You know, initially, word gets around, but then you got a scale. Uh, maybe you could talk a little bit about how how you did that. How you're doing that. You know where your hiring how you're hiring, what your philosophy is on on scaling. >>Sure. Look, I think the key for us is just surrounding ourselves with the right people. You know, the right mentors, advisors and investors to help us really take the business to the next level. Uh, you know, we had no pride of authorship. We're building this and recognize that there are a lot of people out there who have been there, done that and can really guide us and show us the way. I know you had interviewed Marc Roberge on on the show previously. Formerly the C r. O of hubspot. Mark was someone that we you know, we we read his book and had taken sales advice from him from an early age. And over the over time, we got him a little bit more familiar with the company. And and ultimately, Mark and his partner, J Po at Stage two Capital ended up investing in Oculus and really helping us understand how to build the right go to market engine. Um, as the company got bigger, we took on investments from really reputable firms in the financial services space. So our largest investors are okay, H C F T fintech collective and and QED investors. Uh, you know, QED was was founded by Nigel Morris, who is the co founder of Capital One. They backed Sophia and Prosper and a lot of big fintech lenders and, you know, bringing the collective expertise from the fintech sector as well as you know, from a sales and go to market strategy. Point of view created the right mix of ingredients for us to to really ramp up significantly. Uh, we had an awesome run over the years. We were pretty recently recognized by magazine as the number one fastest growing fintech company. And, you know, as the momentum is increased and the market conditions have been very favorable to us, we we just want to double down and expand. Mortgage is the biggest area of opportunity for us. And what we're seeking from a hiring perspective is, you know, go to market sales account executive type resources on the mortgage side as well as you know, deeper products expertise both on the mortgage side as well as with machine learning our product. Because we have the human in the loop piece, we create massive amounts of training data on a daily basis. So it's a, I think, a really exciting place for cutting edge machine learning developers to come and and innovate. >>What can you share with our audience about, you know, your company, any metrics and whatever you're comfortable with, how much money you've raised on my head count? If you want to get some companies comfortable giving a r r others on. But what what do you What can you share with us? >>Sure. Um, you know, we we've raised about 50 million in venture capital. We have grown from one to north of 20 million in revenue in the in the last three years. So particularly since you know, 2017, 2018 is what we really started to see. The growth take off, uh, company size. We have about 800 to 900 employees globally. Now we have about 200 corporate employees who perform the, you know, the the day to day functions of Oculus. And then we have a long tail of about 600 or so verifiers who perform data verification and quality control work again, Speaking to the human in the loop piece of the bottle. Uh, we're, you know, we're focused on expanding beyond the fintech customer base, where we serve customers like plaid PayPal lending club so fi square, etcetera into the mortgage space and ultimately into the traditional banking space where you know, the problems, frankly, are extremely similar. Just on a much larger scale. >>San Bobbly. Congratulations on all the success. You You've got a great road ahead. I really appreciate you coming on the Cube, >>Dave. Thanks so much. It's been a great chat. Look forward to keeping in touch. >>Alright, Did our pleasure. Thank you for watching everybody. This is Dave Volonte for the Cube. We'll see you next time