(bright, upbeat music) >> Thank you, Adam, you're looking great in the studio. Those clouds going behind you in that beautiful blue sky. Okay. We're excited here at the Fira in Barcelona at Mobile World Congress 21. Yes, it's on. Yes, it's alive and I'd say it's pretty well. Andriy Zhylenko is here as the CEO of Porta One and Roman Khalenkov is joining us as well He's the Chief Commercial Officer of Porta One. Gents, great to see you. Thanks for coming on the Cube. >> Thank you very much for having us. >> You're very welcome. You guys are local Barcelonans now. That's awesome. You've came in from Russia. You had this great idea for a company. Tell us about Porta One. >> Well, Porta One exists for over 20 years and we focus on helping Telco operators to deliver services more efficiently or create something new by providing an open architecture platform. And we mostly focus on tier two and three operator. So, I think about us as this weapon they can use to fight the Goliath; the large telecom operators because they need flexibility and the ability to get there faster. >> I mean, I love that, right. And we're going to talk about the cloud is a key part of that because you're now giving the smaller operators the capabilities that the big guys have had but actually doing it a way that may be cleaner and more agile, it's cloud based, they can price differently. It's a whole new ball game, right? I mean, what are you seeing when you talk to customers? What's that? What's the initial conversation like? >> Well, people still, to some extent, are afraid of the cloud but we try to give them different options on premises or in the cloud. It's a software after all. >> Dave: What, what are they afraid of with the cloud? >> They're afraid of not having the full control and usually people are afraid of things, which they don't completely understand and I guess having us here helps them to overcome that fear. >> Well, we saw this with the traditional enterprise IT when we used to have financial services executives on the cube. 10 years ago, they go, we will never put our data in the cloud. It's never going to happen. It was financial services, one of the fastest growing and largest customer segments for the cloud. But you're focusing on, you say, the tier two and tier three, I would think they have a greater motivation, right? Because they see the opportunity to disrupt. Right? >> That's true. I see cloud and other technologies such as SDN as this great equalizer because now it doesn't matter that much how much of the fiber optics you have in the ground or how many base towers you have. The true advantage will come from your platform, from the application and the service you can create. And if there's a company, they can create a great service, if it's in the cloud, it can scale to millions of subscribers easily, they just to find that product market fit. >> And Roman, you've got almost 500 customers, I believe. >> Yes. All around the globe. >> Well, that's the interesting thing, you got like 90 customers or more and so, >> 90 countries >> 90 countries, I meant 500 customers in 90 countries. So you've got local laws, you've got local politics, public policy, different across those countries, you know, provenance etc. etc. How do you see - what's the spectrum like are they open to the tier two and tier three disrupting? I mean, I would imagine some countries are trying to protect, you know, their relationships with the big Telcos because it's such critical infrastructure. What's that spectrum look like? Paint a picture of that diversity. >> It all depends on the specific country. In some countries like South Africa, the market is totally liberalized. You want to become a Telco. Here you go. In other countries like China, for example, it's only for a very small group of national carriers. So we basically follow the lead of the customers. If there are an opportunity in the specific countries, they will pop up like mushrooms. If there is no market liberation, what can you do? >> Right. Okay. So now talk more about what you guys sell to these customers. You're talking about the BSS systems and what exactly am I buying from you? And how is that all working? >> We sell the ability to manage your subscribers, create new services, and then provision and deliver those services to a variety of network elements, equipment and through integrations, and through connections to various types of apps. And right now with the cloud move, I see this as an- it's a challenge and an opportunity at the same time. If Telco has existing infrastructure that's our chance to rethink the architecture and approach. Because if they just think we have a cloud, it's some kind of computer where I'm going to run the applications a bit cheaper, they're missing the point. We were born in Soviet Union and one of my treasures is the jokes from Soviet Union times is one of them is a lady writes to the Central Committee of Communist Party and she says, I work at the Moscow Teapot Factory. And I like my job, I like my colleagues, I'm employee of the month, but, what bothers me; I can never buy a teapot in my store. I go there but they never have teapots. Can you do something? And she receives a reply saying, well, we can not change the way how we distribute goods in the whole country but there's an exception that will allow you to take one part of teapot, bring it home, and you can assemble teapot for yourself. And then two months later, there's now a letter from the same lady saying, Dear comrades, I did as you told me and now in my backyard, I have an intercontinental ballistic missile SS20 but I still don't have a teapot. So you cannot replicate what already had to just bring it piece by piece into the cloud and expect it's going to be something different, it's going to be better. >> Dave: We call it the Lunar Landing Module, very complex. Okay! Let's talk about the move from and the journey from on-prem maybe through hybrid but to the cloud, ultimately, and it starts with the customer conversation. First of all, they got to be willing. Right? Okay. But what's that journey look like? What are the phases that we should- how should we think about that? >> Over the last 20 years we've been offering our platform on premises and usually with unlimited license. So, whatever you can squeeze out of your physical machines is all yours. We don't count that. And that was a pretty straightforward model because you own your servers. We give you the license to the product, and it's fully separated. In the cloud it's not possible by default. You will provide both the physical infrastructure and software infrastructure. So, we need to change that model and we need to explain to our customers first of all. The next step; no Telco is the same. So, they provide different set of services. They offer their products to different audiences of the end-users. So it can be hosted PPBX or IP Centrics environments. So, we would then price our platform based on the number of active seats or it can be a mobile operator, a full mobile network operator or virtual mobile operator MVNO, or even enabler MVNE. So in that case, we would price our platform based on number of active sims. Many manual customers prefer to diversify. They want to choose different models, serve different market segments and not only deliver voice, but also data, messaging, value added services. We have a huge customer in Brazil, for example, they don't have a single end-user customer because everything what they do is pure IOT. So how do we price the platform? Because the variety of business models is so huge. We use the idea of billable events. So any call, any message, any data session, subscription, or anything which can produce a rate-able file can counter against the capacity of what the customer uses. So it gives a full transparency for the customer and it's easy to predict the future costs >> And you're able to charge accordingly and transparently because you've written software to do that. >> Roman: Absolutely. >> Its in the cloud, I presume. And so, you're able to show your customers exactly what you're paying for and the seat in that instance is somebody who's creating those services or somebody who's administering those services, or it's a developer? >> It's an extension >> Somebody who's using the service. So the end user. >> Ah, right. Yeah, okay. >> And actually we use our own software to charge our customers for using our software. >> Okay so you eat your own dog food or drink your own champagne as people like to say, right? How about from an engineering standpoint? Going from on-prem to the cloud, how should we think about architecting that? What are some of the roadblocks that we potentially see? >> The biggest roadblock we see in the developing countries is data centers not being available yet. That customer in Brazil, they were like knocking on the doors of the data center >> 9: 00 AM when it just opened, because they've been waiting for so long. We have about 15 customers in South Africa. They still are waiting for proper cloud at the center to be open there. But that's just the question of time. We just have to wait a little bit and this will get improved. And then that's a big thing. that you have your data center, you have your cloud software, and then you have your existing operations. You have your systems. So how do you move there? And I'm a proponent of gradual migration and gradual movement because every Telco, if they were in business for at least a few years, they have accumulated the variety of different systems, legacy, different products, different departments. It's difficult to jump in the cloud in one jump. So let's build a ladder. And with our customers, we use a technology called Dual-Version with RADIUS. It's a gradual migration. You don't move it at once You first with the pilot batch of customers, observe them, then add more customers, add more customers, and you keep going until everybody's on the new version. And it helps tremendously with new technology, or just with different user experience, because maybe some things which were improved in our perspective from some users, they don't like the change or they need some adjustments. So we see a way to the cloud. It's starting the small steps and then get them to the cloud and the process doesn't start there because once you get to version one of Clio cloud software, it's going to be version two and version three and version four. So the first is a general change in the mentality of telco, all this constant gradual improvements. >> You call it radio? Gradual? >> Gradual. >> Okay, so, gradual migration. So when you do a migration and it's gradual what, do you create some kind of abstraction layer so they don't have to freeze everything, right? Or, maybe I do freeze it but I can still operate with the pieces that have moved. >> Exactly. >> So I'm not shutting down my business. >> No, no way. >> That's the problem with migrations, right? I got to, I got to freeze it. And then, so I say, forget it. I don't ever do a migration, but technology allows you to hide that. >> Right. Some freeze may be required because maybe you should not add a new product or change one, which is currently being immigrated. >> Right. >> But to try to minimize the amount of those freezes from a product catalog perspective and the amount of potential inconveniences for the end user while they be integrated. >> Let's talk about the business value. We know that before, we know what it's like, it's a hairball. You described that spaghetti code. It's slow. It's not transparent. It's expensive. What are you seeing in the after state with some of your tier two and tier three customers, in particular, the ones that are disrupting the Telcos, what do you see? Roman. >> It Brings value, first of all. Because the scalability is no longer an issue. Their ability to migrate, ability to update the system to the new releases is also, much more easier in the cloud. So, the industry's changing fast. The consumers are instantly moving from one preferred way of communicating to another. So the Telcos need to change as well, pretty rapidly. So we are trying to give them that set of tools so they are not being dragged behind by the changes. So update faster, scale faster, introduce new products faster, configure new subscription, and get more customers. >> And then that leads to compress time to monetization. >> Roman: Exactly >> Better customer satisfaction. If we talked in this industry about NPS and how it's so negative. Usually people talk about "my NPS is better than Apple's". When they, in this industry, it's like we need to improve the NPS. Unique approach. Okay! Guys, we're almost out of time. Andriy, I'll give you the last word, put a bow on Mobile World Congress 2021 and how poor to seize it. >> Well, I think it's very symbolic, this place we are in right now, it's a space which used to belong to a large telecom software vendor. And now there's a variety of smaller disruptive companies. And I think that's the future. So the days when Telco would shop for a single huge RFP to solve all of their problems, are gone for good. Because now with the cloud, with integration, with API, You, the Telcos, have the power to build what they need, peak the solutions to integrate and create something which will deliver value and allow them to have it (indistinct) >> Fantastic. We are tracking the transformation of Telco and it just coincides with the exit of the post isolation economy. We're really excited to be here in cloud city. Adam, back to you in the studio.

>> Announcer: Live from Vancouver Canada, it's theCUBE covering OpenStack Summit North America 2018. Brought to you by RedHat, the OpenStack foundation and its ecosystem partners. >> Welcome back to theCUBE's coverage of OpenStack Summit 2018 in Vancouver. I'm Stu Miniman with my co-host for the week John Troyer. And helping us to bring it on home we have Roman Alekseenkov who's the co-founder of Aptomi. Brand new start up, I feel we've got the exclusive here to help you know, we have some blog posts out there and the like, but help to introduce you to our community and some of the broader world. Thanks for joining us. >> Yep, my first time at theCUBE. >> Alright so Roman, give us a little bit about your background and you know, we need with any, you know, founder the why of your company. >> Okay so I guess let's start with a background. So I used to work for one of the cloud infrastructure startups called Mirantis. And I worked there for a very long time. And last year I decided to start something on my own. Right, so now I am one of the main guys and one of the core contributors to the project called Aptomi. So and, I don't know if it's relevant, but before Mirantis, I've been doing a lot of the programming competitions like Google Code Jam, ACMICPC and Top Coder. My team ended up winning ACMICPC world finals. So I have like a decent background in algorithms, computer science, data structures, and things like that. >> Yeah. >> So that's me. >> We always see people are always humble there. It's, we know Mike Dvorkin is on your team. >> He is. >> People in the networking world, you know, might have run across Mike, and so super smart people. Give us the you know, the problem statement that your company's looking to solve. >> Right, so... I think it's going to be not one sentence answer. It's going to be a slightly longer answer. So when we talk to a number of companies who are using Kubernetes and who are building apps on top of Kubernetes, we looked into CI space and the CD space. And we looked at the CI, and in the CI for the most part, most of the problems seem to be solved, right. Everything that starts from your source code and then Docker file, how you build your artifacts, how you test it, and how you publish the binary to the repo, all that part seems to be streamlined. You take Jenkins, you take Docker, you take all the tools. You write some Kubernetes key, so this part, packaging components, it's not a big deal. And what we saw is where all the people are struggling is actually in the CD space, right. Once you start putting multi-container complex applications out of those pieces once you start wiring those pieces together, maybe microservices, maybe not, but once you start wiring things together, once you start running them across multiple environments, multiple clusters, right, that's where the things become really, really difficult for people who just rely on the tool set that we have today. Right, and that's where we saw an opportunity to build this service abstraction which allow people to wire things together and run them and operate them in a controllable way across multiple clusters and multiple environments integrated obviously with the continuous delivery pipelines. >> So if people weren't using Aptomi, what would they be using now? Or what kind of, what kinds of tools and processes are they bringing together if they're not doing this? Are they doing everything by hand, or how do you compare it to some of the other tools? >> Right, so a lot of people, they use some homegrown frameworks right now on top of Kubernetes and Helm. Or maybe on top of Kubernetes and YAML files. Or maybe Kubernetes and JSON is also one of the ways to do this. But there are some drawbacks in, in the approaches, right? Because we think that you want to start reasoning about those as actually applications and services not as like as a bunch of YAMLs and containers right? And so once you start talking about this as services as well as rules around those services, right maybe I want to say like hey everything that goes in my production environment should be secure or I want all my services with label "X" deployed to the dev environment or to cluster US east right? I mean the things become easier for you, 'cause you don't have to deal with the YAML file. >> Kind of from the abstraction layer up to maybe up, say to in other part of IT you might say it's policy driven almost, it's declarative, intent driven; I want this to happen rather than writing this kind of crazy YAML. Actually one of the Kubernetes founders, I dunno recently on Twitter or somewhere I was reading was saying that YAML was never supposed to be written by humans, that was kind of a mistake we meant for it to be under the covers but here we are. >> Roman: Right, but you are exactly right. It's services as well as intent around the services. >> Stu: Roman, I want to get your thoughts on just the Kubernetes ecosystem itself, you know for years here at OpenStack it was "Oh wait there's a lot of different distributions", you know, moving between one or the other wasn't necessarily easy. Kubernetes seems like we're a little bit better, a little further along, might've learned from some of the issues that we've had here. There's, last I saw it was getting around 40 different options but you know the thing I also wonder about is Kubernetes tends to get baked into platforms so you've got people that will build their own, just take the code, but you know Red Hat has a platform, all the public clouds have a platform, then there's a number of startups there. What's that like from your standpoint kind of being in this ecosystem is it, and maybe give us a little comparison compared to what it would have been like in the OpenStack world? >> Roman: Sounds good, so for us we actually we don't really care on what Kubernetes we run because we run, we help people to deliver apps and services on top. But if you talk about Kubernetes itself, we don't actually last year we haven't seen a lot of issues with Kubernetes right because we run a cluster in our lab, it just works. JKE always doesn't let me down, we also run things on Azure so speaking about the Kubernetes infrastructure I think the state of Kubernetes right now it's pretty reliable. So we don't see a lot of issues with that. But you also mentioned the platform, right so Kubernetes is part of the platform and that's the interesting part because a couple of years ago everyone was talking about Pass. It's Pass, Pass, Pass, Pass everywhere. Now you see a lot of conversations about Pass because Pass is like a monolith platform, doesn't exist anymore because it basically gets decomposed into what people call I guess containers of service and the modular tool set. And container orchestration is one part, and there is like 15 or 16 different parts from ad definition, to orchestration, and CD pipelines and security components, right? And that's why you see so many products out there with overlapping functionality. >> I mean do you think that the concept of Pass is going away at this point? Will we continue to redefine what a Pass is? I think every few years maybe that's the pattern. >> My personal opinion is that the concept of Pass is gone. There's is no more Pass. The future is the modular stack and the modular tool set. >> Stu: Yeah, so absolutely the future is becoming more distributed. I'm curious your thoughts then on something like Serverless which tends to change that even a little bit more than what we've been looking at. >> Roman: Sure well Serverless is, I guess it's not for everyone. It also depends on the type of workload that you run. If you want to run something compute intensive I guess it's still going to be containers or even VMs but likely containers. But if you have some stateless front-end or API, something that you sometimes make a call to and have to do something and get a response back sure Serverless is great, and Serverless actually fits quite well into what Mike and are tying to do with Aptomi. >> John: Roman I also wanted to ask about dependency mapping and visualizing dependencies. Hybrid cloud has been a big theme this week. It's actually a big theme in enterprise and elsewhere. When that happens when you have separate components whether they are monolithic components that are talking to each other down to microservices, dependencies are huge at that, the application level dependencies, especially as you move to hybrid cloud because you might be moving some component away from the rest and you better know what's talking to the other components. Any thoughts on how that is developing as architecture, application architectures and what you guys are doing to help there? >> Roman: Yeah so there's basically two ways how you can approach this so one way is the traditional way where you just open up your Kubernetes to a bunch of developers and people just run their things in different namespaces. If you use that approach I think those dependencies between different components, what relies on what, who's talking to whom, they become non-obvious, it's really hard to discover them once you got things deployed. So we are taking a slightly different approach because we require a little bit more information upfront about dependencies between components so once you deploy things through Aptomi we kind of already know what exists on the clusters and why, and who owns the resources, and who asked for certain services to be deployed. So we do provide some contextual visibility into that. And what's really nice is we're trying to build this, or we are building this on top of the community standards, we are not reinventing the whole platform, or trying to invent a new language, it's basically build ontop of Kubernetes and Helm. It's just a simple declarative service based abstraction and it rules. >> Stu: Last thing I wanted to ask, Aptomi itself, you know what's the state of the project? Is it a 1.0, are you looking for contributors, where are you with customers, help round off the understanding of the company and project. >> Sounds good, so we are one year into the project. The project is completely open source, it's on Github. It has 4 contributors right now and close to 2,000 commits maybe a little bit more. 100 star, 100+ on Github, so we're getting some traction, in the open source. Speaking about the readiness I think it's we're not 1.0 yet but we're getting close to 1.0. And the core of it, and the whole project is completely open source right, it's 100% Apache 2.0, but what we also do we also offer a hosted version with support. Right so when people come and they can just get the complete CD system with the service based layer and abstraction through our hosted version with support and that's what we are charging money for and revenue wise we do have paying customers, but it's only a year in so. Not a big amount but, there's going to be more. >> Stu: Alright well, Roman Alekseenkov really appreciate you sharing with us. Congratulations on the progress so far, seen an item I'd like working for us and for John Troyer. I'm Stu Miniman, we thank you for joining for 3 days of live wall-to-wall coverage of big final shout-out to the OpenStack Foundation and the supports of theCUBE for the whole crew here. Thank you for watching theCUBE. >> (electro-dance music) >> (soft piano) >> Astronaught: I recommend you activate my bit-ray over.

[Music] in the 2010s organizations became keenly aware that data would become the key ingredient in driving competitive advantage differentiation and growth but to this day putting data to work remains a difficult challenge for many if not most organizations now as the cloud matures it has become a game changer for data practitioners by making cheap storage and massive processing power readily accessible we've also seen better tooling in the form of data workflows streaming machine intelligence ai developer tools security observability automation new databases and the like these innovations they accelerate data proficiency but at the same time they had complexity for practitioners data lakes data hubs data warehouses data marts data fabrics data meshes data catalogs data oceans are forming they're evolving and exploding onto the scene so in an effort to bring perspective to the sea of optionality we've brought together the brightest minds in the data analyst community to discuss how data management is morphing and what practitioners should expect in 2022 and beyond hello everyone my name is dave vellante with the cube and i'd like to welcome you to a special cube presentation analyst predictions 2022 the future of data management we've gathered six of the best analysts in data and data management who are going to present and discuss their top predictions and trends for 2022 in the first half of this decade let me introduce our six power panelists sanjeev mohan is former gartner analyst and principal at sanjamo tony bear is principal at db insight carl olufsen is well-known research vice president with idc dave meninger is senior vice president and research director at ventana research brad shimon chief analyst at ai platforms analytics and data management at omnia and doug henschen vice president and principal analyst at constellation research gentlemen welcome to the program and thanks for coming on thecube today great to be here thank you all right here's the format we're going to use i as moderator are going to call on each analyst separately who then will deliver their prediction or mega trend and then in the interest of time management and pace two analysts will have the opportunity to comment if we have more time we'll elongate it but let's get started right away sanjeev mohan please kick it off you want to talk about governance go ahead sir thank you dave i i believe that data governance which we've been talking about for many years is now not only going to be mainstream it's going to be table stakes and all the things that you mentioned you know with data oceans data lakes lake houses data fabric meshes the common glue is metadata if we don't understand what data we have and we are governing it there is no way we can manage it so we saw informatica when public last year after a hiatus of six years i've i'm predicting that this year we see some more companies go public uh my bet is on colibra most likely and maybe alation we'll see go public this year we we i'm also predicting that the scope of data governance is going to expand beyond just data it's not just data and reports we are going to see more transformations like spark jaws python even airflow we're going to see more of streaming data so from kafka schema registry for example we will see ai models become part of this whole governance suite so the governance suite is going to be very comprehensive very detailed lineage impact analysis and then even expand into data quality we already seen that happen with some of the tools where they are buying these smaller companies and bringing in data quality monitoring and integrating it with metadata management data catalogs also data access governance so these so what we are going to see is that once the data governance platforms become the key entry point into these modern architectures i'm predicting that the usage the number of users of a data catalog is going to exceed that of a bi tool that will take time and we already seen that that trajectory right now if you look at bi tools i would say there are 100 users to a bi tool to one data catalog and i i see that evening out over a period of time and at some point data catalogs will really become you know the main way for us to access data data catalog will help us visualize data but if we want to do more in-depth analysis it'll be the jumping-off point into the bi tool the data science tool and and that is that is the journey i see for the data governance products excellent thank you some comments maybe maybe doug a lot a lot of things to weigh in on there maybe you could comment yeah sanjeev i think you're spot on a lot of the trends uh the one disagreement i think it's it's really still far from mainstream as you say we've been talking about this for years it's like god motherhood apple pie everyone agrees it's important but too few organizations are really practicing good governance because it's hard and because the incentives have been lacking i think one thing that deserves uh mention in this context is uh esg mandates and guidelines these are environmental social and governance regs and guidelines we've seen the environmental rags and guidelines imposed in industries particularly the carbon intensive industries we've seen the social mandates particularly diversity imposed on suppliers by companies that are leading on this topic we've seen governance guidelines now being imposed by banks and investors so these esgs are presenting new carrots and sticks and it's going to demand more solid data it's going to demand more detailed reporting and solid reporting tighter governance but we're still far from mainstream adoption we have a lot of uh you know best of breed niche players in the space i think the signs that it's going to be more mainstream are starting with things like azure purview google dataplex the big cloud platform uh players seem to be uh upping the ante and and addressing starting to address governance excellent thank you doug brad i wonder if you could chime in as well yeah i would love to be a believer in data catalogs um but uh to doug's point i think that it's going to take some more pressure for for that to happen i recall metadata being something every enterprise thought they were going to get under control when we were working on service oriented architecture back in the 90s and that didn't happen quite the way we we anticipated and and uh to sanjeev's point it's because it is really complex and really difficult to do my hope is that you know we won't sort of uh how do we put this fade out into this nebulous nebula of uh domain catalogs that are specific to individual use cases like purview for getting data quality right or like data governance and cyber security and instead we have some tooling that can actually be adaptive to gather metadata to create something i know is important to you sanjeev and that is this idea of observability if you can get enough metadata without moving your data around but understanding the entirety of a system that's running on this data you can do a lot to help with with the governance that doug is talking about so so i just want to add that you know data governance like many other initiatives did not succeed even ai went into an ai window but that's a different topic but a lot of these things did not succeed because to your point the incentives were not there i i remember when starbucks oxley had come into the scene if if a bank did not do service obviously they were very happy to a million dollar fine that was like you know pocket change for them instead of doing the right thing but i think the stakes are much higher now with gdpr uh the floodgates open now you know california you know has ccpa but even ccpa is being outdated with cpra which is much more gdpr like so we are very rapidly entering a space where every pretty much every major country in the world is coming up with its own uh compliance regulatory requirements data residence is becoming really important and and i i think we are going to reach a stage where uh it won't be optional anymore so whether we like it or not and i think the reason data catalogs were not successful in the past is because we did not have the right focus on adoption we were focused on features and these features were disconnected very hard for business to stop these are built by it people for it departments to to take a look at technical metadata not business metadata today the tables have turned cdo's are driving this uh initiative uh regulatory compliances are beating down hard so i think the time might be right yeah so guys we have to move on here and uh but there's some some real meat on the bone here sanjeev i like the fact that you late you called out calibra and alation so we can look back a year from now and say okay he made the call he stuck it and then the ratio of bi tools the data catalogs that's another sort of measurement that we can we can take even though some skepticism there that's something that we can watch and i wonder if someday if we'll have more metadata than data but i want to move to tony baer you want to talk about data mesh and speaking you know coming off of governance i mean wow you know the whole concept of data mesh is decentralized data and then governance becomes you know a nightmare there but take it away tony we'll put it this way um data mesh you know the the idea at least is proposed by thoughtworks um you know basically was unleashed a couple years ago and the press has been almost uniformly almost uncritical um a good reason for that is for all the problems that basically that sanjeev and doug and brad were just you know we're just speaking about which is that we have all this data out there and we don't know what to do about it um now that's not a new problem that was a problem we had enterprise data warehouses it was a problem when we had our hadoop data clusters it's even more of a problem now the data's out in the cloud where the data is not only your data like is not only s3 it's all over the place and it's also including streaming which i know we'll be talking about later so the data mesh was a response to that the idea of that we need to debate you know who are the folks that really know best about governance is the domain experts so it was basically data mesh was an architectural pattern and a process my prediction for this year is that data mesh is going to hit cold hard reality because if you if you do a google search um basically the the published work the articles and databases have been largely you know pretty uncritical um so far you know that you know basically learning is basically being a very revolutionary new idea i don't think it's that revolutionary because we've talked about ideas like this brad and i you and i met years ago when we were talking about so and decentralizing all of us was at the application level now we're talking about at the data level and now we have microservices so there's this thought of oh if we manage if we're apps in cloud native through microservices why don't we think of data in the same way um my sense this year is that you know this and this has been a very active search if you look at google search trends is that now companies are going to you know enterprises are going to look at this seriously and as they look at seriously it's going to attract its first real hard scrutiny it's going to attract its first backlash that's not necessarily a bad thing it means that it's being taken seriously um the reason why i think that that uh that it will you'll start to see basically the cold hard light of day shine on data mesh is that it's still a work in progress you know this idea is basically a couple years old and there's still some pretty major gaps um the biggest gap is in is in the area of federated governance now federated governance itself is not a new issue uh federated governance position we're trying to figure out like how can we basically strike the balance between getting let's say you know between basically consistent enterprise policy consistent enterprise governance but yet the groups that understand the data know how to basically you know that you know how do we basically sort of balance the two there's a huge there's a huge gap there in practice and knowledge um also to a lesser extent there's a technology gap which is basically in the self-service technologies that will help teams essentially govern data you know basically through the full life cycle from developed from selecting the data from you know building the other pipelines from determining your access control determining looking at quality looking at basically whether data is fresh or whether or not it's trending of course so my predictions is that it will really receive the first harsh scrutiny this year you are going to see some organization enterprises declare premature victory when they've uh when they build some federated query implementations you're going to see vendors start to data mesh wash their products anybody in the data management space they're going to say that whether it's basically a pipelining tool whether it's basically elt whether it's a catalog um or confederated query tool they're all going to be like you know basically promoting the fact of how they support this hopefully nobody is going to call themselves a data mesh tool because data mesh is not a technology we're going to see one other thing come out of this and this harks back to the metadata that sanji was talking about and the catalogs that he was talking about which is that there's going to be a new focus on every renewed focus on metadata and i think that's going to spur interest in data fabrics now data fabrics are pretty vaguely defined but if we just take the most elemental definition which is a common metadata back plane i think that if anybody is going to get serious about data mesh they need to look at a data fabric because we all at the end of the day need to speak you know need to read from the same sheet of music so thank you tony dave dave meninger i mean one of the things that people like about data mesh is it pretty crisply articulates some of the flaws in today's organizational approaches to data what are your thoughts on this well i think we have to start by defining data mesh right the the term is already getting corrupted right tony said it's going to see the cold hard uh light of day and there's a problem right now that there are a number of overlapping terms that are similar but not identical so we've got data virtualization data fabric excuse me for a second sorry about that data virtualization data fabric uh uh data federation right uh so i i think that it's not really clear what each vendor means by these terms i see data mesh and data fabric becoming quite popular i've i've interpreted data mesh as referring primarily to the governance aspects as originally you know intended and specified but that's not the way i see vendors using i see vendors using it much more to mean data fabric and data virtualization so i'm going to comment on the group of those things i think the group of those things is going to happen they're going to happen they're going to become more robust our research suggests that a quarter of organizations are already using virtualized access to their data lakes and another half so a total of three quarters will eventually be accessing their data lakes using some sort of virtualized access again whether you define it as mesh or fabric or virtualization isn't really the point here but this notion that there are different elements of data metadata and governance within an organization that all need to be managed collectively the interesting thing is when you look at the satisfaction rates of those organizations using virtualization versus those that are not it's almost double 68 of organizations i'm i'm sorry um 79 of organizations that were using virtualized access express satisfaction with their access to the data lake only 39 expressed satisfaction if they weren't using virtualized access so thank you uh dave uh sanjeev we just got about a couple minutes on this topic but i know you're speaking or maybe you've spoken already on a panel with jamal dagani who sort of invented the concept governance obviously is a big sticking point but what are your thoughts on this you are mute so my message to your mark and uh and to the community is uh as opposed to what dave said let's not define it we spent the whole year defining it there are four principles domain product data infrastructure and governance let's take it to the next level i get a lot of questions on what is the difference between data fabric and data mesh and i'm like i can compare the two because data mesh is a business concept data fabric is a data integration pattern how do you define how do you compare the two you have to bring data mesh level down so to tony's point i'm on a warp path in 2022 to take it down to what does a data product look like how do we handle shared data across domains and govern it and i think we are going to see more of that in 2022 is operationalization of data mesh i think we could have a whole hour on this topic couldn't we uh maybe we should do that uh but let's go to let's move to carl said carl your database guy you've been around that that block for a while now you want to talk about graph databases bring it on oh yeah okay thanks so i regard graph database as basically the next truly revolutionary database management technology i'm looking forward to for the graph database market which of course we haven't defined yet so obviously i have a little wiggle room in what i'm about to say but that this market will grow by about 600 percent over the next 10 years now 10 years is a long time but over the next five years we expect to see gradual growth as people start to learn how to use it problem isn't that it's used the problem is not that it's not useful is that people don't know how to use it so let me explain before i go any further what a graph database is because some of the folks on the call may not may not know what it is a graph database organizes data according to a mathematical structure called a graph a graph has elements called nodes and edges so a data element drops into a node the nodes are connected by edges the edges connect one node to another node combinations of edges create structures that you can analyze to determine how things are related in some cases the nodes and edges can have properties attached to them which add additional informative material that makes it richer that's called a property graph okay there are two principal use cases for graph databases there's there's semantic proper graphs which are used to break down human language text uh into the semantic structures then you can search it organize it and and and answer complicated questions a lot of ai is aimed at semantic graphs another kind is the property graph that i just mentioned which has a dazzling number of use cases i want to just point out is as i talk about this people are probably wondering well we have relational databases isn't that good enough okay so a relational database defines it uses um it supports what i call definitional relationships that means you define the relationships in a fixed structure the database drops into that structure there's a value foreign key value that relates one table to another and that value is fixed you don't change it if you change it the database becomes unstable it's not clear what you're looking at in a graph database the system is designed to handle change so that it can reflect the true state of the things that it's being used to track so um let me just give you some examples of use cases for this um they include uh entity resolution data lineage uh um social media analysis customer 360 fraud prevention there's cyber security there's strong supply chain is a big one actually there's explainable ai and this is going to become important too because a lot of people are adopting ai but they want a system after the fact to say how did the ai system come to that conclusion how did it make that recommendation right now we don't have really good ways of tracking that okay machine machine learning in general um social network i already mentioned that and then we've got oh gosh we've got data governance data compliance risk management we've got recommendation we've got personalization anti-money money laundering that's another big one identity and access management network and i.t operations is already becoming a key one where you actually have mapped out your operation your your you know whatever it is your data center and you you can track what's going on as things happen there root cause analysis fraud detection is a huge one a number of major credit card companies use graph databases for fraud detection risk analysis tracking and tracing churn analysis next best action what-if analysis impact analysis entity resolution and i would add one other thing or just a few other things to this list metadata management so sanjay here you go this is your engine okay because i was in metadata management for quite a while in my past life and one of the things i found was that none of the data management technologies that were available to us could efficiently handle metadata because of the kinds of structures that result from it but grass can okay grafts can do things like say this term in this context means this but in that context it means that okay things like that and in fact uh logistics management supply chain it also because it handles recursive relationships by recursive relationships i mean objects that own other objects that are of the same type you can do things like bill materials you know so like parts explosion you can do an hr analysis who reports to whom how many levels up the chain and that kind of thing you can do that with relational databases but yes it takes a lot of programming in fact you can do almost any of these things with relational databases but the problem is you have to program it it's not it's not supported in the database and whenever you have to program something that means you can't trace it you can't define it you can't publish it in terms of its functionality and it's really really hard to maintain over time so carl thank you i wonder if we could bring brad in i mean brad i'm sitting there wondering okay is this incremental to the market is it disruptive and replaceable what are your thoughts on this space it's already disrupted the market i mean like carl said go to any bank and ask them are you using graph databases to do to get fraud detection under control and they'll say absolutely that's the only way to solve this problem and it is frankly um and it's the only way to solve a lot of the problems that carl mentioned and that is i think it's it's achilles heel in some ways because you know it's like finding the best way to cross the seven bridges of konigsberg you know it's always going to kind of be tied to those use cases because it's really special and it's really unique and because it's special and it's unique uh it it still unfortunately kind of stands apart from the rest of the community that's building let's say ai outcomes as the great great example here the graph databases and ai as carl mentioned are like chocolate and peanut butter but technologically they don't know how to talk to one another they're completely different um and you know it's you can't just stand up sql and query them you've got to to learn um yeah what is that carlos specter or uh special uh uh yeah thank you uh to actually get to the data in there and if you're gonna scale that data that graph database especially a property graph if you're gonna do something really complex like try to understand uh you know all of the metadata in your organization you might just end up with you know a graph database winter like we had the ai winter simply because you run out of performance to make the thing happen so i i think it's already disrupted but we we need to like treat it like a first-class citizen in in the data analytics and ai community we need to bring it into the fold we need to equip it with the tools it needs to do that the magic it does and to do it not just for specialized use cases but for everything because i i'm with carl i i think it's absolutely revolutionary so i had also identified the principal achilles heel of the technology which is scaling now when these when these things get large and complex enough that they spill over what a single server can handle you start to have difficulties because the relationships span things that have to be resolved over a network and then you get network latency and that slows the system down so that's still a problem to be solved sanjeev any quick thoughts on this i mean i think metadata on the on the on the word cloud is going to be the the largest font uh but what are your thoughts here i want to like step away so people don't you know associate me with only meta data so i want to talk about something a little bit slightly different uh dbengines.com has done an amazing job i think almost everyone knows that they chronicle all the major databases that are in use today in january of 2022 there are 381 databases on its list of ranked list of databases the largest category is rdbms the second largest category is actually divided into two property graphs and rdf graphs these two together make up the second largest number of data databases so talking about accolades here this is a problem the problem is that there's so many graph databases to choose from they come in different shapes and forms uh to bright's point there's so many query languages in rdbms is sql end of the story here we've got sci-fi we've got gremlin we've got gql and then your proprietary languages so i think there's a lot of disparity in this space but excellent all excellent points sanji i must say and that is a problem the languages need to be sorted and standardized and it needs people need to have a road map as to what they can do with it because as you say you can do so many things and so many of those things are unrelated that you sort of say well what do we use this for i'm reminded of the saying i learned a bunch of years ago when somebody said that the digital computer is the only tool man has ever devised that has no particular purpose all right guys we gotta we gotta move on to dave uh meninger uh we've heard about streaming uh your prediction is in that realm so please take it away sure so i like to say that historical databases are to become a thing of the past but i don't mean that they're going to go away that's not my point i mean we need historical databases but streaming data is going to become the default way in which we operate with data so in the next say three to five years i would expect the data platforms and and we're using the term data platforms to represent the evolution of databases and data lakes that the data platforms will incorporate these streaming capabilities we're going to process data as it streams into an organization and then it's going to roll off into historical databases so historical databases don't go away but they become a thing of the past they store the data that occurred previously and as data is occurring we're going to be processing it we're going to be analyzing we're going to be acting on it i mean we we only ever ended up with historical databases because we were limited by the technology that was available to us data doesn't occur in batches but we processed it in batches because that was the best we could do and it wasn't bad and we've continued to improve and we've improved and we've improved but streaming data today is still the exception it's not the rule right there's there are projects within organizations that deal with streaming data but it's not the default way in which we deal with data yet and so that that's my prediction is that this is going to change we're going to have um streaming data be the default way in which we deal with data and and how you label it what you call it you know maybe these databases and data platforms just evolve to be able to handle it but we're going to deal with data in a different way and our research shows that already about half of the participants in our analytics and data benchmark research are using streaming data you know another third are planning to use streaming technologies so that gets us to about eight out of ten organizations need to use this technology that doesn't mean they have to use it throughout the whole organization but but it's pretty widespread in its use today and has continued to grow if you think about the consumerization of i.t we've all been conditioned to expect immediate access to information immediate responsiveness you know we want to know if an uh item is on the shelf at our local retail store and we can go in and pick it up right now you know that's the world we live in and that's spilling over into the enterprise i.t world where we have to provide those same types of capabilities um so that's my prediction historical database has become a thing of the past streaming data becomes the default way in which we we operate with data all right thank you david well so what what say you uh carl a guy who's followed historical databases for a long time well one thing actually every database is historical because as soon as you put data in it it's now history it's no longer it no longer reflects the present state of things but even if that history is only a millisecond old it's still history but um i would say i mean i know you're trying to be a little bit provocative in saying this dave because you know as well as i do that people still need to do their taxes they still need to do accounting they still need to run general ledger programs and things like that that all involves historical data that's not going to go away unless you want to go to jail so you're going to have to deal with that but as far as the leading edge functionality i'm totally with you on that and i'm just you know i'm just kind of wondering um if this chain if this requires a change in the way that we perceive applications in order to truly be manifested and rethinking the way m applications work um saying that uh an application should respond instantly as soon as the state of things changes what do you say about that i i think that's true i think we do have to think about things differently that's you know it's not the way we design systems in the past uh we're seeing more and more systems designed that way but again it's not the default and and agree 100 with you that we do need historical databases you know that that's clear and even some of those historical databases will be used in conjunction with the streaming data right so absolutely i mean you know let's take the data warehouse example where you're using the data warehouse as context and the streaming data as the present you're saying here's a sequence of things that's happening right now have we seen that sequence before and where what what does that pattern look like in past situations and can we learn from that so tony bear i wonder if you could comment i mean if you when you think about you know real-time inferencing at the edge for instance which is something that a lot of people talk about um a lot of what we're discussing here in this segment looks like it's got great potential what are your thoughts yeah well i mean i think you nailed it right you know you hit it right on the head there which is that i think a key what i'm seeing is that essentially and basically i'm going to split this one down the middle is i don't see that basically streaming is the default what i see is streaming and basically and transaction databases um and analytics data you know data warehouses data lakes whatever are converging and what allows us technically to converge is cloud native architecture where you can basically distribute things so you could have you can have a note here that's doing the real-time processing that's also doing it and this is what your leads in we're maybe doing some of that real-time predictive analytics to take a look at well look we're looking at this customer journey what's happening with you know you know with with what the customer is doing right now and this is correlated with what other customers are doing so what i so the thing is that in the cloud you can basically partition this and because of basically you know the speed of the infrastructure um that you can basically bring these together and or and so and kind of orchestrate them sort of loosely coupled manner the other part is that the use cases are demanding and this is part that goes back to what dave is saying is that you know when you look at customer 360 when you look at let's say smart you know smart utility grids when you look at any type of operational problem it has a real-time component and it has a historical component and having predictives and so like you know you know my sense here is that there that technically we can bring this together through the cloud and i think the use case is that is that we we can apply some some real-time sort of you know predictive analytics on these streams and feed this into the transactions so that when we make a decision in terms of what to do as a result of a transaction we have this real time you know input sanjeev did you have a comment yeah i was just going to say that to this point you know we have to think of streaming very different because in the historical databases we used to bring the data and store the data and then we used to run rules on top uh aggregations and all but in case of streaming the mindset changes because the rules normally the inference all of that is fixed but the data is constantly changing so it's a completely reverse way of thinking of uh and building applications on top of that so dave menninger there seemed to be some disagreement about the default or now what kind of time frame are you are you thinking about is this end of decade it becomes the default what would you pin i i think around you know between between five to ten years i think this becomes the reality um i think you know it'll be more and more common between now and then but it becomes the default and i also want sanjeev at some point maybe in one of our subsequent conversations we need to talk about governing streaming data because that's a whole other set of challenges we've also talked about it rather in a two dimensions historical and streaming and there's lots of low latency micro batch sub second that's not quite streaming but in many cases it's fast enough and we're seeing a lot of adoption of near real time not quite real time as uh good enough for most for many applications because nobody's really taking the hardware dimension of this information like how do we that'll just happen carl so near real time maybe before you lose the customer however you define that right okay um let's move on to brad brad you want to talk about automation ai uh the the the pipeline people feel like hey we can just automate everything what's your prediction yeah uh i'm i'm an ai fiction auto so apologies in advance for that but uh you know um i i think that um we've been seeing automation at play within ai for some time now and it's helped us do do a lot of things for especially for practitioners that are building ai outcomes in the enterprise uh it's it's helped them to fill skills gaps it's helped them to speed development and it's helped them to to actually make ai better uh because it you know in some ways provides some swim lanes and and for example with technologies like ottawa milk and can auto document and create that sort of transparency that that we talked about a little bit earlier um but i i think it's there's an interesting kind of conversion happening with this idea of automation um and and that is that uh we've had the automation that started happening for practitioners it's it's trying to move outside of the traditional bounds of things like i'm just trying to get my features i'm just trying to pick the right algorithm i'm just trying to build the right model uh and it's expanding across that full life cycle of building an ai outcome to start at the very beginning of data and to then continue on to the end which is this continuous delivery and continuous uh automation of of that outcome to make sure it's right and it hasn't drifted and stuff like that and because of that because it's become kind of powerful we're starting to to actually see this weird thing happen where the practitioners are starting to converge with the users and that is to say that okay if i'm in tableau right now i can stand up salesforce einstein discovery and it will automatically create a nice predictive algorithm for me um given the data that i that i pull in um but what's starting to happen and we're seeing this from the the the companies that create business software so salesforce oracle sap and others is that they're starting to actually use these same ideals and a lot of deep learning to to basically stand up these out of the box flip a switch and you've got an ai outcome at the ready for business users and um i i'm very much you know i think that that's that's the way that it's going to go and what it means is that ai is is slowly disappearing uh and i don't think that's a bad thing i think if anything what we're going to see in 2022 and maybe into 2023 is this sort of rush to to put this idea of disappearing ai into practice and have as many of these solutions in the enterprise as possible you can see like for example sap is going to roll out this quarter this thing called adaptive recommendation services which which basically is a cold start ai outcome that can work across a whole bunch of different vertical markets and use cases it's just a recommendation engine for whatever you need it to do in the line of business so basically you're you're an sap user you look up to turn on your software one day and you're a sales professional let's say and suddenly you have a recommendation for customer churn it's going that's great well i i don't know i i think that's terrifying in some ways i think it is the future that ai is going to disappear like that but i am absolutely terrified of it because um i i think that what it what it really does is it calls attention to a lot of the issues that we already see around ai um specific to this idea of what what we like to call it omdia responsible ai which is you know how do you build an ai outcome that is free of bias that is inclusive that is fair that is safe that is secure that it's audible etc etc etc etc that takes some a lot of work to do and so if you imagine a customer that that's just a sales force customer let's say and they're turning on einstein discovery within their sales software you need some guidance to make sure that when you flip that switch that the outcome you're going to get is correct and that's that's going to take some work and so i think we're going to see this let's roll this out and suddenly there's going to be a lot of a lot of problems a lot of pushback uh that we're going to see and some of that's going to come from gdpr and others that sam jeeve was mentioning earlier a lot of it's going to come from internal csr requirements within companies that are saying hey hey whoa hold up we can't do this all at once let's take the slow route let's make ai automated in a smart way and that's going to take time yeah so a couple predictions there that i heard i mean ai essentially you disappear it becomes invisible maybe if i can restate that and then if if i understand it correctly brad you're saying there's a backlash in the near term people can say oh slow down let's automate what we can those attributes that you talked about are non trivial to achieve is that why you're a bit of a skeptic yeah i think that we don't have any sort of standards that companies can look to and understand and we certainly within these companies especially those that haven't already stood up in internal data science team they don't have the knowledge to understand what that when they flip that switch for an automated ai outcome that it's it's gonna do what they think it's gonna do and so we need some sort of standard standard methodology and practice best practices that every company that's going to consume this invisible ai can make use of and one of the things that you know is sort of started that google kicked off a few years back that's picking up some momentum and the companies i just mentioned are starting to use it is this idea of model cards where at least you have some transparency about what these things are doing you know so like for the sap example we know for example that it's convolutional neural network with a long short-term memory model that it's using we know that it only works on roman english uh and therefore me as a consumer can say oh well i know that i need to do this internationally so i should not just turn this on today great thank you carl can you add anything any context here yeah we've talked about some of the things brad mentioned here at idc in the our future of intelligence group regarding in particular the moral and legal implications of having a fully automated you know ai uh driven system uh because we already know and we've seen that ai systems are biased by the data that they get right so if if they get data that pushes them in a certain direction i think there was a story last week about an hr system that was uh that was recommending promotions for white people over black people because in the past um you know white people were promoted and and more productive than black people but not it had no context as to why which is you know because they were being historically discriminated black people being historically discriminated against but the system doesn't know that so you know you have to be aware of that and i think that at the very least there should be controls when a decision has either a moral or a legal implication when when you want when you really need a human judgment it could lay out the options for you but a person actually needs to authorize that that action and i also think that we always will have to be vigilant regarding the kind of data we use to train our systems to make sure that it doesn't introduce unintended biases and to some extent they always will so we'll always be chasing after them that's that's absolutely carl yeah i think that what you have to bear in mind as a as a consumer of ai is that it is a reflection of us and we are a very flawed species uh and so if you look at all the really fantastic magical looking supermodels we see like gpt three and four that's coming out z they're xenophobic and hateful uh because the people the data that's built upon them and the algorithms and the people that build them are us so ai is a reflection of us we need to keep that in mind yeah we're the ai's by us because humans are biased all right great okay let's move on doug henson you know a lot of people that said that data lake that term's not not going to not going to live on but it appears to be have some legs here uh you want to talk about lake house bring it on yes i do my prediction is that lake house and this idea of a combined data warehouse and data lake platform is going to emerge as the dominant data management offering i say offering that doesn't mean it's going to be the dominant thing that organizations have out there but it's going to be the predominant vendor offering in 2022. now heading into 2021 we already had cloudera data bricks microsoft snowflake as proponents in 2021 sap oracle and several of these fabric virtualization mesh vendors join the bandwagon the promise is that you have one platform that manages your structured unstructured and semi-structured information and it addresses both the beyond analytics needs and the data science needs the real promise there is simplicity and lower cost but i think end users have to answer a few questions the first is does your organization really have a center of data gravity or is it is the data highly distributed multiple data warehouses multiple data lakes on-premises cloud if it if it's very distributed and you you know you have difficulty consolidating and that's not really a goal for you then maybe that single platform is unrealistic and not likely to add value to you um you know also the fabric and virtualization vendors the the mesh idea that's where if you have this highly distributed situation that might be a better path forward the second question if you are looking at one of these lake house offerings you are looking at consolidating simplifying bringing together to a single platform you have to make sure that it meets both the warehouse need and the data lake need so you have vendors like data bricks microsoft with azure synapse new really to the data warehouse space and they're having to prove that these data warehouse capabilities on their platforms can meet the scaling requirements can meet the user and query concurrency requirements meet those tight slas and then on the other hand you have the or the oracle sap snowflake the data warehouse uh folks coming into the data science world and they have to prove that they can manage the unstructured information and meet the needs of the data scientists i'm seeing a lot of the lake house offerings from the warehouse crowd managing that unstructured information in columns and rows and some of these vendors snowflake in particular is really relying on partners for the data science needs so you really got to look at a lake house offering and make sure that it meets both the warehouse and the data lake requirement well thank you doug well tony if those two worlds are going to come together as doug was saying the analytics and the data science world does it need to be some kind of semantic layer in between i don't know weigh in on this topic if you would oh didn't we talk about data fabrics before common metadata layer um actually i'm almost tempted to say let's declare victory and go home in that this is actually been going on for a while i actually agree with uh you know much what doug is saying there which is that i mean we i remembered as far back as i think it was like 2014 i was doing a a study you know it was still at ovum predecessor omnia um looking at all these specialized databases that were coming up and seeing that you know there's overlap with the edges but yet there was still going to be a reason at the time that you would have let's say a document database for json you'd have a relational database for tran you know for transactions and for data warehouse and you had you know and you had basically something at that time that that resembles to do for what we're considering a day of life fast fo and the thing is what i was saying at the time is that you're seeing basically blur you know sort of blending at the edges that i was saying like about five or six years ago um that's all and the the lake house is essentially you know the amount of the the current manifestation of that idea there is a dichotomy in terms of you know it's the old argument do we centralize this all you know you know in in in in in a single place or do we or do we virtualize and i think it's always going to be a yin and yang there's never going to be a single single silver silver bullet i do see um that they're also going to be questions and these are things that points that doug raised they're you know what your what do you need of of of your of you know for your performance there or for your you know pre-performance characteristics do you need for instance hiking currency you need the ability to do some very sophisticated joins or is your requirement more to be able to distribute and you know distribute our processing is you know as far as possible to get you know to essentially do a kind of brute force approach all these approaches are valid based on you know based on the used case um i just see that essentially that the lake house is the culmination of it's nothing it's just it's a relatively new term introduced by databricks a couple years ago this is the culmination of basically what's been a long time trend and what we see in the cloud is that as we start seeing data warehouses as a checkbox item say hey we can basically source data in cloud and cloud storage and s3 azure blob store you know whatever um as long as it's in certain formats like you know like you know parquet or csv or something like that you know i see that as becoming kind of you know a check box item so to that extent i think that the lake house depending on how you define it is already reality um and in some in some cases maybe new terminology but not a whole heck of a lot new under the sun yeah and dave menger i mean a lot of this thank you tony but a lot of this is going to come down to you know vendor marketing right some people try to co-opt the term we talked about data mesh washing what are your thoughts on this yeah so um i used the term data platform earlier and and part of the reason i use that term is that it's more vendor neutral uh we've we've tried to uh sort of stay out of the the vendor uh terminology patenting world right whether whether the term lake house is what sticks or not the concept is certainly going to stick and we have some data to back it up about a quarter of organizations that are using data lakes today already incorporate data warehouse functionality into it so they consider their data lake house and data warehouse one in the same about a quarter of organizations a little less but about a quarter of organizations feed the data lake from the data warehouse and about a quarter of organizations feed the data warehouse from the data lake so it's pretty obvious that three quarters of organizations need to bring this stuff together right the need is there the need is apparent the technology is going to continue to verge converge i i like to talk about you know you've got data lakes over here at one end and i'm not going to talk about why people thought data lakes were a bad idea because they thought you just throw stuff in a in a server and you ignore it right that's not what a data lake is so you've got data lake people over here and you've got database people over here data warehouse people over here database vendors are adding data lake capabilities and data lake vendors are adding data warehouse capabilities so it's obvious that they're going to meet in the middle i mean i think it's like tony says i think we should there declare victory and go home and so so i it's just a follow-up on that so are you saying these the specialized lake and the specialized warehouse do they go away i mean johnny tony data mesh practitioners would say or or advocates would say well they could all live as just a node on the on the mesh but based on what dave just said are we going to see those all morph together well number one as i was saying before there's always going to be this sort of you know kind of you know centrifugal force or this tug of war between do we centralize the data do we do it virtualize and the fact is i don't think that work there's ever going to be any single answer i think in terms of data mesh data mesh has nothing to do with how you physically implement the data you could have a data mesh on a basically uh on a data warehouse it's just that you know the difference being is that if we use the same you know physical data store but everybody's logically manual basically governing it differently you know um a data mission is basically it's not a technology it's a process it's a governance process um so essentially um you know you know i basically see that you know as as i was saying before that this is basically the culmination of a long time trend we're essentially seeing a lot of blurring but there are going to be cases where for instance if i need let's say like observe i need like high concurrency or something like that there are certain things that i'm not going to be able to get efficiently get out of a data lake um and you know we're basically i'm doing a system where i'm just doing really brute forcing very fast file scanning and that type of thing so i think there always will be some delineations but i would agree with dave and with doug that we are seeing basically a a confluence of requirements that we need to essentially have basically the element you know the ability of a data lake and a data laid out their warehouse we these need to come together so i think what we're likely to see is organizations look for a converged platform that can handle both sides for their center of data gravity the mesh and the fabric vendors the the fabric virtualization vendors they're all on board with the idea of this converged platform and they're saying hey we'll handle all the edge cases of the stuff that isn't in that center of data gradient that is off distributed in a cloud or at a remote location so you can have that single platform for the center of of your your data and then bring in virtualization mesh what have you for reaching out to the distributed data bingo as they basically said people are happy when they virtualize data i i think yes at this point but to this uh dave meningas point you know they have convert they are converging snowflake has introduced support for unstructured data so now we are literally splitting here now what uh databricks is saying is that aha but it's easy to go from data lake to data warehouse than it is from data warehouse to data lake so i think we're getting into semantics but we've already seen these two converge so is that so it takes something like aws who's got what 15 data stores are they're going to have 15 converged data stores that's going to be interesting to watch all right guys i'm going to go down the list and do like a one i'm going to one word each and you guys each of the analysts if you wouldn't just add a very brief sort of course correction for me so sanjeev i mean governance is going to be the maybe it's the dog that wags the tail now i mean it's coming to the fore all this ransomware stuff which really didn't talk much about security but but but what's the one word in your prediction that you would leave us with on governance it's uh it's going to be mainstream mainstream okay tony bear mesh washing is what i wrote down that's that's what we're going to see in uh in in 2022 a little reality check you you want to add to that reality check is i hope that no vendor you know jumps the shark and calls their offering a data mesh project yeah yeah let's hope that doesn't happen if they do we're going to call them out uh carl i mean graph databases thank you for sharing some some you know high growth metrics i know it's early days but magic is what i took away from that it's the magic database yeah i would actually i've said this to people too i i kind of look at it as a swiss army knife of data because you can pretty much do anything you want with it it doesn't mean you should i mean that's definitely the case that if you're you know managing things that are in a fixed schematic relationship probably a relational database is a better choice there are you know times when the document database is a better choice it can handle those things but maybe not it may not be the best choice for that use case but for a great many especially the new emerging use cases i listed it's the best choice thank you and dave meninger thank you by the way for bringing the data in i like how you supported all your comments with with some some data points but streaming data becomes the sort of default uh paradigm if you will what would you add yeah um i would say think fast right that's the world we live in you got to think fast fast love it uh and brad shimon uh i love it i mean on the one hand i was saying okay great i'm afraid i might get disrupted by one of these internet giants who are ai experts so i'm gonna be able to buy instead of build ai but then again you know i've got some real issues there's a potential backlash there so give us the there's your bumper sticker yeah i i would say um going with dave think fast and also think slow uh to to talk about the book that everyone talks about i would say really that this is all about trust trust in the idea of automation and of a transparent invisible ai across the enterprise but verify verify before you do anything and then doug henson i mean i i look i think the the trend is your friend here on this prediction with lake house is uh really becoming dominant i liked the way you set up that notion of you know the the the data warehouse folks coming at it from the analytics perspective but then you got the data science worlds coming together i still feel as though there's this piece in the middle that we're missing but your your final thoughts we'll give you the last well i think the idea of consolidation and simplification uh always prevails that's why the appeal of a single platform is going to be there um we've already seen that with uh you know hadoop platforms moving toward cloud moving toward object storage and object storage becoming really the common storage point for whether it's a lake or a warehouse uh and that second point uh i think esg mandates are uh are gonna come in alongside uh gdpr and things like that to uh up the ante for uh good governance yeah thank you for calling that out okay folks hey that's all the time that that we have here your your experience and depth of understanding on these key issues and in data and data management really on point and they were on display today i want to thank you for your your contributions really appreciate your time enjoyed it thank you now in addition to this video we're going to be making available transcripts of the discussion we're going to do clips of this as well we're going to put them out on social media i'll write this up and publish the discussion on wikibon.com and siliconangle.com no doubt several of the analysts on the panel will take the opportunity to publish written content social commentary or both i want to thank the power panelist and thanks for watching this special cube presentation this is dave vellante be well and we'll see you next time [Music] you

(bright music) >> In the 2010s, organizations became keenly aware that data would become the key ingredient to driving competitive advantage, differentiation, and growth. But to this day, putting data to work remains a difficult challenge for many, if not most organizations. Now, as the cloud matures, it has become a game changer for data practitioners by making cheap storage and massive processing power readily accessible. We've also seen better tooling in the form of data workflows, streaming, machine intelligence, AI, developer tools, security, observability, automation, new databases and the like. These innovations they accelerate data proficiency, but at the same time, they add complexity for practitioners. Data lakes, data hubs, data warehouses, data marts, data fabrics, data meshes, data catalogs, data oceans are forming, they're evolving and exploding onto the scene. So in an effort to bring perspective to the sea of optionality, we've brought together the brightest minds in the data analyst community to discuss how data management is morphing and what practitioners should expect in 2022 and beyond. Hello everyone, my name is Dave Velannte with theCUBE, and I'd like to welcome you to a special Cube presentation, analysts predictions 2022: the future of data management. We've gathered six of the best analysts in data and data management who are going to present and discuss their top predictions and trends for 2022 in the first half of this decade. Let me introduce our six power panelists. Sanjeev Mohan is former Gartner Analyst and Principal at SanjMo. Tony Baer, principal at dbInsight, Carl Olofson is well-known Research Vice President with IDC, Dave Menninger is Senior Vice President and Research Director at Ventana Research, Brad Shimmin, Chief Analyst, AI Platforms, Analytics and Data Management at Omdia and Doug Henschen, Vice President and Principal Analyst at Constellation Research. Gentlemen, welcome to the program and thanks for coming on theCUBE today. >> Great to be here. >> Thank you. >> All right, here's the format we're going to use. I as moderator, I'm going to call on each analyst separately who then will deliver their prediction or mega trend, and then in the interest of time management and pace, two analysts will have the opportunity to comment. If we have more time, we'll elongate it, but let's get started right away. Sanjeev Mohan, please kick it off. You want to talk about governance, go ahead sir. >> Thank you Dave. I believe that data governance which we've been talking about for many years is now not only going to be mainstream, it's going to be table stakes. And all the things that you mentioned, you know, the data, ocean data lake, lake houses, data fabric, meshes, the common glue is metadata. If we don't understand what data we have and we are governing it, there is no way we can manage it. So we saw Informatica went public last year after a hiatus of six. I'm predicting that this year we see some more companies go public. My bet is on Culebra, most likely and maybe Alation we'll see go public this year. I'm also predicting that the scope of data governance is going to expand beyond just data. It's not just data and reports. We are going to see more transformations like spark jawsxxxxx, Python even Air Flow. We're going to see more of a streaming data. So from Kafka Schema Registry, for example. We will see AI models become part of this whole governance suite. So the governance suite is going to be very comprehensive, very detailed lineage, impact analysis, and then even expand into data quality. We already seen that happen with some of the tools where they are buying these smaller companies and bringing in data quality monitoring and integrating it with metadata management, data catalogs, also data access governance. So what we are going to see is that once the data governance platforms become the key entry point into these modern architectures, I'm predicting that the usage, the number of users of a data catalog is going to exceed that of a BI tool. That will take time and we already seen that trajectory. Right now if you look at BI tools, I would say there a hundred users to BI tool to one data catalog. And I see that evening out over a period of time and at some point data catalogs will really become the main way for us to access data. Data catalog will help us visualize data, but if we want to do more in-depth analysis, it'll be the jumping off point into the BI tool, the data science tool and that is the journey I see for the data governance products. >> Excellent, thank you. Some comments. Maybe Doug, a lot of things to weigh in on there, maybe you can comment. >> Yeah, Sanjeev I think you're spot on, a lot of the trends the one disagreement, I think it's really still far from mainstream. As you say, we've been talking about this for years, it's like God, motherhood, apple pie, everyone agrees it's important, but too few organizations are really practicing good governance because it's hard and because the incentives have been lacking. I think one thing that deserves mention in this context is ESG mandates and guidelines, these are environmental, social and governance, regs and guidelines. We've seen the environmental regs and guidelines and posts in industries, particularly the carbon-intensive industries. We've seen the social mandates, particularly diversity imposed on suppliers by companies that are leading on this topic. We've seen governance guidelines now being imposed by banks on investors. So these ESGs are presenting new carrots and sticks, and it's going to demand more solid data. It's going to demand more detailed reporting and solid reporting, tighter governance. But we're still far from mainstream adoption. We have a lot of, you know, best of breed niche players in the space. I think the signs that it's going to be more mainstream are starting with things like Azure Purview, Google Dataplex, the big cloud platform players seem to be upping the ante and starting to address governance. >> Excellent, thank you Doug. Brad, I wonder if you could chime in as well. >> Yeah, I would love to be a believer in data catalogs. But to Doug's point, I think that it's going to take some more pressure for that to happen. I recall metadata being something every enterprise thought they were going to get under control when we were working on service oriented architecture back in the nineties and that didn't happen quite the way we anticipated. And so to Sanjeev's point it's because it is really complex and really difficult to do. My hope is that, you know, we won't sort of, how do I put this? Fade out into this nebula of domain catalogs that are specific to individual use cases like Purview for getting data quality right or like data governance and cybersecurity. And instead we have some tooling that can actually be adaptive to gather metadata to create something. And I know its important to you, Sanjeev and that is this idea of observability. If you can get enough metadata without moving your data around, but understanding the entirety of a system that's running on this data, you can do a lot. So to help with the governance that Doug is talking about. >> So I just want to add that, data governance, like any other initiatives did not succeed even AI went into an AI window, but that's a different topic. But a lot of these things did not succeed because to your point, the incentives were not there. I remember when Sarbanes Oxley had come into the scene, if a bank did not do Sarbanes Oxley, they were very happy to a million dollar fine. That was like, you know, pocket change for them instead of doing the right thing. But I think the stakes are much higher now. With GDPR, the flood gates opened. Now, you know, California, you know, has CCPA but even CCPA is being outdated with CPRA, which is much more GDPR like. So we are very rapidly entering a space where pretty much every major country in the world is coming up with its own compliance regulatory requirements, data residents is becoming really important. And I think we are going to reach a stage where it won't be optional anymore. So whether we like it or not, and I think the reason data catalogs were not successful in the past is because we did not have the right focus on adoption. We were focused on features and these features were disconnected, very hard for business to adopt. These are built by IT people for IT departments to take a look at technical metadata, not business metadata. Today the tables have turned. CDOs are driving this initiative, regulatory compliances are beating down hard, so I think the time might be right. >> Yeah so guys, we have to move on here. But there's some real meat on the bone here, Sanjeev. I like the fact that you called out Culebra and Alation, so we can look back a year from now and say, okay, he made the call, he stuck it. And then the ratio of BI tools to data catalogs that's another sort of measurement that we can take even though with some skepticism there, that's something that we can watch. And I wonder if someday, if we'll have more metadata than data. But I want to move to Tony Baer, you want to talk about data mesh and speaking, you know, coming off of governance. I mean, wow, you know the whole concept of data mesh is, decentralized data, and then governance becomes, you know, a nightmare there, but take it away, Tony. >> We'll put this way, data mesh, you know, the idea at least as proposed by ThoughtWorks. You know, basically it was at least a couple of years ago and the press has been almost uniformly almost uncritical. A good reason for that is for all the problems that basically Sanjeev and Doug and Brad we're just speaking about, which is that we have all this data out there and we don't know what to do about it. Now, that's not a new problem. That was a problem we had in enterprise data warehouses, it was a problem when we had over DoOP data clusters, it's even more of a problem now that data is out in the cloud where the data is not only your data lake, is not only us three, it's all over the place. And it's also including streaming, which I know we'll be talking about later. So the data mesh was a response to that, the idea of that we need to bait, you know, who are the folks that really know best about governance? It's the domain experts. So it was basically data mesh was an architectural pattern and a process. My prediction for this year is that data mesh is going to hit cold heart reality. Because if you do a Google search, basically the published work, the articles on data mesh have been largely, you know, pretty uncritical so far. Basically loading and is basically being a very revolutionary new idea. I don't think it's that revolutionary because we've talked about ideas like this. Brad now you and I met years ago when we were talking about so and decentralizing all of us, but it was at the application level. Now we're talking about it at the data level. And now we have microservices. So there's this thought of have we managed if we're deconstructing apps in cloud native to microservices, why don't we think of data in the same way? My sense this year is that, you know, this has been a very active search if you look at Google search trends, is that now companies, like enterprise are going to look at this seriously. And as they look at it seriously, it's going to attract its first real hard scrutiny, it's going to attract its first backlash. That's not necessarily a bad thing. It means that it's being taken seriously. The reason why I think that you'll start to see basically the cold hearted light of day shine on data mesh is that it's still a work in progress. You know, this idea is basically a couple of years old and there's still some pretty major gaps. The biggest gap is in the area of federated governance. Now federated governance itself is not a new issue. Federated governance decision, we started figuring out like, how can we basically strike the balance between getting let's say between basically consistent enterprise policy, consistent enterprise governance, but yet the groups that understand the data and know how to basically, you know, that, you know, how do we basically sort of balance the two? There's a huge gap there in practice and knowledge. Also to a lesser extent, there's a technology gap which is basically in the self-service technologies that will help teams essentially govern data. You know, basically through the full life cycle, from develop, from selecting the data from, you know, building the pipelines from, you know, determining your access control, looking at quality, looking at basically whether the data is fresh or whether it's trending off course. So my prediction is that it will receive the first harsh scrutiny this year. You are going to see some organization and enterprises declare premature victory when they build some federated query implementations. You going to see vendors start with data mesh wash their products anybody in the data management space that they are going to say that where this basically a pipelining tool, whether it's basically ELT, whether it's a catalog or federated query tool, they will all going to get like, you know, basically promoting the fact of how they support this. Hopefully nobody's going to call themselves a data mesh tool because data mesh is not a technology. We're going to see one other thing come out of this. And this harks back to the metadata that Sanjeev was talking about and of the catalog just as he was talking about. Which is that there's going to be a new focus, every renewed focus on metadata. And I think that's going to spur interest in data fabrics. Now data fabrics are pretty vaguely defined, but if we just take the most elemental definition, which is a common metadata back plane, I think that if anybody is going to get serious about data mesh, they need to look at the data fabric because we all at the end of the day, need to speak, you know, need to read from the same sheet of music. >> So thank you Tony. Dave Menninger, I mean, one of the things that people like about data mesh is it pretty crisply articulate some of the flaws in today's organizational approaches to data. What are your thoughts on this? >> Well, I think we have to start by defining data mesh, right? The term is already getting corrupted, right? Tony said it's going to see the cold hard light of day. And there's a problem right now that there are a number of overlapping terms that are similar but not identical. So we've got data virtualization, data fabric, excuse me for a second. (clears throat) Sorry about that. Data virtualization, data fabric, data federation, right? So I think that it's not really clear what each vendor means by these terms. I see data mesh and data fabric becoming quite popular. I've interpreted data mesh as referring primarily to the governance aspects as originally intended and specified. But that's not the way I see vendors using it. I see vendors using it much more to mean data fabric and data virtualization. So I'm going to comment on the group of those things. I think the group of those things is going to happen. They're going to happen, they're going to become more robust. Our research suggests that a quarter of organizations are already using virtualized access to their data lakes and another half, so a total of three quarters will eventually be accessing their data lakes using some sort of virtualized access. Again, whether you define it as mesh or fabric or virtualization isn't really the point here. But this notion that there are different elements of data, metadata and governance within an organization that all need to be managed collectively. The interesting thing is when you look at the satisfaction rates of those organizations using virtualization versus those that are not, it's almost double, 68% of organizations, I'm sorry, 79% of organizations that were using virtualized access express satisfaction with their access to the data lake. Only 39% express satisfaction if they weren't using virtualized access. >> Oh thank you Dave. Sanjeev we just got about a couple of minutes on this topic, but I know you're speaking or maybe you've always spoken already on a panel with (indistinct) who sort of invented the concept. Governance obviously is a big sticking point, but what are your thoughts on this? You're on mute. (panelist chuckling) >> So my message to (indistinct) and to the community is as opposed to what they said, let's not define it. We spent a whole year defining it, there are four principles, domain, product, data infrastructure, and governance. Let's take it to the next level. I get a lot of questions on what is the difference between data fabric and data mesh? And I'm like I can't compare the two because data mesh is a business concept, data fabric is a data integration pattern. How do you compare the two? You have to bring data mesh a level down. So to Tony's point, I'm on a warpath in 2022 to take it down to what does a data product look like? How do we handle shared data across domains and governance? And I think we are going to see more of that in 2022, or is "operationalization" of data mesh. >> I think we could have a whole hour on this topic, couldn't we? Maybe we should do that. But let's corner. Let's move to Carl. So Carl, you're a database guy, you've been around that block for a while now, you want to talk about graph databases, bring it on. >> Oh yeah. Okay thanks. So I regard graph database as basically the next truly revolutionary database management technology. I'm looking forward for the graph database market, which of course we haven't defined yet. So obviously I have a little wiggle room in what I'm about to say. But this market will grow by about 600% over the next 10 years. Now, 10 years is a long time. But over the next five years, we expect to see gradual growth as people start to learn how to use it. The problem is not that it's not useful, its that people don't know how to use it. So let me explain before I go any further what a graph database is because some of the folks on the call may not know what it is. A graph database organizes data according to a mathematical structure called a graph. The graph has elements called nodes and edges. So a data element drops into a node, the nodes are connected by edges, the edges connect one node to another node. Combinations of edges create structures that you can analyze to determine how things are related. In some cases, the nodes and edges can have properties attached to them which add additional informative material that makes it richer, that's called a property graph. There are two principle use cases for graph databases. There's semantic property graphs, which are use to break down human language texts into the semantic structures. Then you can search it, organize it and answer complicated questions. A lot of AI is aimed at semantic graphs. Another kind is the property graph that I just mentioned, which has a dazzling number of use cases. I want to just point out as I talk about this, people are probably wondering, well, we have relation databases, isn't that good enough? So a relational database defines... It supports what I call definitional relationships. That means you define the relationships in a fixed structure. The database drops into that structure, there's a value, foreign key value, that relates one table to another and that value is fixed. You don't change it. If you change it, the database becomes unstable, it's not clear what you're looking at. In a graph database, the system is designed to handle change so that it can reflect the true state of the things that it's being used to track. So let me just give you some examples of use cases for this. They include entity resolution, data lineage, social media analysis, Customer 360, fraud prevention. There's cybersecurity, there's strong supply chain is a big one actually. There is explainable AI and this is going to become important too because a lot of people are adopting AI. But they want a system after the fact to say, how do the AI system come to that conclusion? How did it make that recommendation? Right now we don't have really good ways of tracking that. Machine learning in general, social network, I already mentioned that. And then we've got, oh gosh, we've got data governance, data compliance, risk management. We've got recommendation, we've got personalization, anti money laundering, that's another big one, identity and access management, network and IT operations is already becoming a key one where you actually have mapped out your operation, you know, whatever it is, your data center and you can track what's going on as things happen there, root cause analysis, fraud detection is a huge one. A number of major credit card companies use graph databases for fraud detection, risk analysis, tracking and tracing turn analysis, next best action, what if analysis, impact analysis, entity resolution and I would add one other thing or just a few other things to this list, metadata management. So Sanjeev, here you go, this is your engine. Because I was in metadata management for quite a while in my past life. And one of the things I found was that none of the data management technologies that were available to us could efficiently handle metadata because of the kinds of structures that result from it, but graphs can, okay? Graphs can do things like say, this term in this context means this, but in that context, it means that, okay? Things like that. And in fact, logistics management, supply chain. And also because it handles recursive relationships, by recursive relationships I mean objects that own other objects that are of the same type. You can do things like build materials, you know, so like parts explosion. Or you can do an HR analysis, who reports to whom, how many levels up the chain and that kind of thing. You can do that with relational databases, but yet it takes a lot of programming. In fact, you can do almost any of these things with relational databases, but the problem is, you have to program it. It's not supported in the database. And whenever you have to program something, that means you can't trace it, you can't define it. You can't publish it in terms of its functionality and it's really, really hard to maintain over time. >> Carl, thank you. I wonder if we could bring Brad in, I mean. Brad, I'm sitting here wondering, okay, is this incremental to the market? Is it disruptive and replacement? What are your thoughts on this phase? >> It's already disrupted the market. I mean, like Carl said, go to any bank and ask them are you using graph databases to get fraud detection under control? And they'll say, absolutely, that's the only way to solve this problem. And it is frankly. And it's the only way to solve a lot of the problems that Carl mentioned. And that is, I think it's Achilles heel in some ways. Because, you know, it's like finding the best way to cross the seven bridges of Koenigsberg. You know, it's always going to kind of be tied to those use cases because it's really special and it's really unique and because it's special and it's unique, it's still unfortunately kind of stands apart from the rest of the community that's building, let's say AI outcomes, as a great example here. Graph databases and AI, as Carl mentioned, are like chocolate and peanut butter. But technologically, you think don't know how to talk to one another, they're completely different. And you know, you can't just stand up SQL and query them. You've got to learn, know what is the Carl? Specter special. Yeah, thank you to, to actually get to the data in there. And if you're going to scale that data, that graph database, especially a property graph, if you're going to do something really complex, like try to understand you know, all of the metadata in your organization, you might just end up with, you know, a graph database winter like we had the AI winter simply because you run out of performance to make the thing happen. So, I think it's already disrupted, but we need to like treat it like a first-class citizen in the data analytics and AI community. We need to bring it into the fold. We need to equip it with the tools it needs to do the magic it does and to do it not just for specialized use cases, but for everything. 'Cause I'm with Carl. I think it's absolutely revolutionary. >> Brad identified the principal, Achilles' heel of the technology which is scaling. When these things get large and complex enough that they spill over what a single server can handle, you start to have difficulties because the relationships span things that have to be resolved over a network and then you get network latency and that slows the system down. So that's still a problem to be solved. >> Sanjeev, any quick thoughts on this? I mean, I think metadata on the word cloud is going to be the largest font, but what are your thoughts here? >> I want to (indistinct) So people don't associate me with only metadata, so I want to talk about something slightly different. dbengines.com has done an amazing job. I think almost everyone knows that they chronicle all the major databases that are in use today. In January of 2022, there are 381 databases on a ranked list of databases. The largest category is RDBMS. The second largest category is actually divided into two property graphs and IDF graphs. These two together make up the second largest number databases. So talking about Achilles heel, this is a problem. The problem is that there's so many graph databases to choose from. They come in different shapes and forms. To Brad's point, there's so many query languages in RDBMS, in SQL. I know the story, but here We've got cipher, we've got gremlin, we've got GQL and then we're proprietary languages. So I think there's a lot of disparity in this space. >> Well, excellent. All excellent points, Sanjeev, if I must say. And that is a problem that the languages need to be sorted and standardized. People need to have a roadmap as to what they can do with it. Because as you say, you can do so many things. And so many of those things are unrelated that you sort of say, well, what do we use this for? And I'm reminded of the saying I learned a bunch of years ago. And somebody said that the digital computer is the only tool man has ever device that has no particular purpose. (panelists chuckle) >> All right guys, we got to move on to Dave Menninger. We've heard about streaming. Your prediction is in that realm, so please take it away. >> Sure. So I like to say that historical databases are going to become a thing of the past. By that I don't mean that they're going to go away, that's not my point. I mean, we need historical databases, but streaming data is going to become the default way in which we operate with data. So in the next say three to five years, I would expect that data platforms and we're using the term data platforms to represent the evolution of databases and data lakes, that the data platforms will incorporate these streaming capabilities. We're going to process data as it streams into an organization and then it's going to roll off into historical database. So historical databases don't go away, but they become a thing of the past. They store the data that occurred previously. And as data is occurring, we're going to be processing it, we're going to be analyzing it, we're going to be acting on it. I mean we only ever ended up with historical databases because we were limited by the technology that was available to us. Data doesn't occur in patches. But we processed it in patches because that was the best we could do. And it wasn't bad and we've continued to improve and we've improved and we've improved. But streaming data today is still the exception. It's not the rule, right? There are projects within organizations that deal with streaming data. But it's not the default way in which we deal with data yet. And so that's my prediction is that this is going to change, we're going to have streaming data be the default way in which we deal with data and how you label it and what you call it. You know, maybe these databases and data platforms just evolved to be able to handle it. But we're going to deal with data in a different way. And our research shows that already, about half of the participants in our analytics and data benchmark research, are using streaming data. You know, another third are planning to use streaming technologies. So that gets us to about eight out of 10 organizations need to use this technology. And that doesn't mean they have to use it throughout the whole organization, but it's pretty widespread in its use today and has continued to grow. If you think about the consumerization of IT, we've all been conditioned to expect immediate access to information, immediate responsiveness. You know, we want to know if an item is on the shelf at our local retail store and we can go in and pick it up right now. You know, that's the world we live in and that's spilling over into the enterprise IT world We have to provide those same types of capabilities. So that's my prediction, historical databases become a thing of the past, streaming data becomes the default way in which we operate with data. >> All right thank you David. Well, so what say you, Carl, the guy who has followed historical databases for a long time? >> Well, one thing actually, every database is historical because as soon as you put data in it, it's now history. They'll no longer reflect the present state of things. But even if that history is only a millisecond old, it's still history. But I would say, I mean, I know you're trying to be a little bit provocative in saying this Dave 'cause you know, as well as I do that people still need to do their taxes, they still need to do accounting, they still need to run general ledger programs and things like that. That all involves historical data. That's not going to go away unless you want to go to jail. So you're going to have to deal with that. But as far as the leading edge functionality, I'm totally with you on that. And I'm just, you know, I'm just kind of wondering if this requires a change in the way that we perceive applications in order to truly be manifested and rethinking the way applications work. Saying that an application should respond instantly, as soon as the state of things changes. What do you say about that? >> I think that's true. I think we do have to think about things differently. It's not the way we designed systems in the past. We're seeing more and more systems designed that way. But again, it's not the default. And I agree 100% with you that we do need historical databases you know, that's clear. And even some of those historical databases will be used in conjunction with the streaming data, right? >> Absolutely. I mean, you know, let's take the data warehouse example where you're using the data warehouse as its context and the streaming data as the present and you're saying, here's the sequence of things that's happening right now. Have we seen that sequence before? And where? What does that pattern look like in past situations? And can we learn from that? >> So Tony Baer, I wonder if you could comment? I mean, when you think about, you know, real time inferencing at the edge, for instance, which is something that a lot of people talk about, a lot of what we're discussing here in this segment, it looks like it's got a great potential. What are your thoughts? >> Yeah, I mean, I think you nailed it right. You know, you hit it right on the head there. Which is that, what I'm seeing is that essentially. Then based on I'm going to split this one down the middle is that I don't see that basically streaming is the default. What I see is streaming and basically and transaction databases and analytics data, you know, data warehouses, data lakes whatever are converging. And what allows us technically to converge is cloud native architecture, where you can basically distribute things. So you can have a node here that's doing the real-time processing, that's also doing... And this is where it leads in or maybe doing some of that real time predictive analytics to take a look at, well look, we're looking at this customer journey what's happening with what the customer is doing right now and this is correlated with what other customers are doing. So the thing is that in the cloud, you can basically partition this and because of basically the speed of the infrastructure then you can basically bring these together and kind of orchestrate them sort of a loosely coupled manner. The other parts that the use cases are demanding, and this is part of it goes back to what Dave is saying. Is that, you know, when you look at Customer 360, when you look at let's say Smart Utility products, when you look at any type of operational problem, it has a real time component and it has an historical component. And having predictive and so like, you know, my sense here is that technically we can bring this together through the cloud. And I think the use case is that we can apply some real time sort of predictive analytics on these streams and feed this into the transactions so that when we make a decision in terms of what to do as a result of a transaction, we have this real-time input. >> Sanjeev, did you have a comment? >> Yeah, I was just going to say that to Dave's point, you know, we have to think of streaming very different because in the historical databases, we used to bring the data and store the data and then we used to run rules on top, aggregations and all. But in case of streaming, the mindset changes because the rules are normally the inference, all of that is fixed, but the data is constantly changing. So it's a completely reversed way of thinking and building applications on top of that. >> So Dave Menninger, there seem to be some disagreement about the default. What kind of timeframe are you thinking about? Is this end of decade it becomes the default? What would you pin? >> I think around, you know, between five to 10 years, I think this becomes the reality. >> I think its... >> It'll be more and more common between now and then, but it becomes the default. And I also want Sanjeev at some point, maybe in one of our subsequent conversations, we need to talk about governing streaming data. 'Cause that's a whole nother set of challenges. >> We've also talked about it rather in two dimensions, historical and streaming, and there's lots of low latency, micro batch, sub-second, that's not quite streaming, but in many cases its fast enough and we're seeing a lot of adoption of near real time, not quite real-time as good enough for many applications. (indistinct cross talk from panelists) >> Because nobody's really taking the hardware dimension (mumbles). >> That'll just happened, Carl. (panelists laughing) >> So near real time. But maybe before you lose the customer, however we define that, right? Okay, let's move on to Brad. Brad, you want to talk about automation, AI, the pipeline people feel like, hey, we can just automate everything. What's your prediction? >> Yeah I'm an AI aficionados so apologies in advance for that. But, you know, I think that we've been seeing automation play within AI for some time now. And it's helped us do a lot of things especially for practitioners that are building AI outcomes in the enterprise. It's helped them to fill skills gaps, it's helped them to speed development and it's helped them to actually make AI better. 'Cause it, you know, in some ways provide some swim lanes and for example, with technologies like AutoML can auto document and create that sort of transparency that we talked about a little bit earlier. But I think there's an interesting kind of conversion happening with this idea of automation. And that is that we've had the automation that started happening for practitioners, it's trying to move out side of the traditional bounds of things like I'm just trying to get my features, I'm just trying to pick the right algorithm, I'm just trying to build the right model and it's expanding across that full life cycle, building an AI outcome, to start at the very beginning of data and to then continue on to the end, which is this continuous delivery and continuous automation of that outcome to make sure it's right and it hasn't drifted and stuff like that. And because of that, because it's become kind of powerful, we're starting to actually see this weird thing happen where the practitioners are starting to converge with the users. And that is to say that, okay, if I'm in Tableau right now, I can stand up Salesforce Einstein Discovery, and it will automatically create a nice predictive algorithm for me given the data that I pull in. But what's starting to happen and we're seeing this from the companies that create business software, so Salesforce, Oracle, SAP, and others is that they're starting to actually use these same ideals and a lot of deep learning (chuckles) to basically stand up these out of the box flip-a-switch, and you've got an AI outcome at the ready for business users. And I am very much, you know, I think that's the way that it's going to go and what it means is that AI is slowly disappearing. And I don't think that's a bad thing. I think if anything, what we're going to see in 2022 and maybe into 2023 is this sort of rush to put this idea of disappearing AI into practice and have as many of these solutions in the enterprise as possible. You can see, like for example, SAP is going to roll out this quarter, this thing called adaptive recommendation services, which basically is a cold start AI outcome that can work across a whole bunch of different vertical markets and use cases. It's just a recommendation engine for whatever you needed to do in the line of business. So basically, you're an SAP user, you look up to turn on your software one day, you're a sales professional let's say, and suddenly you have a recommendation for customer churn. Boom! It's going, that's great. Well, I don't know, I think that's terrifying. In some ways I think it is the future that AI is going to disappear like that, but I'm absolutely terrified of it because I think that what it really does is it calls attention to a lot of the issues that we already see around AI, specific to this idea of what we like to call at Omdia, responsible AI. Which is, you know, how do you build an AI outcome that is free of bias, that is inclusive, that is fair, that is safe, that is secure, that its audible, et cetera, et cetera, et cetera, et cetera. I'd take a lot of work to do. And so if you imagine a customer that's just a Salesforce customer let's say, and they're turning on Einstein Discovery within their sales software, you need some guidance to make sure that when you flip that switch, that the outcome you're going to get is correct. And that's going to take some work. And so, I think we're going to see this move, let's roll this out and suddenly there's going to be a lot of problems, a lot of pushback that we're going to see. And some of that's going to come from GDPR and others that Sanjeev was mentioning earlier. A lot of it is going to come from internal CSR requirements within companies that are saying, "Hey, hey, whoa, hold up, we can't do this all at once. "Let's take the slow route, "let's make AI automated in a smart way." And that's going to take time. >> Yeah, so a couple of predictions there that I heard. AI simply disappear, it becomes invisible. Maybe if I can restate that. And then if I understand it correctly, Brad you're saying there's a backlash in the near term. You'd be able to say, oh, slow down. Let's automate what we can. Those attributes that you talked about are non trivial to achieve, is that why you're a bit of a skeptic? >> Yeah. I think that we don't have any sort of standards that companies can look to and understand. And we certainly, within these companies, especially those that haven't already stood up an internal data science team, they don't have the knowledge to understand when they flip that switch for an automated AI outcome that it's going to do what they think it's going to do. And so we need some sort of standard methodology and practice, best practices that every company that's going to consume this invisible AI can make use of them. And one of the things that you know, is sort of started that Google kicked off a few years back that's picking up some momentum and the companies I just mentioned are starting to use it is this idea of model cards where at least you have some transparency about what these things are doing. You know, so like for the SAP example, we know, for example, if it's convolutional neural network with a long, short term memory model that it's using, we know that it only works on Roman English and therefore me as a consumer can say, "Oh, well I know that I need to do this internationally. "So I should not just turn this on today." >> Thank you. Carl could you add anything, any context here? >> Yeah, we've talked about some of the things Brad mentioned here at IDC and our future of intelligence group regarding in particular, the moral and legal implications of having a fully automated, you know, AI driven system. Because we already know, and we've seen that AI systems are biased by the data that they get, right? So if they get data that pushes them in a certain direction, I think there was a story last week about an HR system that was recommending promotions for White people over Black people, because in the past, you know, White people were promoted and more productive than Black people, but it had no context as to why which is, you know, because they were being historically discriminated, Black people were being historically discriminated against, but the system doesn't know that. So, you know, you have to be aware of that. And I think that at the very least, there should be controls when a decision has either a moral or legal implication. When you really need a human judgment, it could lay out the options for you. But a person actually needs to authorize that action. And I also think that we always will have to be vigilant regarding the kind of data we use to train our systems to make sure that it doesn't introduce unintended biases. In some extent, they always will. So we'll always be chasing after them. But that's (indistinct). >> Absolutely Carl, yeah. I think that what you have to bear in mind as a consumer of AI is that it is a reflection of us and we are a very flawed species. And so if you look at all of the really fantastic, magical looking supermodels we see like GPT-3 and four, that's coming out, they're xenophobic and hateful because the people that the data that's built upon them and the algorithms and the people that build them are us. So AI is a reflection of us. We need to keep that in mind. >> Yeah, where the AI is biased 'cause humans are biased. All right, great. All right let's move on. Doug you mentioned mentioned, you know, lot of people that said that data lake, that term is not going to live on but here's to be, have some lakes here. You want to talk about lake house, bring it on. >> Yes, I do. My prediction is that lake house and this idea of a combined data warehouse and data lake platform is going to emerge as the dominant data management offering. I say offering that doesn't mean it's going to be the dominant thing that organizations have out there, but it's going to be the pro dominant vendor offering in 2022. Now heading into 2021, we already had Cloudera, Databricks, Microsoft, Snowflake as proponents, in 2021, SAP, Oracle, and several of all of these fabric virtualization/mesh vendors joined the bandwagon. The promise is that you have one platform that manages your structured, unstructured and semi-structured information. And it addresses both the BI analytics needs and the data science needs. The real promise there is simplicity and lower cost. But I think end users have to answer a few questions. The first is, does your organization really have a center of data gravity or is the data highly distributed? Multiple data warehouses, multiple data lakes, on premises, cloud. If it's very distributed and you'd have difficulty consolidating and that's not really a goal for you, then maybe that single platform is unrealistic and not likely to add value to you. You know, also the fabric and virtualization vendors, the mesh idea, that's where if you have this highly distributed situation, that might be a better path forward. The second question, if you are looking at one of these lake house offerings, you are looking at consolidating, simplifying, bringing together to a single platform. You have to make sure that it meets both the warehouse need and the data lake need. So you have vendors like Databricks, Microsoft with Azure Synapse. New really to the data warehouse space and they're having to prove that these data warehouse capabilities on their platforms can meet the scaling requirements, can meet the user and query concurrency requirements. Meet those tight SLS. And then on the other hand, you have the Oracle, SAP, Snowflake, the data warehouse folks coming into the data science world, and they have to prove that they can manage the unstructured information and meet the needs of the data scientists. I'm seeing a lot of the lake house offerings from the warehouse crowd, managing that unstructured information in columns and rows. And some of these vendors, Snowflake a particular is really relying on partners for the data science needs. So you really got to look at a lake house offering and make sure that it meets both the warehouse and the data lake requirement. >> Thank you Doug. Well Tony, if those two worlds are going to come together, as Doug was saying, the analytics and the data science world, does it need to be some kind of semantic layer in between? I don't know. Where are you in on this topic? >> (chuckles) Oh, didn't we talk about data fabrics before? Common metadata layer (chuckles). Actually, I'm almost tempted to say let's declare victory and go home. And that this has actually been going on for a while. I actually agree with, you know, much of what Doug is saying there. Which is that, I mean I remember as far back as I think it was like 2014, I was doing a study. I was still at Ovum, (indistinct) Omdia, looking at all these specialized databases that were coming up and seeing that, you know, there's overlap at the edges. But yet, there was still going to be a reason at the time that you would have, let's say a document database for JSON, you'd have a relational database for transactions and for data warehouse and you had basically something at that time that resembles a dupe for what we consider your data life. Fast forward and the thing is what I was seeing at the time is that you were saying they sort of blending at the edges. That was saying like about five to six years ago. And the lake house is essentially on the current manifestation of that idea. There is a dichotomy in terms of, you know, it's the old argument, do we centralize this all you know in a single place or do we virtualize? And I think it's always going to be a union yeah and there's never going to be a single silver bullet. I do see that there are also going to be questions and these are points that Doug raised. That you know, what do you need for your performance there, or for your free performance characteristics? Do you need for instance high concurrency? You need the ability to do some very sophisticated joins, or is your requirement more to be able to distribute and distribute our processing is, you know, as far as possible to get, you know, to essentially do a kind of a brute force approach. All these approaches are valid based on the use case. I just see that essentially that the lake house is the culmination of it's nothing. It's a relatively new term introduced by Databricks a couple of years ago. This is the culmination of basically what's been a long time trend. And what we see in the cloud is that as we start seeing data warehouses as a check box items say, "Hey, we can basically source data in cloud storage, in S3, "Azure Blob Store, you know, whatever, "as long as it's in certain formats, "like, you know parquet or CSP or something like that." I see that as becoming kind of a checkbox item. So to that extent, I think that the lake house, depending on how you define is already reality. And in some cases, maybe new terminology, but not a whole heck of a lot new under the sun. >> Yeah. And Dave Menninger, I mean a lot of these, thank you Tony, but a lot of this is going to come down to, you know, vendor marketing, right? Some people just kind of co-op the term, we talked about you know, data mesh washing, what are your thoughts on this? (laughing) >> Yeah, so I used the term data platform earlier. And part of the reason I use that term is that it's more vendor neutral. We've tried to sort of stay out of the vendor terminology patenting world, right? Whether the term lake houses, what sticks or not, the concept is certainly going to stick. And we have some data to back it up. About a quarter of organizations that are using data lakes today, already incorporate data warehouse functionality into it. So they consider their data lake house and data warehouse one in the same, about a quarter of organizations, a little less, but about a quarter of organizations feed the data lake from the data warehouse and about a quarter of organizations feed the data warehouse from the data lake. So it's pretty obvious that three quarters of organizations need to bring this stuff together, right? The need is there, the need is apparent. The technology is going to continue to converge. I like to talk about it, you know, you've got data lakes over here at one end, and I'm not going to talk about why people thought data lakes were a bad idea because they thought you just throw stuff in a server and you ignore it, right? That's not what a data lake is. So you've got data lake people over here and you've got database people over here, data warehouse people over here, database vendors are adding data lake capabilities and data lake vendors are adding data warehouse capabilities. So it's obvious that they're going to meet in the middle. I mean, I think it's like Tony says, I think we should declare victory and go home. >> As hell. So just a follow-up on that, so are you saying the specialized lake and the specialized warehouse, do they go away? I mean, Tony data mesh practitioners would say or advocates would say, well, they could all live. It's just a node on the mesh. But based on what Dave just said, are we gona see those all morphed together? >> Well, number one, as I was saying before, there's always going to be this sort of, you know, centrifugal force or this tug of war between do we centralize the data, do we virtualize? And the fact is I don't think that there's ever going to be any single answer. I think in terms of data mesh, data mesh has nothing to do with how you're physically implement the data. You could have a data mesh basically on a data warehouse. It's just that, you know, the difference being is that if we use the same physical data store, but everybody's logically you know, basically governing it differently, you know? Data mesh in space, it's not a technology, it's processes, it's governance process. So essentially, you know, I basically see that, you know, as I was saying before that this is basically the culmination of a long time trend we're essentially seeing a lot of blurring, but there are going to be cases where, for instance, if I need, let's say like, Upserve, I need like high concurrency or something like that. There are certain things that I'm not going to be able to get efficiently get out of a data lake. And, you know, I'm doing a system where I'm just doing really brute forcing very fast file scanning and that type of thing. So I think there always will be some delineations, but I would agree with Dave and with Doug, that we are seeing basically a confluence of requirements that we need to essentially have basically either the element, you know, the ability of a data lake and the data warehouse, these need to come together, so I think. >> I think what we're likely to see is organizations look for a converge platform that can handle both sides for their center of data gravity, the mesh and the fabric virtualization vendors, they're all on board with the idea of this converged platform and they're saying, "Hey, we'll handle all the edge cases "of the stuff that isn't in that center of data gravity "but that is off distributed in a cloud "or at a remote location." So you can have that single platform for the center of your data and then bring in virtualization, mesh, what have you, for reaching out to the distributed data. >> As Dave basically said, people are happy when they virtualized data. >> I think we have at this point, but to Dave Menninger's point, they are converging, Snowflake has introduced support for unstructured data. So obviously literally splitting here. Now what Databricks is saying is that "aha, but it's easy to go from data lake to data warehouse "than it is from databases to data lake." So I think we're getting into semantics, but we're already seeing these two converge. >> So take somebody like AWS has got what? 15 data stores. Are they're going to 15 converge data stores? This is going to be interesting to watch. All right, guys, I'm going to go down and list do like a one, I'm going to one word each and you guys, each of the analyst, if you would just add a very brief sort of course correction for me. So Sanjeev, I mean, governance is going to to be... Maybe it's the dog that wags the tail now. I mean, it's coming to the fore, all this ransomware stuff, which you really didn't talk much about security, but what's the one word in your prediction that you would leave us with on governance? >> It's going to be mainstream. >> Mainstream. Okay. Tony Baer, mesh washing is what I wrote down. That's what we're going to see in 2022, a little reality check, you want to add to that? >> Reality check, 'cause I hope that no vendor jumps the shark and close they're offering a data niche product. >> Yeah, let's hope that doesn't happen. If they do, we're going to call them out. Carl, I mean, graph databases, thank you for sharing some high growth metrics. I know it's early days, but magic is what I took away from that, so magic database. >> Yeah, I would actually, I've said this to people too. I kind of look at it as a Swiss Army knife of data because you can pretty much do anything you want with it. That doesn't mean you should. I mean, there's definitely the case that if you're managing things that are in fixed schematic relationship, probably a relation database is a better choice. There are times when the document database is a better choice. It can handle those things, but maybe not. It may not be the best choice for that use case. But for a great many, especially with the new emerging use cases I listed, it's the best choice. >> Thank you. And Dave Menninger, thank you by the way, for bringing the data in, I like how you supported all your comments with some data points. But streaming data becomes the sort of default paradigm, if you will, what would you add? >> Yeah, I would say think fast, right? That's the world we live in, you got to think fast. >> Think fast, love it. And Brad Shimmin, love it. I mean, on the one hand I was saying, okay, great. I'm afraid I might get disrupted by one of these internet giants who are AI experts. I'm going to be able to buy instead of build AI. But then again, you know, I've got some real issues. There's a potential backlash there. So give us your bumper sticker. >> I'm would say, going with Dave, think fast and also think slow to talk about the book that everyone talks about. I would say really that this is all about trust, trust in the idea of automation and a transparent and visible AI across the enterprise. And verify, verify before you do anything. >> And then Doug Henschen, I mean, I think the trend is your friend here on this prediction with lake house is really becoming dominant. I liked the way you set up that notion of, you know, the data warehouse folks coming at it from the analytics perspective and then you get the data science worlds coming together. I still feel as though there's this piece in the middle that we're missing, but your, your final thoughts will give you the (indistinct). >> I think the idea of consolidation and simplification always prevails. That's why the appeal of a single platform is going to be there. We've already seen that with, you know, DoOP platforms and moving toward cloud, moving toward object storage and object storage, becoming really the common storage point for whether it's a lake or a warehouse. And that second point, I think ESG mandates are going to come in alongside GDPR and things like that to up the ante for good governance. >> Yeah, thank you for calling that out. Okay folks, hey that's all the time that we have here, your experience and depth of understanding on these key issues on data and data management really on point and they were on display today. I want to thank you for your contributions. Really appreciate your time. >> Enjoyed it. >> Thank you. >> Thanks for having me. >> In addition to this video, we're going to be making available transcripts of the discussion. We're going to do clips of this as well we're going to put them out on social media. I'll write this up and publish the discussion on wikibon.com and siliconangle.com. No doubt, several of the analysts on the panel will take the opportunity to publish written content, social commentary or both. I want to thank the power panelists and thanks for watching this special CUBE presentation. This is Dave Vellante, be well and we'll see you next time. (bright music)

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. Welcome back to the Cubes Coverage of 80 Bus Reinvent 2020. This is the Cube virtual. I'm your host, John Ferrier normally were there in person, a lot of great face to face, but not this year with the pandemic. We're doing a lot of remote, and he's got a great great content guest here. Rebecca Weekly, who's the senior director and senior principal engineer at for Intel's hyper scale strategy and execution. Rebecca. Thanks for coming on. A lot of great news going on around Intel on AWS. Thanks for coming on. >>Thanks for having me done. >>So Tell us first, what's your role in Intel? Because obviously compute being reimagined. It's going to the next level, and we're seeing the sea change that with Cove in 19, it's putting a lot of pressure on faster, smaller, cheaper. This is the cadence of Moore's law. This is kind of what we need. More horsepower. This is big theme of the event. What's what's your role in intel? >>Oh, well, my team looks after a joint development for product and service offerings with Intel and A W s. So we've been working with AWS for more than 14 years. Um, various projects collaborations that deliver a steady beat of infrastructure service offerings for cloud applications. So Data Analytics, ai ml high performance computing, Internet of things, you name it. We've had a project or partnership, several in those the main faces on thanks to that relationship. You know, today, customers Committee choose from over 220 different instance types on AWS global footprint. So those feature Intel processors S, P. J s ai accelerators and more, and it's been incredibly rewarding an incredibly rewarding partnership. >>You know, we've been covering Intel since silicon angle in the Cube was formed 10 years ago, and this is what we've been to every reinvent since the first one was kind of a smaller one. Intel's always had a big presence. You've always been a big partner, and we really appreciate the contribution of the industry. Um, you've been there with with Amazon. From the beginning, you've seen it grow. You've seen Amazon Web services become, ah, big important player in the enterprise. What's different this year from your perspective. >>Well, 2020 has been a challenging here for sure. I was deeply moved by the kinds of partnership that we were able to join forces on within telling a W s, uh, to really help those communities across the globe and to address all the different crisis is because it it hasn't just been one. This has been, ah, year of of multiple. Um, sometimes it feels like rolling crisis is So When the pandemic broke out in India in March of this year, there were schools that were forced to close, obviously to slow the spread of the disease. And with very little warning, a bunch of students had to find themselves in remote school out of school. Uh, so the Department of Education in India engaged career launcher, which is a partner program that we also sponsor and partner with, and it really they had to come up with a distance learning solutions very quickly, uh, that, you know, really would provide Children access to quality education while they were remote. For a long as they needed to be so Korean launcher turned to intel and to a W s. We helped design infrastructure solution to meet this challenge and really, you know, within the first, the first week, more than 100 teachers were instructing classes using that online portal, and today it serves more than 165,000 students, and it's going to accommodate more than a million over the fear. Um, to me, that's just a perfect example of how Cove it comes together with technology, Thio rapidly address a major shift in how we're approaching education in the times of the pandemic. Um, we also, you know, saw kind of a climate change set of challenges with the wildfires that occurred this year in 2020. So we worked with a partner, Roman, as well as a partner who is a partner with AWS end until and used the EEC Thio C five instances that have the second Gen Beyond available processors. And we use them to be able to help the Australian researchers who were dealing with that wildfire increase over 60 fold the number of parallel wildfire simulations that they could perform so they could do better forecasting of who needed to leave their homes how they could manage those scenarios. Um, and we also were able toe work with them on a project to actually thwart the extinction of the Tasmanian Devils. Uh, in also in Australia. So again, that was, you know, an HPC application. And basically, by moving that to the AWS cloud and leveraging those e c two instances, we were able to take their analysis time from 10 days to six hours. And that's the kind of thing that makes the cloud amazing, right? We work on technology. We hope that we get thio, empower people through that technology. But when you can deploy that technology a cloud scale and watch the world's solve problems faster, that has made, I would say 2020 unique in the positivity, right? >>Yeah. You don't wanna wish this on anyone, but that's a real upside for societal change. I mean, I love your passion on that. I think this is a super important worth calling out that the cloud and the cloud scale With that kind of compute power and differentiation, you gets faster speed to value not just horsepower, but speed to value. This is really important. And it saved lives that changes lives. You know, this is classic change. The world kind of stuff, and it really is on center stage on full display with Cove. I really appreciate, uh, you making that point? It's awesome. Now with that, I gotta ask you, as the strategist for hyper scale intel, um, this is your wheelhouse. You get the fashion for the cloud. What kind of investments are you making at Intel To make more advancements in the clock? You take a minute, Thio, share your vision and what intel is working on? >>Sure. I mean, obviously were known more for our semiconductor set of investments. But there's so much that we actually do kind of across the cloud innovation landscape, both in standards, open standards and bodies to enable people to work together across solutions across the world. But really, I mean, even with what we do with Intel Capital, right, we're investing. We've invested in a bunch of born in the cloud start up, many of whom are on top of AWS infrastructure. Uh, and I have found that to be a great source of insights, partnerships, you know, again how we can move the needle together, Thio go forward. So, in the space of autonomous learning and adopt is one of the start ups we invested in. And they've really worked to use methodologies to improve European Health Co network monitoring. So they were actually getting a ton of false positive running in their previous infrastructure, and they were able to take it down from 50 k False positive the day to 50 using again a I on top of AWS in the public cloud. Um, using obviously and a dog, you know, technology in the space of a I, um we've also seen Capsule eight, which is an amazing company that's enabling enterprisers enterprises to modernize and migrate their workloads without compromising security again, Fully born in the cloud able to run on AWS and help those customers migrate to the public cloud with security, we have found them to be an incredible partner. Um, using simple voice commands on your on your smartphone hypersonic is another one of the companies that we've invested in that lets business decision makers quickly visualized insects insight from their disparate data sources. So really large unstructured data, which is the vast majority of data stored in the world that is exploding. Being able to quickly discern what should we do with this. How should we change something about our company using the power of the public cloud? I'm one of the last ones that I absolutely love to cover kind of the wide scope of the waves. That cloud is changing the innovation landscape, Um, Model mine, which is basically a company that allows people thio take decades of insights out of the mainframe data and do something with it. They actually use Amazon's cloud Service, the cloud storage service. So they were able Teoh Teik again. Mainframe data used that and be able to use Amazon's capabilities. Thio actually create, you know, meaningful insights for business users. So all of those again are really exciting. There's a bunch of information on the Intel sponsor channel with demos and videos with those customer stories and many, many, many more. Using Amazon instances built on Intel technology, >>you know that Amazon has always been in about startup born in the cloud. You mentioned that Intel has always been investing with Intel Capital, um, generations of great investments. Great call out there. Can you tell us more about what, uh, Amazon technology about the new offerings and Amazon has that's built on Intel because, as you mentioned at the top of the interview, there's been a long, long standing partnership since inception, and it continues. Can you take a minute to explain some of the offerings built on the Intel technology that Amazon's offering? >>Well, I've always happened to talk about Amazon offerings on Intel products. That's my day job. You know, really, we've spent a lot of time this year listening to our customer feedback and working with Amazon to make sure that we are delivering instances that are optimized for fastest compute, uh, better virtual memory, greater storage access, and that's really being driven by a couple of very specific workloads. So one of the first that we are introducing here it reinvents is the n five the n instant, and that's really ah, high frequency, high speed, low Leighton see network variants of what was, you know, the traditional Amazon E. C two and five. Um, it's powered by a second Gen Intel scalable processors, The Cascade late processors and really these have the highest all court turbo CPU performance from the on scalable processors in the club, with a frequency up to 4.5 gigahertz. That is really exciting for HPC work clothes, uh, for gaining for financial applications. Simulation modeling applications thes are ones where you know, automation, Um, in the automotive space in the aerospace industries, energy, Telkom, all of them can really benefit from that super low late and see high frequency. So that's really what the M five man is all about, um, on the br to others that we've introduced here today and that they are five beats and that is that can utilize up thio 60 gigabits per second of Amazon elastic block storage and really again that bandwidth and the 260 I ops that it can deliver is great for large relational databases. So the database file systems kind of workload. This is really where we are super excited. And again, this is built on Cascade Lake. The 2nd 10. Yeah, and it takes It takes advantage of many different aspects of how we're optimizing in that processor. So we were excited to partner with customers again using E. B s as well as various other solutions to ensure that data ingestion times for applications are reduced and they can see the delivery to what you were mentioning before right time to results. It's all about time to results on the last one is t three e. N. 33 e n is really the new D three instant. It's again on the Alexa Cascade Lake. We offer those for high density with high density local hard drive storage so very cost optimized but really allowing you to have significantly higher network speed and disk throughput. So very cost optimized for storage applications that seven x more storage capacity, 80% lower costs given terabytes of storage compared to the previous B two instances. So we will really find that that would be ideal for workloads in distributed and clustered file system, Big data and analytics. Of course, you need a lot of capacity on high capacity data lakes. You know, normally you want to optimize a day late for performance, but if you need tons of capacity, you need to walk that line. And I think the three and really will help you do that. And and of course, I would be absolutely remiss to not mention that last month we announced the Amazon Web Services Partnership with us on an Intel select solution, which is the first, you know, cloud Service provider to really launching until select solution there. Um, and it's an HPC space, So this is really about in high performance computing. Developers can spend weeks for months researching, you know, to manage compute storage network software configuration options. It's not a field that has gone fully cloud native by default, and those recipes air still coming together. So this is where the AWS parallel cluster solution using. It's an Intel Select solution for simulation and modeling on top of AWS. We're really excited about how it's going to make it easier for scientists and researchers like the ones I mentioned before, but also I t administrators to deploy and manage and just automatically scale those high performance computing clusters in Aws Cloud. >>Wow, that's a lot. A lot of purpose built e mean, no, you guys were really nailing. I mean, low late and see you got stories, you got density. I mean, these air use cases where there's riel workloads that require that kind of specialty and or e means beyond general purpose. Now, you're kind of the general purpose of the of the use case. This is what cloud does this is amazing. Um, final comments this year. I want to get your thoughts because you mentioned Cloud Service provider. You meant to the select program, which is an elite thing, right? Okay, we're anticipating Mawr Cloud service providers. We're expecting Mawr innovation around chips and silicon and software. This is just getting going. It feels like to me, it's just the pulse is different this year. It's faster. The cadence has changed. As a strategist, What's your final comments? Where is this all going? Because this is pretty different. Its's not what it was pre code, but I feel like this is going to continue transforming and being faster. What's your thoughts? >>Absolutely. I mean, the cloud has been one of the biggest winners in a time of, you know, incredible crisis for our world. I don't think anybody has come out of this time without understanding remote work, you know, uh, remote retail, and certainly a business transformation is inevitable and required thio deliver in a disaster recovery kind of business continuity environment. So the cloud will absolutely continue on continue to grow as we enable more and more people to come to it. Um, I personally, I couldn't be more excited than to be able Thio leverage a long term partnership, incredible strength of that insulin AWS partnership and these partnerships with key customers across the ecosystem. We do so much with SVS Os Vives s eyes MSP, you know, name your favorite flavor of acronym, uh, to help end users experience that digital transformation effectively, whatever it might be. And as we learn, we try and take those learnings into any environment. We don't care where workloads run. We care that they run best on our architecture. Er and that's really what we're designing. Thio. And when we partner between the software, the algorithm on the hardware, that's really where we enable the best and user demand and the end use their time to incite and use your time to market >>best. >>Um, so that's really what I'm most excited about. That's obviously what my team does every day. So that's of course, what I'm gonna be most excited about. Um, but that's certainly that's that's the future that you see. And I think it is a bright and rosy one. Um, you know, I I won't say things I'm not supposed to say, but certainly do be sure to tune into the Cube interview with It's on. And you know, also Chatan, who's the CEO of Havana and obviously shaken, is here at A W s, a Z. They talk about some exciting new projects in the AI face because I think that is when we talk about the software, the algorithms and the hardware coming together, the specialization of compute where it needs to go to help us move forward. But also, the complexity of managing that heterogeneity at scale on what that will take and how much more we need to do is an industry and as partners to make that happen. Um, that is the next five years of managing. You know how we are exploding and specialized hardware. I'm excited about that, >>Rebecca. Thank you for your great insight there and thanks for mentioning the Cube interviews. And we've got some great news coming. We'll be breaking that as it gets announced. The chips in the Havana labs will be great stuff. I wouldn't be remiss if I didn't call out the intel. Um, work hard, play hard philosophy. Amazon has a similar approach. You guys do sponsor the party every year replay party, which is not gonna be this year. So we're gonna miss that. I think they gonna have some goodies, as Andy Jassy says, Plan. But, um, you guys have done a great job with the chips and the performance in the cloud. And and I know you guys have a great partner. Concerts provide a customer in Amazon. It's great showcase. Congratulations. >>Thank you so much. I hope you all enjoy olive reinvents even as you adapt to New time. >>Rebecca Weekly here, senior director and senior principal engineer. Intel's hyper scale strategy and execution here in the queue breaking down the Intel partnership with a W s. Ah, lot of good stuff happening under the covers and compute. I'm John for your host of the Cube. We are the Cube. Virtual Thanks for watching

>> Live from Las Vegas, it's theCUBE. Covering Dell Technologies World 2019. Brought to you by Dell Technologies and its ecosystem partners. >> Hi, Lisa Martin with theCUBE, live day three of theCUBE's double set coverage of Dell Technologies World 2019 I am with Stu Miniman. We've got one alumni back. We've got Gil Schneorson, Senior Vice President and General Manager of Vxrail. Gil welcome back. >> Thank you nice to be back. >> And it's show and tell you brought Niv Raz CTO of Harel Insurance one of your successful customers, Niv it's great to have you on the program. >> Thank you and great to be here. >> So Niv let's start with you. Give our audience an understanding of Harel Insurance where you're located, what it is that you do and then we'll get into why think Dell EMC is so fantastic. >> Harel Insurance is a insurance company doing a life, now life insurances very wide portfolio of business products in the insurance and investments in Israel. More than 5000 employees and three million customers managing around 240 billion shekels in 2018. So it's very innovative company to work in. >> So Niv interesting. Dell has a podcast and I'm just given a little plug here 'cause at the gym this morning the latest episode by Walter Issacson talks about transformation going on in the insurance business. Some people think, oh insurance has been around a long time, I mean heck to the Roman Era when they had some of this but today Insurance is changing fast. Can you give us at a macro level, give us what are some the changes and stresses on the company and how's that impact your job. >> It's funny you mentioning that. In 2015 our CEO has declared innovative program named Recalculating Routes. The purpose of the program the strategic plan was to take a role from traditional insurance company to more digital transform, data transform. We Israel has the brokers. The brokers are our sales person but once the customer and the sales part, the onboarding part, you want a more innovative service after that. The post service part is very hard in insurance and we investing a lot to make the post service customer experience very advantaged. >> We talk a lot about customer insurance at every, oh sorry, customer insurance, well that's important too, customer experience is the word I was going for. It's essential right because in 2019 customers of any type of product or service have so much choice. So talk to us Niv from looking through that lens of delivering an outstanding customer experience obviously your sales folks need to have innovative technology to deliver that outstanding customer experience. But when a company says we've got to transform digitally we've got to stay ahead of the market, delight our customers Where do you start? Talk to us about maybe a phased approach that you're taking to digital transformation. >> Digital transformation is all about how customer experience feel like in your environment. So if a person entering your website and trying to do some post service and running into some old fashionable process that is very hard to him and its really frustrating to do that. And actually if I look about what our approach about it, we're thinking about the digital transformation, we're thinking about how to take the onboarding part for our brokers, the post service for our customers, to make the process, the services we are offering to our customers easy as possible to just can submit. >> All right so Gil let's bring you into the discussion here. And I think back Converge Infrastructure, Hyper-converge Infrastructure you've been riding the rocket ship that is Vxrail, digital transformation wasn't the leading use for that when we started. It was simplification driving that wave of virtualization, we've heard Vxrail everywhere in the discussion this week. It was like all of these different cloud pieces, what's underneath them, VxRail. Help us connect the dots, the transformations that your customers are going where VxRail and the new solutions built with VxRail help enable your customers. >> Yeah thanks Stu. We talk about a digital transformation a lot. Reality is that many of the customers, not all of them are transformative like Harel Insurance right. Many of them look at ATI and VxRail as the next simple tech refresh. They see the agility, they see the economical benefit but there's a growing majority of customers who look to this is as transformational. And so that's where you see ATI and VxRail specifically in our case starting to grow beyond being an infrastructure for workloads to be an infrastructure for their hybrid cloud and multi cloud environment. So what is so exciting about this show is because we've been very successful we're growing very fast, but by putting this building block in many of our customers' data centers they've made the choice that will enable them to now embark on a more transformational strategy. And I think we demonstrated in the last two days that hybrid cloud is here and it's sellable, operational and with VxRail and the integration with VML cloud foundation and the ability to add and burst into a cloud move workloads It's here and its now, I thinks that's what's nice about this whole thing. >> All right so Gil it's great for you to say it even as an analyst as a media organization for us to say it but what we love is that you brought a customer here to tell us the reality as to where cloud fits into your overall discussion. And I would love your feedback as to what Gil's saying. What's the reality in your world and the impact on your work >> I would connect the previous question this one because it's like a very rolling on questions about it. So you as the customers your expectations about the company is to do every operation from everywhere very easy way and the mobility and the digital transformation itself all the mobile applications, all the things that's taking the customer experience to the next level will took the organization to a phase that I need understand how to scalable the systems. So in this journey when you're looking about digital transformation you must have a infrastructure that support the scalability, the elasticity, the availability that the customer demands. You don't think to yourself that you are enter some E-commerce customer and they will send you on application. sorry Sir, we currently offline the management reasons or maintenance reasons. That thing in 2019 you will not think about and it's not be acceptable. So to do a scalability our multi cloud strategy in Harel is to have infrastructure free environment to focus on the service applications and not to focus on the infrastructure management part. That's the big concerns of our IT teams was how to care about support and matrix's and compatibility and maintenance and when you go into the private cloud environment, the private cloud environment, that's VxRail on the bottom and VML cloud foundation on the top allow Harel is to start the journey to a phase that said okay we're going to our infrastructure free road map. >> Tell us about the outcomes that for example go back to, what we were talking about your brokers who need to be able to deliver any service. I imagine they're out in the field sometimes with customers depending on the types of services that they need to deliver. What has been some of the feedback or maybe the outcomes for the brokers. Are they able to do their jobs faster, deliver quotes faster to customers. What are some of the exciting outcomes that you're seeing as a result of the infrastructure that Dell EMC is helping you to establish. >> Part of digital transformation we're talking about micro servicing a lot of old virtual machines I'm saying that. So service applications on the password virtual machine now your micro services, why you micro servicing it because in 8:00 a.m, perhaps there is 20 persons that's selling your policies but perhaps on the 11:00 after some TV show said something about Harel you can have thousands of customers entering to your website. So how you can support that? So again brokers need the tools to support the operation, the sales operations and the customers need the tools to support the post service for themselves, how to claim, how to do claims how to do more preventives aspects of insurances. So basically again when you're looking about what exciting is, is the reality that I'm seeing a process of a customer and is saying, wow that was easy. So taking the digital transformation to make our customer experience better. >> All right Gil help us zoom out a little bit. We talked to one customer here but the business overall joint product development between Dell EMC and the VMware teams is something that we think was transformational and helped accelerate the HTI growth. What are some the big drivers what's changed in the business. Give us the overall update. >> Yeah look, I think that when we discovered that working together pays off through our joint leadership through examples like VxRail and others we started looking at every part of the business and how collaboration could enable us to add even more value and any value transfer to finances and there's a very strong interest in so this recent innovation we've introduced with integration with cloud foundation, people don't realize how much work goes into integrating two products regardless, even between 1 company you're talking about engineers co-location, you're talking about joint sprints you're talking about test fests, design workshop, customers interaction and so, but you know what I mean, it pays off. You deliver a new outcome that didn't exist before now with VCF and VxRail you can have a full life cycle management of the entire VMX stack and the entire hardware stack drivers, framework everything life cycled together, it's a very, very impressive outcome and it's ready now and I'm really thinking that shift is going to be more than just ATI, people are going to start embracing the full stack because they can, because we're simplifying it. In addition to that Stu I think it's important to understand or I'd like the people to know that the other way we're taking the ATI stack and the full stack is into much more intelligence so machine learning and predictability all the way eventually to remediation and so in this show we introduced the analytical consulting engine for VxRail and we put it out there as a field trial, as an early access. The thought process is we have a very large amount of intelligent customers that could tell us where they need this to take them. What's exciting about it is that every product these days is trying to be intelligent because we have a full stack we have a lot of context, a lot of things we could correlate. So we're very excited about this and we're hoping that our customers will participate in that design, I'm sure Harel will as soon as we can give it to them, the access and, not only full stack but make it much more intelligent, I think it's going to be very exciting year til next time we speak. >> Harel you have? >> Something to say about it. We are customers, us as an organization understand the public cloud allowed us to be infrastructure free and now they said okay some workloads are good for public cloud some workloads are good for private cloud and the multi cloud approach that VMcloud Foundation gives us the infrastructure free to just focus on the services. You need to understand the manageability of traditional infrastructure is very costly. Why? You need to manage it, you need to support it, you need to upgrade the frameworks, the buyers, the drivers and all the time to be concerned about if everything is supportable, how you do that all the job and again once you taking the VxRail as a hardware platform for that and the VMcloud foundation the software you getting a complete life cycle that assist you to just focusing about to be a service broker just add new services to the exist environment. >> Well Niv, thank you so much for stopping by theCUBE and sharing with Stu and me where you guys are on this digital transformation journey, the successes you've achieved so far with Dell EMC, Gil again always great to have you on the program and we can't wait to hear more next year maybe Ace is going to give us some really insightful insights that will be groundbreaking. >> I believe so. Thank you very much. >> For Stu Minneman, I'm Lisa Martin. You're watching us on theCUBE, live from day three of our coverage of Dell Technologies World. Thanks for watching. (upbeat music)

>> Live from Orlando, Florida It's the Cube covering Enterprise Connect twenty nineteen brought to you by five nine. >> Hello from Orlando. We are at Enterprise Connect twenty nineteen, and we're being very graciously hosted by five nine, which is the intelligent Cloud Contact center. We had a great few days, two minute minute myself talking with customers, partners, vendors on this massive change and enterprise, communication and collaboration. We're excited to welcome back to the key one of our alumni, Zs Caravella, the founder and principal analyst at Zeke Research. These It's great to have you here, >> Dawson. To me. Here >> you are. You should have the i p status at Enterprise Connect because you have been to this event some twenty times. >> I believe it's my twentieth. >> Can't imagine. So they didn't They should have rolled out the red carpet. Maybe we'll put a note >> in next year, >> but Yeah. There you go. I >> want to get my own booth. >> There you go. But I can't imagine how much this event has changed. And just your perspectives on Day three here of e. C nineteen and some of the vendors that you're like, Wow. A few years ago, you would never have seen a so and so here. >> Yeah, the shows massive compared to what it used to be the Remember when I first started coming to the show floor was maybe if I was a quarter the size, I mean generous, and it was really dominated by just a handful of companies. But since then, it's gone through several transitions the i p to software to the cloud on. That's gotten a lot more companies interested. And I think also, finally, businesses starting understand that if you're going to transform digitally right, communications has to be part of that fact. If you look at any piece of research right that I know there's a walker study throwing around saying by twenty twenty customer experience to be the number one brand differentiator, that's that's already happening. It's already the number one brand differentiator. And so because of that, more and more companies are now interested in communications. So, you know, ten years ago, fifteen years ago, we didn't have Amazon here. We didn't have Microsoft here. We didn't have Oracle here, but it's been a great thing for the show to see all these other companies that really have really great presidents validate what we've been saying for a long time, and it's a much different show today than it was. >> Yeah, it's really interesting that the thing that opened my eye is some of the companies that air here. I wish I knew which brand used these technologies so that if and when I do have an issue, I'm not gonna have that horrible customer experience that you know we've had in the past. It's like, you know, if I wanted to make a call, it's like, Can I even make a call? And, you know, do I actually get through the I V R. Things like that? I like how you set it up there. Some of these pendulums swings some of these waves of technology. Um, let's talk a little bit about voice because this used to be called Voice Khan, and it went through a rebranding because, you know, voice was in a little bit of kind. But, you know, we know voices. It's still very important. How does that fit in the hall >> when I went through that rebound, Frankly, voice wasn't sexy anymore. Everyone is talking about unified communications. No one was going to call anybody ever again. We're just gonna message or social each other to death and what's happened is voice is kind of important, right? And I think one of the undersea and friends to look at is that voice is becoming simultaneously less important and more important. What I mean by that is that they sound like a little bit of an oxymoron. But if you look across all age demographics right there, everybody has a prefered mode of communications, and it's rarely voice to start a conversation with the company. You message them your social, um, send them an e mail. But somewhere in there, you you eventually want to talk to somebody, and a that moment s o to start the conversation voices less important. But at that moment, you now want to have a conversation with uneducated agent who knows what your problem is and can help you quickly. And so now voices Mohr important than it's ever been before where, but I think the buried entry wasn't all that high, but voices, you know, it's it's important, it's sexy, and especially when people are dealing with emotional issues, they're dealing with money problems right in front of get a refund. If I'm trying to check on the status of my health, I want to talk to somebody. But when I want to talk to somebody, I want to get that conversation with over. It's possible. I think the bar's been raised as you mentioned to. You used to think that the dreaded Ivy are. If you have a dread and ivy are experience, you just want to business that company anymore, right? And so the stakes are higher than the bar's been raised on. What voices >> are you saying that the customers that you were talking to are now starting to get much more prescriptive in terms of understanding their customer journeys and their preferences? You know, before they used to go, we assume we're talking to millennials. They only want they only want ASA Master. Our company's starting to get more focused on. Alright, let's actually do analysis and determine if a voice only one of the next channels that we need to enable, >> uh, well, I wish they were. I think we're really in the early early innings that I think the best companies in the world are doing that. If you look at companies with very high, uh, NPS scores and customer SAT scores there doing that thing already and I think it's a good lesson for the rest of the industry. If you're not doing that, you're gonna fall behind pretty quickly. And I think that is driving companies more to the Saami Channel experience Where, uh, from, uh, from an analytic standpoint, you really have to understand your customer, not at the demographic level, but almost at a custom level because everyone's different, right? I think that's, uh, that's never been possible before. But today, because we've got bigger data sets. Things were in the cloud rise of artificial intelligence. It's made all the stuff possible. So companies like I said, the best cos the world to taken advantage of and they're having a, you know, big differences. That's why there's been such a huge swings in the market leadership right there cos we never heard of before. Market leaders and brands we trusted loved before they're gone. >> Yeah, I'm glad you brought that up, because every company we talked to this week that that CX is at the center of what they're talking about. So, in your research, what is differentiating though those new leaders and, you know, causing some of those swings in the market place hot out of the customer. Look at these and help differentiate and and ever changing marketplace. >> Well, it's what's going on today. It's really about being more contextual, having a deeper understanding a wire. Customers calling, uh, how you could help him faster understanding maybe what products they own. You know what? What are some of the adjacent ones? Ah, no. I think that's going very quickly, become table stakes. And I think where we're moving to is we're going to shift customer service from being largely inbound, driven and reactive. And that's where they I can help react faster to being Mohr, outbound driven and pearl active. Right? So, for instance, let's say I buy a connected refrigerator and my water filter needs changing. Well, right now, I still have to recognize that. And maybe I call that refrigerator company and they can proactively help me because they understand what I have. And they've got a great arm, the Channel contact center. But ultimately that should be a reverse. They should contact me, maybe through a text message saying, Hey, you're we noticed your water filter needs changing. Can we send you one? Yes, it comes and then maybe I call the agent and say, Can you help me install it? Right? So I think within the next three, four years, we're going to see a lot of customer service, Uh, where contextual is the table stakes and then the ability to predict what your customer wants. That's going to be the differentiator. And frankly, that's really exciting. I mean, if you think we've seen change of this industry as you mentioned in the last five years, wait for the next five. >> When you're talking with customers or even doing research and and other venues, it's to mention CX. We talk. We've been talking about it all week, but I get curious when I hear the customer experience and the agent experience just think, How are they not how they separate because of the Asian isn't empowered to be able to, whether it's no the right channel. But I want to be communicated with or have the information where the context about why I'm calling, then the customer experience, right? >> Yeah, well, they're very tightly linked together. You can't have a good customer experience that a good agent experience and you may have the best trained agents in the world that are the most empathetic that are incredibly sensitive with what people want. But if they don't have the data, you're going frustrate your customer. And everybody's been through that situation where you get transferred to somebody else and you gotta start that whole conversation over again and eventually you just hang up and say, I don't want ever to business. So I think you're right. Agent experience Customer experience are very tightly interwoven, and they're they're really dependent on one another. You can't you can't do without the data. And again, that's where all these friends of a I come into play because they're able to send better information to the agents faster, really, through an assistive technology versus replacement. Right? >> So when we came into this show, we knew that the wave of cloud had made a big transformation. We're starting to hear a I is the next wave everybody's talking about. I believe I read something that that you had written that was talking about, you know, whether that is something just internal the company build in versus how it interacts with the customer. Where do you see I having the biggest impact kind of in the short term, and nowhere is that more long. >> It's a great question because I ask my customers all the time. Should we be using intelligence bots? Or if you saw the Google Duplex Nemo, where they have on a I call in order pizza I think it was or something like that. So is a I ready to talk to people? And I think if you think of the entire world of interactions on a two by two grid is an analyst would like to buy two grids, right? And you put complexity of conversation on one axis and frequency of interactions if it's hiking, or if it's low complexity, high frequency, that might be okay to try and automate through a But other than that, everything should flipped. Agent. And I think right now we're very early in the cycle, and so is a business. I'm not sure I trust today. I tow always have the right answer, but it makes a great assistant technology to recommend to the agent. This is what you should say, and the great thing about that is, if the agent says no, that's stupid and says that wasn't helpful. That becomes the input to the learning mechanism for the A I so overtime will get smarter and smarter. But if you if you want to think about just the role of it now, I always use the analogy is like a self driving car. I'm not sure if either one of you would want to jump in a car that has no driver, no steering wheel, no controls. But there's a lot of great aye aye technology in a car like lane change assist, parallel parking assist things like that that can make you a better driver. So let's make our agents better drivers by giving him those assistive technologies. And that's the the short term vision long term. Who knows? But I But I think oh, if company's heir to aggressively they II, they're actually gonna create a nod. The opposite effect, where they hurt customer experience. It's the people that make a difference, so let's make those people better. >> That's one of the things that we've heard consistently throughout this event is the empathy factor machines can't bring. That's really got to be the humans with the A I to deliver on idea, hopefully optimal experience, too. Whatever customer has whatever issue on the back end. >> Yeah, in fact, Roman always talks about that as well. The CEO of five nine and I think he's right from that. Regarded is about having the knowledge of the customer in the empathy to understand. Put yourself in the customer's position and this to your point. Lisa, about CX. In Asian experience, we tied a couple together. If the Asian distressed because they don't have the right information and they're trying a message, this person, or look something up in the database, that frustration is going to come through to the customer. And that further frustrates the customer, right? So of the agents, armed with the right information, they can spend more time focused on the customer and less time trying to find the data that, frankly, they should have at their fingertips all the time. >> So speaking of five nine, you recently attended their analyst event. >> I did >> on. We've had the executives on the team. You know, Jonathan on earlier this week, you know, rock star background. We're goingto throwing on a little bit later. We know him from his Cisco days without breaking any India's, you know, give us a little bit of the insight as to, you know, five nine. You know, what have they been doing? Well, what's what's the new team driving them forward towards? >> Well, I mean, if you look at their stock price from Roland joined, it's it's more than doubled. So obviously there's, um, some good growth there. I think. What? I've always believed that it's very difficult to compete on product alone, right? And if you believe this whole world of it is this customer experience, that's what they do really well, the customers, their customers have a great experience here with five nine, they have a great service organization that makes sure that when you buy five nine, you have a good on boarding experience that set up the way you want it, and that services business makes a big difference. Now they've always had that. Now, where I think the new executive team has made a difference is helping the company understand the scale, move upmarket, more enterprises because the needs their different than down market. And so I think you know, they're gonna have a big impact on the future of five nine. Frankly, I think a lot of what you've seen for growth in the last year has been stuff that was put in place. But I know they're working on a lot of the AI capabilities. We're not breaking in the NBA's. I can tell you that the demonstrations that Jonathan Rosenberg, who's in there incredibly smart guy, I mean he might be the smartest guy in this industry was giving around. How a I can impact customer experience was the best set of concrete examples that I've seen today because it's really easy to give me a pie in the sky hypothetical things. But he really boiled it down in a very grand your level of this possible. This is possible and I'm expecting over the next year, five nine customers will see those things. >> They've done really well in the enterprise market. I think last year in twenty eighteen, they closed very, very strongly. Also, a lot of growth in there. Custom enterprise customers with a Million and Ahrar plus What are you seeing, though, in terms of some of the smaller businesses that probably are facing a lot of the same challenges that enterprises are? Is this an area where they can also leverage five nine two really dial up and deliver Great CX, >> Yeah, but the line has moved up right of people interested in cloud services that used to be too small businesses, and now it's all kinds. But I think for a small business, you can look like a much larger business. I think there's a lot of companies people sometimes think that's a little risky deal the small company. But five nine is a very, very valuable tool because by having that information right away that agents fingertips, they're able to actually replicates, uh, large company experience and on almost validate that the customer made the right decision using them. So I think up and down the stack it for five nine. They provide value tow companies of all sizes. Today, one of them, you know, the interesting aspects of what I've seen two is everybody talks about this twenty four billion dollars tam for Contact Center. I know I've been in that eye, and may I say that because that twenty four billion dollars tam is based on giving contact, Senator people contact center tools, but what I've been noticing over the last years, when people buy five nine, often it's not contact center people using that using it. It's sales people in marketing people, field service. Anybody that needs customer info is using it. And I'll give an example. One of the customers that was at the five nine day I can't see you. They say who they are. They migrated all fifty contacts and regions five nine. And since then they've added one hundred mohr sales people using the tools. So now we've got one hundred fifty people using five nine when there was only fifty contacts. Generations you can see the value is starting to spread across the company, and I think that's a pretty exciting thing. >> It's been interesting we've seen at the show. And in some of the interviews, that line between kind of unified communications and contact center seems to be blurring. It seems to be that >> well, everybody needs that data on the customer info. I actually cameras closer to forty. Forty five billion. To be frank, really, every anybody who uses a serum tool should have five nine capabilities. >> Zia's Thank you so much for sharing your insights and your energy on Day three. If Enterprise connect nineteen, we appreciate your time Thank you. First two minute, man. I'm Lisa Martin. You're watching the Cube?

>> Hello, and welcome to the special. Keep conversation here in Palo Alto, California John Furrier, Co-Host of the Cube. We're here with Jonathan Rosenberg, CTO chief technology officer and head of AI for Five9. Jonathan. Great. Great to see you. Thanks for coming in. >> Thanks. My pleasure to be here. >> So you've had a stellar career? Certainly. Technical career going way back to Lucent Technologies. Now here at Five9, Cisco along the way. You've been a really technical guru. You've seen the movie before. This's happening. Every wave of innovation, multiple ways you've been on. Now you're on the next wave, which is cloud AI, CTO Five9. Rapidly growing company. Yes, it is. What attracted you to five? >> Yeah, Great question. There's actually a lot of things that brought me to Five9. I think probably the most important thing is that I've got this belief, and I'm very motivated for myself. A least to do technology and innovate and create new things. And this belief that were on the cusp of the next generation of technology in the collaboration industry. And that next generation is going to be powered by artificial intelligence, and one of the ways I sort of talked about this is that if you look at the entire history of collaboration, up til now meetings, telephony, messaging was to figure out, a way to get the bits of data from one person to another person fast enough to have a conversation. That's it. You know, once we got the audio connected, we just moved the audio packets in the video packets and messaging from one place to another. And we didn't actually analyze any of that because we couldn't. We didn't have the technology to do that. But now, with the arrival of artificial intelligence and particular speech recognition, natural language processing, we can apply those technologies to that content and take all this dark data that's been basically thrown away the instant it was received, to process it and do things. And that is going to completely transform every field of collaboration, from meetings to messaging, to telephony. And I believe that so strongly, that is, That's great. That's going to be my next job. I wanna work on that. And it's going to start in the Contact Center because a contact center is the ideal place to do that. It's the tip of the spear for AI in collaboration, >> and it's in a really great area. Disruptive innovation are absolutely so Take us through the impact was one of things I have observed in this industry is you have You know, I don't want to say mainframe clients served to go back to date myself, but there was that wave of client server computer >> mainframes. Cool again. We just called clout. Now, hey, is >> exactly. So you have these structural industry waves take us through the waves of how we got here and what's different now? And why can't the old guard or the older incumbents surviving if you're not out in front that next wave your driftwood. So what? What's What's his ways mean? Why is this important? What has to change to be successful? >> Exactly. So there's been this this whole like you said these waves. So the first wave of telecommunications was like hardware: circuit switching, big iron switches, sitting in telco data centers, you know, And then that era transitioned to software and that was with the arrival voiceover IP and technologies like SIP, and that made it more less expensive. And anyone could do it, and it transformed the industry. The next wave, the third wave were still like halfway through and in some areas, actually, just beginning contact, center was early here, the third wave is cloud, right is now we're moving that software to a totally new delivery vehicle that allows us to deliver innovation and speed. And that wave has now enabled us to start the next wave, which is on ly in its infancy, which is AI right, and the application of machine learning techniques to automate all kinds of aspects of how people communicate in collaborate. >> I think cloud is a great example of Seen a. I, which had been a concept around when I was in computer science. Back in the eighties, there was a guy you know theory, and it's the science of it is not so much change, but computing's available. The data to be analysed for the first time is available. Yeah, you mentioned analyzing the bits writings. There's now a key part. What does it actually mean? Teo. Someone who's has a contact center has a large enterprise. Says, you know what? I got to modernize. How does A I fit them? What is actually going on, >> right? Great question. So a I actually consult lots different problem at the end of the day again, Hey, eyes like this, Let's. It's the biggest buzz word right on. It's in my title. So, like I'm a little guilty, right? >> We'll get a pay raise for, But >> what? It comes down to this, really this Korean machine learning, which is really like a fancy new algorithmic technique for taking a bunch of data and sort of making a decision based on it. So And it turns out, as we've learned that if you have enough data and you can have enough computing and we optimize the algorithms, you could do some amazing things, right? And it's been applied to areas like speech recognition and image recognition and all these kind of things. Self driving cars that are all about decision process is, Do I go left? I go right? Is this Bob? Is this Alice? Did the users say and or did they say or write those air all decision process? Is that these tools economy? What does it mean? The Contact Center? It means everything in the context. And if you look at the conduct center. It's all about decision. Process is, you know, where should this call get routed? What's the right agent to handle the call right now? When the agent gets the call, what kind of things should they be saying? What I do with the call after the call is done, How should the agent use their time? All those things are decision processes and their key to the contact center. So so, aye, aye. And Emily going to transform every aspect of it and, most importantly, analyzing what the person is saying connecting with the customer, allowing the age to >> be more. You know, I think this is really one of the most cutting edge areas of the business. And the technology and throw in CEO was talking about an emotional cognitive recognition around. Yeah, connecting with customers and data certainly is going to be a part of that. But as machine learning continues to get it, Sea legs. Yeah, you seeing kind of two schools of thought? I call it the Berklee School. Hard core mathematics. Throw math at it. And then you've got this other side of a machine learning which is much more learning. Yeah, it's less math. More about adaptive and self learning. One's deterministic one's non deterministic is starting to see these use cases where Yeah, there's a deterministic outcome, right throw machine learning at a great exactly helped humans come curate, create knowledge, create value that you've got a new emerging use case of non deterministic, like machine learning environments where I could be driving my test Look down the road or my company's run the Contact Center. I gotto understand what's gonna happen before it happens. Right? Talkabout this. What's your thoughts on this is This isn't really new, pioneering area. What's your view on >> this? Yeah, so I think it actually straight sort of a key point. I wantto narrow enough from what she said, which is that a lot of these problems still, it's about the combination of man and machine, right? It's that there's things that you know are going to be hard for the machine to predict. So the human in their usage of the product, teaches the machine, and the machine, as it observes, helped the human achieved mastery. And that human part, by the way, is even more important in the conduct centre than anywhere else. At the end of the day, your customer and you call up, you're reaching for human connection. You're calling this. You want to talk, you've got a problem. You need someone to not just give you the answers, but empathize with youto understand you. Right? And if you go back to anything about the best experience you've ever had when you called up for support or get a question answered. He was like it was someone who understood you who's friendly, polite, empathetic, funny. And they knew exactly what they were doing, right? And they solve it for you. So the way I think about that, is that actually the future of the context. Dinner is a combination of human and machine, and the human delivers the heart, and the machine delivers the master. >> And I just noticed your I'm looking at Twitter, right? And you just tweeted this forty minutes to go the future of Contact Center. Nice. A combination of human and machine human delivers heart. The machines lose mastery. I think this is so important because unpacking that words like trust come out True relationship. So you asked about my experiences is when I've gotten what I needed, You know, all ledger, the outcome I wanted. Plus I felt good about right. I trusted it. I trusted the truth. It was. And he's seeing that in media today with fake news. You're seeing it with Digital has kind of almost created, anonymous, non trustworthy its data. There's been no real human. Yeah, packaging. So I think you're I'm hearing you You're on the side of humans and machines, not just machines being the silver bullet. >> Absolutely, absolutely. And again, it goes back to sort of the history of the contact centre has been this desire to, like, just make it cheaper, right? But as the world is changing, and as customer experience is more important than ever before and is now, technology is enabling us to allow agents and human beings to be more effective through this. The symbiotic relationship that we're going to form with each other, like we can actually deliver amazing customer experiences. And that's what really matters. And that idea of trust I want to come back to that word that's like super Central to this entire thing. You know, you have that as a user, you have to trust the brand you have to trust the information you're getting from the agent. You have to trust the product that you're calling them talking about, and that's central to everything that we need to do. In fact, it's a It's a fundamental aspect of our entire business. In fact, if you again think about it for a moment here, we're going to customers who are looking to buy a context, and we're saying, Trust us, we're going to put it in the cloud, We're going to run it, We're going to operate it for you and we're going to deliver a great, highly reliable experience that takes trust to sew one of things that back to your early early question. Why did come two, five, nine? One of the things it has done is build this amazing trust with its customers to its huge, amazing reliability. Up time, a great human process of how we go in work with our customers. It's about building trust in every single >> way. So I want to put in the spot because I know you've seen many ways of innovation. You've seen a lot of different times, but now it's more accelerated. Got cloud computing at a much more accelerated innovation cycle. So as users expect interact with certain kind of environment. Roman talked about this in his interview. CEO Control. So you just want to be served on the channels that they want to be served in. So having a system that they have to go to to get support, They wanted where they are. And so how is the future of the customer interaction? Whether it's support our engagement is going to take place in context to nonlinear discovery, progression, meaning or digging a service themselves in the organic digital space. I honestly want to go to a site per se. How do you see the future evolving around this notion of organic discovery? Talking to their friends, finding things out? Does that impact how Five9 sees the future? >> Yeah, absolutely. And I think it gets back to sort of an old idea of Omni channel. I mean, this is something that the context people been talking about for, like forever, like the last ten years, right? And and its original meeting was just this idea. Oh, you know, you can talk to us via chat, or you can send us an e mail or you can send us a text or you could call us right and we'll work with you on any of those, like you said. Actually, what's more interesting is as customers and users moved between those things, and it actually switches from reactive to proactive right where we actually treat those channels as well. Depending on what the situation is, we're going to gather information from all these different data sources, and then we're going toe, find the right way to reach out to you and allow you to reach out to us in the most official. >> So you see a real change in user expectation experience with relative rule contact? >> Yeah, I mean, I mean, the one thing that technology is delivered is a change in user expectations on how things work. And if you look at the way we as human beings communicate with each other, it's dramatically different today than it was really just just a few years ago. >> So, Johnny, let's look under the hood now in terms of the customer environment, because certainly I've seen Legacy after Legacy sisters being deployed. It's almost like cyber security kind of matches the same kind of trend that in your world, which is throw money at something and build it out. So there's a lot of sprawl of solutions out there and trying to solve these problems. How does the customer deal with that? And they're going forward there on this new wave. They want to be modernized, but they got legacy. They had legacy process, legacy, culture. What's the key technical architecture, How you see them deploying this? What's the steps of the patient and her opinion? >> It will surprise you not one drop when I say it's go to the cloud, all right, and there are real reasons for it and by the way, this is going to be going to be talking about this at Enterprise Connect. So, So tune in Enterprise Connect. I'm going to be talking about this. Um, there's a ton of reasons, five huge ones, actually, about why people need to get to the cloud. And one of them is actually one of the ones we've been talking about here, which is a lot of this. Modernization is rooted in artificial intelligence. It turns out you just cannot do artificial intelligence on promise you cannot. So the traditional gear, which used to be installed and operated by legacy vendors like a VIA, you know, they go in, and Genesis, they go in the install a thing and it works just for one customer at a time. The oly way artificial intelligence works is when it gathers data across multiple customers. So multi tendency and artificial intelligence go hand in hand. And so if you want to take any benefit from the stuff that we've been talking about this conversation, the first step is you gotta take your context int the cloud just to begin building and adding your data on the set and then leverage the technologies and they come out >> So data is the central equation And in all this because good data feed's good machine learning good machine learning feeds Great a. I So data is the heart of this, yes. So data making data in the cloud addressable seems to be a key. Thought Your reaction and what are you guys doing with? >> Absolutely, absolutely. And this is, by the way, another reason why I joined five nine, that I've been speculating here. I said, All right, if Date if ya if the future is about a I miss, I said, That's what I want to do in collaboration. You need data to do that. You actually have to work for a company that has a lot of data. So market leadership matters. And if you go look at the contact center and you go look at all the industry and analyst reports like it made it pretty obvious, like who to go to there is like the leader in cloud Conduct. Sonar with with tons of agents and tons of data is Five9 and ah, and so that's That's why you're so building the data aggregating data. That's one of the first things I'm working on here is how do we increase and utilize the data that we've been gathering for years. >> And and a lot of that we've had this conscious with many customs before about Silas Silas. Kill innovation When it comes to data address ability, your thoughts on that and what customs Khun due to start thinking about breaking down those silent >> exactly so In fact, Silas have been a big part of the history of especially on premise systems. Once in fact, Afghan one silo for inbound contacts and are different for outbound. Different departments, by the way, also had their own different comic centers. And then you had other tools that on the other data, if you don't like a separate tool over there for serum and a different tool over there for WFOR debut Fam and something else for Q M. And all these things were like barely integrated together in the cloud that becomes much more natural. Spring these technologies together and the data can begin to flow from the systems in and out of each other. And that means that we have a much greater access to data and correlated data across these different things that allows us to automate all over the place. So it's this positive reinforcement sile cycle that you only get one year when you've gone to the club. >> The question I want to ask you, it's more customers on pretend I'm a customer for second. I won't ask you, Jonathan, what's the core innovation for me to think about and bring to my organization? If I want to go down the modern monitors you. How do you answer that question? What is the core innovation? Stretch it. I should have Marcy moving through the cloud is one beyond that is itjust cloud. Then what else? What, Juanito? Be preaching internally and organizing my culture >> around. Yeah, great questions. So, I mean, I think the cloud is sort of the enabler of many of these different pieces of innovation. Right? So velocity and speed is one of them. And then setting up and adjusting these things used to be super super hard. Ah, you wanted to add agents seats? Oh, my gosh, enough to go binding hardware and racket stack boxes and whatever. So even simple things like reactive nous, right? That's something that's important to talk about is that many of our customers and our businesses are highly seasonal. Right? We've seen like someone showed me a graph. This was like, Oh, my gosh, it was It was a company that was doing ah, telethon. And they said, Here's how many agents they have over this year. It was like two agents, and then it shut up. It's like five hundred agents of phones. Two days exactly. Drop back down. And I'm like, if you think about a business like that, you could never even do that. And so the so cloud is nice, but the way you talk about it, and as an I t buyer of these technologies, you talk your business owners about reacted nous speed, velocity, right? That's what matters to a business and then customer experience. >> You're one of the things that just to kind of end of second, I want to get your thoughts on. I'm gonna bring kind of industry trend. That's I think, might be a way to kind of talk about some of these core problems on data. Most mainstream people look at Facebook and saying, Well, what a debacle. They used my data. These men against me. I'm not in control of my data. You're seeing that weaponization people saying elections were rigged. So weaponizing data for bad is this content, and this context ends right? An infrastructure that's right, >> that's right. >> But there's also the other side, which is, you actually make it for good. So you started thinking about this people starting to realize Wow, I should be thinking about my data and the infrastructure that I have to create a better outcome. That's right, Your thoughts on that as people start to think about II in terms of the business context, right? How did they get to that moment where they can saying, I don't want anyone weaponizing did against me. I want to use it for good. How did the head of the company comes back to >> trust, by the way, right? Is that you know, on and to some degree that's an uphill battle due to some of these debacles that you just talked about. But Contact Center is a different beast of the whole thing. And interestingly, it's an area where there's already been an assumption by users that when they interact with the contact center, that data is sort of used to improve the experience. I mean, every contacts and the first thing I say, by the way, this call may be recorded for training. Um, honoring purses, Captain, that they are right. It's it's already opt in. There's an assumption that that's exactly how that is being used. So it's This is another reason. By the way, what's a contact center is? It was the tip of the spear because it was a place where there was already permission, where the data is exactly the kind of stuff that had already been subject to analysis and Attock customer expectation that that's actually what was happening. The expectation was there they building action, that data what was missing. So now we're filling in the ability to action on that All that data with artificial intelligence >> and final question. What's your vision going forward? A CTO and aye, aye. What's the vision of Five9? What do what do you see? The twenty miles stair for Five9 within consciousness. We just talked about >> it. So? So it's It's about revolution. I'll be honest. Right on. I tell people like, I'm not like an incremental, steady Eddie CTo like I do things because I want to make big changes. And I believe that the context and R is on the cusp of a massive change. And my boss, Rohan said this and this has been actually central to how I'm thinking about this. The Contact Center in the next five years will be totally different than the twenty five years before that. It's a technologist. I say. Wow, five years like that's not very long in terms of softer development. That's what we were going pretty much rewrite our entire stack over the next five years. And show. What should that start to look like? So for me, it's about how do we completely reimagine every single aspect of the context center to revolutionize the experience by merging together, human and machine and totally new >> and the innovation strategies cloud in a cloud and and and data great job and great to have you on pleasure. Great, great conversation. Quick plug for you guys. Going to be a enterprise, connect to Cuba. Lbi. They're covering the event as well. What you going to talk about that? What? Some of the interactions? What will be the hallway conversations? What's your objective? What's your focus >> exactly? So so I'm going to be having my own session. We're going to be talking about the five reasons that you may not think about to goto context on the cloud. I've hinted already. A James of them. I think we're too well. That's you can you know, A. I is clearly central and I'm going to start to talk about the other four. >> Great, great conversation. A lot of change. Massive change happening. Great innovation Stretch. Great mission here at Five9. Great, great mission around. Changing and reimagine. More change the next five years in the past twenty five years. Again cloud computing eyes doing it will be winners. Will be losers will be following it here on the Cube. Jonathan Rosenberg, CTO ahead of AI at Five9. I'm John Furrier with the Cube. Thanks for watching.

(bright samba music) >> Narrator: Live from San Juan, Puerto Rico. It's The Cube. Covering Blockchain Unbound. Brought to you by Blockchain Industries. (bright samba music) >> Hello everyone and welcome back to The Cube's exclusive coverage here in Puerto Rico for Blockchain Unbound global conference where leaders from around the world, Silicon Valley, Miami, New York, all over the United States and Puerto Rico and Moscow and South Africa, all over the world come together to talk about the impact of blockchain, cryptocurrency, and a decentralized internet and the impact on society. Our next guest is David Orban. He's the managing director of Networking Society Ventures. Also does some investing. On the keynote speech of the closing session here on Day 1 of Blockchain Unbound. Thanks for joining me. >> Thank you very much for having me. >> So one of the big things that we're seeing in this revolution with blockchain and cryptocurrency is an awareness of how to reimagine democracy, society, and among other things, money transfer, and how that's impacting the world, from entrepreneurship to NGO's and society for good, AI for good, technology for good. So I got to ask ya, I heard some of your presentation, is there's some good tailwinds and some good headwinds in this industry, what's your assessment right now of the state of the globe with respect to how a network society will evolve and what are some of your observations and conclusions? >> One of our fundamental assumptions is that social change is only possible if sustainable technologies emerge to catalyze it. You know, if a slave rebellion won under the Roman Empire, the night of the victory, the slaves would be around the fire to decide who would be the slave the next morning, because they needed slaves to do everything. Today, not only we have achieved a level in our human civilization to outlaw slavery, we have incredible new inventions, like blockchain, to imagine a new social contract that is going to unstoppably come. >> This social contract is interesting, because now you have, I mean, democratization in digital transformation has been kicked around for a long time. Where are some real good examples that you can point to where you see really bright lights of innovation around democratization and digital transformation where it's working, and also where it's not working and what we need to do better? >> Certainly it is fashionable to pretend that technology hasn't helped. And one of the reasons why many people take that stance, is because they are confused. Too many simultaneous changes make the future even harder to predict today that it used to be the case 10, 20 or a hundred years ago. This is especially hard for those who are in charge of making those predictions, politicians, regulators, policy makers. We appointed or elected them in order to make decisions for everybody else. It is an impossible job, but they cannot afford to say that is the case. >> Yeah, and certainly we're in the media business and our model is open media, and even in the media you still have these gate keepers. So we see interesting trends, right so we're seeing disruption horizontally across all industries. If you look at blockchain and some of the things that are coming out, it's spurring real creativity from entrepreneurs as well as leaders, progressives if you will, that are being focused on efficiencies, which is spawning these little spots of innovation. I saw your use case around the solar panel. It was working. They killed it. So, you know, this is examples of where you see people get the value of really fast. So where are the efficiencies? Where's the value of creation coming from? What is blockchain? What is crypto? What is decentralized apps enabling? Is it, are we running too fast? Is it an enabling technology? What's your reaction, the thoughts? >> Some of us have been around in the first internet boom, 20 years ago, and the big three trillion dollars of value have been achieved by the dot-coms as measured by their market capitalization. And you would say, well, that bubble burst, and it all disappeared, but it didn't. We are still using the transatlantic fiber optic cables that were laid down then, and that created the premise for the next 20 years of technology based economic growth. So with blockchain, we are seeing the same, except that contrary to that, which was a quite provincial Silicon Valley phenomenon, blockchain innovation is today, global. So it is going to incredible places incredibly fast, and it is extremely competitive. There are projects that are doing the same thing, addressing the same challenge, all over the world. And it is fantastic. We even have a name for it. It's called forking or ray forking. >> Yeah, forking creates competition, but also faster time the value. Let's talk about the bubble. The dot-com bubble, which I lived through, and you have as well, was again, a Silicon Valley phenomenon, some New York, mostly America, basically, but everything happened. So everything that was talked about actually happened. But at that time, we didn't have a very wired community. Today we have organic communities in place, whether it's from open source communities online to actually a connected global network, AKA mobile internet. The role of communities now, seems to be that counter balancing self-governing opportunity. So I want to get your thoughts. Is the bubble going to be predicated, or letting some air out of that bubble, can it come from the communities? Because you could argue that efficiency in the communities with sourcing the truth if exposing the data can create very fast efficiencies around the transparency, so the thesis is, with the bubble behavior, also comes a connected community. So what's your view on the role of the community as a mechanism to continue to clean up or sanitize or whatever word we want to use to manage and help the self-governing? Because if it's organic ground swell, the communities should theoretically be monitoring and self-governing the growth. You thoughts. >> Those that are afraid of what is happening are incredibly capable of accusing the blockchain world of a thing and its opposite. Because they are saying, oh my god, the value, the metric value, which some mistakenly call the market capitalization, of tokens is increasing too fast, this is a bubble. And then maybe a month later, they will say, oh look it, everything is going to zero, I told you so. Well, either one is the problem or the other is the problem, but not both. The answer to your question is that yes, the community is expressing what is going on at the fine granularity that was not possible before because you would measure that by the subsequent venture funding stages of a startup, and maybe there would be a year or two years or more between one or the other of the stages. Today with tokens, every minute we are measuring the heartbeat of the project and the sentiment of the community around it. And everybody can vote with their tokens. Do I want to be part of this? Or I don't feel aligned anymore. And it is beautiful. But an even more important fact is that yes, today the community is global. When in 1976, Richard Dawkins wrote The Selfish Gene and the last chapter defined memes, which were the unit of the evolution of culture, he didn't mean silly images on the internet with captions. What he meant is, we should really be able to build a new science here. Memetic Engineering is what is fake news, and it is up to people like you and me who believe in the positive role of technology to show that we can actually have memetic engineering that benefits society and the markets. >> I mean, who'd have fake news is two things, the payload of fake news and actually the infrastructure gamification of what it did. I postulate that for, on one end of the spectrum is fake news, you could almost move to the other side of the spectrum and say, this good news. So clickbait equals fake news equals bad behavior, real bait, content, equals real news, real community. So there's a spectrum that you can almost say, we could actually weaponize content for good. >> Evolving our tool set in order to make sure that the wisdom of the crowds creates incredible investment and wealth creation opportunities for billions, not only for the gate keepers is what should be the regulators' best job, and they should be excited to have it rather than panicking. >> I want to ask you a question, philosophically. You mentioned tokens and governance, what we can vote for what people can vote with their coins and or some sort of consensus, gesture, or actually, real token transfer, as a way of voting. This actually, could solve the truth problem, because if you think about it, this is a new mechanism to understand sentiment within, whether it's a project or society, this new mechanism could be a source of truth, hence, but no centralized control, so you got the decentralization thing happening, but that's all predicated on going around a central authority, but the token dynamic, actually if you think about it, could be a token of truth, because statistically, it should work that way. Is that how you see it happening? And is that a directional correct statement? >> For too many years, we believed that Churchill's quip, democracy's the worst kind of government, except every other kind of government, was just a joke. He was giving us a challenge. And we were too weak to step up to that challenge and to design better governance mechanisms, better political instruments, and that is what is happening today. More and more people realize that they are freed up by technology where their relationship with the nation state that pretends to own them through citizenship and taxation can and will be renegotiated. >> I got to ask you a question. I love your logo, you've got a network graph up there that show the network society, implying that we're all connected, almost, you can argue, border-less nations, if you will. But I got to ask you, as that vision of a network society implies we're all connected, so we're all in one big tribe, although maybe, with different characteristics, but how do you see the future as we look at the current internet as almost a 30 year old stack, I mean, we're talkin' ancient relic by today's standards. So how do you see the stack evolving to match this criteria of a network society where the expectations of users and communities in society, whether it's government or groups are expecting new kinds of experiences, new kinds of outcomes? What in the stack is evolving? I mean, blockchain is one piece of it, but we're dealing with an old stack. I mean, it's old guard stuff, keep company's legacy. But the stack needs to be modernized. How does a stack modernize to intersect with your vision of a network society? >> Biological evolution has never been able to go meta. Our eyes are still so badly designed that the nerves bringing signals to our brain puncture the screen on which the images are projected. It's so stupid. We are able to understand when our designs are bad, and we are able to go deep, and actually rip out what has been the best way of going about certain things. This has happened in energy, where we are still in the process of electrifying a lot of things, many stoves are still gas stoves rather than electric as they should be. Or in transportation where we went from horses to cars and now we going to rapidly go to electric transportation. The internet is very young. It's just 30 years old, and the consumer space, just 50, 60 years old as a technology, but it must be fundamentally rebuilt and rethought. >> Yeah, it needs an engine change. It needs a tune up. >> What is dangerous is that there are very powerfully faulty memes being planted into the brains of too many people bringing desirable vulnerabilities in our infrastructure. And too few understand that those vulnerabilities caught everyone, whether they are friends, or real or pretend enemies. We have to build sustainable human civilization on a solid foundation. Nobody is served by maintaining those vulnerabilities that are still poking holes in vital infrastructure around us. >> Yeah, I mean, vital infrastructure and also the soft infrastructure, AKA the human psyche, AK memes in one tactic, to control the belief system and the narrative. But that's an attention driven mindset, so we're seeing that that fake news weaponizing content really prayed on the attention aspect of people where the reputation piece wasn't there. A lot of people now realizing that. How important is reputation in this new era of society, because there's something that's been challenging. We've seen every project, I mean, every project that I've seen, that I like, has an element of reputation in it. So there's a, because you have identity. Identity is super important. Attention, we know what does there. Get my attention. But the new discovery, the new navigation, the new progression to proficiency or value needs to be trusted. Reputation is an important part. Your reaction. >> Blockchain is making a lot of things measurable that were not before, and measuring them, it is able to assign value to them, and wherever there is value, new markets are being born. That is how incredible resources are now being poured in problems that were ignored for many, many years. And what is beautiful, is that blockchain is doing it open source. That is why new sustainable business models are evolving so fast. Back in the days, we would say an internet year is 3 months. I am now saying a blockchain year is 3 weeks. >> So that's fundamental, this value piece. That seems to be the equation that seems to be consistent. That's what you're saying, this value measurement seems to be a key metric and store, that's what the value is going to circulate around? Is that? >> So um, for the moment, our lives are bounded, limited. We have a given number of years to live, and more and more people realize that what they need to maximize is their benefit together with everybody else's benefit, because that is what makes human society valuable to its components and as a whole. So that kind of new outlook is being driven in the blockchain industry by people who don't necessarily need the second billion or the second million, but there are too many people who need to make ends meet, and it is just plain unacceptable that we let them live a life that does not fulfill their potential. >> This is a new opportunity to reimagine that equation. So I got to ask you, I love your work on social economic impact with blockchain, one of the things that we're observing in our reporting and analysis is, societal entrepreneurship is now emerging, what used to be a waterfall philanthropy exercise of NGO's and whatnot, fund something, stand up some servers, build a data center, uh, funding's over, project's over, start all over again. You're kind of chasing this tail. We're seeing real action with people who understand the businesses of nonprofits. We're turning that domain expertise into real, viable ventures. This is now an emerging trend, we're seeing certainly in Washington DC, where they have networks of people that they know, and now building on a tech stack is easier than it every was, so you're starting to see these real business opportunities getting funded and growing, that never would have gotten funding before, whether it's a, you know, an app for missing and exploited children, human trafficking, battered women, to water saving, water purification, all these things are now happening. What's your view on this, because this is kind of an unreported area around this entrepreneurship trend that things are getting to value faster? Do you see the same thing? >> If you ask the founder of the Ford Motor Company if he believed that damaging a community was a good business practice, he would have probably punched you, or at least laughed. Because today, those who feel that maximizing profit is a sacred duty of any capitalistic enterprise, even if it does include extracting and harming communities, employees, stakeholders, is extremely misguided. Positive impact is not counter to profit. They go hand in hand. >> Mission driven enterprises can exist. It's not just for the philanthropy. >> There is nothing else, but a sustainable business. In a long term an unsustainable business cannot be sustained. So if you want to build a business that lasts, you must build it sustainably, ecologically, socially, but of course, also in terms of it being profitable. And what is beautiful about the blockchain is that it completely decoupled the long term sustainability of a project from this silly decision. Should it be a for profit? Should it be a nonprofit? Who cares? What it should be is an inspiration for millions of people to align their creativity and passion with that project. And profits and sustainability will follow. >> And the funding's there and the opportunity time to value is shorter than ever before. Thank you so much for spending the time coming on The Cube and sharing your ideas and your mission and vision. And thanks for coming on. The Cube appreciate it. Okay, we are here with Dave Orban, managing director, Network Society Ventures, changing the world, societal economic impact. I'm John Furrier, your Washington Cube. More live coverage, day 2 tomorrow. We're here both days, Thursday and Friday. Here in Puerto Rico, for Blockchain Unbound, I'm John Furrier. Thanks for watching. (light techno music) (light techno music)

>> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at Data Privacy Day at Twitter's World Headquarters in downtown San Francisco. Full-day event, a lot of seminars and sessions talking about the issue of privacy. Even though Scott McNealy in 1999 said, "Privacy's dead, get over it," everyone here would beg to differ; and it's a really important topic. We're excited to have Michelle Dennedy. She's the Chief Privacy Officer from Cisco. Welcome, Michelle. >> Indeed, thank you. And when Scott said that, I was his Chief Privacy Officer. >> Oh you were? >> I'm well acquainted with my young friend Scott's feelings on the subject. >> It's pretty interesting, 'cause that was eight years before the iPhone, so a completely different world than actually one of the prior guests we were talking about privacy is an issue in the Harvard Business Review from 125 years ago. So this is not new. >> Absolutely. >> So how have things changed? I mean that's a great perspective that you were there. What was he kind of thinking about and really what are the privacy challenges now compared to 1999? >> So different. Such a different world. I mean fascinating that when that statement was made the discussion was a press conference where we were introducing Connectivity. It was an offshoot of Java, and it basically allowed you to send from your personal computer a wireless message to your printer so that a document could come out (gasp). >> That's what it was? >> Yeah. >> Wireless printing? >> Wireless printing. And really it was gyro technology, so anything wirelessly could start talking to each other in an internet of things world. >> Right. >> So, good news bad news. The world has exploded from there, obviously; but the base premise of, can I be mobile, can I live in a world of connectivity, and still have control over my story, who I am, where I am, what I'm doing? And it was really a reframing moment of when you say privacy is dead, if what you mean by that is secrecy and hiding away and not being connected to the world around you, I may agree with you. However, privacy as a functional definition of how we define ourselves, how we live in a culture, what we can expect in terms of morality, ethics, respect, and security, alive and well, baby. Alive and well. >> (laughs) No shortage of opportunity to keep you busy. We talked to a lot of people who go to a lot of tech conferences. I have to say I don't know that we've ever talked to a Chief Privacy Officer. >> You're missing out. >> I know, so not you get to define the role, I love it. So what are your priorities as Chief Priority Officer? What are you keeping an eye on day to day as well as what are your more strategic objectives? >> It's a great question. So the rise of the Chief Privacy Officer, actually Scott was a big help in that and gave me exactly the right amount of rope to hang myself with. The way I look at it is, probably the simplest analogy is, should you have a Chief Financial Officer? >> Yeah. >> I would guess yeah, right? That didn't exist about 100 years ago. We just kind of loped along, and whoever had the biggest bag of money at the end was deemed to be successful. Where if somebody else who had no money left at the end but bought another store, you would have no way of measuring that. So the Chief Privacy Officer is that person for your digital currency. I look at the pros and the cons, the profit and the loss, of data and the data footprint for our company and for all the people to whom we sell. We think about, what are those control mechanisms for data? So think of me as your data financial officer. >> Right, right. But the data in and of itself is just stagnant, right? It's really just the data in the context of all these other applications. How it's used, where it's used, when it's used, what it's combined with, that really starts to trip into areas of value as well as potential problems. >> I feel like we scripted this before, but we didn't. >> Jeff: We did not script it, we don't script the-- >> So if I took out a rectangle out of my wallet, and it had a number on it, and it was green, what would you say that thing probably is? >> Probably Andrew Jackson on the front. >> Yeah, probably Andrew Jackson. What is that? >> A 20 dollar bill. >> Why is that a 20 dollar bill? >> Because we agree that you're going to give it to me and it has that much value, and thankfully the guy at Starbucks will give me 20 bucks worth of coffee for it. >> (laughs) Exactly. Well which could be a cup the way we're going. >> Which could be a cup. >> But that's exactly right. So is that 20 dollar bill stagnant? Yes. That 20 dollar bill just sitting on the table between us is nothing. I could burn it up, I could put it in my pocket and lose it and never see it again. I could flush it down the toilet. That's how we used to treat our data. If you recognize instead the story that we share about that piece of currency, we happen to be in a place where it's really easy to alienate that currency. I could go downstairs here and spend it. If I was in Beijing I probably would have to go and convert it into a different currency, and we'd tell a story about that conversion because our standards interface is different. Data is exactly the same way. The story that we share together today is a valuable story because we're communicating out, we're here for a purpose. >> Right. >> We're making friends. I'm liking you because you're asking me all these great questions that I would have fed you had I been able to feed you questions. >> Jeff: (laughs) But it's only that context, it's only that communicability that brings it value. We now assume as a populous that paper currency is valuable. It's just paper. It's only as good as the story that enlivens it. So now we're looking at smaller, smaller Microdata transactions of how am I tweeting out information to people who follow me? >> Jeff: Right, right. >> How do I share that with your following public, and does that give me a greater opportunity to educate people about security and privacy? Does that allow my company to sell more of my goods and services because we're building ethics and privacy into the fabric of our networks? I would say that's as valuable or more valuable than that Andrew Jackson. >> So it's interesting 'cause you talk about building privacy into the products. We often hear about building security into the products, right? Because the old way of security of building a bigger wall doesn't work any more and you really have to bake it in at all steps of the application: development, the data layer, the database, et cetera, et cetera. When you look at privacy versus security, and especially 'cause Cisco's sitting on, I mean you guys are sitting on the pipes, everything is running through your machines. >> That's right. >> How do you separate the two, how do you prioritize, and how do you make sure the privacy discussion is certainly part of that gets the right amount of relevance within the context of the security conversation? >> It's a glib answer that's much more complicated, but the security is really in many instances the what. I can really secure almost any batch of data. It can be complete gobbley gook zeroes and ones. It could be something really critical. It could be my medical records. The privacy and the data about what that context is, that's the why. I don't see them as one or the other at all. I see security and security not as not a technology but a series of verb things that you actually physically, people process technologies. That enactment should be addressed to a why. So it's kind of Peter Drucker's management of you manage what you measure. That was like incendiary advice when it first came out. Well I wanted to say that you secure what you treasure. So if you treasure a digital interaction with your employees, your customers, and your community, you should probably secure that. >> Right. But it seems like there's a little bit of a disconnect about maybe what should be treasured and what is the value with folks that have grown up. Let's pick on the young kids, not really thinking through or having the time or knowing an impact of a negative event in terms of just clicking and accepting the EULA and using that application on their phone. They just look at in a different way. Is that valid? How do they change that behavior? How do you look at this new generation, and there's this sea of data which is far larger than it used to be coming off all these devices, internet of things, obviously. People are things too. The mobile devices with all that geolocation data, and the sensor data, and then oh by the way it's all going to be in our cars and everything else shortly. How's that landscape changing and challenging you in new ways, and what are you doing about it? >> The speed and dynamics are astronomical. How do you count the stars, right? >> Jeff: (laughs) >> And should you? Isn't that kind of a waste of time? >> Jeff: Right, right. >> It used to be that knowledge, when I was a kid, was knowing what was in A to Z of the Encyclopedia Britannica. Now facts are cheap. Facts used to be expensive. You had to take time and commit to them, and physically find them, and be smart enough to read, and on, and on, and on. The dumbest kid is smarter than I was with my Encyclopedia Britannica because we have search engines. Now their commodity is how do I critically think? How do I make my brand and make my way? How do I ride and surf on a wave of untold quantities of information to create a quality brand for myself? So the young people are actually in a much better position than, I'll still count us as young. >> Jeff: Yeah, Uh huh. >> But maybe less young. >> Less young, less young than we were yesterday. >> We are digital natives, but I think I am hugely optimistic that the kids coming up are really starting to understand the power of brand: personal brand, family brand, cultural brand. And they're feeling very activist about the whole thing. >> Yeah, which is interesting 'cause that was never a factor when there was no personal brand, right? You were part of-- >> No way. >> whatever entity that you were in. >> Well, you were in a clique. >> Right. >> Right? You identified as when I was home I was the third out of four kids. I was a Roman Catholic girl in the Midwest. I was a total dork with a bowl haircut. Now kids can curate who and what and how they are over the network. Young professionals can connect with people with experience. Or they can decide, I get this all the time on Twitter actually. How did you become a Chief Privacy Officer? I'm really interested in taking a pivot in my career. And I love talking to those people 'cause they always educate me, and I hope that I give them a little bit of value too. >> Right, right. Michelle, we could go on for on and on and on. But, unfortunately, I think you got to go cover a session. So we're going to let you go. >> Thank you. >> Michelle Dennedy, thanks for taking a few minutes of your time. >> Thank you, and don't miss another Data Privacy Day. >> I will not. We'll be back next year as well. I'm Jeff Frick. You're watching theCUBE. See you next time.