(gentle bright music) >> Okay, welcome back to our "Supercloud 2" event live coverage here at stage performance in Palo Alto syndicating around the world. I'm John Furrier with Dave Vellante. We've got exclusive news and a scoop here for SiliconANGLE and theCUBE. Zhamak Dehghani, creator of data mesh has formed a new company called NextData.com NextData, she's a cube alumni and contributor to our Supercloud initiative, as well as our coverage and breaking analysis with Dave Vellante on data, the killer app for Supercloud. Zhamak, great to see you. Thank you for coming into the studio and congratulations on your newly formed venture and continued success on the data mesh. >> Thank you so much. It's great to be here. Great to see you in person. >> Dave: Yeah, finally. >> John: Wonderful. Your contributions to the data conversation has been well-documented certainly by us and others in the industry. Data mesh taking the world by storm. Some people are debating it, throwing, you know, cold water on it. Some are, I think, it's the next big thing. Tell us about the data mesh super data apps that are emerging out of cloud. >> I mean, data mesh, as you said, it's, you know, the pain point that it surfaced were universal. Everybody said, "Oh, why didn't I think of that?" You know, it was just an obvious next step and people are approaching it, implementing it. I guess the last few years, I've been involved in many of those implementations, and I guess Supercloud is somewhat a prerequisite for it because it's data mesh and building applications using data mesh is about sharing data responsibly across boundaries. And those boundaries include boundaries, organizational boundaries cloud technology boundaries and trust boundaries. >> I want to bring that up because your venture, NextData which is new, just formed. Tell us about that. What wave is that riding? What specifically are you targeting? What's the pain point? >> Zhamak: Absolutely, yes. So next data is the result of, I suppose, the pains that I suffered from implementing a database for many of the organizations. Basically, a lot of organizations that I've worked with, they want decentralized data. So they really embrace this idea of decentralized ownership of the data, but yet they want interconnectivity through standard APIs, yet they want discoverability and governance. So they want to have policies implemented, they want to govern that data, they want to be able to discover that data and yet they want to decentralize it. And we do that with a developer experience that is easy and native to a generalist developer. So we try to find, I guess, the common denominator that solves those problems and enables that developer experience for data sharing. >> John: Since you just announced the news, what's been the reaction? >> Zhamak: I just announced the news right now, so what's the reaction? >> John: But people in the industry that know you, you did a lot of work in the area. What have been some of the feedback on the new venture in terms of the approach, the customers, problem? >> Yeah, so we've been in stealth modes, so we haven't publicly talked about it, but folks that have been close to us in fact have reached out. We already have implementations of our pilot platform with early customers, which is super exciting. And we're going to have multiple of those. Of course, we're a tiny, tiny company. We can have many of those where we are going to have multiple pilots, implementations of our platform in real world. We're real global large scale organizations that have real world problems. So we're not going to build our platform in vacuum. And that's what's happening right now. >> Zhamak: When I think about your role at ThoughtWorks, you had a very wide observation space with a number of clients helping them implement data mesh and other things as well prior to your data mesh initiative. But when I look at data mesh, at least the ones that I've seen, they're very narrow. I think of JPMC, I think of HelloFresh. They're generally obviously not surprising. They don't include the big vision of inclusivity across clouds across different data stores. But it seems like people are having to go through some gymnastics to get to, you know, the organizational reality of decentralizing data, and at least pushing data ownership to the line of business. How are you approaching or are you approaching, solving that problem? Are you taking a narrow slice? What can you tell us about Next Data? >> Zhamak: Sure, yeah, absolutely. Gymnastics, the cute word to describe what the organizations have to go through. And one of those problems is that, you know, the data, as you know, resides on different platforms. It's owned by different people, it's processed by pipelines that who owns them. So there's this very disparate and disconnected set of technologies that were very useful for when we thought about data and processing as a centralized problem. But when you think about data as a decentralized problem, the cost of integration of these technologies in a cohesive developer experience is what's missing. And we want to focus on that cohesive end-to-end developer experience to share data responsibly in this autonomous units, we call them data products, I guess in data mesh, right? That constitutes computation, that governs that data policies, discoverability. So I guess, I heard this expression in the last talks that you can have your cake and eat it too. So we want people have their cakes, which is, you know, data in different places, decentralization and eat it too, which is interconnected access to it. So we start with standardizing and codifying this idea of a data product container that encapsulates data computation, APIs to get to it in a technology agnostic way, in an open way. And then, sit on top and use existing existing tech, you know, Snowflake, Databricks, whatever exists, you know, the millions of dollars of investments that companies have made, sit on top of those but create this cohesive, integrated experience where data product is a first class primitive. And that's really key here, that the language, and the modeling that we use is really native to data mesh is that I will make a data product, I'm sharing a data product, and that encapsulates on providing metadata about this. I'm providing computation that's constantly changing the data. I'm providing the API for that. So we're trying to kind of codify and create a new developer experience based on that. And developer, both from provider side and user side connected to peer-to-peer data sharing with data product as a primitive first class concept. >> Okay, so the idea would be developers would build applications leveraging those data products which are discoverable and governed. Now, today you see some companies, you know, take a snowflake for example. >> Zhamak: Yeah. >> Attempting to do that within their own little walled garden. They even, at one point, used the term, "Mesh." I dunno if they pull back on that. And then they sort of became aware of some of your work. But a lot of the things that they're doing within their little insulated environment, you know, support that, that, you know, governance, they're building out an ecosystem. What's different in your vision? >> Exactly. So we realize that, you know, and this is a reality, like you go to organizations, they have a snowflake and half of the organization happily operates on Snowflake. And on the other half, oh, we are on, you know, bare infrastructure on AWS, or we are on Databricks. This is the realities, you know, this Supercloud that's written up here. It's about working across boundaries of technology. So we try to embrace that. And even for our own technology with the way we're building it, we say, "Okay, nobody's going to use next data mesh operating system. People will have different platforms." So you have to build with openness in mind, and in case of Snowflake, I think, you know, they have I'm sure very happy customers as long as customers can be on Snowflake. But once you cross that boundary of platforms then that becomes a problem. And we try to keep that in mind in our solution. >> So, it's worth reviewing that basically, the concept of data mesh is that, whether you're a data lake or a data warehouse, an S3 bucket, an Oracle database as well, they should be inclusive inside of the data. >> We did a session with AWS on the startup showcase, data as code. And remember, I wrote a blog post in 2007 called, "Data's the new developer kit." Back then, they used to call 'em developer kits, if you remember. And that we said at that time, whoever can code data >> Zhamak: Yes. >> Will have a competitive advantage. >> Aren't there machines going to be doing that? Didn't we just hear that? >> Well we have, and you know, Hey Siri, hey Cube. Find me that best video for data mesh. There it is. I mean, this is the point, like what's happening is that, now, data has to be addressable >> Zhamak: Yes. >> For machines and for coding. >> Zhamak: Yes. >> Because as you need to call the data. So the question is, how do you manage the complexity of big things as promiscuous as possible, making it available as well as then governing it because it's a trade off. The more you make open >> Zhamak: Definitely. >> The better the machine learning. >> Zhamak: Yes. >> But yet, the governance issue, so this is the, you need an OS to handle this maybe. >> Yes, well, we call our mental model for our platform is an OS operating system. Operating systems, you know, have shown us how you can kind of abstract what's complex and take care of, you know, a lot of complexities, but yet provide an open and, you know, dynamic enough interface. So we think about it that way. We try to solve the problem of policies live with the data. An enforcement of the policies happens at the most granular level which is, in this concept, the data product. And that would happen whether you read, write, or access a data product. But we can never imagine what are these policies could be. So our thinking is, okay, we should have a open policy framework that can allow organizations write their own policy drivers, and policy definitions, and encode it and encapsulated in this data product container. But I'm not going to fool myself to say that, you know, that's going to solve the problem that you just described. I think we are in this, I don't know, if I look into my crystal ball, what I think might happen is that right now, the primitives that we work with to train machine-learning model are still bits and bites in data. They're fields, rows, columns, right? And that creates quite a large surface area, an attack area for, you know, for privacy of the data. So perhaps, one of the trends that we might see is this evolution of data APIs to become more and more computational aware to bring the compute to the data to reduce that surface area so you can really leave the control of the data to the sovereign owners of that data, right? So that data product. So I think the evolution of our data APIs perhaps will become more and more computational. So you describe what you want, and the data owner decides, you know, how to manage the- >> John: That's interesting, Dave, 'cause it's almost like we just talked about ChatGPT in the last segment with you, who's a machine learning, could really been around the industry. It's almost as if you're starting to see reason come into the data, reasoning. It's like you starting to see not just metadata, using the data to reason so that you don't have to expose the raw data. It's almost like a, I won't say curation layer, but an intelligence layer. >> Zhamak: Exactly. >> Can you share your vision on that 'cause that seems to be where the dots are connecting. >> Zhamak: Yes, this is perhaps further into the future because just from where we stand, we have to create still that bridge of familiarity between that future and present. So we are still in that bridge-making mode, however, by just the basic notion of saying, "I'm going to put an API in front of my data, and that API today might be as primitive as a level of indirection as in you tell me what you want, tell me who you are, let me go process that, all the policies and lineage, and insert all of this intelligence that need to happen. And then I will, today, I will still give you a file. But by just defining that API and standardizing it, now we have this amazing extension point that we can say, "Well, the next revision of this API, you not just tell me who you are, but you actually tell me what intelligence you're after. What's a logic that I need to go and now compute on your API?" And you can kind of evolve that, right? Now you have a point of evolution to this very futuristic, I guess, future where you just describe the question that you're asking from the chat. >> Well, this is the Supercloud, Dave. >> I have a question from a fan, I got to get it in. It's George Gilbert. And so, his question is, you're blowing away the way we synchronize data from operational systems to the data stack to applications. So the concern that he has, and he wants your feedback on this, "Is the data product app devs get exposed to more complexity with respect to moving data between data products or maybe it's attributes between data products, how do you respond to that? How do you see, is that a problem or is that something that is overstated, or do you have an answer for that?" >> Zhamak: Absolutely. So I think there's a sweet spot in getting data developers, data product developers closer to the app, but yet not burdening them with the complexity of the application and application logic, and yet reducing their cognitive load by localizing what they need to know about which is that domain where they're operating within. Because what's happening right now? what's happening right now is that data engineers, a ton of empathy for them for their high threshold of pain that they can, you know, deal with, they have been centralized, they've put into the data team, and they have been given this unbelievable task of make meaning out of data, put semantic over it, curates it, cleans it, and so on. So what we are saying is that get those folks embedded into the domain closer to the application developers, these are still separately moving units. Your app and your data products are independent but yet tightly closed with each other, tightly coupled with each other based on the context of the domain, so reduce cognitive load by localizing what they need to know about to the domain, get them closer to the application but yet have them them separate from app because app provides a very different service. Transactional data for my e-commerce transaction, data product provides a very different service, longitudinal data for the, you know, variety of this intelligent analysis that I can do on the data. But yet, it's all within the domain of e-commerce or sales or whatnot. >> So a lot of decoupling and coupling create that cohesiveness. >> Zhamak: Absolutely. >> Architecture. So I have to ask you, this is an interesting question 'cause it came up on theCUBE all last year. Back on the old server, data center days and cloud, SRE, Google coined the term, "Site Reliability Engineer" for someone to look over the hundreds of thousands of servers. We asked a question to data engineering community who have been suffering, by the way, agree. Is there an SRE-like role for data? Because in a way, data engineering, that platform engineer, they are like the SRE for data. In other words, managing the large scale to enable automation and cell service. What's your thoughts and reaction to that? >> Zhamak: Yes, exactly. So, maybe we go through that history of how SRE came to be. So we had the first DevOps movement which was, remove the wall between dev and ops and bring them together. So you have one cross-functional units of the organization that's responsible for, you build it you run it, right? So then there is no, I'm going to just shoot my application over the wall for somebody else to manage it. So we did that, and then we said, "Okay, as we decentralized and had this many microservices running around, we had to create a layer that abstracted a lot of the complexity around running now a lot or monitoring, observing and running a lot while giving autonomy to this cross-functional team." And that's where the SRE, a new generation of engineers came to exist. So I think if I just look- >> Hence Borg, hence Kubernetes. >> Hence, hence, exactly. Hence chaos engineering, hence embracing the complexity and messiness, right? And putting engineering discipline to embrace that and yet give a cohesive and high integrity experience of those systems. So I think, if we look at that evolution, perhaps something like that is happening by bringing data and apps closer and make them these domain-oriented data product teams or domain oriented cross-functional teams, full stop, and still have a very advanced maybe at the platform infrastructure level kind of operational team that they're not busy doing two jobs which is taking care of domains and the infrastructure, but they're building infrastructure that is embracing that complexity, interconnectivity of this data process. >> John: So you see similarities. >> Absolutely, but I feel like we're probably in a more early days of that movement. >> So it's a data DevOps kind of thing happening where scales happening. It's good things are happening yet. Eh, a little bit fast and loose with some complexities to clean up. >> Yes, yes. This is a different restructure. As you said we, you know, the job of this industry as a whole on architects is decompose, recompose, decompose, recomposing a new way, and now we're like decomposing centralized team, recomposing them as domains and- >> John: So is data mesh the killer app for Supercloud? >> You had to do this for me. >> Dave: Sorry, I couldn't- (John and Dave laughing) >> Zhamak: What do you want me to say, Dave? >> John: Yes. >> Zhamak: Yes of course. >> I mean Supercloud, I think it's, really the terminology's Supercloud, Opencloud. But I think, in spirits of it, this embracing of diversity and giving autonomy for people to make decisions for what's right for them and not yet lock them in. I think just embracing that is baked into how data mesh assume the world would work. >> John: Well thank you so much for coming on Supercloud too, really appreciate it. Data has driven this conversation. Your success of data mesh has really opened up the conversation and exposed the slow moving data industry. >> Dave: Been a great catalyst. (John laughs) >> John: That's now going well. We can move faster, so thanks for coming on. >> Thank you for hosting me. It was wonderful. >> Okay, Supercloud 2 live here in Palo Alto. Our stage performance, I'm John Furrier with Dave Vellante. We're back with more after this short break, Stay with us all day for Supercloud 2. (gentle bright music)

foreign welcome back to re invent 2022 we're wrapping up four days well one evening and three solid days wall-to-wall of cube coverage I'm Dave vellante John furrier's birthday is today he's on a plane to London to go see his nephew get married his his great Sister Janet awesome family the furriers uh spanning the globe and uh and John I know you wanted to be here you're watching in Newark or you were waiting to uh to get in the plane so all the best to you happy birthday one year the Amazon PR people brought a cake out to celebrate John's birthday because he's always here at AWS re invented his birthday so I'm really pleased to have two really special guests uh former Cube host Cube Alum great wikibon contributor Stu miniman now with red hat still good to see you again great to be here Dave yeah I was here for that cake uh the twitterverse uh was uh really helping to celebrate John's birthday today and uh you know always great to be here with you and then with this you know Awesome event this week and friend of the cube of many time Cube often Cube contributor as here's a cube analyst this week as his own consultancy sarbj johal great to see you thanks for coming on good to see you Dave uh great to see you stu I'm always happy to participate in these discussions and um I enjoy the discussion every time so this is kind of cool because you know usually the last day is a getaway day and this is a getaway day but this place is still packed I mean it's I mean yeah it's definitely lighter you can at least walk and not get slammed but I subjit I'm going to start with you I I wanted to have you as the the tail end here because cause you participated in the analyst sessions you've been watching this event from from the first moment and now you've got four days of the Kool-Aid injection but you're also talking to customers developers Partners the ecosystem where do you want to go what's your big takeaways I think big takeaways that Amazon sort of innovation machine is chugging along they are I was listening to some of the accessions and when I was back to my room at nine so they're filling the holes in some areas but in some areas they're moving forward there's a lot to fix still it doesn't seem like that it seems like we are done with the cloud or The Innovation is done now we are building at the millisecond level so where do you go next there's a lot of room to grow on the storage side on the network side uh the improvements we need and and also making sure that the software which is you know which fits the hardware like there's a specialized software um sorry specialized hardware for certain software you know so there was a lot of talk around that and I attended some of those sessions where I asked the questions around like we have a specialized database for each kind of workload specialized processes processors for each kind of workload yeah the graviton section and actually the the one interesting before I forget that the arbitration was I asked that like why there are so many so many databases and IRS for the egress costs and all that stuff can you are you guys thinking about reducing that you know um the answer was no egress cost is not a big big sort of uh um show stopper for many of the customers but but the from all that sort of little discussion with with the folks sitting who build these products over there was that the plethora of choice is given to the customers to to make them feel that there's no vendor lock-in so if you are using some open source you know um soft software it can be on the you know platform side or can be database side you have database site you have that option at AWS so this is a lot there because I always thought that that AWS is the mother of all lock-ins but it's got an ecosystem and we're going to talk about exactly we'll talk about Stu what's working within AWS when you talk to customers and where are the challenges yeah I I got a comment on open source Dave of course there because I mean look we criticized to Amazon for years about their lack of contribution they've gotten better they're doing more in open source but is Amazon the mother of all lock-ins many times absolutely there's certain people inside Amazon I'm saying you know many of us talk Cloud native they're like well let's do Amazon native which means you're like full stack is things from Amazon and do things the way that we want to do things and you know I talk to a lot of customers they use more than one Cloud Dave and therefore certain things absolutely I want to Leverage The Innovation that Amazon has brought I do think we're past building all the main building blocks in many ways we are like in day two yes Amazon is fanatically customer focused and will always stay that way but you know there wasn't anything that jumped out at me last year or this year that was like Wow new category whole new way of thinking about something we're in a vocals last year Dave said you know we have over 200 services and if we listen to you the customer we'd have over two thousand his session this week actually got some great buzz from my friends in the serverless ecosystem they love some of the things tying together we're using data the next flywheel that we're going to see for the next 10 years Amazon's at the center of the cloud ecosystem in the IT world so you know there's a lot of good things here and to your point Dave the ecosystem one of the things I always look at is you know was there a booth that they're all going to be crying in their beer after Amazon made an announcement there was not a tech vendor that I saw this week that was like oh gosh there was an announcement and all of a sudden our business is gone where I did hear some rumbling is Amazon might be the next GSI to really move forward and we've seen all the gsis pushing really deep into supporting Cloud bringing workloads to the cloud and there's a little bit of rumbling as to that balance between what Amazon will do and their uh their go to market so a couple things so I think I think we all agree that a lot of the the announcements here today were taping seams right I call it and as it relates to the mother of all lock-in the reason why I say that it's it's obviously very much a pejorative compare Oracle company you know really well with Amazon's lock-in for Amazon's lock-in is about bringing this ecosystem together so that you actually have Choice Within the the house so you don't have to leave you know there's a there's a lot to eat at the table yeah you look at oracle's ecosystem it's like yeah you know oracle is oracle's ecosystem so so that is how I think they do lock in customers by incenting them not to leave because there's so much Choice Dave I agree with you a thousand I mean I'm here I'm a I'm a good partner of AWS and all of the partners here want to be successful with Amazon and Amazon is open to that it's not our way or get out which Oracle tries how much do you extract from the overall I.T budget you know are you a YouTube where you give the people that help you create a large sum of the money YouTube hasn't been all that profitable Amazon I think is doing a good balance of the ecosystem makes money you know we used to talk Dave about you know how much dollars does VMware make versus there um I think you know Amazon is a much bigger you know VMware 2.0 we used to think talk about all the time that VMware for every dollar spent on VMware licenses 15 or or 12 or 20 were spent in the ecosystem I would think the ratio is even higher here sarbji and an Oracle I would say it's I don't know yeah actually 1 to 0.5 maybe I don't know but I want to pick on your discussion about the the ecosystem the the partner ecosystem is so it's it's robust strong because it's wider I was I was not saying that there's no lock-in with with Amazon right AWS there's lock-in there's lock-in with everything there's lock-in with open source as well but but the point is that they're they're the the circle is so big you don't feel like locked in but they're playing smart as well they're bringing in the software the the platforms from the open source they're picking up those packages and saying we'll bring it in and cater that to you through AWS make it better perform better and also throw in their custom chips on top of that hey this MySQL runs better here so like what do you do I said oh Oracle because it's oracle's product if you will right so they are I think think they're filing or not slenders from their go to market strategy from their engineering and they listen to they're listening to customers like very closely and that has sort of side effects as well listening to customers creates a sprawl of services they have so many services and I criticized them last year for calling everything a new service I said don't call it a new service it's a feature of a existing service sure a lot of features a lot of features this is egress our egress costs a real problem or is it just the the on-prem guys picking at the the scab I mean what do you hear from customers so I mean Dave you know I I look at what Corey Quinn talks about all the time and Amazon charges on that are more expensive than any other Cloud the cloud providers and partly because Amazon is you know probably not a word they'd use they are dominant when it comes to the infrastructure space and therefore they do want to make it a little bit harder to do that they can get away with it um because um yeah you know we've seen some of the cloud providers have special Partnerships where you can actually you know leave and you're not going to be charged and Amazon they've been a little bit more flexible but absolutely I've heard customers say that they wish some good tunning and tongue-in-cheek stuff what else you got we lay it on us so do our players okay this year I think the focus was on the upside it's shifting gradually this was more focused on offside there were less talk of of developers from the main stage from from all sort of quadrants if you will from all Keynotes right so even Werner this morning he had a little bit for he was talking about he he was talking he he's job is to Rally up the builders right yeah so he talks about the go build right AWS pipes I thought was kind of cool then I said like I'm making glue easier I thought that was good you know I know some folks don't use that I I couldn't attend the whole session but but I heard in between right so it is really adopt or die you know I am Cloud Pro for last you know 10 years and I think it's the best model for a technology consumption right um because of economies of scale but more importantly because of division of labor because of specialization because you can't afford to hire the best security people the best you know the arm chip designers uh you can't you know there's one actually I came up with a bumper sticker you guys talked about bumper sticker I came up with that like last couple of weeks The Innovation favorite scale they have scale they have Innovation so that's where the Innovation is and it's it's not there again they actually say the market sets the price Market you as a customer don't set the price the vendor doesn't set the price Market sets the price so if somebody's complaining about their margins or egress and all that I think that's BS um yeah I I have a few more notes on the the partner if you you concur yeah Dave you know with just coming back to some of this commentary about like can Amazon actually enable something we used to call like Community clouds uh your companies like you know Goldman and NASDAQ and the like where Industries will actually be able to share data uh and you know expand the usage and you know Amazon's going to help drive that API economy forward some so it's good to see those things because you know we all know you know all of us are smarter than just any uh single company together so again some of that's open source but some of that is you know I think Amazon is is you know allowing Innovation to thrive I think the word you're looking for is super cloud there well yeah I mean it it's uh Dave if you want to go there with the super cloud because you know there's a metaphor for exactly what you described NASDAQ Goldman Sachs we you know and and you know a number of other companies that are few weeks at the Berkeley Sky Computing paper yeah you know that's a former supercloud Dave Linthicum calls it metacloud I'm not really careful I mean you know I go back to the the challenge we've been you know working at for a decade is the distributed architecture you know if you talk about AI architectures you know what lives in the cloud what lives at the edge where do we train things where do we do inferences um locations should matter a lot less Amazon you know I I didn't hear a lot about it this show but when they came out with like local zones and oh my gosh out you know all the things that Amazon is building to push out to the edge and also enabling that technology and software and the partner ecosystem helps expand that and Pull It in it's no longer you know Dave it was Hotel California all of the data eventually is going to end up in the public cloud and lock it in it's like I don't think that's going to be the case we know that there will be so much data out at the edge Amazon absolutely is super important um there some of those examples we're giving it's not necessarily multi-cloud but there's collaboration happening like in the healthcare world you know universities and hospitals can all share what they're doing uh regardless of you know where they live well Stephen Armstrong in the analyst session did say that you know we're going to talk about multi-cloud we're not going to lead with it necessarily but we are going to actually talk about it and that's different to your points too than in the fullness of time all the data will be in the cloud that's a new narrative but go ahead yeah actually Amazon is a leader in the cloud so if they push the cloud even if they don't say AWS or Amazon with it they benefit from it right and and the narrative is that way there's the proof is there right so again Innovation favorite scale there are chips which are being made for high scale their software being tweaked for high scale you as a Bank of America or for the Chrysler as a typical Enterprise you cannot afford to do those things in-house what cloud providers can I'm not saying just AWS Google cloud is there Azure guys are there and few others who are behind them and and you guys are there as well so IBM has IBM by the way congratulations to your red hat I know but IBM won the award um right you know very good partner and yeah but yeah people are dragging their feet people usually do on the change and they are in denial denial they they drag their feet and they came in IBM director feed the cave Den Dell drag their feed the cave in yeah you mean by Dragon vs cloud deniers cloud deniers right so server Huggers I call them but they they actually are sitting in Amazon Cloud Marketplace everybody is buying stuff from there the marketplace is the new model OKAY Amazon created the marketplace for b2c they are leading the marketplace of B2B as well on the technology side and other people are copying it so there are multiple marketplaces now so now actually it's like if you're in in a mobile app development there are two main platforms Android and Apple you first write the application for Apple right then for Android hex same here as a technology provider as and I I and and I actually you put your stuff to AWS first then you go anywhere else yeah they are later yeah the Enterprise app store is what we've wanted for a long time the question is is Amazon alone the Enterprise app store or are they partner of a of a larger portfolio because there's a lot of SAS companies out there uh that that play into yeah what we need well and this is what you're talking about the future but I just want to make a point about the past you talking about dragging their feet because the Cube's been following this and Stu you remember this in 2013 IBM actually you know got in a big fight with with Amazon over the CIA deal you know and it all became public judge wheeler eviscerated you know IBM and it ended up IBM ended up buying you know soft layer and then we know what happened there and it Joe Tucci thought the cloud was Mosey right so it's just amazing to see we have booksellers you know VMware called them books I wasn't not all of them are like talking about how great Partnerships they are it's amazing like you said sub GC and IBM uh with the the GSI you know Partnership of the year but what you guys were just talking about was the future and that's what I wanted to get to is because you know Amazon's been leading the way I I was listening to Werner this morning and that just reminded me of back in the days when we used to listen to IBM educate us give us a master class on system design and decoupled systems and and IO and everything else now Amazon is you know the master educator and it got me thinking how long will that last you know will they go the way of you know the other you know incumbents will they be disrupted or will they you know keep innovating maybe it's going to take 10 or 20 years I don't know yeah I mean Dave you actually you did some research I believe it was a year or so ago yeah but what will stop Amazon and the one thing that worries me a little bit um is the two Pizza teams when you have over 202 Pizza teams the amount of things that each one of those groups needs to take care of was more than any human could take care of people burn out they run out of people how many amazonians only last two or three years and then leave because it is tough I bumped into plenty of friends of mine that have been you know six ten years at Amazon and love it but it is a tough culture and they are driving werner's keynote I thought did look to from a product standpoint you could say tape over some of the seams some of those solutions to bring Beyond just a single product and bring them together and leverage data so there are some signs that they might be able to get past some of those limitations but I still worry structurally culturally there could be some challenges for Amazon to keep the momentum going especially with the global economic impact that we are likely to see in the next year bring us home I think the future side like we could talk about the vendors all day right to serve the community out there I think we should talk about how what's the future of technology consumption from the consumer side so from the supplier side just a quick note I think the only danger AWS has has that that you know Fred's going after them you know too big you know like we will break you up and that can cause some disruption there other than that I think they they have some more steam to go for a few more years at least before we start thinking about like oh this thing is falling apart or anything like that so they have a lot more they have momentum and it's continuing so okay from the I think game is on retail by the way is going to get disrupted before AWS yeah go ahead from the buyer's side I think um the the future of the sort of Technology consumption is based on the paper uh use and they actually are turning all their services to uh they are sort of becoming serverless behind the scenes right all analytics service they had one service left they they did that this year so every service is serverless so that means you pay exactly for the amount you use the compute the iops the the storage so all these three layers of course Network we talked about the egress stuff and that's a problem there because of the network design mainly because Google has a flatter design and they have lower cost so so they are actually squeezing the their their designing this their services in a way that you don't waste any resources as a buyer so for example very simple example when early earlier In This Cloud you will get a VM right in Cloud that's how we started so and you can get 20 use 20 percent of the VM 80 is getting wasted that's not happening now that that has been reduced to the most extent so now your VM grows as you grow the usage and if you go higher than the tier you picked they will charge you otherwise they will not charge you extra so that's why there's still a lot of instances like many different types you have to pick one I think the future is that those instances will go away the the instance will be formed for you on the fly so that is the future serverless all right give us bumper sticker Stu and then Serb G I'll give you my quick one and then we'll wrap yeah so just Dave to play off of sharp G and to wrap it up you actually wrote about it on your preview post for here uh serverless we're talking about how developers think about things um and you know Amazon in many ways you know is the new default server uh you know for the cloud um and containerization fits into the whole serverless Paradigm uh it's the space that I live in uh you know every day here and you know I was happy to see the last few years serverless and containers there's a blurring a line and you know subject we're still going to see VMS for a long time yeah yeah we will see that so give us give us your book Instagram my number six is innovation favorite scale that's my bumper sticker and and Amazon has that but also I I want everybody else to like the viewers to take a look at the the Google Cloud as well as well as IBM with others like maybe you have a better price to Performance there for certain workloads and by the way one vendor cannot do it alone we know that for sure the market is so big there's a lot of room for uh Red Hats of the world and and and Microsoft's the world to innovate so keep an eye on them they we need the competition actually and that's why competition Will Keep Us to a place where Market sets the price one vendor doesn't so the only only danger is if if AWS is a monopoly then I will be worried I think ecosystems are the Hallmark of a great Cloud company and Amazon's got the the biggest and baddest ecosystem and I think the other thing to watch for is Industries building on top of the cloud you mentioned the Goldman Sachs NASDAQ Capital One and Warner media these all these industries are building their own clouds and that's where the real money is going to be made in the latter half of the 2020s all right we're a wrap this is Dave Valente I want to first of all thank thanks to our great sponsors AWS for for having us here this is our 10th year at the cube AMD you know sponsoring as well the the the cube here Accenture sponsor to third set upstairs upstairs on the fifth floor all the ecosystem partners that came on the cube this week and supported our mission for free content our content is always free we try to give more to the community and we we take back so go to thecube.net and you'll see all these videos go to siliconangle com for all the news wikibon.com I publish weekly a breaking analysis series I want to thank our amazing crew here you guys we have probably 30 35 people unbelievable our awesome last session John Walls uh Paul Gillen Lisa Martin Savannah Peterson John Furrier who's on a plane we appreciate Andrew and Leonard in our ear and all of our our crew Palo Alto Boston and across the country thank you so much really appreciate it all right we are a wrap AWS re invent 2022 we'll see you in two weeks we'll see you two weeks at Palo Alto ignite back here in Vegas thanks for watching thecube the leader in Enterprise and emerging Tech coverage [Music]

>> From The Cube Studios in Palo Alto and Boston, bringing you data driven insights from The Cube at ETR. This is "Breaking Analysis" with Dave Vellante. >> The reverse momentum in tech stocks caused by rising interest rates, less attractive discounted cash flow models, and more tepid forward guidance, can be easily measured by public market valuations. And while there's lots of discussion about the impact on private companies and cash runway and 409A valuations, measuring the performance of non-public companies isn't as easy. IPOs have dried up and public statements by private companies, of course, they accentuate the good and they kind of hide the bad. Real data, unless you're an insider, is hard to find. Hello and welcome to this week's "Wikibon Cube Insights" powered by ETR. In this "Breaking Analysis", we unlock some of the secrets that non-public, emerging tech companies may or may not be sharing. And we do this by introducing you to a capability from ETR that we've not exposed you to over the past couple of years, it's called the Emerging Technologies Survey, and it is packed with sentiment data and performance data based on surveys of more than a thousand CIOs and IT buyers covering more than 400 companies. And we've invited back our colleague, Erik Bradley of ETR to help explain the survey and the data that we're going to cover today. Erik, this survey is something that I've not personally spent much time on, but I'm blown away at the data. It's really unique and detailed. First of all, welcome. Good to see you again. >> Great to see you too, Dave, and I'm really happy to be talking about the ETS or the Emerging Technology Survey. Even our own clients of constituents probably don't spend as much time in here as they should. >> Yeah, because there's so much in the mainstream, but let's pull up a slide to bring out the survey composition. Tell us about the study. How often do you run it? What's the background and the methodology? >> Yeah, you were just spot on the way you were talking about the private tech companies out there. So what we did is we decided to take all the vendors that we track that are not yet public and move 'em over to the ETS. And there isn't a lot of information out there. If you're not in Silicon (indistinct), you're not going to get this stuff. So PitchBook and Tech Crunch are two out there that gives some data on these guys. But what we really wanted to do was go out to our community. We have 6,000, ITDMs in our community. We wanted to ask them, "Are you aware of these companies? And if so, are you allocating any resources to them? Are you planning to evaluate them," and really just kind of figure out what we can do. So this particular survey, as you can see, 1000 plus responses, over 450 vendors that we track. And essentially what we're trying to do here is talk about your evaluation and awareness of these companies and also your utilization. And also if you're not utilizing 'em, then we can also figure out your sales conversion or churn. So this is interesting, not only for the ITDMs themselves to figure out what their peers are evaluating and what they should put in POCs against the big guys when contracts come up. But it's also really interesting for the tech vendors themselves to see how they're performing. >> And you can see 2/3 of the respondents are director level of above. You got 28% is C-suite. There is of course a North America bias, 70, 75% is North America. But these smaller companies, you know, that's when they start doing business. So, okay. We're going to do a couple of things here today. First, we're going to give you the big picture across the sectors that ETR covers within the ETS survey. And then we're going to look at the high and low sentiment for the larger private companies. And then we're going to do the same for the smaller private companies, the ones that don't have as much mindshare. And then I'm going to put those two groups together and we're going to look at two dimensions, actually three dimensions, which companies are being evaluated the most. Second, companies are getting the most usage and adoption of their offerings. And then third, which companies are seeing the highest churn rates, which of course is a silent killer of companies. And then finally, we're going to look at the sentiment and mindshare for two key areas that we like to cover often here on "Breaking Analysis", security and data. And data comprises database, including data warehousing, and then big data analytics is the second part of data. And then machine learning and AI is the third section within data that we're going to look at. Now, one other thing before we get into it, ETR very often will include open source offerings in the mix, even though they're not companies like TensorFlow or Kubernetes, for example. And we'll call that out during this discussion. The reason this is done is for context, because everyone is using open source. It is the heart of innovation and many business models are super glued to an open source offering, like take MariaDB, for example. There's the foundation and then there's with the open source code and then there, of course, the company that sells services around the offering. Okay, so let's first look at the highest and lowest sentiment among these private firms, the ones that have the highest mindshare. So they're naturally going to be somewhat larger. And we do this on two dimensions, sentiment on the vertical axis and mindshare on the horizontal axis and note the open source tool, see Kubernetes, Postgres, Kafka, TensorFlow, Jenkins, Grafana, et cetera. So Erik, please explain what we're looking at here, how it's derived and what the data tells us. >> Certainly, so there is a lot here, so we're going to break it down first of all by explaining just what mindshare and net sentiment is. You explain the axis. We have so many evaluation metrics, but we need to aggregate them into one so that way we can rank against each other. Net sentiment is really the aggregation of all the positive and subtracting out the negative. So the net sentiment is a very quick way of looking at where these companies stand versus their peers in their sectors and sub sectors. Mindshare is basically the awareness of them, which is good for very early stage companies. And you'll see some names on here that are obviously been around for a very long time. And they're clearly be the bigger on the axis on the outside. Kubernetes, for instance, as you mentioned, is open source. This de facto standard for all container orchestration, and it should be that far up into the right, because that's what everyone's using. In fact, the open source leaders are so prevalent in the emerging technology survey that we break them out later in our analysis, 'cause it's really not fair to include them and compare them to the actual companies that are providing the support and the security around that open source technology. But no survey, no analysis, no research would be complete without including these open source tech. So what we're looking at here, if I can just get away from the open source names, we see other things like Databricks and OneTrust . They're repeating as top net sentiment performers here. And then also the design vendors. People don't spend a lot of time on 'em, but Miro and Figma. This is their third survey in a row where they're just dominating that sentiment overall. And Adobe should probably take note of that because they're really coming after them. But Databricks, we all know probably would've been a public company by now if the market hadn't turned, but you can see just how dominant they are in a survey of nothing but private companies. And we'll see that again when we talk about the database later. >> And I'll just add, so you see automation anywhere on there, the big UiPath competitor company that was not able to get to the public markets. They've been trying. Snyk, Peter McKay's company, they've raised a bunch of money, big security player. They're doing some really interesting things in developer security, helping developers secure the data flow, H2O.ai, Dataiku AI company. We saw them at the Snowflake Summit. Redis Labs, Netskope and security. So a lot of names that we know that ultimately we think are probably going to be hitting the public market. Okay, here's the same view for private companies with less mindshare, Erik. Take us through this one. >> On the previous slide too real quickly, I wanted to pull that security scorecard and we'll get back into it. But this is a newcomer, that I couldn't believe how strong their data was, but we'll bring that up in a second. Now, when we go to the ones of lower mindshare, it's interesting to talk about open source, right? Kubernetes was all the way on the top right. Everyone uses containers. Here we see Istio up there. Not everyone is using service mesh as much. And that's why Istio is in the smaller breakout. But still when you talk about net sentiment, it's about the leader, it's the highest one there is. So really interesting to point out. Then we see other names like Collibra in the data side really performing well. And again, as always security, very well represented here. We have Aqua, Wiz, Armis, which is a standout in this survey this time around. They do IoT security. I hadn't even heard of them until I started digging into the data here. And I couldn't believe how well they were doing. And then of course you have AnyScale, which is doing a second best in this and the best name in the survey Hugging Face, which is a machine learning AI tool. Also doing really well on a net sentiment, but they're not as far along on that access of mindshare just yet. So these are again, emerging companies that might not be as well represented in the enterprise as they will be in a couple of years. >> Hugging Face sounds like something you do with your two year old. Like you said, you see high performers, AnyScale do machine learning and you mentioned them. They came out of Berkeley. Collibra Governance, InfluxData is on there. InfluxDB's a time series database. And yeah, of course, Alex, if you bring that back up, you get a big group of red dots, right? That's the bad zone, I guess, which Sisense does vis, Yellowbrick Data is a NPP database. How should we interpret the red dots, Erik? I mean, is it necessarily a bad thing? Could it be misinterpreted? What's your take on that? >> Sure, well, let me just explain the definition of it first from a data science perspective, right? We're a data company first. So the gray dots that you're seeing that aren't named, that's the mean that's the average. So in order for you to be on this chart, you have to be at least one standard deviation above or below that average. So that gray is where we're saying, "Hey, this is where the lump of average comes in. This is where everyone normally stands." So you either have to be an outperformer or an underperformer to even show up in this analysis. So by definition, yes, the red dots are bad. You're at least one standard deviation below the average of your peers. It's not where you want to be. And if you're on the lower left, not only are you not performing well from a utilization or an actual usage rate, but people don't even know who you are. So that's a problem, obviously. And the VCs and the PEs out there that are backing these companies, they're the ones who mostly are interested in this data. >> Yeah. Oh, that's great explanation. Thank you for that. No, nice benchmarking there and yeah, you don't want to be in the red. All right, let's get into the next segment here. Here going to look at evaluation rates, adoption and the all important churn. First new evaluations. Let's bring up that slide. And Erik, take us through this. >> So essentially I just want to explain what evaluation means is that people will cite that they either plan to evaluate the company or they're currently evaluating. So that means we're aware of 'em and we are choosing to do a POC of them. And then we'll see later how that turns into utilization, which is what a company wants to see, awareness, evaluation, and then actually utilizing them. That's sort of the life cycle for these emerging companies. So what we're seeing here, again, with very high evaluation rates. H2O, we mentioned. SecurityScorecard jumped up again. Chargebee, Snyk, Salt Security, Armis. A lot of security names are up here, Aqua, Netskope, which God has been around forever. I still can't believe it's in an Emerging Technology Survey But so many of these names fall in data and security again, which is why we decided to pick those out Dave. And on the lower side, Vena, Acton, those unfortunately took the dubious award of the lowest evaluations in our survey, but I prefer to focus on the positive. So SecurityScorecard, again, real standout in this one, they're in a security assessment space, basically. They'll come in and assess for you how your security hygiene is. And it's an area of a real interest right now amongst our ITDM community. >> Yeah, I mean, I think those, and then Arctic Wolf is up there too. They're doing managed services. You had mentioned Netskope. Yeah, okay. All right, let's look at now adoption. These are the companies whose offerings are being used the most and are above that standard deviation in the green. Take us through this, Erik. >> Sure, yet again, what we're looking at is, okay, we went from awareness, we went to evaluation. Now it's about utilization, which means a survey respondent's going to state "Yes, we evaluated and we plan to utilize it" or "It's already in our enterprise and we're actually allocating further resources to it." Not surprising, again, a lot of open source, the reason why, it's free. So it's really easy to grow your utilization on something that's free. But as you and I both know, as Red Hat proved, there's a lot of money to be made once the open source is adopted, right? You need the governance, you need the security, you need the support wrapped around it. So here we're seeing Kubernetes, Postgres, Apache Kafka, Jenkins, Grafana. These are all open source based names. But if we're looking at names that are non open source, we're going to see Databricks, Automation Anywhere, Rubrik all have the highest mindshare. So these are the names, not surprisingly, all names that probably should have been public by now. Everyone's expecting an IPO imminently. These are the names that have the highest mindshare. If we talk about the highest utilization rates, again, Miro and Figma pop up, and I know they're not household names, but they are just dominant in this survey. These are applications that are meant for design software and, again, they're going after an Autodesk or a CAD or Adobe type of thing. It is just dominant how high the utilization rates are here, which again is something Adobe should be paying attention to. And then you'll see a little bit lower, but also interesting, we see Collibra again, we see Hugging Face again. And these are names that are obviously in the data governance, ML, AI side. So we're seeing a ton of data, a ton of security and Rubrik was interesting in this one, too, high utilization and high mindshare. We know how pervasive they are in the enterprise already. >> Erik, Alex, keep that up for a second, if you would. So yeah, you mentioned Rubrik. Cohesity's not on there. They're sort of the big one. We're going to talk about them in a moment. Puppet is interesting to me because you remember the early days of that sort of space, you had Puppet and Chef and then you had Ansible. Red Hat bought Ansible and then Ansible really took off. So it's interesting to see Puppet on there as well. Okay. So now let's look at the churn because this one is where you don't want to be. It's, of course, all red 'cause churn is bad. Take us through this, Erik. >> Yeah, definitely don't want to be here and I don't love to dwell on the negative. So we won't spend as much time. But to your point, there's one thing I want to point out that think it's important. So you see Rubrik in the same spot, but Rubrik has so many citations in our survey that it actually would make sense that they're both being high utilization and churn just because they're so well represented. They have such a high overall representation in our survey. And the reason I call that out is Cohesity. Cohesity has an extremely high churn rate here about 17% and unlike Rubrik, they were not on the utilization side. So Rubrik is seeing both, Cohesity is not. It's not being utilized, but it's seeing a high churn. So that's the way you can look at this data and say, "Hm." Same thing with Puppet. You noticed that it was on the other slide. It's also on this one. So basically what it means is a lot of people are giving Puppet a shot, but it's starting to churn, which means it's not as sticky as we would like. One that was surprising on here for me was Tanium. It's kind of jumbled in there. It's hard to see in the middle, but Tanium, I was very surprised to see as high of a churn because what I do hear from our end user community is that people that use it, like it. It really kind of spreads into not only vulnerability management, but also that endpoint detection and response side. So I was surprised by that one, mostly to see Tanium in here. Mural, again, was another one of those application design softwares that's seeing a very high churn as well. >> So you're saying if you're in both... Alex, bring that back up if you would. So if you're in both like MariaDB is for example, I think, yeah, they're in both. They're both green in the previous one and red here, that's not as bad. You mentioned Rubrik is going to be in both. Cohesity is a bit of a concern. Cohesity just brought on Sanjay Poonen. So this could be a go to market issue, right? I mean, 'cause Cohesity has got a great product and they got really happy customers. So they're just maybe having to figure out, okay, what's the right ideal customer profile and Sanjay Poonen, I guarantee, is going to have that company cranking. I mean they had been doing very well on the surveys and had fallen off of a bit. The other interesting things wondering the previous survey I saw Cvent, which is an event platform. My only reason I pay attention to that is 'cause we actually have an event platform. We don't sell it separately. We bundle it as part of our offerings. And you see Hopin on here. Hopin raised a billion dollars during the pandemic. And we were like, "Wow, that's going to blow up." And so you see Hopin on the churn and you didn't see 'em in the previous chart, but that's sort of interesting. Like you said, let's not kind of dwell on the negative, but you really don't. You know, churn is a real big concern. Okay, now we're going to drill down into two sectors, security and data. Where data comprises three areas, database and data warehousing, machine learning and AI and big data analytics. So first let's take a look at the security sector. Now this is interesting because not only is it a sector drill down, but also gives an indicator of how much money the firm has raised, which is the size of that bubble. And to tell us if a company is punching above its weight and efficiently using its venture capital. Erik, take us through this slide. Explain the dots, the size of the dots. Set this up please. >> Yeah. So again, the axis is still the same, net sentiment and mindshare, but what we've done this time is we've taken publicly available information on how much capital company is raised and that'll be the size of the circle you see around the name. And then whether it's green or red is basically saying relative to the amount of money they've raised, how are they doing in our data? So when you see a Netskope, which has been around forever, raised a lot of money, that's why you're going to see them more leading towards red, 'cause it's just been around forever and kind of would expect it. Versus a name like SecurityScorecard, which is only raised a little bit of money and it's actually performing just as well, if not better than a name, like a Netskope. OneTrust doing absolutely incredible right now. BeyondTrust. We've seen the issues with Okta, right. So those are two names that play in that space that obviously are probably getting some looks about what's going on right now. Wiz, we've all heard about right? So raised a ton of money. It's doing well on net sentiment, but the mindshare isn't as well as you'd want, which is why you're going to see a little bit of that red versus a name like Aqua, which is doing container and application security. And hasn't raised as much money, but is really neck and neck with a name like Wiz. So that is why on a relative basis, you'll see that more green. As we all know, information security is never going away. But as we'll get to later in the program, Dave, I'm not sure in this current market environment, if people are as willing to do POCs and switch away from their security provider, right. There's a little bit of tepidness out there, a little trepidation. So right now we're seeing overall a slight pause, a slight cooling in overall evaluations on the security side versus historical levels a year ago. >> Now let's stay on here for a second. So a couple things I want to point out. So it's interesting. Now Snyk has raised over, I think $800 million but you can see them, they're high on the vertical and the horizontal, but now compare that to Lacework. It's hard to see, but they're kind of buried in the middle there. That's the biggest dot in this whole thing. I think I'm interpreting this correctly. They've raised over a billion dollars. It's a Mike Speiser company. He was the founding investor in Snowflake. So people watch that very closely, but that's an example of where they're not punching above their weight. They recently had a layoff and they got to fine tune things, but I'm still confident they they're going to do well. 'Cause they're approaching security as a data problem, which is probably people having trouble getting their arms around that. And then again, I see Arctic Wolf. They're not red, they're not green, but they've raised fair amount of money, but it's showing up to the right and decent level there. And a couple of the other ones that you mentioned, Netskope. Yeah, they've raised a lot of money, but they're actually performing where you want. What you don't want is where Lacework is, right. They've got some work to do to really take advantage of the money that they raised last November and prior to that. >> Yeah, if you're seeing that more neutral color, like you're calling out with an Arctic Wolf, like that means relative to their peers, this is where they should be. It's when you're seeing that red on a Lacework where we all know, wow, you raised a ton of money and your mindshare isn't where it should be. Your net sentiment is not where it should be comparatively. And then you see these great standouts, like Salt Security and SecurityScorecard and Abnormal. You know they haven't raised that much money yet, but their net sentiment's higher and their mindshare's doing well. So those basically in a nutshell, if you're a PE or a VC and you see a small green circle, then you're doing well, then it means you made a good investment. >> Some of these guys, I don't know, but you see these small green circles. Those are the ones you want to start digging into and maybe help them catch a wave. Okay, let's get into the data discussion. And again, three areas, database slash data warehousing, big data analytics and ML AI. First, we're going to look at the database sector. So Alex, thank you for bringing that up. Alright, take us through this, Erik. Actually, let me just say Postgres SQL. I got to ask you about this. It shows some funding, but that actually could be a mix of EDB, the company that commercializes Postgres and Postgres the open source database, which is a transaction system and kind of an open source Oracle. You see MariaDB is a database, but open source database. But the companies they've raised over $200 million and they filed an S-4. So Erik looks like this might be a little bit of mashup of companies and open source products. Help us understand this. >> Yeah, it's tough when you start dealing with the open source side and I'll be honest with you, there is a little bit of a mashup here. There are certain names here that are a hundred percent for profit companies. And then there are others that are obviously open source based like Redis is open source, but Redis Labs is the one trying to monetize the support around it. So you're a hundred percent accurate on this slide. I think one of the things here that's important to note though, is just how important open source is to data. If you're going to be going to any of these areas, it's going to be open source based to begin with. And Neo4j is one I want to call out here. It's not one everyone's familiar with, but it's basically geographical charting database, which is a name that we're seeing on a net sentiment side actually really, really high. When you think about it's the third overall net sentiment for a niche database play. It's not as big on the mindshare 'cause it's use cases aren't as often, but third biggest play on net sentiment. I found really interesting on this slide. >> And again, so MariaDB, as I said, they filed an S-4 I think $50 million in revenue, that might even be ARR. So they're not huge, but they're getting there. And by the way, MariaDB, if you don't know, was the company that was formed the day that Oracle bought Sun in which they got MySQL and MariaDB has done a really good job of replacing a lot of MySQL instances. Oracle has responded with MySQL HeatWave, which was kind of the Oracle version of MySQL. So there's some interesting battles going on there. If you think about the LAMP stack, the M in the LAMP stack was MySQL. And so now it's all MariaDB replacing that MySQL for a large part. And then you see again, the red, you know, you got to have some concerns about there. Aerospike's been around for a long time. SingleStore changed their name a couple years ago, last year. Yellowbrick Data, Fire Bolt was kind of going after Snowflake for a while, but yeah, you want to get out of that red zone. So they got some work to do. >> And Dave, real quick for the people that aren't aware, I just want to let them know that we can cut this data with the public company data as well. So we can cross over this with that because some of these names are competing with the larger public company names as well. So we can go ahead and cross reference like a MariaDB with a Mongo, for instance, or of something of that nature. So it's not in this slide, but at another point we can certainly explain on a relative basis how these private names are doing compared to the other ones as well. >> All right, let's take a quick look at analytics. Alex, bring that up if you would. Go ahead, Erik. >> Yeah, I mean, essentially here, I can't see it on my screen, my apologies. I just kind of went to blank on that. So gimme one second to catch up. >> So I could set it up while you're doing that. You got Grafana up and to the right. I mean, this is huge right. >> Got it thank you. I lost my screen there for a second. Yep. Again, open source name Grafana, absolutely up and to the right. But as we know, Grafana Labs is actually picking up a lot of speed based on Grafana, of course. And I think we might actually hear some noise from them coming this year. The names that are actually a little bit more disappointing than I want to call out are names like ThoughtSpot. It's been around forever. Their mindshare of course is second best here but based on the amount of time they've been around and the amount of money they've raised, it's not actually outperforming the way it should be. We're seeing Moogsoft obviously make some waves. That's very high net sentiment for that company. It's, you know, what, third, fourth position overall in this entire area, Another name like Fivetran, Matillion is doing well. Fivetran, even though it's got a high net sentiment, again, it's raised so much money that we would've expected a little bit more at this point. I know you know this space extremely well, but basically what we're looking at here and to the bottom left, you're going to see some names with a lot of red, large circles that really just aren't performing that well. InfluxData, however, second highest net sentiment. And it's really pretty early on in this stage and the feedback we're getting on this name is the use cases are great, the efficacy's great. And I think it's one to watch out for. >> InfluxData, time series database. The other interesting things I just noticed here, you got Tamer on here, which is that little small green. Those are the ones we were saying before, look for those guys. They might be some of the interesting companies out there and then observe Jeremy Burton's company. They do observability on top of Snowflake, not green, but kind of in that gray. So that's kind of cool. Monte Carlo is another one, they're sort of slightly green. They are doing some really interesting things in data and data mesh. So yeah, okay. So I can spend all day on this stuff, Erik, phenomenal data. I got to get back and really dig in. Let's end with machine learning and AI. Now this chart it's similar in its dimensions, of course, except for the money raised. We're not showing that size of the bubble, but AI is so hot. We wanted to cover that here, Erik, explain this please. Why TensorFlow is highlighted and walk us through this chart. >> Yeah, it's funny yet again, right? Another open source name, TensorFlow being up there. And I just want to explain, we do break out machine learning, AI is its own sector. A lot of this of course really is intertwined with the data side, but it is on its own area. And one of the things I think that's most important here to break out is Databricks. We started to cover Databricks in machine learning, AI. That company has grown into much, much more than that. So I do want to state to you Dave, and also the audience out there that moving forward, we're going to be moving Databricks out of only the MA/AI into other sectors. So we can kind of value them against their peers a little bit better. But in this instance, you could just see how dominant they are in this area. And one thing that's not here, but I do want to point out is that we have the ability to break this down by industry vertical, organization size. And when I break this down into Fortune 500 and Fortune 1000, both Databricks and Tensorflow are even better than you see here. So it's quite interesting to see that the names that are succeeding are also succeeding with the largest organizations in the world. And as we know, large organizations means large budgets. So this is one area that I just thought was really interesting to point out that as we break it down, the data by vertical, these two names still are the outstanding players. >> I just also want to call it H2O.ai. They're getting a lot of buzz in the marketplace and I'm seeing them a lot more. Anaconda, another one. Dataiku consistently popping up. DataRobot is also interesting because all the kerfuffle that's going on there. The Cube guy, Cube alum, Chris Lynch stepped down as executive chairman. All this stuff came out about how the executives were taking money off the table and didn't allow the employees to participate in that money raising deal. So that's pissed a lot of people off. And so they're now going through some kind of uncomfortable things, which is unfortunate because DataRobot, I noticed, we haven't covered them that much in "Breaking Analysis", but I've noticed them oftentimes, Erik, in the surveys doing really well. So you would think that company has a lot of potential. But yeah, it's an important space that we're going to continue to watch. Let me ask you Erik, can you contextualize this from a time series standpoint? I mean, how is this changed over time? >> Yeah, again, not show here, but in the data. I'm sorry, go ahead. >> No, I'm sorry. What I meant, I should have interjected. In other words, you would think in a downturn that these emerging companies would be less interesting to buyers 'cause they're more risky. What have you seen? >> Yeah, and it was interesting before we went live, you and I were having this conversation about "Is the downturn stopping people from evaluating these private companies or not," right. In a larger sense, that's really what we're doing here. How are these private companies doing when it comes down to the actual practitioners? The people with the budget, the people with the decision making. And so what I did is, we have historical data as you know, I went back to the Emerging Technology Survey we did in November of 21, right at the crest right before the market started to really fall and everything kind of started to fall apart there. And what I noticed is on the security side, very much so, we're seeing less evaluations than we were in November 21. So I broke it down. On cloud security, net sentiment went from 21% to 16% from November '21. That's a pretty big drop. And again, that sentiment is our one aggregate metric for overall positivity, meaning utilization and actual evaluation of the name. Again in database, we saw it drop a little bit from 19% to 13%. However, in analytics we actually saw it stay steady. So it's pretty interesting that yes, cloud security and security in general is always going to be important. But right now we're seeing less overall net sentiment in that space. But within analytics, we're seeing steady with growing mindshare. And also to your point earlier in machine learning, AI, we're seeing steady net sentiment and mindshare has grown a whopping 25% to 30%. So despite the downturn, we're seeing more awareness of these companies in analytics and machine learning and a steady, actual utilization of them. I can't say the same in security and database. They're actually shrinking a little bit since the end of last year. >> You know it's interesting, we were on a round table, Erik does these round tables with CISOs and CIOs, and I remember one time you had asked the question, "How do you think about some of these emerging tech companies?" And one of the executives said, "I always include somebody in the bottom left of the Gartner Magic Quadrant in my RFPs. I think he said, "That's how I found," I don't know, it was Zscaler or something like that years before anybody ever knew of them "Because they're going to help me get to the next level." So it's interesting to see Erik in these sectors, how they're holding up in many cases. >> Yeah. It's a very important part for the actual IT practitioners themselves. There's always contracts coming up and you always have to worry about your next round of negotiations. And that's one of the roles these guys play. You have to do a POC when contracts come up, but it's also their job to stay on top of the new technology. You can't fall behind. Like everyone's a software company. Now everyone's a tech company, no matter what you're doing. So these guys have to stay in on top of it. And that's what this ETS can do. You can go in here and look and say, "All right, I'm going to evaluate their technology," and it could be twofold. It might be that you're ready to upgrade your technology and they're actually pushing the envelope or it simply might be I'm using them as a negotiation ploy. So when I go back to the big guy who I have full intentions of writing that contract to, at least I have some negotiation leverage. >> Erik, we got to leave it there. I could spend all day. I'm going to definitely dig into this on my own time. Thank you for introducing this, really appreciate your time today. >> I always enjoy it, Dave and I hope everyone out there has a great holiday weekend. Enjoy the rest of the summer. And, you know, I love to talk data. So anytime you want, just point the camera on me and I'll start talking data. >> You got it. I also want to thank the team at ETR, not only Erik, but Darren Bramen who's a data scientist, really helped prepare this data, the entire team over at ETR. I cannot tell you how much additional data there is. We are just scratching the surface in this "Breaking Analysis". So great job guys. I want to thank Alex Myerson. Who's on production and he manages the podcast. Ken Shifman as well, who's just coming back from VMware Explore. Kristen Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our editor in chief over at SiliconANGLE. Does some great editing for us. Thank you. All of you guys. Remember these episodes, they're all available as podcast, wherever you listen. All you got to do is just search "Breaking Analysis" podcast. I publish each week on wikibon.com and siliconangle.com. Or you can email me to get in touch david.vellante@siliconangle.com. You can DM me at dvellante or comment on my LinkedIn posts and please do check out etr.ai for the best survey data in the enterprise tech business. This is Dave Vellante for Erik Bradley and The Cube Insights powered by ETR. Thanks for watching. Be well. And we'll see you next time on "Breaking Analysis". (upbeat music)

>> live from San Francisco, celebrating 10 years of high tech coverage. It's the Cube covering Veum World 2019. Brought to you by VM Wear and its ecosystem partners. >> Welcome back to San Francisco, everybody. My name is David Lantz. I'm here with my co host John Troia. This is Day three of V M World 2019 2 sets. >> This is >> our 10th year at the M. World Cube is the leader in live enterprise tech coverage. Marry on Blondie is here. He's the C m o and chief evangelist that 10 tree by DDN Yes, sir. He's joined by Graham Breezes The Field CTO at 10 Tree also by DDN Recent acquisition jets Great to see you. >> Likewise, as they say, we're back. I like I like to call it a hibernation in the sense that people may have not known where did Ian or 10 Trias and Tension by Dede and, as the name implies, were acquired a year ago at the M World August 31st of 2018. And in the year since, we've been ableto invest in engineering support, my joining the company in marketing to take this solution, we've been able to save thousands of customers millions of man hours and bring it to a larger number of users. Way >> first saw 10 tree, we said, Wow, this is all about simplification. And Jonah Course you remember that when you go back to the early early Dick Cube days of of'em World, very complex storage was a major challenge. 10 Tree was all about simplifying that. Of course, we know DDN as well is the high performance specialist and have worked with those guys for a number of years. But take >> us >> back Married to the original vision of 10 Cherie. Is that original vision still alive? How was it evolved? >> Well, I'd say that it's, ah, number one reason why we're a part of the DD and family of brands because, as, ah, portfolio company, they're looking good. Bring technologies. I'm the marketing guy for our enterprise or virtual ization audience, and the product sets that cover high performance computing have their own audience. So for me, I'm focused on that. Graham's also focused on that, and, uh, really what continues to make us different today is the fact we were designed to learn from the beginning to understand how virtual machines end to end work with infrastructure. And that's really the foundation of what makes us different today. The same thing, right? >> So from the very beginning we were we were built to understand the work clothes that we service in the data center. So and that was virtual machines. We service those on multiple hyper visors today in terms of being able to understand those workloads intrinsically gives us a tremendous capability. Thio place. I owe again understanding that the infrastructure network storage, hyper visor, uh, weaken view that end end in terms of a latent a graph and give customers and insight into the infrastructure how it's performing. I would say that we're actually extending that further ways in terms of additional workload that we're gonna be able to take on later this year. >> So I know a lot >> of storage admits, although I I only play one on >> TV, but, uh, no, consistently >> throughout the years, right? 10 tree user experiences that is the forefront there. And in fact, they they often some people have said, You know what? I really want to get something done. I grab my tent Reeboks and so it can't talk. Maybe some examples of one example of why the user experience how the user experiences differ or why, why it's different. >> I'll start off by saying that I had a chance being new to the company just two weeks to meet a lot of 10 tree users. And prior to taking the job, I talkto us some folks behind the scenes, and they all told me the same thing. But what I was so interested to hear is that if they didn't have 10 tree, they'd otherwise not have the time to do the automation work, the research work, the strategy work or even the firefighting that's vital to their everyday operations. Right? So it's like, of course, I don't need to manage it. If I did, I wouldn't be able to do all these other things. And I think that's it. Rings true right that it's hard to quantify that time savings because people say, 0 1/2 of it. See, that's really not much of the greater scheme of things. I don't know. 1/2 50. Working on strategic program is a huge opportunity. Let's see >> the value of 10 tree to our end users and we've heard from a lot of them this week actually spent a fantastic event hearing from many of our passionate consumers. From the very beginning. We wanted to build a product that ultimately customers care about, and we've seen that this week in droves. But I would say the going back to what they get out of it. It's the values and what they don't have to do, so they don't have to carve up ones. They don't have to carve up volumes. All they have to do is work with the units of infrastructure that air native to their environment, v ems. They deal with everything in their environment from our virtual machine perspective, virtual machines, one thing across the infrastructure. Again, they can add those virtual machines seamlessly. They can add those in seconds they don't have toe size and add anything in terms of how am I gonna divide up the storage coming in a provisional I Oh, how am I going to get the technical pieces right? Uh, they basically just get place v EMS, and we have a very simplistic way to give them Ah, visualization into that because we understand that virtual machine and what it takes to service. It comes right back to them in terms of time savings that are tremendous in terms of that. >> So let's deal with the elephant in the room. So, so 10 tree. We've talked about all the great stuff in the original founding vision. But then I ran into some troubles, right? And so what? How do you deal with that with customers in terms of just their perception of what what occurred you guys did the eye poets, et cetera, take us through how you're making sure customers are cool with you guys. >> I'm naturally, glass is half full kind of guy from previous, uh, times on the Cube. The interesting thing is, not a lot of people actually knew. Maybe we didn't create enough brand recognition in the past for people to even know that there was a transition. There were even some of our customers. And Graham, you can pile on this that because they don't manage the product every day because they don't have to. It's kind of so easy they even for gotten a lot about it on don't spend a lot of time. I'd say that the reason why we are able to continue. Invest today a year after the acquisition is because retaining existing customers was something that was very successful, and to a lot of them, you can add comments. It wasn't easy to switch to something. They could just switch to something else because there's no other product, does these automatic things and provides the predictive modeling that they're used to. So it's like what we switched to so they just kept going, and to them, they've given us a lot of great feedback. Being owned by the largest private storage company on planet Earth has the advantages of strong source of supply. Great Leverett reverse logistics partnerships with suppliers as a bigger company to be able to service them. Long >> trial wasn't broke, so you didn't need to fix it. And you were ableto maintain obviously a large portion of that customer base. And what was this service experience like? And how is that evolving? And what is Dede and bring to the table? >> So, uh, boy DD and brings so many resources in terms of bringing this from the point when they bought us last year. A year ago today, I think we transition with about 40 people in the company. We're up about 200 now, so Ah, serious investment. Obviously, that's ah have been a pretty heavy job in terms of building that thing back up. Uh, service and support we've put all of the resource is the stated goal coming across the acquisition was they have, ah, 10. Tree support tender by DNC would be better than where 10 tree support was. We fought them on >> rate scores, too. So it's hard to go from there. Right? And >> I would say what we've been doing on that today. I mean, in terms of the S L. A's, I think those were as good as they've ever been from that perspective. So we have a big team behind us that are working really hard to make sure that the customer experience is exactly what we want. A 10 tree experience to be >> So big messages at this This show, of course, multi cloud kubernetes solving climate change, fixing the homeless problem in San Francisco. I'm not hearing that from you guys. What's what's your key message to the VM world? >> Well, I personally believe that there's a lot of opportunity to invest in improving operations that are already pretty darn stable, operating these environments, talking to folks here on the floor. These new technologies you're talking about are certainly gonna change the way we deploy things. But there's gonna be a lot of time left Still operating virtualized server infrastructure and accelerating VD I deployments to just operationalized things better. We're hoping that folks choose some new technologies out there. I mean, there's a bill was a lot of hype in past years. About what technology to choose. We're all flash infrastructure, but well, I'd liketo for the say were intelligent infrastructure. We have 10 and 40 get boards were all flash, but that's not what you choose this. You choose this because you're able to take their operations and spend more your time on the apse because you're not messing around with that low level infrastructure. I think that there's a renaissance of, of, of investment and opportunity to innovate in that space into Graham's point about going further up the stack. We now have data database technology that we can show gives database administrators the direct ability to self service their own cloning, their own, staging their own operations, which otherwise would be a complex set of trouble tickets internally to provision the environment. Everyone loves to self service. That's really big. I think our customers love. It's a self service aspect. I see the self service and >> the ability to d'oh again, not have to worry about all the things that they don't have to do in terms of again not having to get into those details. A cz Morrow mentioned in terms of the database side, that's, ah, workload, the workload intelligence that we've already had for virtual machines. We can now service that database object natively. We're going to do sequel server later this year, uh, being ableto again, being able to see where whether or not they've got a host or a network or a storage problem being able to see where those the that unit they're serving, having that inside is tremendously powerful. Also being able the snapshot to be able to clone to be able thio manage and protect that database in a native way. Not having to worry about, you know, going into a console, worrying about the underlying every structure, the ones, the volumes, all the pieces that might people people would have to get involved with maybe moving from, like, production to test and those kinds of things. So it's the simplicity, eyes all the things that you really don't have to do across the getting down in terms of one's the volumes, the sizing exercises one of our customers put it. Best thing. You know, I hear a lot of things back from different customer. If he says the country, the sentry box is the best employee has >> I see that way? Reinvest, Reinvest. I haven't heard a customer yet that talks about reducing staff. Their I t staff is really, really critical. They want to invest up Kai throw buzzword out there, Dev. Ops. You didn't mention that it's all about Dev ops, right? And one thing that's interesting here is were or ah, technology that supports virtual environments and how many software developers use virtual environments to write, test and and basically developed programmes lots and being able to give those developers the ability to create new machines and be very agile in the way they do. Their test of is awesome and in terms of just taking big amounts of data from a nap, if I can circling APP, which is these virtual machines be ableto look at that on the infrastructure and more of her copy data so that I can do stuff with that data. All in the flying virtualization we think of Dev Ops is being very much a cloud thing. I'd say that virtual ization specifically server virtualization is the perfect foundation for Dav ops like functionality. And what we've been able to do is provide that user experience directly to those folks up the stacks of the infrastructure. Guy doesn't have to touch it. I wanted to pull >> a couple of threads together, and I think because we talked about the original vision kind of E m r centric, VM centric multiple hyper visors now multi cloud here in the world. So what >> are you seeing >> in the customers? Is that is it? Is it a multi cloud portfolio? What? What are you seeing your customers going to in the future with both on premise hybrid cloud public. So where does where does 10 tree fit into the storage portfolio? >> And they kind of >> fit all over the map. I think in terms of the most of the customers that we have ultimately have infrastructure on site and in their own control. We do have some that ultimately put those out in places that are quote unquote clouds, if you will, but they're not in the service. Vendor clouds actually have a couple folks, actually, that our cloud providers. So they're building their own clouds to service customers using market. What >> differentiates service is for serving better d our offerings because they can offer something that's very end end for that customer. And so there's more. They monetize it. Yeah, and I think those type of customers, like the more regional provider or more of a specialty service provider rather than the roll your own stuff, I'd say that Generally speaking, folks want tohave a level of abstraction as they go into new architecture's so multi cloud from a past life I wrote a lot about. This is this idea that I don't have to worry about which cloud I'm on to do what I'm doing. I want to be able to do it and then regards of which clouded on it just works. And so I think that our philosophy is how we can continue to move up the stack and provide not US access to our analytics because all that analytic stuff we do in machine learning is available via a P I We have ah v r o plug in and all that sort of stuff to be able allow that to happen. But when we're talking now about APS and how those APS work across multiple, you know, pieces of infrastructure, multiple V EMS, we can now develop build a composite view of what those analytics mean in a way that really now gives them new inside test. So how can I move it over here? Can I move over here? What's gonna happen if I move it over here over there? And I think that's the part that should at least delineate from your average garden variety infrastructure and what we like to call intelligent infrastructure stopping that can, Actually that's doing stuff to be able to give you that data because there's always a way you could do with the long way. Just nobody has time to do with the long way, huh? No. And I would actually say that you >> know what you just touched on, uh, going back to a fundamental 10 tree. Different churches, getting that level of abstraction, right is absolutely the key to what we do. We understand that workload. That virtual machine is the level of abstraction. It's the unit infrastructure within a virtual environment in terms of somebody who's running databases. Databases are the unit of infrastructure that they want to manage. So we line exactly to the fundamental building blocks that they're doing in those containers, certainly moving forward. It's certainly another piece we're looking. We've actually, uh I think for about three years now, we've been looking pretty hard of containers. We've been waiting to see where customers were at. Obviously Of'em were put. Put some things on the map this week in terms of that they were pretty excited about in terms of looking in terms of how we would support. >> Well, it certainly makes it more interesting if you're gonna lean into it with someone like Vienna where behind it. I mean, I still think there are some questions, but I actually like the strategy of because if I understand it correctly of Visa, the sphere admin is going to see the spear. But ah ah, developers going to see kubernetes. So >> yeah, that's kind of cool. And we just want to give people an experience, allows them to self service under the control of the I T department so that they can spend less time on infrastructure. Just the end of the I haven't met a developer that even likes infrastructure. They love to not have to deal with it at all. They only do it out. It assessed even database folks They love infrastructural because they had to think about it. They wanted to avoid the pitfalls of bad infrastructure infrastructures Code is yeah, way we believe in that >> question. Go to market. Uh, you preserve the 10 tree name so that says a lot. What's to go to market like? How are you guys structuring the >> organizational in terms of, ah, parent company perspective or a wholly owned subsidiary of DDN? So 10 tree by DDN our go to market model is channel centric in the sense that still a vast majority of people who procure I t infrastructure prefer to use an integrator or reseller some sort of thing. As far as that goes, what you'll see from us, probably more than you did historically, is more work with some of the folks in the ecosystem. Let's say in the data protection space, we see a rubric as an example, and I think you can talk to some of that scene where historically 10 Tree hadn't really done. It's much collaboration there, but I think now, given the overall stability of the segment and people knowing exactly where value could be added, we have a really cool joint story and you're talking about because your team does that. >> Yeah, so I would certainly say, you know, in terms of go to market Side, we've been very much channel lead. Actually, it's been very interesting to go through this with the channel folks. It's a There's also a couple other pieces I mentioned you mentioned some of the cloud provider. Some of those certainly crossed lines between whether they're MSP is whether they are resellers, especially as we go to our friends across the pond. Maybe that's the VM it'll Barcelona discussion, but some of those were all three, right? So there are customer their service providers there. Ah ah, channel partner if you want terms of a resellers. So, um, it's been pretty interesting from that perspective. I think the thing is a lot of opportunity interview that Certainly, uh, I would say where we're at in terms of, we're trying to very much. Uh, we understand customers have ecosystems. I mean, Marco Mitchem, the backup spaces, right? Uh, customers. We're doing new and different things in there, and they want us to fit into those pieces. Ah, and I'd certainly say in the world that we're in, we're not tryingto go solve and boil the ocean in terms of all the problems ourselves we're trying to figure out are the things that we can bring to the table that make it easier for them to integrate with us And maybe in some new and novel, right, >> So question So what's the number one customer problem that when you guys hear you say, that's our wheelhouse, we're gonna crush the competition. >> I'll let you go first, >> So I'd say, you know, if they have a virtualized environment, I mean, we belong there. Vermin. Actually, somebody said this bed is the best Earlier again. Today in the booze is like, you know, the person who doesn't have entries, a person who doesn't know about 10 tree. If they have a virtual environment, you know, the, uh I would say that this week's been pretty interesting. Lots of customer meetings. So it's been pretty, pretty awesome, getting a lot of things back. But I would say the things that they're asking us to solve our not impossible things. They're looking for evolution's. They're looking for things in terms of better insights in their environment, maybe deeper insights. One of the things we're looking to do with the tremendous amount of data we've got coming back, Um, got almost a million machines coming back to us in terms of auto support data every single night. About 2.3 trillion data points for the last three years, eh? So we're looking to make that data that we've gotten into meaningful consumable information for them. That's actionable. So again, again, what can we see in a virtual environment, not just 10 tree things in terms of storage of those kinds of things, but maybe what patches they have installed that might be affecting a network driver, which might affect the certain configuration and being able to expose and and give them some actionable ways to go take care of those problems. >> All right, we gotta go marry. I'll give you. The last word >> stated simply if you are using virtual, is a Shinto abstract infrastructure. As a wayto accelerate your operations, I run the M where, if you have ah 100 virtual machine, 150 virtual machines, you could really benefit from maybe choosing a different way to do that. Do infrastructure. I can't say the competition doesn't work. Of course, the products work. We just want hope wanted hope that folks could see that doing it differently may produce a different outcome. And different outcomes could be good. >> All right, Mario Graham, Thanks very much for coming to the cubes. Great. Thank you so much. All right. Thank you for watching John Troy a day Volante. We'll be back with our next guest right after this short break. You're watching the cube?

>> Narrator: Live from Orlando, Florida, it's theCube covering PentahoWorld 2017 brought to you by Hitachi Ventara. >> Welcome back to theCube's live coverage of PentahoWorld brought to you by Hitachi Ventara. My name is Rebecca Knight, I'm your host along with my co-host, Dave Vellante. We're joined by Michael Weiss, he is the senior manager at NASDAQ, and Shere Saidon, who is analytics manager at NASDAQ. Thanks so much for coming back to theCube, I should say, you're Cube veterans now. >> We are, at least I am. This is his first year, this is his first time at PentahoWorld. So, excited to bring him along. >> Okay so you're a newbie but you're a veteran so. (laughing) >> Great. So, tell us a little bit about what has changed since the last time you came on, which was 2015, back then? >> So the biggest thing that's happened in the past 18 months is we've launched seven new exchanges. Integrated seven new exchanges. We bought the ISE, the International Stock Exchange, which is three options markets. We just completed that integration in August. We've also bought the Canadian, CHI-X, the Canadian Exchange, which also had three equities markets, so we integrated them, and we went live with a dark pool offering for Goldman back in June. So now we operate a dark pool for Goldman Sachs, and we're looking to kind of expand that offering at this point. >> So you're just getting bigger and bigger. So tell our viewers a little bit how Pentaho fits into this. >> So Pentaho is the engine that kind of does all our analytics behind the scenes at post trade, right. So we do a lot of traditionally TL, where we're doing batch processing. In the back-end we're doing a little bit more with the Hadoop ecosystem leveraging things like EMR, Spark, Presto, that type of stuff, And Pentaho kind of helps blend that stuff together a little bit. We use it for reporting, we do some of the BA, we're actually now looking to have the data Pentaho generates plug in a little bit of Tableau. So, we're looking to expand it and really leverage that data in other ways at this point. Even doing some things more externally, doing more data offerings via Pentaho externally. >> So I got to do a NASDAQ 101 for my 13 year-old. Came up to me the other day and said, "Daddy, what's the NASDAQ index and how does it work?" Well, give us a 20 second answer. >> Michael: On the NASDAQ index? >> Yeah, what's the NASDAQ Index and how does it work? >> Probably the wrong person to answer that one but, the index is generally just a blend of various stocks. So the S&P 500 is a blend of different stocks, much like that the cues, are NASDAQ's equivalent of the S&P, right, so, we use a different algorithm to determine the companies that make up that blend, but it's an index just like at the S&P. >> They're weighted by market cap- >> Michael: Right, yeah. >> And that determines the number at the end- >> Michael: Correct. >> And it goes up and down based on what the stock's index. >> Right, and that's how most people know NASDAQ, right. They see the S&P went up by 5 points, The Dow went down by 3 and the NASDAQ went up by a point, right. But most people don't realize that NASDAQ also operates 27 exchanges worldwide, I think it is now. So, probably a little bit more, maybe closer to 32, but... >> So you mentioned that you're doing a dark pool for Goldman >> Michael: Yes. >> So that's interesting. We were talking off camera about HFT and kind of the old days, and dark pools were criticized at the time. Now Goldman was one of the ones shown to be honest and above board, but what does that mean the dark pool for your business and how does that all tie in? >> Michael: So, dark pools are isolated markets, right, so they don't necessarily interact with the NASDAQ exchange themselves, it's all done within the pool. You interact with only people trading on that pool. What NASDAQ has done is we took our technology and we now host it for Goldman so, we have I-NETs our trading system, so we gave them I-NET, we built all the surrounding solutions, how you manage symbols, how you manage membership. Even the data, we curate their data in the AWS. We do some Pentaho transformations for them. We do some analytics for them. And that's actually going to start expanding, but yeah, we've provided them an entire solution, so now they don't have to manage their own dark pool. And now we're going to look to expand that to other potential clients. >> Dave: So that's NASDAQ as a technology >> Yes. >> Dave: Provider. Very interesting. So I was saying, earlier, the Hong Kong Stock Exchange is basically closing the facility where they house humans, again another example of machines replacing humans. So the joining, well NASDAQ, kind of, but NYSE, London Stock Exchange, Singapore, now Hong Kong... Essentially, electronic trading. So, brings us to the sort of technology underpinnings of NASDAQ. Shere, maybe you can talk a little bit about your role, and paint a picture of the technology infrastructure. >> Yeah so I focus primarily on the financial side of corporate finance. So we leverage Pentaho to do a lot of data integration, allow us to really answer our business questions. So, previously it would take days to put basic reporting together, now you've got it all automated, or we're working towards getting it mostly automated, and it just answer the questions that we need. And no longer use our gut to drive decisions, we're using hard data. And so that's helped us instrumentally in a lot of different places. >> Dave: So, talk more about the data pipeline, where the data's coming from, how you're blending it, and how you're bringing it through the pipeline and operationalizing it. >> Yeah, so we've got a lot of different billing systems, so we integrate companies, and historically we've let them keep their billings systems. So just kind of bring it all together into our core ERP, seeing how quantities...and just getting the data, and just figuring out on the basic side, how much do we make from a certain customer? What are we making from them? What happens in different scenarios if they consolidate, or if they default? And some of the pipeline there is just blending it all together, normalizing the data, making sure it's all in the same format, and then putting it in a format where our executives or business managers can actually make decisions off of it. >> Well you're talking about the decision making process, and you said it's no longer gut, you're using data to drive your decisions, to know which direction is the right direction. How big a change is that, just culturally speaking? How has that changed? >> Yeah, it's huge, at least on our side, it's making us a long more confident in the decisions we're making. We're no longer going in saying, hey this is probably how we should do it. No, the numbers are showing us that this is going to pay off, and we stick to it and look at the hard facts, rather than what do we think is going to happen? >> So, talk a little bit about what you guys are seeing here, and you're doing a lot of speaking here, we were joking earlier, you're kind of losing your voice. You're telling your story, what kind of reactions you getting? Share with us the behind the scenes at the conference. >> I think at this conference you're seeing a lot of people kind of fall in line with similar ideas that we're trying to get to. Taking advantage more instead of your traditional MPPs, or your traditional relational databases, moving more towards this Hadoop ecosystem. Leveraging Spark, Presto, Flume, all these various new technologies that have emerged over the past two to five years, and are now more viable than ever. They're easier to scale, if you look at your traditional MPPs, like we're a big Redshift user, but every time you scale it there's a cost with that, and we don't necessarily need to maintain all that data all the time, so something in the Hadoop ecosystem now lets us maintain that data without all the unnecessary cost. I see a lot of more of that than I did two years ago, a lot more people are following that trend. I think the other interesting trend I've seen this week is this idea of becoming more cloud agnostic. Where do you operate, and how do you store your data should be irrelevant to the data processing, and I think it's going to be a tough nut to crack for Pentaho, or any vendor. But if you can figure out a way to either do some type of cloud parity, where you have support across all your services, but you don't have to know which service you deploy to when you design your pipelines, I think that's going to be huge. I think we're a little ways from that, but that's been a common theme this week as well, both private and your big three cloud providers right now, your Googles, your Azures, and your AWS. >> So when I asked you said cloud agnostic, that's great, good vision and aspiration. The follow up would be, am I correct that you don't see it as data location agnostic, right, you want to bring the cloud model to your data, versus try to force your data into a cloud? Or not necessarily? >> A lot of it I think is being driven by not wanting to be vendor locked in, so they want to have the ability to, and I think this is easier said than done, the ability to move your data to different cloud providers based on pricing or offerings, right, and right now going from AWS to Google to Azure would be a very painful process. So you move petabytes of data across, it's not cost efficient and all the savings you want to realize by moving to maybe a Google in the future, are not going to be realized cause of all the effort it's going to take to get there. >> Dave: We had CERN on earlier, and they were working on that problem... >> Yeah, it's not a trivial problem to solve, but if you can crack that, and you can then say hey I wanna...even if I have a service offering, Like our operating a dark pool for Goldman. We also have a market tech side, where we sell our trading platform and various solutions to other exchanges worldwide. If we can come up with a way to be able to deploy to any cloud provider, even on an on-prem cloud, without having to do a bunch of customizations each time, that would be huge, it would revolutionize what we do. We're, as our own company, starting to look at that, and talking with Pentaho, they're also... are going to eye that as a potential way to go, with abstractions and things like that, but it's going to take some time. >> We're you guys here yesterday for the keynotes? >> Michael: Saw some of the keynotes, yes. >> The big messaging, like every conference that you go to, is be the disruptor, or you're going to get disrupted. We talked earlier off camera... Trading volumes are down, so the way you traditionally did business is changing, and made money is changing. >> Michael: Right. >> We talked earlier about you guys becoming a technology provider, I wonder if you could help us understand that a little bit, from the standpoint of NASDAQ strategy, when we hear your CEOs talk, real visionary, technology driven transformations. >> Yeah, I think Adena's coming in is definitely looking at that as a trend, right? Trading volumes are down, they've been going down, they've kind of stabilized a little bit, and we're stable able to make money in that space, but the problem is there's not a ton of growth. We acquire the ISE, we acquire the CHI-X, we're buying market share at that point. So you increase revenue, but you also increase overhead in that way. And you can only do so many major acquisitions at a time, you can only do how many one billion dollar acquisitions a year before you have to call it a day. And we can look at more strategic, smaller acquisitions for exchanges, but that doesn't necessarily bring you the transformation, the net revenue you're looking for. So what Adena has started to look at is, how do we transform to more of a technology company? We're really good at operating exchanges, how do we take that, and we already have market tech doing it, but how do we make that more scalable, not just to the financial sector, but to your other exchanges, your Ubers or your StubHubs of the world? How do you become a service provider, or a platform as a service for these other companies, to come in and use your tech? So we're looking at how do we rewrite our entire platform, from trading to the back-end, to do things like: Can we deploy to any cloud provider? Can we deploy on-prem? Can we be a little bit more technology agnostic so to speak, and offer these as services, and offer a bunch of microservices, so that if a startup comes up and wants to set up an exchange, they can do it, they can leverage our services, then build whatever other applications they want on top of it. I think that's a transformation we need to go through, I think it's good vision, and I'm looking forward to executing it. It's going to be a couple years before we see the fruits of that labor, but Adena's really doing a great job of coming in, and really driving that innovation, and Brad Peterson as well, our CIO, has really been pushing this vision, and I think it's really going to work out for us, assuming we can execute. >> Well you know what's interesting about that, if I may, is financial services is usually so secretive about their technology, right? But your business, you guys are becoming a technology provider, so you got to face the world and start marketing your capabilities now, and opening about that. It's sort of an interesting change. >> I think you'll see that starting to become more of a thing over the next year or two, as we start actually looking to build out the platform and figure it out. We do market on the market tech side, I mean it's not a small business, but we're more strategic about who we market to, cause we're still targeting your financial exchanges, more internationally than in the U.S., but there's only so many of them, again you have to start looking at rebranding, rebuilding, and rethinking how we think about exchanges in general, and not thinking of them as just a financial thing. >> Well that's what I wanted to get into, because you're talking about this rebranding, and this rebuilding, this transformation, to the backdrop within an industry that is changing rapidly, and we have sort of the threat of legislative reform, perhaps some administrative reforms coming down all the time, so how do you manage that? I mean, those are a lot of pressures there, are you constantly trying to push the envelope right up until any changes take place? Or what would you say Shere and Michael? >> Probably again not the right person to ask about this, but we're definitely trying to stay on top of the cutting edge in innovation and the technologies out there that, whether it be Blockchain, or different types of technologies. I mean we're definitely trying to make sure we're investing in them, while maintaining our core businesses. >> Right, it's trying to find that balance right now of when to make the next step in the technology food chain, and when to balance that with regulatory obligations. And if you look at it, going back to the idea of being able to launch marketplaces, I think what you're ending up seeing over the coming years is your Ubers, your StubHubs, I think they're going to become more regulated at some level. And we're good at operating more regulated markets, so I think that's where we can kind of come in and play a role, and help wade through those regulations a little bit more, and help build software to adhere to those regulations. >> Since you brought up Blockchain, Jamie Dimon craps all over Blockchain, or you know, Bitcoin, and then clarifies his remarks, saying look, technology underneath is here to stay. Thoughts on Blockchain? Obviously Financial Services is looking at it very closely, doing some really advanced stuff, what can you tell us? >> Yeah, I think there's no argument that it's definitely an innovation and a disruptive technology. I think that it's definitely in it's early stages across the board, so we're investing in it where we can, and trying to keep a close eye on it. We think that there's a lot of potential in a lot of different applications. >> As the NASDAQ transforms its business, how does that effect the sort of back-end analytics activity and infrastructure? >> The data is just growing, that's like the biggest challenge we have now. Data that used to be done in Excel, it's just no longer an option, so now in order to get the insights that we used to get just from having a couple people doing Excel transformations, you need to now invest in the infrastructure in the back-end, and so there's a lot that needs to go into building out an infrastructure to be able to ingest the data, and then also having the UI on the front-end, so that the business can actually view it the way they want. >> So skills wise, how's that affecting who you guys are hiring and training? And how's that transformation going? >> Michael: I'll let you go first. >> I think there's definitely, data analytics is a hot field. It's very new, there's definitely a big skills gap in administrative work and in the analytics side. Usually you have people could perform analytical functions just by being administrative or operational, and now it's really, we're investing in analysts, and making sure that we have the right people in place to be able to do these transformations, or pull the data and get the answers that we need from them. >> I mean from the tech side, I think what you're seeing is where we traditionally would just plug a developer in there, whether a Java developer, or an ETL developer, I think what you're seeing now is we're looking to bring more of a business minded data analyst to the tech side, right? So we're looking to bring a data engineer, so to speak, more to the tech side. So we're not looking to hire a traditional four year Computer Science degree, or Software Engineering degree, you're looking for a different breed of person, cause quite honestly because you're traditional Java dev. or C++ developer, they're not skilled or geared towards data. And when we've tried to plug that paradigm in, it just doesn't really work, so we're looking now to hiring more of an analyst, but someone who's a little bit more techie as well. They still need to have those skills to do some level of coding, and what we are finding is that skill gap is still very much... There's a gap there. There's a huge gap. And I think it's closing, but- >> And as you have to fund those for the new areas, I presume, like many companies in your business, you're trying to move away from the sort of undifferentiated low-level infrastructure deployment hassles, and the IT labor costs there, especially as we move to the cloud, presumably, so is that shift palpable? I mean, can you see that going on? >> Yeah, I think we made a lot of progress over the past couple years in doing that. We do more one button deployments, where the operation cost is a lot lower, a lot more automation around alerting, around when things go wrong, so there's not necessarily a human being sitting there watching a computer. We've invested a lot in that area to kind of reduce the costs, and make the experience better for our end user. And even from a development side, the cost of a new application is a lot less every time you have to do a release. The question is, how do you balance that with the regulations, and make sure you still have a good process in place. The idea of putting single button deployments in place is a great one, but you still have to balance that with making sure that what you push to productions been tested, well defined, and it meets the need, and you're not just arbitrarily throwing things out there. So we're still trying to hit that balance a little bit, it's more on the back-end side. The trading system is not quite there for obvious reasons, we're way more protective of what goes out there, then surrounding it a lot of the times, but I can see a future where, again going back to this idea of transforming our business, where you can stand up and do an exchange with the click of a button. I think that's a trend we're looking at. >> Rebecca: It's not too far in the future. >> No, I don't think it is. >> Last question, Pentaho report card. What are they doing really well? What do you want to see them do better? >> I think they continue to focus in the right areas, focusing more on the data processing side, and with the big data technologies, trying to fill that gap in the big data, and be the layer that you don't have to tie yourself to ike vCloud Air or MapR, you can kind of be a little bit more plug and play. I think they still need to do some improvements on there visualizations in their front-ends. I think they've been so much more focused on the data processing, that part of it, that the visualization's kind of lacked behind, so I think they need to put a little more focus into that, but all in all, they're an A, and we've been extremely happy with them as a software provider. >> Great. >> Shere: I think the visualization part is the part that allows people to understand that value being created at Pentaho. So I think being able to maybe improve a little bit on the visualization could go a far way. >> Michael, Shere, it's been so much fun having you on theCube, and having this conversation, keep that bull market coming please, do whatever you can. >> We'll do our best. >> I'm Rebecca Knight. We are here at PentahoWorld, sponsored by Hitachi Vantara. For Dave Vellante, we will have more from theCube in just a little bit.

>> Announcer: Live from Munich, Germany; it's The Cube. Covering IBM; fast-track your data. Brought to you by IBM. (upbeat techno music) >> For you here at the show, generally; and specifically, what are you doing here today? >> There's really three things going on at the show, three high level things. One is we're talking about our new... How we're repositioning our hybrid data management portfolio, specifically some announcements around DB2 in a hybrid environment, and some highly transactional offerings around DB2. We're talking about our unified governance portfolio; so actually delivering a platform for unified governance that allows our clients to interact with governance and data management kind of products in a more streamlined way, and help them actually solve a problem instead of just offering products. The third is really around data science and machine learning. Specifically we're talking about our machine learning hub that we're launching here in Germany. Prior to this we had a machine learning hub in San Francisco, Toronto, one in Asia, and now we're launching one here in Europe. >> Seth, can you describe what this hub is all about? This is a data center where you're hosting machine learning services, or is it something else? >> Yeah, so this is where clients can come and learn how to do data science. They can bring their problems, bring their data to our facilities, learn how to solve a data science problem in a more team oriented way; interacting with data scientists, machine learning engineers, basically, data engineers, developers, to solve a problem for their business around data science. These previous hubs have been completely booked, so we wanted to launch them in other areas to try and expand the capacity of them. >> You're hosting a round table today, right, on the main tent? >> Yep. >> And you got a customer on, you guys going to be talking about sort of applying practices and financial and other areas. Maybe describe that a little bit. >> We have a customer on from ING, Heinrich, who's the chief architect for ING. ING, IBM, and Horton Works have a consortium, if you would, or a framework that we're doing around Apache Atlas and Ranger, as the kind of open-source operating system for our unified governance platform. So much as IBM has positioned Spark as a unified, kind of open-source operating system for analytics, for a unified governance platform... For a governance platform to be truly unified, you need to be able to integrate metadata. The biggest challenge about connecting your data environments, if you're an enterprise that was not internet born, or cloud born, is that you have proprietary metadata platforms that all want to be the master. When everyone wants to be the master, you can't really get anything done. So what we're doing around Apache Atlas is we are setting up Apache Atlas as kind of a virtual translator, if you would, or a dictionary between all the different proprietary metadata platforms so that you can get a single unified view of your data environment across hybrid clouds, on premise, in the cloud, and across different proprietary vendor platforms. Because it's open-sourced, there are these connectors that can go in and out of the proprietary platforms. >> So Seth, you seem like you're pretty tuned in to the portfolio within the analytics group. How are you spending your time as the Chief Data Officer? How do you balance it between customer visits, maybe talking about some of the products, and then you're sort of day job? >> I actually have three days jobs. My job's actually split into kind of three pieces. The first, my primary mission, is really around transforming IBM's internal business unit, internal business workings, to use data and analytics to run our business. So kind of internal business unit transformation. Part of that business unit transformation is also making sure that we're compliant with regulations like GDBR and other regulations. Another third is really around kind of rethinking our offerings from a CDO perspective. As a CDO, and as you, Dave, I've only been with IBM for seven months. As a former client recently, and as a CDO, what is it that I want to see from IBM's offerings? We kind of hit on it a little bit with the unified governance platform, where I think IBM makes fantastic products. But as a client, if a salesperson shows up to me, I don't want them selling me a product, 'cause if I want an MDM solution, I'll call you up and say, "Hey, I need an MDM solution. "Give me a quote." What I want them showing up is saying, "I have a solution that's going to solve "your governance problem across your portfolio." Or, "I'm going to solve your data science problem." Or, "I'm going to help you master your data, "and manage your data across "all these different environments." So really working with the offering management and the Dev teams to define what are these three or four, kind of business platforms that we want to settle on? We know three of them at least, right? We know that we have a hybrid data management. We have unified governance. We have data science and machine learning, and you could think of the Z franchise as a fourth platform. >> Seth, can you net out how governance relates to data science? 'Cause there is governance of the statistical models, machine learning, and so forth, version control. I mean, in an end to end machine learning pipeline, there's various versions of various artifacts they have to be managed in a structured way. Is your unified governance bundle, or portfolio, does it address those requirements? Or just the data governance? >> Yeah, so the unified governance platform really kind of focuses today on data governance and how good data governance can be an enabler of rapid data science. So if you have your data all pre-governed, it makes it much quicker to get access to data and understand what you can and can't do with data; especially being here in Europe, in the context of the EU GDPR. You need to make sure that your data scientists are doing things that are approved by the user, because basically your data, you have to give explicit consent to allow things to be done with it. But long term vision is that... essentially the output of models is data, right? And how you use and deploy those models also need to be governed. So the long term vision is that we will have a governance platform for all those things, as well. I think it makes more sense for those things to be governed in the data science platform, if you would. And we... >> We often hear separate from GDPR and all that, is something called algorithmic accountability; that more is being discussed in policy circles, in government circles around the world, as strongly related to everything you're describing. Being able to trace the lineage of any algorithmic decision back to the data, the metadata, and so forth, and the machine learning models that might have driven it. Is that where IBM's going with this portfolio? >> I think that's the natural extension of it. We're thinking really in the context of them as two different pieces, but if you solve them both and you connect them together, then you have that problem. But I think you're absolutely right. As we're leveraging machine learning and artificial intelligence, in general, we need to be able to understand how we got to a decision, and that includes the model, the data, how the data was gathered, how the data was used and processed. So it is that entire pipeline, 'cause it is a pipeline. You're not doing machine learning or AI in a vacuum. You're doing it in the context of the data, and you're doing it in the context about the individuals or the organizations that you're trying to influence with the output of those models. >> I call it Dev ops for data science. >> Seth, in the early Hadoop days, the real headwind was complexity. It still is, by the way. We know that. Companies like IBM are trying to reduce that complexity. Spark helps a little bit So the technology will evolve, we get that. It seems like one of the other big headwinds right now is that most companies don't have a great understanding of how they can take data and monetize it, turn it into value. Most companies, many anyway, make the mistake of, "Well, I don't really want to sell my data," or, "I'm not really a data supplier." And they're kind of thinking about it, maybe not in the right way. But we seem to be entering a next wave here, where people are beginning to understand I can cut costs, I can do predictive maintenance, I can maybe not sell the data, but I can enhance what I'm doing and increase my revenue, maybe my customer retention. They seem to be tuning, more so; largely, I think 'cause of the chief data officer roles, helping them think that through. I wonder if you would give us your point of view on that narrative. >> I think what you're describing is kind of the digital transformation journey. I think the end game, as enterprises go through a digital transformation, the end game is how do I sell services, outcomes, those types of things. How do I sell an outcome to my end user? That's really the end game of a digital transformation in my mind. But before you can get to that, before you transform your business's objectives, there's a couple of intermediary steps that are required for that. The first is what you're describing, is those kind of data transformations. Enterprises need to really get a handle on their data and become data driven, and start then transforming their current business model; so how do I accelerate my current business leveraging data and analytics? I kind of frame that, that's like the data science kind of transformation aspect of the digital journey. Then the next aspect of it is how do I transform my business and change my business objectives? Part of that first step is in fact, how do I optimize my supply chain? How do I optimize my workforce? How do I optimize my goals? How do I get to my current, you know, the things that Wall Street cares about for business; how do I accelerate those, make those faster, make those better, and really put my company out in front? 'Cause really in the grand scheme of things, there's two types of companies today; there's the company that's going to be the disruptor, and there's companies that's going to get disrupted. Most companies want to be the disruptors, and it's a process to do that. >> So the accounting industry doesn't have standards around valuing data as an asset, and many of us feel as though waiting for that is a mistake. You can't wait for that. You've got to figure out on your own. But again, it seems to be somewhat of a headwind because it puts data and data value in this fuzzy category. But there are clearly the data haves and the data have-nots. What are you seeing in that regard? >> I think the first... When I was in my former role, my former company went through an exercise of valuing our data and our decisions. I'm actually doing that same exercise at IBM right now. We're going through IBM, at least in the analytics business unit, the part I'm responsible for, and going to all the leaders and saying, "What decisions are you making?" "Help me understand the decisions that you're making." "Help me understand the data you need "to make those decisions." And that does two things. Number one, it does get to the point of, how can we value the decisions? 'Cause each one of those decisions has a specific value to the company. You can assign a dollar amount to it. But it also helps you change how people in the enterprise think. Because the first time you go through and ask these questions, they talk about the dashboards they want to help them make their preconceived decisions, validated by data. They have a preconceived notion of the decision they want to make. They want the data to back it up. So they want a dashboard to help them do that. So when you come in and start having this conversation, you kind of stop them and say, "Okay, what you're describing is a dashboard. "That's not a decision. "Let's talk about the decision that you want to make, "and let's understand the real value of that decision." So you're doing two things, you're building a portfolio of decisions that then becomes to your point, Jim, about Dev ops for data science. It's your backlog for your data scientists, in the long run. You then connect those decisions to data that's required to make those, and you can extrapolate the data for each decision to the component that each piece of data makes up to it. So you can group your data logically within an enterprise; customer, product, talent, location, things like that, and you can assign a value to those based on decisions they support. >> Jim: So... >> Dave: Go ahead, please. >> As a CDO, following on that, are you also, as part of that exercise, trying to assess the value of not just the data, but of data science as a capability? Or particular data science assets, like machine learning models? In the overall scheme of things, that kind of valuation can then drive IBM's decision to ramp up their internal data science initiatives, or redeploy it, or, give me a... >> That's exactly what happened. As you build this portfolio of decisions, each decision has a value. So I am now assigning a value to the data science models that my team will build. As CDOs, CDOs are a relatively new role in many organizations. When money gets tight, they say, "What's this guy doing?" (Dave laughing) Having a portfolio of decisions that's saying, "Here's real value I'm adding..." So, number one, "Here's the value I can add in the future," and as you check off those boxes, you can kind of go and say, "Here's value I've added. "Here's where I've changed how the company's operating. "Here's where I've generated X billions of dollars "of new revenue, or cost savings, or cost avoidance, "for the enterprise." >> When you went through these exercises at your previous company, and now at IBM, are you using standardized valuation methodologies? Did you kind of develop your own, or come up with a scoring system? How'd you do that? >> I think there's some things around, like net promoter score, where there's pretty good standards on how to assign value to increases in net promoter score, or decreases in net promoter score for certain aspects of your business. In other ways, you need to kind of decide as an enterprise, how do we value our assets? Do we use a three year, five year, ten year MPV? Do we use some other metric? You need to kind of frame it in the reference that your CFO is used to talking about so that it's in the context that the company is used to talking about. Most companies, it's net present value. >> Okay, and you're measuring that on an ongoing basis. >> Seth: Yep. >> And fine tuning as you go along. Seth, we're out of time. Thanks so much for coming back in The Cube. It was great to see you. >> Seth: Yeah, thanks for having me. >> You're welcome, good luck this afternoon. >> Seth: Alright. >> Keep it right there, buddy. We'll be back. Actually, let me run down the day here for you, just take a second to do that. We're going to end our Cube interviews for the morning, and then we're going to cut over to the main tent. So in about an hour, Rob Thomas is going to kick off the main tent here with a keynote, talking about where data goes next. Hilary Mason's going to be on. There's a session with Dez Blanchfield on data science as a team sport. Then the big session on changing regulations, GDPRs. Seth, you've got some customers that you're going to bring on and talk about these issues. And then, sort of balancing act, the balancing act of hybrid data. Then we're going to come back to The Cube and finish up our Cube interviews for the afternoon. There's also going to be two breakout sessions; one with Hilary Mason, and one on GDPR. You got to go to IBMgo.com and log in and register. It's all free to see those breakout sessions. Everything else is open. You don't even have to register or log in to see that. So keep it right here, everybody. Check out the main tent. Check out siliconangle.com, and of course IBMgo.com for all the action here. Fast track your data. We're live from Munich, Germany; and we'll see you a little later. (upbeat techno music)