(upbeat music) >> Hello, this is Dave Valente with theCUBE, and one of the most gratifying aspects of my role as a host of "theCUBE TV" is I get to cover a wide range of topics. And quite often, we're able to bring to our program a level of expertise that allows us to more deeply explore and unpack some of the topics that we cover throughout the year. And one of our favorite topics, of course, is data. Now, in 2021, after being in isolation for the better part of two years, a group of industry analysts met up at AWS re:Invent and started a collaboration to look at the trends in data and predict what some likely outcomes will be for the coming year. And it resulted in a very popular session that we had last year focused on the future of data management. And I'm very excited and pleased to tell you that the 2023 edition of that predictions episode is back, and with me are five outstanding market analyst, Sanjeev Mohan of SanjMo, Tony Baer of dbInsight, Carl Olofson from IDC, Dave Menninger from Ventana Research, and Doug Henschen, VP and Principal Analyst at Constellation Research. Now, what is it that we're calling you, guys? A data pack like the rat pack? No, no, no, no, that's not it. It's the data crowd, the data crowd, and the crowd includes some of the best minds in the data analyst community. They'll discuss how data management is evolving and what listeners should prepare for in 2023. Guys, welcome back. Great to see you. >> Good to be here. >> Thank you. >> Thanks, Dave. (Tony and Dave faintly speaks) >> All right, before we get into 2023 predictions, we thought it'd be good to do a look back at how we did in 2022 and give a transparent assessment of those predictions. So, let's get right into it. We're going to bring these up here, the predictions from 2022, they're color-coded red, yellow, and green to signify the degree of accuracy. And I'm pleased to report there's no red. Well, maybe some of you will want to debate that grading system. But as always, we want to be open, so you can decide for yourselves. So, we're going to ask each analyst to review their 2022 prediction and explain their rating and what evidence they have that led them to their conclusion. So, Sanjeev, please kick it off. Your prediction was data governance becomes key. I know that's going to knock you guys over, but elaborate, because you had more detail when you double click on that. >> Yeah, absolutely. Thank you so much, Dave, for having us on the show today. And we self-graded ourselves. I could have very easily made my prediction from last year green, but I mentioned why I left it as yellow. I totally fully believe that data governance was in a renaissance in 2022. And why do I say that? You have to look no further than AWS launching its own data catalog called DataZone. Before that, mid-year, we saw Unity Catalog from Databricks went GA. So, overall, I saw there was tremendous movement. When you see these big players launching a new data catalog, you know that they want to be in this space. And this space is highly critical to everything that I feel we will talk about in today's call. Also, if you look at established players, I spoke at Collibra's conference, data.world, work closely with Alation, Informatica, a bunch of other companies, they all added tremendous new capabilities. So, it did become key. The reason I left it as yellow is because I had made a prediction that Collibra would go IPO, and it did not. And I don't think anyone is going IPO right now. The market is really, really down, the funding in VC IPO market. But other than that, data governance had a banner year in 2022. >> Yeah. Well, thank you for that. And of course, you saw data clean rooms being announced at AWS re:Invent, so more evidence. And I like how the fact that you included in your predictions some things that were binary, so you dinged yourself there. So, good job. Okay, Tony Baer, you're up next. Data mesh hits reality check. As you see here, you've given yourself a bright green thumbs up. (Tony laughing) Okay. Let's hear why you feel that was the case. What do you mean by reality check? >> Okay. Thanks, Dave, for having us back again. This is something I just wrote and just tried to get away from, and this just a topic just won't go away. I did speak with a number of folks, early adopters and non-adopters during the year. And I did find that basically that it pretty much validated what I was expecting, which was that there was a lot more, this has now become a front burner issue. And if I had any doubt in my mind, the evidence I would point to is what was originally intended to be a throwaway post on LinkedIn, which I just quickly scribbled down the night before leaving for re:Invent. I was packing at the time, and for some reason, I was doing Google search on data mesh. And I happened to have tripped across this ridiculous article, I will not say where, because it doesn't deserve any publicity, about the eight (Dave laughing) best data mesh software companies of 2022. (Tony laughing) One of my predictions was that you'd see data mesh washing. And I just quickly just hopped on that maybe three sentences and wrote it at about a couple minutes saying this is hogwash, essentially. (laughs) And that just reun... And then, I left for re:Invent. And the next night, when I got into my Vegas hotel room, I clicked on my computer. I saw a 15,000 hits on that post, which was the most hits of any single post I put all year. And the responses were wildly pro and con. So, it pretty much validates my expectation in that data mesh really did hit a lot more scrutiny over this past year. >> Yeah, thank you for that. I remember that article. I remember rolling my eyes when I saw it, and then I recently, (Tony laughing) I talked to Walmart and they actually invoked Martin Fowler and they said that they're working through their data mesh. So, it takes a really lot of thought, and it really, as we've talked about, is really as much an organizational construct. You're not buying data mesh >> Bingo. >> to your point. Okay. Thank you, Tony. Carl Olofson, here we go. You've graded yourself a yellow in the prediction of graph databases. Take off. Please elaborate. >> Yeah, sure. So, I realized in looking at the prediction that it seemed to imply that graph databases could be a major factor in the data world in 2022, which obviously didn't become the case. It was an error on my part in that I should have said it in the right context. It's really a three to five-year time period that graph databases will really become significant, because they still need accepted methodologies that can be applied in a business context as well as proper tools in order for people to be able to use them seriously. But I stand by the idea that it is taking off, because for one thing, Neo4j, which is the leading independent graph database provider, had a very good year. And also, we're seeing interesting developments in terms of things like AWS with Neptune and with Oracle providing graph support in Oracle database this past year. Those things are, as I said, growing gradually. There are other companies like TigerGraph and so forth, that deserve watching as well. But as far as becoming mainstream, it's going to be a few years before we get all the elements together to make that happen. Like any new technology, you have to create an environment in which ordinary people without a whole ton of technical training can actually apply the technology to solve business problems. >> Yeah, thank you for that. These specialized databases, graph databases, time series databases, you see them embedded into mainstream data platforms, but there's a place for these specialized databases, I would suspect we're going to see new types of databases emerge with all this cloud sprawl that we have and maybe to the edge. >> Well, part of it is that it's not as specialized as you might think it. You can apply graphs to great many workloads and use cases. It's just that people have yet to fully explore and discover what those are. >> Yeah. >> And so, it's going to be a process. (laughs) >> All right, Dave Menninger, streaming data permeates the landscape. You gave yourself a yellow. Why? >> Well, I couldn't think of a appropriate combination of yellow and green. Maybe I should have used chartreuse, (Dave laughing) but I was probably a little hard on myself making it yellow. This is another type of specialized data processing like Carl was talking about graph databases is a stream processing, and nearly every data platform offers streaming capabilities now. Often, it's based on Kafka. If you look at Confluent, their revenues have grown at more than 50%, continue to grow at more than 50% a year. They're expected to do more than half a billion dollars in revenue this year. But the thing that hasn't happened yet, and to be honest, they didn't necessarily expect it to happen in one year, is that streaming hasn't become the default way in which we deal with data. It's still a sidecar to data at rest. And I do expect that we'll continue to see streaming become more and more mainstream. I do expect perhaps in the five-year timeframe that we will first deal with data as streaming and then at rest, but the worlds are starting to merge. And we even see some vendors bringing products to market, such as K2View, Hazelcast, and RisingWave Labs. So, in addition to all those core data platform vendors adding these capabilities, there are new vendors approaching this market as well. >> I like the tough grading system, and it's not trivial. And when you talk to practitioners doing this stuff, there's still some complications in the data pipeline. And so, but I think, you're right, it probably was a yellow plus. Doug Henschen, data lakehouses will emerge as dominant. When you talk to people about lakehouses, practitioners, they all use that term. They certainly use the term data lake, but now, they're using lakehouse more and more. What's your thoughts on here? Why the green? What's your evidence there? >> Well, I think, I was accurate. I spoke about it specifically as something that vendors would be pursuing. And we saw yet more lakehouse advocacy in 2022. Google introduced its BigLake service alongside BigQuery. Salesforce introduced Genie, which is really a lakehouse architecture. And it was a safe prediction to say vendors are going to be pursuing this in that AWS, Cloudera, Databricks, Microsoft, Oracle, SAP, Salesforce now, IBM, all advocate this idea of a single platform for all of your data. Now, the trend was also supported in 2023, in that we saw a big embrace of Apache Iceberg in 2022. That's a structured table format. It's used with these lakehouse platforms. It's open, so it ensures portability and it also ensures performance. And that's a structured table that helps with the warehouse side performance. But among those announcements, Snowflake, Google, Cloud Era, SAP, Salesforce, IBM, all embraced Iceberg. But keep in mind, again, I'm talking about this as something that vendors are pursuing as their approach. So, they're advocating end users. It's very cutting edge. I'd say the top, leading edge, 5% of of companies have really embraced the lakehouse. I think, we're now seeing the fast followers, the next 20 to 25% of firms embracing this idea and embracing a lakehouse architecture. I recall Christian Kleinerman at the big Snowflake event last summer, making the announcement about Iceberg, and he asked for a show of hands for any of you in the audience at the keynote, have you heard of Iceberg? And just a smattering of hands went up. So, the vendors are ahead of the curve. They're pushing this trend, and we're now seeing a little bit more mainstream uptake. >> Good. Doug, I was there. It was you, me, and I think, two other hands were up. That was just humorous. (Doug laughing) All right, well, so I liked the fact that we had some yellow and some green. When you think about these things, there's the prediction itself. Did it come true or not? There are the sub predictions that you guys make, and of course, the degree of difficulty. So, thank you for that open assessment. All right, let's get into the 2023 predictions. Let's bring up the predictions. Sanjeev, you're going first. You've got a prediction around unified metadata. What's the prediction, please? >> So, my prediction is that metadata space is currently a mess. It needs to get unified. There are too many use cases of metadata, which are being addressed by disparate systems. For example, data quality has become really big in the last couple of years, data observability, the whole catalog space is actually, people don't like to use the word data catalog anymore, because data catalog sounds like it's a catalog, a museum, if you may, of metadata that you go and admire. So, what I'm saying is that in 2023, we will see that metadata will become the driving force behind things like data ops, things like orchestration of tasks using metadata, not rules. Not saying that if this fails, then do this, if this succeeds, go do that. But it's like getting to the metadata level, and then making a decision as to what to orchestrate, what to automate, how to do data quality check, data observability. So, this space is starting to gel, and I see there'll be more maturation in the metadata space. Even security privacy, some of these topics, which are handled separately. And I'm just talking about data security and data privacy. I'm not talking about infrastructure security. These also need to merge into a unified metadata management piece with some knowledge graph, semantic layer on top, so you can do analytics on it. So, it's no longer something that sits on the side, it's limited in its scope. It is actually the very engine, the very glue that is going to connect data producers and consumers. >> Great. Thank you for that. Doug. Doug Henschen, any thoughts on what Sanjeev just said? Do you agree? Do you disagree? >> Well, I agree with many aspects of what he says. I think, there's a huge opportunity for consolidation and streamlining of these as aspects of governance. Last year, Sanjeev, you said something like, we'll see more people using catalogs than BI. And I have to disagree. I don't think this is a category that's headed for mainstream adoption. It's a behind the scenes activity for the wonky few, or better yet, companies want machine learning and automation to take care of these messy details. We've seen these waves of management technologies, some of the latest data observability, customer data platform, but they failed to sweep away all the earlier investments in data quality and master data management. So, yes, I hope the latest tech offers, glimmers that there's going to be a better, cleaner way of addressing these things. But to my mind, the business leaders, including the CIO, only want to spend as much time and effort and money and resources on these sorts of things to avoid getting breached, ending up in headlines, getting fired or going to jail. So, vendors bring on the ML and AI smarts and the automation of these sorts of activities. >> So, if I may say something, the reason why we have this dichotomy between data catalog and the BI vendors is because data catalogs are very soon, not going to be standalone products, in my opinion. They're going to get embedded. So, when you use a BI tool, you'll actually use the catalog to find out what is it that you want to do, whether you are looking for data or you're looking for an existing dashboard. So, the catalog becomes embedded into the BI tool. >> Hey, Dave Menninger, sometimes you have some data in your back pocket. Do you have any stats (chuckles) on this topic? >> No, I'm glad you asked, because I'm going to... Now, data catalogs are something that's interesting. Sanjeev made a statement that data catalogs are falling out of favor. I don't care what you call them. They're valuable to organizations. Our research shows that organizations that have adequate data catalog technologies are three times more likely to express satisfaction with their analytics for just the reasons that Sanjeev was talking about. You can find what you want, you know you're getting the right information, you know whether or not it's trusted. So, those are good things. So, we expect to see the capabilities, whether it's embedded or separate. We expect to see those capabilities continue to permeate the market. >> And a lot of those catalogs are driven now by machine learning and things. So, they're learning from those patterns of usage by people when people use the data. (airy laughs) >> All right. Okay. Thank you, guys. All right. Let's move on to the next one. Tony Bear, let's bring up the predictions. You got something in here about the modern data stack. We need to rethink it. Is the modern data stack getting long at the tooth? Is it not so modern anymore? >> I think, in a way, it's got almost too modern. It's gotten too, I don't know if it's being long in the tooth, but it is getting long. The modern data stack, it's traditionally been defined as basically you have the data platform, which would be the operational database and the data warehouse. And in between, you have all the tools that are necessary to essentially get that data from the operational realm or the streaming realm for that matter into basically the data warehouse, or as we might be seeing more and more, the data lakehouse. And I think, what's important here is that, or I think, we have seen a lot of progress, and this would be in the cloud, is with the SaaS services. And especially you see that in the modern data stack, which is like all these players, not just the MongoDBs or the Oracles or the Amazons have their database platforms. You see they have the Informatica's, and all the other players there in Fivetrans have their own SaaS services. And within those SaaS services, you get a certain degree of simplicity, which is it takes all the housekeeping off the shoulders of the customers. That's a good thing. The problem is that what we're getting to unfortunately is what I would call lots of islands of simplicity, which means that it leads it (Dave laughing) to the customer to have to integrate or put all that stuff together. It's a complex tool chain. And so, what we really need to think about here, we have too many pieces. And going back to the discussion of catalogs, it's like we have so many catalogs out there, which one do we use? 'Cause chances are of most organizations do not rely on a single catalog at this point. What I'm calling on all the data providers or all the SaaS service providers, is to literally get it together and essentially make this modern data stack less of a stack, make it more of a blending of an end-to-end solution. And that can come in a number of different ways. Part of it is that we're data platform providers have been adding services that are adjacent. And there's some very good examples of this. We've seen progress over the past year or so. For instance, MongoDB integrating search. It's a very common, I guess, sort of tool that basically, that the applications that are developed on MongoDB use, so MongoDB then built it into the database rather than requiring an extra elastic search or open search stack. Amazon just... AWS just did the zero-ETL, which is a first step towards simplifying the process from going from Aurora to Redshift. You've seen same thing with Google, BigQuery integrating basically streaming pipelines. And you're seeing also a lot of movement in database machine learning. So, there's some good moves in this direction. I expect to see more than this year. Part of it's from basically the SaaS platform is adding some functionality. But I also see more importantly, because you're never going to get... This is like asking your data team and your developers, herding cats to standardizing the same tool. In most organizations, that is not going to happen. So, take a look at the most popular combinations of tools and start to come up with some pre-built integrations and pre-built orchestrations, and offer some promotional pricing, maybe not quite two for, but in other words, get two products for the price of two services or for the price of one and a half. I see a lot of potential for this. And it's to me, if the class was to simplify things, this is the next logical step and I expect to see more of this here. >> Yeah, and you see in Oracle, MySQL heat wave, yet another example of eliminating that ETL. Carl Olofson, today, if you think about the data stack and the application stack, they're largely separate. Do you have any thoughts on how that's going to play out? Does that play into this prediction? What do you think? >> Well, I think, that the... I really like Tony's phrase, islands of simplification. It really says (Tony chuckles) what's going on here, which is that all these different vendors you ask about, about how these stacks work. All these different vendors have their own stack vision. And you can... One application group is going to use one, and another application group is going to use another. And some people will say, let's go to, like you go to a Informatica conference and they say, we should be the center of your universe, but you can't connect everything in your universe to Informatica, so you need to use other things. So, the challenge is how do we make those things work together? As Tony has said, and I totally agree, we're never going to get to the point where people standardize on one organizing system. So, the alternative is to have metadata that can be shared amongst those systems and protocols that allow those systems to coordinate their operations. This is standard stuff. It's not easy. But the motive for the vendors is that they can become more active critical players in the enterprise. And of course, the motive for the customer is that things will run better and more completely. So, I've been looking at this in terms of two kinds of metadata. One is the meaning metadata, which says what data can be put together. The other is the operational metadata, which says basically where did it come from? Who created it? What's its current state? What's the security level? Et cetera, et cetera, et cetera. The good news is the operational stuff can actually be done automatically, whereas the meaning stuff requires some human intervention. And as we've already heard from, was it Doug, I think, people are disinclined to put a lot of definition into meaning metadata. So, that may be the harder one, but coordination is key. This problem has been with us forever, but with the addition of new data sources, with streaming data with data in different formats, the whole thing has, it's been like what a customer of mine used to say, "I understand your product can make my system run faster, but right now I just feel I'm putting my problems on roller skates. (chuckles) I don't need that to accelerate what's already not working." >> Excellent. Okay, Carl, let's stay with you. I remember in the early days of the big data movement, Hadoop movement, NoSQL was the big thing. And I remember Amr Awadallah said to us in theCUBE that SQL is the killer app for big data. So, your prediction here, if we bring that up is SQL is back. Please elaborate. >> Yeah. So, of course, some people would say, well, it never left. Actually, that's probably closer to true, but in the perception of the marketplace, there's been all this noise about alternative ways of storing, retrieving data, whether it's in key value stores or document databases and so forth. We're getting a lot of messaging that for a while had persuaded people that, oh, we're not going to do analytics in SQL anymore. We're going to use Spark for everything, except that only a handful of people know how to use Spark. Oh, well, that's a problem. Well, how about, and for ordinary conventional business analytics, Spark is like an over-engineered solution to the problem. SQL works just great. What's happened in the past couple years, and what's going to continue to happen is that SQL is insinuating itself into everything we're seeing. We're seeing all the major data lake providers offering SQL support, whether it's Databricks or... And of course, Snowflake is loving this, because that is what they do, and their success is certainly points to the success of SQL, even MongoDB. And we were all, I think, at the MongoDB conference where on one day, we hear SQL is dead. They're not teaching SQL in schools anymore, and this kind of thing. And then, a couple days later at the same conference, they announced we're adding a new analytic capability-based on SQL. But didn't you just say SQL is dead? So, the reality is that SQL is better understood than most other methods of certainly of retrieving and finding data in a data collection, no matter whether it happens to be relational or non-relational. And even in systems that are very non-relational, such as graph and document databases, their query languages are being built or extended to resemble SQL, because SQL is something people understand. >> Now, you remember when we were in high school and you had had to take the... Your debating in the class and you were forced to take one side and defend it. So, I was was at a Vertica conference one time up on stage with Curt Monash, and I had to take the NoSQL, the world is changing paradigm shift. And so just to be controversial, I said to him, Curt Monash, I said, who really needs acid compliance anyway? Tony Baer. And so, (chuckles) of course, his head exploded, but what are your thoughts (guests laughing) on all this? >> Well, my first thought is congratulations, Dave, for surviving being up on stage with Curt Monash. >> Amen. (group laughing) >> I definitely would concur with Carl. We actually are definitely seeing a SQL renaissance and if there's any proof of the pudding here, I see lakehouse is being icing on the cake. As Doug had predicted last year, now, (clears throat) for the record, I think, Doug was about a year ahead of time in his predictions that this year is really the year that I see (clears throat) the lakehouse ecosystems really firming up. You saw the first shots last year. But anyway, on this, data lakes will not go away. I've actually, I'm on the home stretch of doing a market, a landscape on the lakehouse. And lakehouse will not replace data lakes in terms of that. There is the need for those, data scientists who do know Python, who knows Spark, to go in there and basically do their thing without all the restrictions or the constraints of a pre-built, pre-designed table structure. I get that. Same thing for developing models. But on the other hand, there is huge need. Basically, (clears throat) maybe MongoDB was saying that we're not teaching SQL anymore. Well, maybe we have an oversupply of SQL developers. Well, I'm being facetious there, but there is a huge skills based in SQL. Analytics have been built on SQL. They came with lakehouse and why this really helps to fuel a SQL revival is that the core need in the data lake, what brought on the lakehouse was not so much SQL, it was a need for acid. And what was the best way to do it? It was through a relational table structure. So, the whole idea of acid in the lakehouse was not to turn it into a transaction database, but to make the data trusted, secure, and more granularly governed, where you could govern down to column and row level, which you really could not do in a data lake or a file system. So, while lakehouse can be queried in a manner, you can go in there with Python or whatever, it's built on a relational table structure. And so, for that end, for those types of data lakes, it becomes the end state. You cannot bypass that table structure as I learned the hard way during my research. So, the bottom line I'd say here is that lakehouse is proof that we're starting to see the revenge of the SQL nerds. (Dave chuckles) >> Excellent. Okay, let's bring up back up the predictions. Dave Menninger, this one's really thought-provoking and interesting. We're hearing things like data as code, new data applications, machines actually generating plans with no human involvement. And your prediction is the definition of data is expanding. What do you mean by that? >> So, I think, for too long, we've thought about data as the, I would say facts that we collect the readings off of devices and things like that, but data on its own is really insufficient. Organizations need to manipulate that data and examine derivatives of the data to really understand what's happening in their organization, why has it happened, and to project what might happen in the future. And my comment is that these data derivatives need to be supported and managed just like the data needs to be managed. We can't treat this as entirely separate. Think about all the governance discussions we've had. Think about the metadata discussions we've had. If you separate these things, now you've got more moving parts. We're talking about simplicity and simplifying the stack. So, if these things are treated separately, it creates much more complexity. I also think it creates a little bit of a myopic view on the part of the IT organizations that are acquiring these technologies. They need to think more broadly. So, for instance, metrics. Metric stores are becoming much more common part of the tooling that's part of a data platform. Similarly, feature stores are gaining traction. So, those are designed to promote the reuse and consistency across the AI and ML initiatives. The elements that are used in developing an AI or ML model. And let me go back to metrics and just clarify what I mean by that. So, any type of formula involving the data points. I'm distinguishing metrics from features that are used in AI and ML models. And the data platforms themselves are increasingly managing the models as an element of data. So, just like figuring out how to calculate a metric. Well, if you're going to have the features associated with an AI and ML model, you probably need to be managing the model that's associated with those features. The other element where I see expansion is around external data. Organizations for decades have been focused on the data that they generate within their own organization. We see more and more of these platforms acquiring and publishing data to external third-party sources, whether they're within some sort of a partner ecosystem or whether it's a commercial distribution of that information. And our research shows that when organizations use external data, they derive even more benefits from the various analyses that they're conducting. And the last great frontier in my opinion on this expanding world of data is the world of driver-based planning. Very few of the major data platform providers provide these capabilities today. These are the types of things you would do in a spreadsheet. And we all know the issues associated with spreadsheets. They're hard to govern, they're error-prone. And so, if we can take that type of analysis, collecting the occupancy of a rental property, the projected rise in rental rates, the fluctuations perhaps in occupancy, the interest rates associated with financing that property, we can project forward. And that's a very common thing to do. What the income might look like from that property income, the expenses, we can plan and purchase things appropriately. So, I think, we need this broader purview and I'm beginning to see some of those things happen. And the evidence today I would say, is more focused around the metric stores and the feature stores starting to see vendors offer those capabilities. And we're starting to see the ML ops elements of managing the AI and ML models find their way closer to the data platforms as well. >> Very interesting. When I hear metrics, I think of KPIs, I think of data apps, orchestrate people and places and things to optimize around a set of KPIs. It sounds like a metadata challenge more... Somebody once predicted they'll have more metadata than data. Carl, what are your thoughts on this prediction? >> Yeah, I think that what Dave is describing as data derivatives is in a way, another word for what I was calling operational metadata, which not about the data itself, but how it's used, where it came from, what the rules are governing it, and that kind of thing. If you have a rich enough set of those things, then not only can you do a model of how well your vacation property rental may do in terms of income, but also how well your application that's measuring that is doing for you. In other words, how many times have I used it, how much data have I used and what is the relationship between the data that I've used and the benefits that I've derived from using it? Well, we don't have ways of doing that. What's interesting to me is that folks in the content world are way ahead of us here, because they have always tracked their content using these kinds of attributes. Where did it come from? When was it created, when was it modified? Who modified it? And so on and so forth. We need to do more of that with the structure data that we have, so that we can track what it's used. And also, it tells us how well we're doing with it. Is it really benefiting us? Are we being efficient? Are there improvements in processes that we need to consider? Because maybe data gets created and then it isn't used or it gets used, but it gets altered in some way that actually misleads people. (laughs) So, we need the mechanisms to be able to do that. So, I would say that that's... And I'd say that it's true that we need that stuff. I think, that starting to expand is probably the right way to put it. It's going to be expanding for some time. I think, we're still a distance from having all that stuff really working together. >> Maybe we should say it's gestating. (Dave and Carl laughing) >> Sorry, if I may- >> Sanjeev, yeah, I was going to say this... Sanjeev, please comment. This sounds to me like it supports Zhamak Dehghani's principles, but please. >> Absolutely. So, whether we call it data mesh or not, I'm not getting into that conversation, (Dave chuckles) but data (audio breaking) (Tony laughing) everything that I'm hearing what Dave is saying, Carl, this is the year when data products will start to take off. I'm not saying they'll become mainstream. They may take a couple of years to become so, but this is data products, all this thing about vacation rentals and how is it doing, that data is coming from different sources. I'm packaging it into our data product. And to Carl's point, there's a whole operational metadata associated with it. The idea is for organizations to see things like developer productivity, how many releases am I doing of this? What data products are most popular? I'm actually in right now in the process of formulating this concept that just like we had data catalogs, we are very soon going to be requiring data products catalog. So, I can discover these data products. I'm not just creating data products left, right, and center. I need to know, do they already exist? What is the usage? If no one is using a data product, maybe I want to retire and save cost. But this is a data product. Now, there's a associated thing that is also getting debated quite a bit called data contracts. And a data contract to me is literally just formalization of all these aspects of a product. How do you use it? What is the SLA on it, what is the quality that I am prescribing? So, data product, in my opinion, shifts the conversation to the consumers or to the business people. Up to this point when, Dave, you're talking about data and all of data discovery curation is a very data producer-centric. So, I think, we'll see a shift more into the consumer space. >> Yeah. Dave, can I just jump in there just very quickly there, which is that what Sanjeev has been saying there, this is really central to what Zhamak has been talking about. It's basically about making, one, data products are about the lifecycle management of data. Metadata is just elemental to that. And essentially, one of the things that she calls for is making data products discoverable. That's exactly what Sanjeev was talking about. >> By the way, did everyone just no notice how Sanjeev just snuck in another prediction there? So, we've got- >> Yeah. (group laughing) >> But you- >> Can we also say that he snuck in, I think, the term that we'll remember today, which is metadata museums. >> Yeah, but- >> Yeah. >> And also comment to, Tony, to your last year's prediction, you're really talking about it's not something that you're going to buy from a vendor. >> No. >> It's very specific >> Mm-hmm. >> to an organization, their own data product. So, touche on that one. Okay, last prediction. Let's bring them up. Doug Henschen, BI analytics is headed to embedding. What does that mean? >> Well, we all know that conventional BI dashboarding reporting is really commoditized from a vendor perspective. It never enjoyed truly mainstream adoption. Always that 25% of employees are really using these things. I'm seeing rising interest in embedding concise analytics at the point of decision or better still, using analytics as triggers for automation and workflows, and not even necessitating human interaction with visualizations, for example, if we have confidence in the analytics. So, leading companies are pushing for next generation applications, part of this low-code, no-code movement we've seen. And they want to build that decision support right into the app. So, the analytic is right there. Leading enterprise apps vendors, Salesforce, SAP, Microsoft, Oracle, they're all building smart apps with the analytics predictions, even recommendations built into these applications. And I think, the progressive BI analytics vendors are supporting this idea of driving insight to action, not necessarily necessitating humans interacting with it if there's confidence. So, we want prediction, we want embedding, we want automation. This low-code, no-code development movement is very important to bringing the analytics to where people are doing their work. We got to move beyond the, what I call swivel chair integration, between where people do their work and going off to separate reports and dashboards, and having to interpret and analyze before you can go back and do take action. >> And Dave Menninger, today, if you want, analytics or you want to absorb what's happening in the business, you typically got to go ask an expert, and then wait. So, what are your thoughts on Doug's prediction? >> I'm in total agreement with Doug. I'm going to say that collectively... So, how did we get here? I'm going to say collectively as an industry, we made a mistake. We made BI and analytics separate from the operational systems. Now, okay, it wasn't really a mistake. We were limited by the technology available at the time. Decades ago, we had to separate these two systems, so that the analytics didn't impact the operations. You don't want the operations preventing you from being able to do a transaction. But we've gone beyond that now. We can bring these two systems and worlds together and organizations recognize that need to change. As Doug said, the majority of the workforce and the majority of organizations doesn't have access to analytics. That's wrong. (chuckles) We've got to change that. And one of the ways that's going to change is with embedded analytics. 2/3 of organizations recognize that embedded analytics are important and it even ranks higher in importance than AI and ML in those organizations. So, it's interesting. This is a really important topic to the organizations that are consuming these technologies. The good news is it works. Organizations that have embraced embedded analytics are more comfortable with self-service than those that have not, as opposed to turning somebody loose, in the wild with the data. They're given a guided path to the data. And the research shows that 65% of organizations that have adopted embedded analytics are comfortable with self-service compared with just 40% of organizations that are turning people loose in an ad hoc way with the data. So, totally behind Doug's predictions. >> Can I just break in with something here, a comment on what Dave said about what Doug said, which (laughs) is that I totally agree with what you said about embedded analytics. And at IDC, we made a prediction in our future intelligence, future of intelligence service three years ago that this was going to happen. And the thing that we're waiting for is for developers to build... You have to write the applications to work that way. It just doesn't happen automagically. Developers have to write applications that reference analytic data and apply it while they're running. And that could involve simple things like complex queries against the live data, which is through something that I've been calling analytic transaction processing. Or it could be through something more sophisticated that involves AI operations as Doug has been suggesting, where the result is enacted pretty much automatically unless the scores are too low and you need to have a human being look at it. So, I think that that is definitely something we've been watching for. I'm not sure how soon it will come, because it seems to take a long time for people to change their thinking. But I think, as Dave was saying, once they do and they apply these principles in their application development, the rewards are great. >> Yeah, this is very much, I would say, very consistent with what we were talking about, I was talking about before, about basically rethinking the modern data stack and going into more of an end-to-end solution solution. I think, that what we're talking about clearly here is operational analytics. There'll still be a need for your data scientists to go offline just in their data lakes to do all that very exploratory and that deep modeling. But clearly, it just makes sense to bring operational analytics into where people work into their workspace and further flatten that modern data stack. >> But with all this metadata and all this intelligence, we're talking about injecting AI into applications, it does seem like we're entering a new era of not only data, but new era of apps. Today, most applications are about filling forms out or codifying processes and require a human input. And it seems like there's enough data now and enough intelligence in the system that the system can actually pull data from, whether it's the transaction system, e-commerce, the supply chain, ERP, and actually do something with that data without human involvement, present it to humans. Do you guys see this as a new frontier? >> I think, that's certainly- >> Very much so, but it's going to take a while, as Carl said. You have to design it, you have to get the prediction into the system, you have to get the analytics at the point of decision has to be relevant to that decision point. >> And I also recall basically a lot of the ERP vendors back like 10 years ago, we're promising that. And the fact that we're still looking at the promises shows just how difficult, how much of a challenge it is to get to what Doug's saying. >> One element that could be applied in this case is (indistinct) architecture. If applications are developed that are event-driven rather than following the script or sequence that some programmer or designer had preconceived, then you'll have much more flexible applications. You can inject decisions at various points using this technology much more easily. It's a completely different way of writing applications. And it actually involves a lot more data, which is why we should all like it. (laughs) But in the end (Tony laughing) it's more stable, it's easier to manage, easier to maintain, and it's actually more efficient, which is the result of an MIT study from about 10 years ago, and still, we are not seeing this come to fruition in most business applications. >> And do you think it's going to require a new type of data platform database? Today, data's all far-flung. We see that's all over the clouds and at the edge. Today, you cache- >> We need a super cloud. >> You cache that data, you're throwing into memory. I mentioned, MySQL heat wave. There are other examples where it's a brute force approach, but maybe we need new ways of laying data out on disk and new database architectures, and just when we thought we had it all figured out. >> Well, without referring to disk, which to my mind, is almost like talking about cave painting. I think, that (Dave laughing) all the things that have been mentioned by all of us today are elements of what I'm talking about. In other words, the whole improvement of the data mesh, the improvement of metadata across the board and improvement of the ability to track data and judge its freshness the way we judge the freshness of a melon or something like that, to determine whether we can still use it. Is it still good? That kind of thing. Bringing together data from multiple sources dynamically and real-time requires all the things we've been talking about. All the predictions that we've talked about today add up to elements that can make this happen. >> Well, guys, it's always tremendous to get these wonderful minds together and get your insights, and I love how it shapes the outcome here of the predictions, and let's see how we did. We're going to leave it there. I want to thank Sanjeev, Tony, Carl, David, and Doug. Really appreciate the collaboration and thought that you guys put into these sessions. Really, thank you. >> Thank you. >> Thanks, Dave. >> Thank you for having us. >> Thanks. >> Thank you. >> All right, this is Dave Valente for theCUBE, signing off for now. Follow these guys on social media. Look for coverage on siliconangle.com, theCUBE.net. Thank you for watching. (upbeat music)

>>Live from Las Vegas. It's the cube covering UI path forward Americas 2019 brought to you by UI path. >>Hello everyone and welcome to the cubes live coverage of UI path forward here at the Bellagio. I'm your host hosting alongside of Dave Volante. David's so great to be here with you. I'm so excited to get into this. See Rebecca, so we were, we would use came from the keynote. A lot of high profile UI path executives and important customers were on there too, but then this is the message is it's time to reboot work. It's time to reboot your business, transformed the customer experience, transform the employee experience. I'm wondering as someone who spent a lot of time at these kinds of conferences, and here's a lot of this, these, this kind of messaging, especially in this age of digital transformation, how compelling do you find this value proposition, this, this idea that RPA, robotics, processing automation can do these things? >>The first thing I would mention, Rebecca, is to me it's all about the customers. And you know, it's rare that you see a tech show start with the customers to actually do in the intro. I've seen it before. Nutanix actually does it at his shows, but it's, but it's quite rare because you know, the vendors want to put their message out, they want to control everything, and so they're very, very cautious about that. But, so we had three customers up on stage today doing the intro, which I thought was kind of cool. Tech shows, you know, a lot of smoke, a lot of mirrors and so forth. So you have to try to squint through that. I would say this, it's very clear that the age of automation is here. You know, people have been always concerned about automation for good reason. They're afraid that automation is gonna take away their jobs. >>Having said that, machines have always replace humans. We've talked about this a lot on the cube, but this is the first time in history that machines are replacing humans with cognitive tasks. So that's got to be scaring people a little bit. But when you come back and answer your question, when you talk to customers, they're really happy about software robots because they're doing, they're automating mundane tasks that these folks don't want to do on a day to day basis and they want to do other things. They want to get their weekends back. They don't want to just manually enter data from spreadsheets into applications and back and forth. And so from that standpoint, I think it is real and it is unique. You know, the big question is how much of this is transformational and is it really a path to AI something that UI path and others are really pointing towards and we're going to explore that, >>right? I mean in what you were just saying too is that that that the company's pitch is that we are freeing people. We are liberating them from the mundane, from the drudgery, from the data entry. And as you, as you pointed out, rightfully, a lot of the customers are saying, Oh no, it's giving our time. It's giving our employees time back to focus on the higher level tasks, the more creative aspects of their job. But, but I wonder if it is in fact a w what it really is doing. Two jobs. I mean I think that there was a really telling line in that Forbes profile of uh, Daniel Dina's who is the, the CEO of this company is founder of this company. The first ever bought billionaire exactly. Um, where it was an MIT professor quoted saying, you know, we always say to the companies that we say, give, give us your data and we'll tell you if it is in fact, uh, having this job killing effect. And he said, the companies don't want to give, give that up. >>Right? So now just look at the why is Daniel didn't as a billionaire, it will here, here, here's why. >>Yeah, walk, walk us through this. >>So UI path is up to 3,400 employees. 34 50 is the actual number. Now back in 2017, two years ago, this company did $25 million in annual recurring revenue. Now, ARR is a metric that's very important because you know, even though you book, let's say you book a $12,000 deal, you recognize that $1,000 a month over the 12 month period. So ARR is a very really important metric. So 25 million in 2017 my sources indicate that they'll do over 300 million this year in ARR. So we're talking about a 12 X plus increase in a two year period. They've raised $1 billion. One of their key competitors, automation anywhere has raised similar amounts of money. So they're talking about a couple of billion dollars raised just in the last couple of years. UI past valuation in March was $7 billion. So at that kind of back of napkin, and we're talking about a $10 billion valuation, Daniel obviously owns a lot of that. >>So 20% yeah. So it's, it's pretty substantial in terms of the market impact. Now valuations, as you all know, it's a fleeting metric, right? It comes in, it goes, but so the, but the landscape is very strong right now. It's really interesting to see how much customers are glomming onto this automation tailwind. The other comment I would make is let's lay out the sort of competitive landscape. UI path has gone from kind of a clear third in the marketplace to clear number one. I mean they're kind of separating from the pack, but there are others automation anywhere, blue prism and there are a number of legacy customers as well >>that that's what I wanted to ask you too, is that we have seen a few Microsoft and Google of course are, are, are partnered in their, in their customers, but they also are moving into this area themselves. So I mean will you will let UI path be able to maintain its competitive position as these very established and frankly very smart companies move into this area. Safety's >>another one. SAP bought an RPA company. It's a good question, but, so if you look at, let me start with this sort of underlying trend. If you look at the spending data, so we have access to the enterprise technology, research spending data and it shows the entire space is gaining share relative to other technology initiatives. So when you look at the data for UI path automation, anywhere blue prism, even legacy process automation companies like Pega systems, they're all actually from a spending standpoint attracting a lot of attention. So it's this rising tide lifts all ships. It's still somewhat early in terms of this next generation RPA if you will, you I-PASS advantage is simplicity. They are totally focused on this. You see this all the time. Do we go best of breed or do we go with a suite? So if Oracle comes up with an RPA solution, they throw it in for free, you know, does a customer take that? >>I think it comes down to what the business value is and that's something we're going to explore. It's not uncommon in detect industry that there's a first mover advantage or maybe it's a second mover advantage. You know, Facebook wasn't really first mover, but the one who really gets it right is kind of a winner take most. And so that's where a UI path is going like crazy right now. Trying to scale the company, raise a bunch of money. We saw this week a bunch of bankers sort of sniffing around. All the bankers are here cause they want their business. So I would expect there's some kind of IPO on the horizon, which I think they need to do to be, to your point to be able to compete with the big guys. So bottom line is they have to do it on a better product, more openness, moving faster and getting to scale. And I think they'll be able to reach escape velocity. I don't know if there's enough room for the big three. I would expect that given the spending climate is very good for everybody right now. I would expect within the next two to three years, some consolidation in this space. >>Well. So one of the things that you had just talked about with this next generation RPA, and that is exactly where we're going because these bots have got to become more durable, more smarter and more capable of handling complex tasks. We saw a number of new product announcements today. Oh, I might to get your thoughts and what you think about them and just whether or not they will have this transformational effect. Um, so, so yes, we have some new product announcements, some, some that democratize automation building that all you have to do is know how to run an Excel spreadsheet and you too can build an automation in your company. >>Yeah. It'll take a little bit of training though. >>I know. I think a better idea for those those demos is they should just pluck someone out of the audience and say, okay, you're going to do this. >>No, they would fail. I mean, let's say said, I remember the first time you learned Excel, I'm old enough to remember slash file, retrieve, paste, copy, whatever. You had to go through some training and we went through classes back in the eighties I think it's a similar here. I mean it's not overly complex. It's gonna have a low code theme, but you're right, UI path announced the number of new products. You know, we looked at this a couple of years ago, we went, we went out and we took the big three from the Forrester wave blue prism automation anywhere in UI path and we said, Hey, let's download them and start building some, some, some automations. While the only software we could get ahold of was UI path. Because as they say, they had kind of a simpler or more open model. The other guys were like, well, talk to a reseller is spend some money. >>And we were like, no, we just want to try it before we buy it. And we weren't able to get the other guy software. Now I think automation anywhere has made some strides in that regard in terms of simplification. You know, it's a copycat industry like the NFL. But so let's remember here we're talking about automating mundane tasks. Relatively simple automations. The customers are asking for things like more complex automations. How do we prioritize the automations? How do we figure out where, what's the best bang for the buck? How do we actually have attended bots because many of these are unintended. They'd like to have the human injected into the equation and that's pretty interesting because it brings forth this augmentation scenario that's everybody's talking about in AI and that starts to move us from sort of this tactical, I'm going to save some time on a use case specific or a technology specific automation to something that's more strategic that I can scale across my organization but right now people are saving money on this as a super hot space. As I say, all the bankers are trying to get in because they know some other ideas are coming down the road and the VCs I'm sure are gonna want the air exits. >>I want to talk to you about the leadership of this company. This is Daniel Dienes and you have interviewed him many times. Do minimun has as well. He he, he seems like a different kind of CEO. I mean, first of all, he is, he's a Romanian. Uh, he grew up, uh, behind the iron curtain. Uh, he was a professional bridge player for awhile, at least play competitive bridge player play competitive bridge and now he is a company headquartered in New York city. He still spends a lot of time in Bucharest but I'm curious to hear your thoughts about his leadership style and the kind of culture he's created at UI path and whether or not, because he's made some key hires from AWS, from Google, some, some of the more established tech players, whether or not he is, whether or not he'll be able to keep that startup culture, that startup mindset as the company becomes so much bigger. Well >>I think it's a concern and something that we want to ask about when you ask Daniel about, you know, how have you been able to do this? He'll talk about the mistakes that they made, how they sort of, they had a build it and you and they shall come mentality, which is kind of kind of old thinking these days and they sort of lucked into this RPA space. He also emphasize, emphasize as humble, and he's a very humble guy. I mean, you'll, you'll, you'll meet him I think last year he came on and you know, he's a developer. He had a tee shirt on. He's a coder right now. He's a billionaire coder. So maybe, maybe he'll, he'll dress up a little bit, but you know, maybe a fancy tee shirt, I don't know. Or maybe a collared shirt that says UI path on it. We'll see. >>But so they end, they want to move fast. They believe in openness there. They believe in transparency. I think those things worked in today's marketplace. People love the guy. I mean the customers love them. The employees love them. As you said, they're pulling people in from the hottest companies. Google, AWS. We, I got a on the shoulder today from, from a gentleman and I know from Google, he was in sales at Google. It's not me. There's no, Oh, I'm day three. And so people want to be part of this, this rocket ship. And I think it's gonna move very, very fast. Like I say, I think you're going to see some moves in the marketplace. I think you're going to see some exits and consolidations. We saw some M and a today UI path announced the acquisition of company called process gold that actually competes with a partner of UI path. So it's again, people are going to be on collision courses and they recently made another acquisition of a company called step shots and we're seeing some M and a, you know, relatively small MNA, but it's all about how can they transform from this little startup to this major player. To your point that can compete with the Microsofts and the SAP and the big whales of the world. >>And what do you think is his bigger selling point? Is it that it is transforming the employee experience, which as we know that that should not be discounted because an employee who is doing less mundane tasks able to focus on the more creative interesting parts of his or her job is a happier employee, happier your employees, more productive employee. A more productive employee means a healthier bottom line. So that's now funding to discount. Also the customer experience, as you said, which is clearly a huge top priority for this company. But, but I think the question is, is this technology now is a transformative enough? >>You know, as you asked that question, it kind of reminds me in a different way of a company that we've followed for years service now. When service now first came out, it was kind of doing what people saw as help desk, improving help desk, and they disrupted an industry and they made it better, which is kind of boring. It's kind of mundane, but actually having good it where you're not constantly down and you're not complaining and stuff's not falling through the cracks actually can be somewhat transformative. Kind of boring, but really important. And I see a similar sort of pattern here now the vision is, you know, a robot for every worker and the path to AI and we'll see. But right now the trans, the transformation is we're going to take away all this crap that you hate doing all these crap locations or mundane tasks and we're going to make your life better. >>And people workers want that and it's going to be in theory, a productivity boost as a result of that. That in and of itself, I think Rebecca can be transformative because it'll, it'll help with morale, it'll help with culture, it'll allow people to shift their emphasis on more strategic work and drive more value for the companies. And so, and I think companies that invest in RPA are, are seeing returns in terms of quality, just in terms of employee morale. You'll hear that from the customers that we talked to today. So I think in that sense it can be transformative like service now was now can it take the next step or is this really just paving the cow path? Is it just taking mundane known processes, automating them as opposed to really rethinking what process automation should look like. And that's some of the criticism of RPA and the RPA hype. And you know, we're going to talk about that. We're going to talk to customers about that. We've got analysts from HFS coming on, Kathy from Gartner's coming on. So excited to hear their perspectives as well. >>Exactly. And I, I want to reiterate that point that you're absolutely right. Their question is should we actually think about redesigning the process itself rather than automating the, the the flawed process? >>Yeah, and I mean I guess part of me says yes strategically we should be doing that, but another part of me says, look, I don't have to change anything. And I think that's the big advantage of UI path and these other players is you can basically automate what you have today. You don't have to redesign the process because process redesign is a heavy lift. So if I don't have to do a heavy lift, if I can improve what I'm doing today and it works, yeah, it's the old, if it ain't broke, why fix it, but just improve it. I think that's a very powerful, I think the big question I have is, is that like a big hit of a step function or is it really transformative? I feel like today's tech is a step function, which is important. You're going to get that step function, but I think you're going to absorb that benefit fast and then people are going to say, okay, now what? >>Another good example is virtualization. When I first saw virtualization and the ability to spin up a server, my jaw dropped and went, Oh my God, I could spin up a server in five minutes and it used to take weeks, months to spin up a server. That's game changing. Nobody talks about virtualization anymore. It was a, you know, a five year absorption of productivity for it and now it's like, yeah, I've been there, done that. That's yesterday's news. I think the same thing is going to happen with today's RPA and the big question is can they cross that strategic chasm into what the gentleman from Pepsi, the executive from Pepsi was saying, this automation fabric across the enterprise as a, as a platform for automation and artificial intelligence. That's a big leap. These guys get big plans. Daniel Dienes is a big thinker, go big or go home. So I don't, I don't have the crystal ball on that, I think, but I think there's a decent opportunity given that there's enough attention on this business right now that it, that it could be transformed. >>All right, well, hopefully we'll know more at the end of these two days. Dave, I've, I'm looking forward to getting into with you. I'm Rebecca Knight for Dave Volante. Stay tuned for more. You're watching the cube.

>> live from San Francisco, celebrating 10 years of high tech coverage. It's the Cube covering Veum World 2019 brought to you by IBM Wear and its ecosystem partners. >> Oh, good afternoon and welcome back as we continue our coverage live here on the cue from Mosconi North in beautiful San Francisco. Clouds of melted away In a way, of course, we're still talking about hybrid Multi. They're not going anywhere. In fact, there are very much entrenched into this show. John Wall's Justin Warren. Glad to have You with us. Joined now by Kevin Chats. Camera. Who's the vice president of Product management Enterprise and SP Solutions of Dental Technologies. Kevin. Good to see you again, sir. Nice to see you, too. Two shots in one week on the Q. We love that and Wait Holmes, who's the director of technical product management at Veum? Where? Wade, Good to see you this afternoon. >> But if you also >> so this this is kind of your party here, VM where? I mean, just give me your impression so far. First off, just kind of what you're sensing that the vibe here of the show and, ah, the kind of work that you're getting done. >> So the vibe here is excitement. I mean, I think everyone's excited about a lot of the announcements around either probably Pacific and how we're redefining the V's Fair platform and Tan Xue and now these capabilities on how these capabilities are going to be able to enhance our capabilities of our cloud provider partners. So I'm part of our club fighter salt for business unit, who specifically makes products and solutions for our cloud provider V, C P P program. And I think couldn't beam or excitement. And they've been a crescendo the past few years and be anywhere and b m world. And I think this has been one of the best ever. >> If the waves hitting the shore big time now. So you you talk about cloud providers about service providers. I mean, one of the same. Or Or how do you guys define that now? Or how do you separate that? >> Yeah, I think these terms are largely used interchangeably. To a large degree, I think if we look att at the cloud industry in the provider industry over the last several years, maybe about 5 to 7 years ago, there was a belief from every single cloud provider that they needed to build a scaled platform like a W s like Microsoft Azure like Google Compute. And that they were all in the business of a race to building the most robust, most scalable, most feature rich, most differentiated cloud that was largely erased the bottom from an economics perspective. And I think just about all of all of the service providers and now these cloud providers that we work with have really moved to a different model. What they've recognized is first off. The race to the hyper scale is not a profitable business that you want to race against. Number two. Ah, the transition for large enterprise I t small enterprise medium business to the cloud is so complex that it's not a game of building clouds and not a game of building platforms. It's a game of building practices at this point and cloud providers or building practices that allow them to find their own niche and differentiation off differentiated offerings. Whether that be on Prem Private Cloud hosted Private Cloud and then partnering with the hyper scale er's for the massively scaled multi tenant cloud world. And when we start to realize that this managed offering these cloud practices are there to help the enterprise and small medium business in their transition to the public cloud in transition to cloud and moving towards more managed I t offerings. What we're finding is the reemergence of these cloud providers in a meaningful way, starting to bridge the gap of skill, set, mismatches and expertise. Mismatches at Enterprise I t just doesn't have to embrace cloud technology. >> Yeah, for a long time there, there was the cloud Geraghty, who were saying that the public cloud is the only way this is gonna happen. Everything's going to be there. And some some of us I would count myself among them was a little bit skeptical about that. That approach to things and a lot of it with a lot of the pressure on on service providers was you don't even bother getting into the cloud business. Just shut up shop and go home. This is never going to be a good idea for you to compete in this at all. And it sounds like that that some of these providers have actually gone. You know what we've We've got a viable business here. There are customers here who need things done that we do really well that are not available out in public Cloud. So what are some of the things that some of the things that you're hearing from these cloud cloud providers, that that they are finding from customers that they value, that they not finding anywhere else? >> So I grew 100% that the club wider there, find their business is still growing, and it's due to their expertise. Is Kevin said, that the building practices they understand enterprise customers? Veum, Where business? They understand the platform that they're running the enterprise and are able to provide additional differentiated service's while leverage in the technology that the enterprise they're utilizing in their own data centers. So it's able to pride value out of service is with the same platform that air using in their own premises and providing those capability of same platform in a cloud model. So, given a pragmatic way for enterprises to be able to migrate to a cloud in a hybrid cloud, >> are there specific practices you noticing that is that kind of stand out as being particularly common? >> Yeah, s so I think that through the answer is yes, right? And the answer is that vertical expertise is king here, right? Understanding the industries in which the cloud platforms get deployed and how those industries consume. Resource is the use cases. How they monetize their business is key for success. But I think that what we where we've lived over the last several years is that the building blocks for all of these vertical industries, the only uniform way you had to do it was with the massively scaled public cloud providers. The hyper scale er's what we're doing now, Adele Technologies Cloud is we're enabling a consistent set of building blocks for all of these vertical industries that all of these vertical X three experts in the vertical industries across the cloud providers can then bring a common building block and go address the complex problems of building the use cases, building the monetization models, building the differentiated feature set. >> So I mean, can you give me an example? I mean, what you talking about? It's like if you're going about health care versus transportation versus manufacturing, some things that were going to a different way, we're going to slice this That's right. It's a different >> set of ecosystem partners. It's a different set of vertical applications, a different set of problems. It's different set of monetization models across the board, right? You know, retail has very specific requirements around Leighton See sensitivity and the need to be able to address micro transactions. Security capabilities of those transactions or what not, Health care is governed by hip on various other legislative. When you build in Europe, you have, ah, various data protection and privacy implications to keep in mind. It's right, so all of this is not typically available in public Cloud Public Cloud is built for a lowest common denominator. One size fits all, and then you come bring differentiation. On top of that now is enterprise. I T organizations start to migrate their workloads to Public Cloud. They're looking for consistency in terms of how they've lived before and how they work before how they've operated before. How do they migrate those applications, right? It's not I'm building everything natively for public cloud is that I have an entire set of applications that were designed in my enterprise i d environment that I just want to find a new way to operate in VM wears a consistent abstraction. Layers is really the path forward, So DT Cloud on Deli emcee and TT Cloud leveraging the public cloud providers in the V M wear abstraction with both feet spheres. Well, it's vey cloud foundations, eyes really a commonality that they can now the uses a foundational building block for all their service is >> yes. So where one of the things that a lot of customers have invested over a decade or Maur envy em where? And they have a lot of processes and tools and skills that they have invested in. And it sounds like for some of these cloud providers specializing in a particular industry, that there's a risk there that you will end up with building blocks that, yes, they're customized for one particular thing. But now I have to operate them a little bit differently. And now I've got a lot of different ways of doing things, and particularly as a provider, then that that adds cost. And I want to try to get some of those costs out there because they think that influences my margin. So is the choice. Of'em were one way of dealing with that because I can maintain that same consistent way of managing things. >> Absolutely. And that's key to some of the work that VM wear and Dell has been working together on two. Allow for Kevin Mention, Adele Technology Cloud Platform, which the baseline of that is being more cloud foundation. So been ableto have that homogeneous operational model, and Mona's data plane set is the same V sphere and XXV sand based originality perspective. So the operational model, whether it's in the providers infrastructure or whether it's on premises within enterprise is similar. >> And I think there's even 1/3 vector to this, which is, um, yeah, one public cloud provider is not gonna win. All of the public cloud providers are going to exist, and the scale of a Microsoft azure and the scale of an AWS on a scale of a Google compute put them in position to continue to lead this industry forward. And it's it's difficult to bet on one horse, right? So the GMC model on the DT Cloud model allows us to be able to scale across all of these different cloud providers and as an enterprise organization that's making specific decisions based on region or based on other financials that some of these workloads are going to say in AWS, and some of them are going to sit in Microsoft Azure, etcetera, etcetera is a common abstraction across all of them. >> But at that point, I mean the fact that you're talking about, um, vertical practices, right? Verticals having practices that might be unique to their particular industry. And now you're talking about them deciding that they might all flowed work Thio, maybe an azure. Maybe in Google. Maybe I'd be it. Whatever, Um, I mean multiple complexities for you in dealing with that because you're gonna be the translator, right? You've got to be. You've got to be multi lingual, not only within in the cloud world, but also in a vertical world too. Right? So tough road for you guys to provide that kind of flexibility and that kind of knowledge. >> Oh, I mean, that's the key to the software and solutions that GM was providing and allowing for solutions and sat space capabilities to provide a modernise, softer, defined capabilities across clouds or a and be able to manage things across, such as cost in via cloud health and other manage service's capabilities by our software platform and then be ableto have this. These capabilities in the Bean Imlay consumed by providers and turnkey fashion by utilizing del technologies, bx rail are and VCF one VX rail and having us all package together, and so that providers no longer have to focus on building a core infrastructure. But they're now able to focus on that integration layer. Focus on the additional higher level service is that are able to stitch together the use this multi cloud environment >> decision logic that our customers have. It's just so complex, and I think that the message that we've heard loud and clear from them is that they feel like once they're in particular ecosystem, they're locked into that ecosystem. And the more that we can do that give them flexibility to bring these ecosystems together and leverage the benefits and the capabilities and the regional and geo location of just about all the different ecosystems that exists and build their own ecosystems. On top of that, especially if you're a cloud provider, is really what they're looking to do. And when the foundational building blocks all look different, the integration look different the automation look different. The orchestration look different in the storage. Later look different. It's just It was impossible, right? It's really on us to provide an abstraction to make that easy for them to accomplish their business. >> Consistent foundation is critical, and that's what we're bringing through the cloud provider today. >> One thing that has changed from from technology of 12 12 15 20 years ago is the consumption model that cloud has provided. S. So what are you seeing around service providers, providing that pretty much you have to provide if your cloud provided you have to provide some kind of consumption model because that's what people have in their minds when they think about about Cloud it is. It's not just about the technology side of things. Actually, we're out the business operations about, you know, the financing and the funding models of things. What are you seeing with the cloud providers and service providers? How are they changing the way that they allow people to finance the buy of this infrastructure? >> So that's one of the pieces that, in being where Rendell is working together to allow for not just software, which through the visa program all of our software solutions are consumed through a subscription like model. So it's pay as you go, but also be able to consume hardware and consume the turnkey patches package so that VCF on Vieques rail and the Cloud Provider platform can be consumed in a pay as you go subscription model, which is a way that providers want to be able to then provides software and capabilities to their enterprise customers. >> Have they completely changed across to being purely consumption? Or do we still have a lot of industries that preferred by things that with Catholics >> it would be fantastic if the world converged on one answer? Everything is always easier when there's one answer. But I think, ah, one of the things we recognize is that, ah, and it's true and technology. It's true in business models. It's true. In operational models, there's never in. It's never just a or answer right. It's always an end, and there's a need for us to embrace multiple different models in order to meet the needs of our customers. And even a single service provider will find particular areas that they wanted, consumption based model and others that they realize that it's a well entrenched business for them, and the risk is a little bit lower, and they're willing to take on that risk and look at a Cap IX base model right there. Certainly financial implications to both an Op X and the Catholics model. There's tax implications, and you know where. We're still a little bit all over the map in terms of their preferences. >> Hopefully, we'll see that shake out a little bit and we'll have some standard patents to match the practices that will just make it a little bit easier to design the solution. >> I think the Saturn standard pattern that I expect to emerge is that we have to do everything >> for everyone >> in every way that they want to see. >> Oh, you left there, Kevin. I can't imagine that being too difficult. Everything. Everyone it all at every time. That's right. All right. Hey, thanks for the time of and the discussion and good luck with handling that. I know. That's a that's a big lift on. I know we're joking, but, uh, it's a great world for you. Certainly exciting time. And we thank you for your time here. >> Thank you. Thank you guys appreciate the time. >> I appreciate being World 2019. Coverage continues right here on the Cube. We're live and we're in San Francisco.

>> Live from Las Vegas. It's the queue covering del Technologies. World twenty nineteen. Brought to you by Del Technologies and its ecosystem partners. >> Good morning. Welcome to the Cubes coverage. Day three. Odell Technologies, World from Las Vegas. Lisa Martin With student Amanda We're pleased to welcome one of our alumni back to the key. We've got Jay Snyder with us SPP of global alliances, service providers and industries chaebol. Thank >> you so much for having me again. >> Our pleasure. So we have been talking for This is our third day of covering the lots of news, lots of technology conversations We know there's a big global Cartner summit. >> It's been fantastic, actually. >> Abd el Technologies World Thriving partner ecosystem Give us an overview of global alliances and some of the feedback from the last few days of the partners. So >> fantastic. Thank you again for having me. I'll tell you this. The feedback is off the chart eye. Don't even I've lost the ability to find new words to describe how excited are partners seem to be with the messaging that we've had here. But what's been consistent is best l technologies world ever and best global partners. Summer that we've ever had and I think the reason behind that is not just because we've done a great job presenting the content. It's because of the content, right. If you think about the partner ecosystem, it's interesting. We've always worked incredibly well with them and our partners love what we do in the products we make. But our messages have never been perfectly aligned. Think about the messages we have now on the main stage. We have four transformations and delivering outcomes and then we have multi cloud and the multi cloud strategy and then think about what the partners do. They deliver the strategy around designing and defining what a multi cloud architecture is going to look like and or being the providers that actually deliver it. Our messages are perfectly aligned, so they're so excited to see that there now at the epicenter of everything that we go and do, and the fact that I would say probably more exciting is our entire sales force is trained on those messages, understanding those messages and embracing those messages. So they're getting huge lift now from our cellars, as opposed to kind of. I wouldn't say we were never at conflict. But we're Maurin Parallel. And now we're really lock. Step. Well, does that make sense? >> It does, Jay. And and he brought up a really good point, you know? Congratulations. Glad to hear everybody's in lock step. Because I remember we talked about the transformation of the channel. Yeah, and I go back when converge infrastructure first rolled out. They're people. Oh, my gosh. I make millions of dollars racking it, Stacking, shoveling stuff. I need to shift Cloud that there was, you know, at VM wears partner Summit, you know, one of the executive V M. Where you know, every time Amazon winds, you know, we all lose. Sure. So helped us for today. You know, cloud big theme of the message. How Teo his partners fit into those environments. And how have they gotten to over the fear of cloud and to be fully embracing in executing a multi cloud? >> Maybe I should just context to about who my partners are, so that would be helpful. So we representing alliance is the largest global systems integrators. So think about firms like in HCL, Deloitte dating, censure. And I hate to leave anybody out, but there's eighteen of them. And then we represent the clouds of the cloud service provider ecosystem. So a couple of hundred cloud providers that actually do provide manage private clouds off from or public clouds. So they're super excited about the message because they fit in on both ends, right, As I was just describing right there, the ones that are really gonna have to deliver the strategy around what it's going to look like and how they're going to get their customers ask us all the time. Hey, I want to get to the cloud, but they don't really know what it means. So we have to ask them, What do you really trying to accomplish and why? Right, Once we understand that we can engage with these partners, and it's a perfect entree for them to go figure out, articulating design that architecture. And then last time I checked, we're actually not a cloud company, right? We have great products. We have great services. We've rate platforms, but we're not a cloud company, right? We don't provide those types of capabilities. So when you think about being able to leverage >> multi cloud and it started just clever, you're saying you're not a public cloud company because company Private Cloud absolutely se Eun apart >> from Private Cloud, right? But when we want to go off from and create that multi claude environment based on use case now all those partners fit into that play and they have the ability through the capabilities we just announced with Del Technologies clown tow leverage, those hyper scale er's. So where they used to see them as foe. They're now part of the solution, and they can deliver that solution through our new platform that we just brought to market. So again it gets back to we used to fight it. Now we're embracing it and leveraging it and delivered a comprehensive solution. >> So starting Monday, when Michael walked out on stage your hat with Jeff, the message over lying on, of course, with salt from Microsoft was collaboration integration. So really starting to see all the layers of Del technologies and its brands come together in a much more cohesive way than we've seen so far in terms of what the partners are now enabled to deliver. Some of the feedback on that is, do they feel that it's been made more simplified that has been made more streamlined, that it's opening up new market opportunities with, you know, the Del Technologies Cloud and some of the related announcements. >> So So it's a complicated question you're actually asking, because for years the partners have been saying We'd love to view you as a single company, right? That's kind of the missing ingredient to really a lot unlock the full potential. I think the first big piece big mover in this is the Del Technology Cloud platform. It's really the end, Stan, she ation of what Michael's been talking about for the last three years, which is I'm going to bring all this stuff together and create a force in the industry where we compete in the market together, not against one another. So we're seeing that so the partners are ecstatic right there, seeing the best of all the piece parts come together in that platform, and we've told him that's the first step. But we have been working with them for years to provide what I'LL call an umbrella effect across all the different companies to allow them to tap into all those resource is. So in some degree, we've been doing it already. We've been playing that multi cloud game and working cross strategically aligned business to bring those values to life. But now we put our money where our mouth is, and we have simplified the approach with the product and the platform to make it easier for them to go tomorrow. Way to have a little bit. We do have a little bit a ways to go, though. I want to be clear. >> So, yeah, and Jay really good points there because I I one article recently about hybrid cloud cut a lot of history with it and simplifying a piece of the overall puzzle. But as you said, those hyper scales fit into it. Sergeant Dellape, upstate eight of us, a strong partner on VM where you know, Google announcement. You know, just a few weeks ago, those s eyes that air your partner's There are some of the critical pieces because there's a lot of complexity out there and we need key partners to be a help us to do there. You know, the Del of Technology family is a piece of it, but those s eyes air really thie arms and legs that are going to go help all of the customers understand. Try to get their arms around and, you know, hopefully simplify. And what what I said is they need to turn from a bunch of point pieces in the new overall solution. They do that, help me drive innovation and drive by. Visit forward, not trying to manage all of the pieces >> We had talked about it yesterday. I mean, I D c. Says that sixty two percent of customers will have a multi cloud architecture. But for my partner Rico system, it's more interesting. You know that seventy percent of the customers are going to choose a provider to design, architect and manage that infrastructure. So if you think about that seven ten, customers will use one of those global systems integrators and or cloud service writers or, more likely both to deliver on their vision and their outcomes that they need to achieve to change their business models, which is again great for our business. >> How influential are your is your partner ecosystem in terms of some of the announces that we've heard this week? They're out feet on the street there, talking with customers about the challenges that they're having emerging trends. A. M L. What's that sort of center? Just a partner. Feedback loop like that helps Del Technologies, right thruster >> way Run partner advisory boards in each major theater multiple times a year, and these are the exact things we ask them. What tribe trends are you seeing? We map it against our product portfolio in our solutions to identify where there's gaps. Five g's a great example, right? We're looking at where the market's going happen. Have responsibility for a big chunk of our telco vertical as well within the company. So it's a hot topic and, you know, for a while we were. We were honestly lagging in this particular space. If I think back two years ago, we talked Telco, but we didn't walk Telco. We've made a lot of investments over the last two years to build a product business unit specifically around Telco solutions, and I'm proud to say, especially coming out of Mobile World Congress this year that we have arrived. We have incredible products solutions that really are exactly what are partners are looking for and our end user customers looking for. And it's an interesting dynamic because a lot of our partners, our customers. If you think about the telco community that's really gonna embrace and drive five G, we both sell to them and we sell through them. So we love the fact they'LL consume our underlying technology. But more importantly, I love the fact that we can use them as a route to market to expose hundreds thousands of customers to those capabilities in the broader scale. >> Yeah, J that the networking is such a critical component of that service fighter piece. So how much of that solution that you're talking about? Polls in some of the aspects from GM wear, you know, NSX, the SD win. Those pieces seem natural fit to help drive that overall solution. >> Yeah, I would actually tell you that my opinion is probably the first products that we brought to market that were really crossed Company cross collaboration. You know, even before we got to the Del Technologies cloud were exactly what you're talking about. Some of those networking asked it some security assets that vm where has integrated with some of our products server technology to build some integrated telco specific things for the core and the edge, which is really where they're operating specifically around the edge. Fellow cloud is going to be a huge piece of that SD. When we see the telcos, has a huge route to market again for that particular product and as a massive consumer of that particular product, we understand they have to cannibalize some of their own business. But it's the way the markets going. So the answer is yes. We're seeing great integration, great collaboration between our product business unit under cabin, Kevin Shots Camera in Telco and his V M or counterparts. And I think I said his name right there, too. >> Yeah, I had to interview him once, and absolutely nothing I'm getting that right was tough. You know, one of things always at the show is just the feedback that you get from from customers and from from your partners. So gives the mood, you know, Where are they? What are some of, you know, key opportunities, challenges? What? What's top of mind issues for? >> I'm telling you like I can't make this up. The mood is off the chart, right? They've said consistently best sessions ever. I was talking to one particular partner last night. I won't say his name, but he's worked in this industry for thirty years. He's worked for major companies ASAP. Adobe, Microsoft. This is his first time Adele Technologies world working as a partner of ours, he said. Hands down. This is the best partner driven partner content partner event I've ever seen in the industry. So excited about the focus Del Technologies has as a company on our ecosystem and the types of conversations we're having to actually not just sell to us, but sell through us, right? We're really, I think we've really worked hard to view our partners not as customers, but truly as partners. It's all about the business. We build together, not about the business we do together. If that makes sense, right >> well, that trust trusting relationship is absolutely table stakes. It is for an organization. It sounds like you guys have really done a tremendous amount of work in the last few years to get that to the highest level that it's ever been on. >> I would agree. I think we've come a long way from where we were. We have a lot more work to do it .'LL never end, but I'm super excited with what achieved. I think our partners are, too, because the results they're getting are fantastic. I talked about the profitability of our business and their business together, which means what we're selling has value, which is fantastic as well. So it's good to know that we're not just winning in the market, but we're winning with high value, and again it gets back to where this conversation started, which is everyone talking about transformation and outcomes. It's hard to deliver value if you're not delivering an outcome or vice versa, right >> J. One of the areas that I I think your partner's and the solutions that your help bringing to market what would have some good opinion on is this move from kind of the Catholics, the optics model, you know, one of things. We look at the cloud announcements and it's like, Okay, wait, which of these air as a service? Which one of these he's, you know, can I do financing on and which one of these you know are mostly built on hardware? We're just that fit in the overall discussion, and it's what what do you get feedback from your partners and to cultivate that >> users? It's literally in every single conversation we have. So I can't think of a particular partner conversation that doesn't send around a variety of things. One is always our technology. One is our go to market engine and how we can leverage that and the other is commercials. And it's not the price. It's the consumption, right? How are we going to consume your technology, CAF, ex office and everything in between? And that everything in between used to be one or two things. Now it's ten or fifteen things right. The models have got very complex and very dynamic, so it's top of mind. And the beautiful thing is, you know, a few years ago the only way to get a consumption model on as a service model. It was through my partner Rico system. Now Dell's done a good job to catch up to some degree. But to truly deliver what a lot of the customers air accident for, which is pure op X, no caf X pays you grow. Models were still leveraging heavily our partner ecosystem to Babel. Deliver that, and the challenge for us is to be able to keep up with them, right? They're moving at such a rapid pace and the dynamics of those models Archangel. We have to evolve too quickly to be able to offer what our competitors aire doing. I'm excited to say, so far, so good, but we're doing a great job of that. But I would I would agree with you, right? The commercial model, The consumption models are top of mind, and every conversation had to today right on how we're going to structure these things. And it's really exciting, right? Because when we do it right, it tends to be not only great for Dell and great for the partner, but great for the customer. So it really is. It's the classic win win win. >> Are you know, one of the things that it seems that Dell has been technologies working to Dio for awhile now has become this sort of one stop shop for all things partners. Are they looking to have that single trusted source Do they appreciate now that they've got that, that they can really go today l technologies and enable their customers and your customers to transform security work for us? We heard a lot about work first. Urination, >> very common, >> are they now seeing Dallas? This Hey, this is this really a one stop shop. We can actually deliver everything that our customers are looking for. >> They're definitely seeing because we're telling it to him all the time, right? But yes, the answers without question, I think one of the big drivers for our business has been the ability to aggregate the breath of Del Technologies and bring the full portfolio to beer to them. I'd love to see them all standardised on us exclusively. That's my job, right? That's what we do. We try to eliminate white space and own all marketshare. We'LL never get there one hundred percent. But we've seen, you know, we look out of right of metrics in our business. We look at revenue, growth, probability, growth way. Also, look at white space, which is what you're talking about. Have we consume the white space where competitors used to be with inside our partners, and we've seen massive growth there in the last two years significant growth across the board. And the reason is because of what you just described. We now have an economies of scale advantage in a breath of portfolio advantage where it just makes sense for them to bet on us to get what they need, right, whether it's a pivotal capability or of'em were capability or Bhumi capability. When we have that, everybody pointed in the same direction. This story is just so much more powerful and there, and I'm not going to say they're buying it. They're believing it and they're seeing it in the field. So again, I talked about it earlier. If weaken transact at that level at Adele Technologies level, it means more value to our partners. But ultimately they can provide more value to their customers. So they're more profitable or customers get better solutions. So yes, yes, and yes, >> everybody went well. Jay, thank you so much for joining student May assuring the tremendous momentum that you guys have achieved. We look forward to hearing next year. >> I do to >> even better news will be Thanks. Thank you again for joining us. >> Thanks for having me. >> Great to meet you. Thanks, Tio for student a man. I'm Lisa Martin. You're watching us on the Cube. Live from jail technology World twenty nineteen day three of the cubes to set coverage continues after this

>> Live, from Bellevue, Washington, it's theCUBE. Covering Smartsheet Engage 18. Brought to you by Smartsheet. >> Welcome back to theCUBE's continuing coverage of Smartsheet Engage 2018, I am Lisa Martin with Jeff Frick in Bellevue, Washington, our first time here. Second annual Smartsheet Engage and we're very please to be joined, welcoming back to theCUBE, Mark Mader, the CEO of Smartsheet. Mark, it's great to have you on the program. >> Thank you, good to be with you. >> Great job on the keynote. >> Thank you, appreciate it. >> So, you can see the buzz behind us, we just got out of the keynote, where, you guys kicked it up, there was a coupla things Jeff and I were talking about that were unique, that I haven't seen very much of at all, in all the keynotes that we go to. One, you started off with an explorer who had a very empowering, enlightening message, all about communication. And then, something that you did that I thought was really cool, that I don't think I've ever seen, is you actually, during your keynote, went into the audience, where you have about 2000 customers here, representing 1100 companies, across 20 countries, and just ad-libbed, hey guys, tell me about your company, how is Smartsheet empowering you, and as you said, that was all natural. >> I think part of it making it real for somebody, is giving you somebody that's relatable. So, we started off the conversation, as you said, with Ed Viesturs, arguably the most famous accomplished climber in the world, today, and he talked about the importance of communication and preparation, and teamwork, and clear decision making, in a context that was spectacularly visual, right, this mountain and those climbing shots, so, people relate to that, and then when you introduces those conducts in the business setting, it's like, oh, yeah, this applies to me, it applies to all of us. So, the notion of getting into the crowd, in a non-rehearsed way, is to really get people comfortable with, hey, I can share something, I can share an experience, and there's no one right answer, it's my experience. >> And that's why you're here, as you said in your keynote, and we know this as well, if companies aren't designing technology for the users, what's the point? >> Yeah, you're right and, one of the things I tried to highlight was, when you say for the user, it's not just for the user, the end user, like developed by a few people, spread to everybody, but it's empowering each and every person to say, hey I want to do something more transformational. I want to manage, automate, scale it, I don't want to be given that solution by someone, I want to do it. And there are hundreds of millions of people, who have the appetite and the interest, and the need for it. So, that's what we're trying to sell into. >> You know, Mark, we got to, so many shows, right, and everyone's chasing innovation. How do we get more innovative? Especially big companies, right? And you did show two really interesting messages, one, was your kind of core message, empowering everyone to improve, how they work, so, like you said, not just the top level decision makers, not down in the developer weave, but everybody up and down this stack. And then you shared a statement covey quote, really talking about how do people, keep 'em engaged and the way people are engaged is that they feel they're empowered to do something for their clients and their customers. So it's such an importannt piece and I think it's easy to talk about, harder to execute, but what is the answer to innovation? Giving more people the data, the tools and the power to take all that and do something for their customers, and thereby unlock all this tremendous value that you already have in your four doors. >> Absolutely, and I think the point of unlocking, so we have, you have 100% of your workforce. If you empower only 4.3% of them, for instance, the developers in your group, you're leaving so much opportunity on the table. And again, you don't get that unlock or that innovative spirit by just using something. You have to live with it, you have to work with it, you have to wrestle with it, And through that, innovation occurs. Ideas get generated. So, if you can get that ideation happening at the midpoint of your company, not the top 5%, huge opportunity. >> I think you were even quoted in the press release, maybe around the IPO that happened a few months ago, congratulations, >> Thank you. >> In saying that, maybe naysayers in the beginning, when you were a company of six, as you were talking about in your keynote, people thought, you're going to build this on a spreadsheet construct? And you said, but four hundred to five hundred million people know that construct. >> Right, right So you're going into an audience if knowledge workers, of which there's a massive percentage, designing something for lines of business, IT, finance, marketing, sales, who actually need to work with that, we're not talking about API's and developer and code speak, you're building this for a very large percentage of the population. >> We are, and I think when we talk about serving a large population, it's tempting to say, well, they can't handle much, let's go with the most common denominator. Let's give them something super, super simple. The problem is, with simple, you don't always get value. So how do you combine relevance and comfort and understanding, with capability. And the product's changed a lot since the early days, it's no longer just a grid, we have dashboards, we have forms, we have card view, we have all these elements that are now being brought forward, but one thing that we've always respected from the beginning is, don't throw away what somebody understands, and is comfortable with. That doesn't necessarily mean that it's the best, but they know it. And people are very nervous about just jettisoning the things they know, so like, embrace it. And then, what we had talked about earlier, was, how do you really listen to that customer's signal, and say okay, I'm comfortable, I like this, but I want more. And that ability to respond to that request, I think has really helped define who Smartsheet is today. You know, 12 years later. >> The other piece you talked on is kind of sideways off of that, is people have systems already in place, they have tools that they use every day. Right, there's this competition for the top layer of the desktop, but the reality is that we have many, many applications that we have to interact with every day. You guys are really taking a coopation approach with all these existing, >> Absolutely >> where it fits, where it's working, to your point, they're already using it and make it work. Integrate with. Don't try to rip and replace all these other systems that're in there. >> Yeah, and I think, you know you come across so many people in life, who want everything. I need total, complete, presence. And you're really discounting what people appreciate. And I think when you take the view of, I'm going to listen to my client, I'm going to listen to what they love and understand, and I'm going to let them articulate how they want it to work, we are in a very diverse, multi-app world today. If you actually march in somewhere and say, yeah all those decisions you made, those were the wrong decisions, you should trust me on everything, you'll be walked out of the building in about 4.2 seconds. So, we're really living that philosophy, and I think in great partnerships with Google, Microsoft and Slack, and Tableau, and others, we're actually able to demonstrate that. >> Yeah, and then to take it from the concept to reality, a great demo, I'm sure you didn't have this planned a couple of weeks ago, was, you talked about the state of North Carolina, and the preparation and the response to Hurricane Florence, and that they were very quickly able to build a super informative dashboard, to let everybody know who needed to know, what they needed to know. >> Correct. >> And how long did that take to put together? Amazing. >> That was under 24 hours. >> 24 hours? >> And the difference here is the difference between building or developing something, and configuring something. So, the difference there is when you actually build something from scratch, we have bare dirt, we need to put a foundation, we need to build a house, we need to shingle it, we need to insulate, that takes you a long time. So how about, we go to a house that exists, let's change the colors of the blinds, let's put in a certain sofa, let's furnish it. And the configuration element, versus construction, that gives people velocity. Now, what they also want is, they want to actually put their own texture to it, they want to make it their own, so the Department of Transportation dashboard that they produced for FEMA and the Coast Guard and the state governor's office, it didn't look like anybody else's dashboard. It was tailored, but it was so quick to build. And the great thing there was, so many people who accessed that site for information on on runway status and power and fuel, they could focus on the citizens as opposed to what the heck is going on, on the ground. >> Right. >> That provides a lot of purpose to our team, when we see our product used that way. >> You talked about speed just a minute ago, and speed, obviously, every enterprise of whatever size, needs to move and quite a bit quickly, to gain competitive advantage, to increase revenues, et cetera, you guys have some really very eye-catching statistics. That you're enabling customers to achieve. I read, enabling an average business leader to save 300 hours a year, 60,000 hours a year saved across on average organization. That's a big impact. How is speed a factor there? >> Yeah, I think speed I look at in a couple dimensions, One is, is it time saved, but there's also an element which is speed of experimentation So we go into an initiative, we say we have this amazing idea and we're going to have all these returns, we think. (chuckling) Well, not all the bets you place actually makes it. Or actually yields, so if you can empower a team to more quickly experiment, configure, try things, see what works and then double down behind those, if you can run five times as many plays as your competitor, you have five times as many chances to find that next winner. And so when we talk about speed, it's again, velocity of decision making, saving time, but also, organizationally, how can you unlock those possibilities? >> Part of that also is enabling cultural change. Which is not easy, it's essential for digital transformation, we talk about that at every event, and it's true, but how do you put that in action? You and I were chatting off camera about one of your customers that is an 125 year old oil and gas company. How do you enable them to kind of absorb and digest a culture of experimentation so that they can really move their business forward as quickly as they need to? >> Well, I think there's a great quote that one of my mentors early gave me. And it was, "All hat, no cattle." And the "All hat, no cattle" refers to the person who talks about how big their ranch is and how big their... Where's your herd? So you can talk a lot, but you have to demonstrate it. So when they go in, and there was another gentleman who talked about this idea of transforming their implementations across 300 project managers, and the quote was, we're going to get you up and running in two to three weeks, and he goes, "Never. No chance." Now, he ended up working with us, and we proved it to him and when you get a win like that, and you can demonstrate speed and impact, those things carry a lot of weight in organizations, but you have to show evidence. And when you talk about why we're landing and expanding in some of the world's largest brands, it's not because we're just talkin' a big game, it's because you're able to demonstrate those wins, and those lead to further growth. >> Right. And then you topped it off with a bit about the catalysts. But even more, I liked the concept of the point guard. Good point guards make everybody else on the team better. They do a little bit on their own, they hit a couple key shots, but they make everybody else better. And you're seeing that in terms of the expansion, and just in the way your go to market is, you don't come in usually as a big enterprise sale, I don't think, you come in small, you come in a group level, and then let the catalyst let those point guards, built successful in their own team, and then branch it out to a broader audience. >> Yeah, and I'm a big believer, and I don't think people can be classified into catalysts and non-catalysts. That's a very sort of blunt force approach. I view it as, you've catalysts, you've catalysts that haven't been unlocked, and then you have people that aren't catalysts. But very often that point guard, is going to activate the power forward, the center and holy smokes, where did that come from? And what we see is, when we see this growth happening in companies, those players around that point guard, get lit, get sparked, and once they're sparked, it's on. And then we see that growth happen for a long, long time. >> We saw some of that quotes, quotes >> We did. (all speaking at once) >> Queen of the world? >> Queen of the world. That's a big statement. >> That's empowerment, right there. >> It is empowerment. >> And the one where, I tweeted this, one of the quotes, I won't share this product name, but it can actually seem smart, she can help reduce work place anxiety. >> Anxiety! >> Which everybody needs. So, it's been six months since the IPO, you have doubled your attendance in your second year only, at Engage, up here in Bellevue, Washington, What are some of the exciting things that you anounced this morning, that have been fueled by the momentum of the IPO has as I imagine, ignited? >> Yeah, couple big things, is we, at every tech conference, you're going to hear about new capabilities. Here are the new bells and whistles and features and capabilities we have. But what we're hearing from customers, they also want us to frame those capabilites and things that are consumable. So, not everybody wants to configure or build as we talked about earlier today, they say I have a need, it's specific to this area, and do you have something for me. More turnkey, like that gentleman I said, two to three weeks to turn and sold him my implementation team. So those are being referred to as accelerators. So we announced a few new accelerators today in the sales realm, in terms of being able to better manage engagement plans with prospects and clients and on sophisticated deals it's a very common thing. And the other piece that I think is really important is, not just talking about business users, which is a huge focus for us, but also how do we better support IT and their needs to regulate, control, have visibility and to how Smartsheet is used. So, those were a couple of highlights, and then the ability to give people more controls over how they share their data. There've been some issues in the news recently, where people have shared too broadly, they've said that's the issue, so we're hearing from our customers, give us some more fine gated controls and confidence over how our corporate information is shared with others. Well, Mark Mader, I wish we had more time, but we thank you so much for stopping by theCUBE, and chatting with Jeff and me. >> Great to see you. >> Great momentum, we look forward to a number of your execs and customers and analysts on the program tonight. >> Great, thank you. >> Thank you, good to see you. >> Thanks, Mark, good to see you again. >> We just want to thank you for watching theCUBE, I'm Lisa Martin with Jeff Frick live from Smartsheet Engage 2018. Stick around, Jeff and I will be right back with our next guest. (techno music)

>> There's a movement that's sweeping across businesses everywhere here in this country and around the world. And it's all about data. Today businesses are being inundated with data. To the tune of over two and a half million gigabytes that'll be generated in the next 60 seconds alone. What do you do with all that data? To extract insights you typically turn to a data scientist. But not necessarily anymore. At least not exclusively. Today the ability to extract value from data is becoming a shared mission. A team effort that spans the organization extending far more widely than ever before. Today, data science is being democratized. >> Data Sciences for All: It's a Whole New Game. >> Welcome everyone, I'm Katie Linendoll. I'm a technology expert writer and I love reporting on all things tech. My fascination with tech started very young. I began coding when I was 12. Received my networking certs by 18 and a degree in IT and new media from Rochester Institute of Technology. So as you can tell, technology has always been a sure passion of mine. Having grown up in the digital age, I love having a career that keeps me at the forefront of science and technology innovations. I spend equal time in the field being hands on as I do on my laptop conducting in depth research. Whether I'm diving underwater with NASA astronauts, witnessing the new ways which mobile technology can help rebuild the Philippine's economy in the wake of super typhoons, or sharing a first look at the newest iPhones on The Today Show, yesterday, I'm always on the hunt for the latest and greatest tech stories. And that's what brought me here. I'll be your host for the next hour and as we explore the new phenomenon that is taking businesses around the world by storm. And data science continues to become democratized and extends beyond the domain of the data scientist. And why there's also a mandate for all of us to become data literate. Now that data science for all drives our AI culture. And we're going to be able to take to the streets and go behind the scenes as we uncover the factors that are fueling this phenomenon and giving rise to a movement that is reshaping how businesses leverage data. And putting organizations on the road to AI. So coming up, I'll be doing interviews with data scientists. We'll see real world demos and take a look at how IBM is changing the game with an open data science platform. We'll also be joined by legendary statistician Nate Silver, founder and editor-in-chief of FiveThirtyEight. Who will shed light on how a data driven mindset is changing everything from business to our culture. We also have a few people who are joining us in our studio, so thank you guys for joining us. Come on, I can do better than that, right? Live studio audience, the fun stuff. And for all of you during the program, I want to remind you to join that conversation on social media using the hashtag DSforAll, it's data science for all. Share your thoughts on what data science and AI means to you and your business. And, let's dive into a whole new game of data science. Now I'd like to welcome my co-host General Manager IBM Analytics, Rob Thomas. >> Hello, Katie. >> Come on guys. >> Yeah, seriously. >> No one's allowed to be quiet during this show, okay? >> Right. >> Or, I'll start calling people out. So Rob, thank you so much. I think you know this conversation, we're calling it a data explosion happening right now. And it's nothing new. And when you and I chatted about it. You've been talking about this for years. You have to ask, is this old news at this point? >> Yeah, I mean, well first of all, the data explosion is not coming, it's here. And everybody's in the middle of it right now. What is different is the economics have changed. And the scale and complexity of the data that organizations are having to deal with has changed. And to this day, 80% of the data in the world still sits behind corporate firewalls. So, that's becoming a problem. It's becoming unmanageable. IT struggles to manage it. The business can't get everything they need. Consumers can't consume it when they want. So we have a challenge here. >> It's challenging in the world of unmanageable. Crazy complexity. If I'm sitting here as an IT manager of my business, I'm probably thinking to myself, this is incredibly frustrating. How in the world am I going to get control of all this data? And probably not just me thinking it. Many individuals here as well. >> Yeah, indeed. Everybody's thinking about how am I going to put data to work in my organization in a way I haven't done before. Look, you've got to have the right expertise, the right tools. The other thing that's happening in the market right now is clients are dealing with multi cloud environments. So data behind the firewall in private cloud, multiple public clouds. And they have to find a way. How am I going to pull meaning out of this data? And that brings us to data science and AI. That's how you get there. >> I understand the data science part but I think we're all starting to hear more about AI. And it's incredible that this buzz word is happening. How do businesses adopt to this AI growth and boom and trend that's happening in this world right now? >> Well, let me define it this way. Data science is a discipline. And machine learning is one technique. And then AI puts both machine learning into practice and applies it to the business. So this is really about how getting your business where it needs to go. And to get to an AI future, you have to lay a data foundation today. I love the phrase, "there's no AI without IA." That means you're not going to get to AI unless you have the right information architecture to start with. >> Can you elaborate though in terms of how businesses can really adopt AI and get started. >> Look, I think there's four things you have to do if you're serious about AI. One is you need a strategy for data acquisition. Two is you need a modern data architecture. Three is you need pervasive automation. And four is you got to expand job roles in the organization. >> Data acquisition. First pillar in this you just discussed. Can we start there and explain why it's so critical in this process? >> Yeah, so let's think about how data acquisition has evolved through the years. 15 years ago, data acquisition was about how do I get data in and out of my ERP system? And that was pretty much solved. Then the mobile revolution happens. And suddenly you've got structured and non-structured data. More than you've ever dealt with. And now you get to where we are today. You're talking terabytes, petabytes of data. >> [Katie] Yottabytes, I heard that word the other day. >> I heard that too. >> Didn't even know what it meant. >> You know how many zeros that is? >> I thought we were in Star Wars. >> Yeah, I think it's a lot of zeroes. >> Yodabytes, it's new. >> So, it's becoming more and more complex in terms of how you acquire data. So that's the new data landscape that every client is dealing with. And if you don't have a strategy for how you acquire that and manage it, you're not going to get to that AI future. >> So a natural segue, if you are one of these businesses, how do you build for the data landscape? >> Yeah, so the question I always hear from customers is we need to evolve our data architecture to be ready for AI. And the way I think about that is it's really about moving from static data repositories to more of a fluid data layer. >> And we continue with the architecture. New data architecture is an interesting buzz word to hear. But it's also one of the four pillars. So if you could dive in there. >> Yeah, I mean it's a new twist on what I would call some core data science concepts. For example, you have to leverage tools with a modern, centralized data warehouse. But your data warehouse can't be stagnant to just what's right there. So you need a way to federate data across different environments. You need to be able to bring your analytics to the data because it's most efficient that way. And ultimately, it's about building an optimized data platform that is designed for data science and AI. Which means it has to be a lot more flexible than what clients have had in the past. >> All right. So we've laid out what you need for driving automation. But where does the machine learning kick in? >> Machine learning is what gives you the ability to automate tasks. And I think about machine learning. It's about predicting and automating. And this will really change the roles of data professionals and IT professionals. For example, a data scientist cannot possibly know every algorithm or every model that they could use. So we can automate the process of algorithm selection. Another example is things like automated data matching. Or metadata creation. Some of these things may not be exciting but they're hugely practical. And so when you think about the real use cases that are driving return on investment today, it's things like that. It's automating the mundane tasks. >> Let's go ahead and come back to something that you mentioned earlier because it's fascinating to be talking about this AI journey, but also significant is the new job roles. And what are those other participants in the analytics pipeline? >> Yeah I think we're just at the start of this idea of new job roles. We have data scientists. We have data engineers. Now you see machine learning engineers. Application developers. What's really happening is that data scientists are no longer allowed to work in their own silo. And so the new job roles is about how does everybody have data first in their mind? And then they're using tools to automate data science, to automate building machine learning into applications. So roles are going to change dramatically in organizations. >> I think that's confusing though because we have several organizations who saying is that highly specialized roles, just for data science? Or is it applicable to everybody across the board? >> Yeah, and that's the big question, right? Cause everybody's thinking how will this apply? Do I want this to be just a small set of people in the organization that will do this? But, our view is data science has to for everybody. It's about bring data science to everybody as a shared mission across the organization. Everybody in the company has to be data literate. And participate in this journey. >> So overall, group effort, has to be a common goal, and we all need to be data literate across the board. >> Absolutely. >> Done deal. But at the end of the day, it's kind of not an easy task. >> It's not. It's not easy but it's maybe not as big of a shift as you would think. Because you have to put data in the hands of people that can do something with it. So, it's very basic. Give access to data. Data's often locked up in a lot of organizations today. Give people the right tools. Embrace the idea of choice or diversity in terms of those tools. That gets you started on this path. >> It's interesting to hear you say essentially you need to train everyone though across the board when it comes to data literacy. And I think people that are coming into the work force don't necessarily have a background or a degree in data science. So how do you manage? >> Yeah, so in many cases that's true. I will tell you some universities are doing amazing work here. One example, University of California Berkeley. They offer a course for all majors. So no matter what you're majoring in, you have a course on foundations of data science. How do you bring data science to every role? So it's starting to happen. We at IBM provide data science courses through CognitiveClass.ai. It's for everybody. It's free. And look, if you want to get your hands on code and just dive right in, you go to datascience.ibm.com. The key point is this though. It's more about attitude than it is aptitude. I think anybody can figure this out. But it's about the attitude to say we're putting data first and we're going to figure out how to make this real in our organization. >> I also have to give a shout out to my alma mater because I have heard that there is an offering in MS in data analytics. And they are always on the forefront of new technologies and new majors and on trend. And I've heard that the placement behind those jobs, people graduating with the MS is high. >> I'm sure it's very high. >> So go Tigers. All right, tangential. Let me get back to something else you touched on earlier because you mentioned that a number of customers ask you how in the world do I get started with AI? It's an overwhelming question. Where do you even begin? What do you tell them? >> Yeah, well things are moving really fast. But the good thing is most organizations I see, they're already on the path, even if they don't know it. They might have a BI practice in place. They've got data warehouses. They've got data lakes. Let me give you an example. AMC Networks. They produce a lot of the shows that I'm sure you watch Katie. >> [Katie] Yes, Breaking Bad, Walking Dead, any fans? >> [Rob] Yeah, we've got a few. >> [Katie] Well you taught me something I didn't even know. Because it's amazing how we have all these different industries, but yet media in itself is impacted too. And this is a good example. >> Absolutely. So, AMC Networks, think about it. They've got ads to place. They want to track viewer behavior. What do people like? What do they dislike? So they have to optimize every aspect of their business from marketing campaigns to promotions to scheduling to ads. And their goal was transform data into business insights and really take the burden off of their IT team that was heavily burdened by obviously a huge increase in data. So their VP of BI took the approach of using machine learning to process large volumes of data. They used a platform that was designed for AI and data processing. It's the IBM analytics system where it's a data warehouse, data science tools are built in. It has in memory data processing. And just like that, they were ready for AI. And they're already seeing that impact in their business. >> Do you think a movement of that nature kind of presses other media conglomerates and organizations to say we need to be doing this too? >> I think it's inevitable that everybody, you're either going to be playing, you're either going to be leading, or you'll be playing catch up. And so, as we talk to clients we think about how do you start down this path now, even if you have to iterate over time? Because otherwise you're going to wake up and you're going to be behind. >> One thing worth noting is we've talked about analytics to the data. It's analytics first to the data, not the other way around. >> Right. So, look. We as a practice, we say you want to bring data to where the data sits. Because it's a lot more efficient that way. It gets you better outcomes in terms of how you train models and it's more efficient. And we think that leads to better outcomes. Other organization will say, "Hey move the data around." And everything becomes a big data movement exercise. But once an organization has started down this path, they're starting to get predictions, they want to do it where it's really easy. And that means analytics applied right where the data sits. >> And worth talking about the role of the data scientist in all of this. It's been called the hot job of the decade. And a Harvard Business Review even dubbed it the sexiest job of the 21st century. >> Yes. >> I want to see this on the cover of Vogue. Like I want to see the first data scientist. Female preferred, on the cover of Vogue. That would be amazing. >> Perhaps you can. >> People agree. So what changes for them? Is this challenging in terms of we talk data science for all. Where do all the data science, is it data science for everyone? And how does it change everything? >> Well, I think of it this way. AI gives software super powers. It really does. It changes the nature of software. And at the center of that is data scientists. So, a data scientist has a set of powers that they've never had before in any organization. And that's why it's a hot profession. Now, on one hand, this has been around for a while. We've had actuaries. We've had statisticians that have really transformed industries. But there are a few things that are new now. We have new tools. New languages. Broader recognition of this need. And while it's important to recognize this critical skill set, you can't just limit it to a few people. This is about scaling it across the organization. And truly making it accessible to all. >> So then do we need more data scientists? Or is this something you train like you said, across the board? >> Well, I think you want to do a little bit of both. We want more. But, we can also train more and make the ones we have more productive. The way I think about it is there's kind of two markets here. And we call it clickers and coders. >> [Katie] I like that. That's good. >> So, let's talk about what that means. So clickers are basically somebody that wants to use tools. Create models visually. It's drag and drop. Something that's very intuitive. Those are the clickers. Nothing wrong with that. It's been valuable for years. There's a new crop of data scientists. They want to code. They want to build with the latest open source tools. They want to write in Python or R. These are the coders. And both approaches are viable. Both approaches are critical. Organizations have to have a way to meet the needs of both of those types. And there's not a lot of things available today that do that. >> Well let's keep going on that. Because I hear you talking about the data scientists role and how it's critical to success, but with the new tools, data science and analytics skills can extend beyond the domain of just the data scientist. >> That's right. So look, we're unifying coders and clickers into a single platform, which we call IBM Data Science Experience. And as the demand for data science expertise grows, so does the need for these kind of tools. To bring them into the same environment. And my view is if you have the right platform, it enables the organization to collaborate. And suddenly you've changed the nature of data science from an individual sport to a team sport. >> So as somebody that, my background is in IT, the question is really is this an additional piece of what IT needs to do in 2017 and beyond? Or is it just another line item to the budget? >> So I'm afraid that some people might view it that way. As just another line item. But, I would challenge that and say data science is going to reinvent IT. It's going to change the nature of IT. And every organization needs to think about what are the skills that are critical? How do we engage a broader team to do this? Because once they get there, this is the chance to reinvent how they're performing IT. >> [Katie] Challenging or not? >> Look it's all a big challenge. Think about everything IT organizations have been through. Some of them were late to things like mobile, but then they caught up. Some were late to cloud, but then they caught up. I would just urge people, don't be late to data science. Use this as your chance to reinvent IT. Start with this notion of clickers and coders. This is a seminal moment. Much like mobile and cloud was. So don't be late. >> And I think it's critical because it could be so costly to wait. And Rob and I were even chatting earlier how data analytics is just moving into all different kinds of industries. And I can tell you even personally being effected by how important the analysis is in working in pediatric cancer for the last seven years. I personally implement virtual reality headsets to pediatric cancer hospitals across the country. And it's great. And it's working phenomenally. And the kids are amazed. And the staff is amazed. But the phase two of this project is putting in little metrics in the hardware that gather the breathing, the heart rate to show that we have data. Proof that we can hand over to the hospitals to continue making this program a success. So just in-- >> That's a great example. >> An interesting example. >> Saving lives? >> Yes. >> That's also applying a lot of what we talked about. >> Exciting stuff in the world of data science. >> Yes. Look, I just add this is an existential moment for every organization. Because what you do in this area is probably going to define how competitive you are going forward. And think about if you don't do something. What if one of your competitors goes and creates an application that's more engaging with clients? So my recommendation is start small. Experiment. Learn. Iterate on projects. Define the business outcomes. Then scale up. It's very doable. But you've got to take the first step. >> First step always critical. And now we're going to get to the fun hands on part of our story. Because in just a moment we're going to take a closer look at what data science can deliver. And where organizations are trying to get to. All right. Thank you Rob and now we've been joined by Siva Anne who is going to help us navigate this demo. First, welcome Siva. Give him a big round of applause. Yeah. All right, Rob break down what we're going to be looking at. You take over this demo. >> All right. So this is going to be pretty interesting. So Siva is going to take us through. So he's going to play the role of a financial adviser. Who wants to help better serve clients through recommendations. And I'm going to really illustrate three things. One is how do you federate data from multiple data sources? Inside the firewall, outside the firewall. How do you apply machine learning to predict and to automate? And then how do you move analytics closer to your data? So, what you're seeing here is a custom application for an investment firm. So, Siva, our financial adviser, welcome. So you can see at the top, we've got market data. We pulled that from an external source. And then we've got Siva's calendar in the middle. He's got clients on the right side. So page down, what else do you see down there Siva? >> [Siva] I can see the recent market news. And in here I can see that JP Morgan is calling for a US dollar rebound in the second half of the year. And, I have upcoming meeting with Leo Rakes. I can get-- >> [Rob] So let's go in there. Why don't you click on Leo Rakes. So, you're sitting at your desk, you're deciding how you're going to spend the day. You know you have a meeting with Leo. So you click on it. You immediately see, all right, so what do we know about him? We've got data governance implemented. So we know his age, we know his degree. We can see he's not that aggressive of a trader. Only six trades in the last few years. But then where it gets interesting is you go to the bottom. You start to see predicted industry affinity. Where did that come from? How do we have that? >> [Siva] So these green lines and red arrows here indicate the trending affinity of Leo Rakes for particular industry stocks. What we've done here is we've built machine learning models using customer's demographic data, his stock portfolios, and browsing behavior to build a model which can predict his affinity for a particular industry. >> [Rob] Interesting. So, I like to think of this, we call it celebrity experiences. So how do you treat every customer like they're a celebrity? So to some extent, we're reading his mind. Because without asking him, we know that he's going to have an affinity for auto stocks. So we go down. Now we look at his portfolio. You can see okay, he's got some different holdings. He's got Amazon, Google, Apple, and then he's got RACE, which is the ticker for Ferrari. You can see that's done incredibly well. And so, as a financial adviser, you look at this and you say, all right, we know he loves auto stocks. Ferrari's done very well. Let's create a hedge. Like what kind of security would interest him as a hedge against his position for Ferrari? Could we go figure that out? >> [Siva] Yes. Given I know that he's gotten an affinity for auto stocks, and I also see that Ferrari has got some terminus gains, I want to lock in these gains by hedging. And I want to do that by picking a auto stock which has got negative correlation with Ferrari. >> [Rob] So this is where we get to the idea of in database analytics. Cause you start clicking that and immediately we're getting instant answers of what's happening. So what did we find here? We're going to compare Ferrari and Honda. >> [Siva] I'm going to compare Ferrari with Honda. And what I see here instantly is that Honda has got a negative correlation with Ferrari, which makes it a perfect mix for his stock portfolio. Given he has an affinity for auto stocks and it correlates negatively with Ferrari. >> [Rob] These are very powerful tools at the hand of a financial adviser. You think about it. As a financial adviser, you wouldn't think about federating data, machine learning, pretty powerful. >> [Siva] Yes. So what we have seen here is that using the common SQL engine, we've been able to federate queries across multiple data sources. Db2 Warehouse in the cloud, IBM's Integrated Analytic System, and Hortonworks powered Hadoop platform for the new speeds. We've been able to use machine learning to derive innovative insights about his stock affinities. And drive the machine learning into the appliance. Closer to where the data resides to deliver high performance analytics. >> [Rob] At scale? >> [Siva] We're able to run millions of these correlations across stocks, currency, other factors. And even score hundreds of customers for their affinities on a daily basis. >> That's great. Siva, thank you for playing the role of financial adviser. So I just want to recap briefly. Cause this really powerful technology that's really simple. So we federated, we aggregated multiple data sources from all over the web and internal systems. And public cloud systems. Machine learning models were built that predicted Leo's affinity for a certain industry. In this case, automotive. And then you see when you deploy analytics next to your data, even a financial adviser, just with the click of a button is getting instant answers so they can go be more productive in their next meeting. This whole idea of celebrity experiences for your customer, that's available for everybody, if you take advantage of these types of capabilities. Katie, I'll hand it back to you. >> Good stuff. Thank you Rob. Thank you Siva. Powerful demonstration on what we've been talking about all afternoon. And thank you again to Siva for helping us navigate. Should be give him one more round of applause? We're going to be back in just a moment to look at how we operationalize all of this data. But in first, here's a message from me. If you're a part of a line of business, your main fear is disruption. You know data is the new goal that can create huge amounts of value. So does your competition. And they may be beating you to it. You're convinced there are new business models and revenue sources hidden in all the data. You just need to figure out how to leverage it. But with the scarcity of data scientists, you really can't rely solely on them. You may need more people throughout the organization that have the ability to extract value from data. And as a data science leader or data scientist, you have a lot of the same concerns. You spend way too much time looking for, prepping, and interpreting data and waiting for models to train. You know you need to operationalize the work you do to provide business value faster. What you want is an easier way to do data prep. And rapidly build models that can be easily deployed, monitored and automatically updated. So whether you're a data scientist, data science leader, or in a line of business, what's the solution? What'll it take to transform the way you work? That's what we're going to explore next. All right, now it's time to delve deeper into the nuts and bolts. The nitty gritty of operationalizing data science and creating a data driven culture. How do you actually do that? Well that's what these experts are here to share with us. I'm joined by Nir Kaldero, who's head of data science at Galvanize, which is an education and training organization. Tricia Wang, who is co-founder of Sudden Compass, a consultancy that helps companies understand people with data. And last, but certainly not least, Michael Li, founder and CEO of Data Incubator, which is a data science train company. All right guys. Shall we get right to it? >> All right. >> So data explosion happening right now. And we are seeing it across the board. I just shared an example of how it's impacting my philanthropic work in pediatric cancer. But you guys each have so many unique roles in your business life. How are you seeing it just blow up in your fields? Nir, your thing? >> Yeah, for example like in Galvanize we train many Fortune 500 companies. And just by looking at the demand of companies that wants us to help them go through this digital transformation is mind-blowing. Data point by itself. >> Okay. Well what we're seeing what's going on is that data science like as a theme, is that it's actually for everyone now. But what's happening is that it's actually meeting non technical people. But what we're seeing is that when non technical people are implementing these tools or coming at these tools without a base line of data literacy, they're often times using it in ways that distance themselves from the customer. Because they're implementing data science tools without a clear purpose, without a clear problem. And so what we do at Sudden Compass is that we work with companies to help them embrace and understand the complexity of their customers. Because often times they are misusing data science to try and flatten their understanding of the customer. As if you can just do more traditional marketing. Where you're putting people into boxes. And I think the whole ROI of data is that you can now understand people's relationships at a much more complex level at a greater scale before. But we have to do this with basic data literacy. And this has to involve technical and non technical people. >> Well you can have all the data in the world, and I think it speaks to, if you're not doing the proper movement with it, forget it. It means nothing at the same time. >> No absolutely. I mean, I think that when you look at the huge explosion in data, that comes with it a huge explosion in data experts. Right, we call them data scientists, data analysts. And sometimes they're people who are very, very talented, like the people here. But sometimes you have people who are maybe re-branding themselves, right? Trying to move up their title one notch to try to attract that higher salary. And I think that that's one of the things that customers are coming to us for, right? They're saying, hey look, there are a lot of people that call themselves data scientists, but we can't really distinguish. So, we have sort of run a fellowship where you help companies hire from a really talented group of folks, who are also truly data scientists and who know all those kind of really important data science tools. And we also help companies internally. Fortune 500 companies who are looking to grow that data science practice that they have. And we help clients like McKinsey, BCG, Bain, train up their customers, also their clients, also their workers to be more data talented. And to build up that data science capabilities. >> And Nir, this is something you work with a lot. A lot of Fortune 500 companies. And when we were speaking earlier, you were saying many of these companies can be in a panic. >> Yeah. >> Explain that. >> Yeah, so you know, not all Fortune 500 companies are fully data driven. And we know that the winners in this fourth industrial revolution, which I like to call the machine intelligence revolution, will be companies who navigate and transform their organization to unlock the power of data science and machine learning. And the companies that are not like that. Or not utilize data science and predictive power well, will pretty much get shredded. So they are in a panic. >> Tricia, companies have to deal with data behind the firewall and in the new multi cloud world. How do organizations start to become driven right to the core? >> I think the most urgent question to become data driven that companies should be asking is how do I bring the complex reality that our customers are experiencing on the ground in to a corporate office? Into the data models. So that question is critical because that's how you actually prevent any big data disasters. And that's how you leverage big data. Because when your data models are really far from your human models, that's when you're going to do things that are really far off from how, it's going to not feel right. That's when Tesco had their terrible big data disaster that they're still recovering from. And so that's why I think it's really important to understand that when you implement big data, you have to further embrace thick data. The qualitative, the emotional stuff, that is difficult to quantify. But then comes the difficult art and science that I think is the next level of data science. Which is that getting non technical and technical people together to ask how do we find those unknown nuggets of insights that are difficult to quantify? Then, how do we do the next step of figuring out how do you mathematically scale those insights into a data model? So that actually is reflective of human understanding? And then we can start making decisions at scale. But you have to have that first. >> That's absolutely right. And I think that when we think about what it means to be a data scientist, right? I always think about it in these sort of three pillars. You have the math side. You have to have that kind of stats, hardcore machine learning background. You have the programming side. You don't work with small amounts of data. You work with large amounts of data. You've got to be able to type the code to make those computers run. But then the last part is that human element. You have to understand the domain expertise. You have to understand what it is that I'm actually analyzing. What's the business proposition? And how are the clients, how are the users actually interacting with the system? That human element that you were talking about. And I think having somebody who understands all of those and not just in isolation, but is able to marry that understanding across those different topics, that's what makes a data scientist. >> But I find that we don't have people with those skill sets. And right now the way I see teams being set up inside companies is that they're creating these isolated data unicorns. These data scientists that have graduated from your programs, which are great. But, they don't involve the people who are the domain experts. They don't involve the designers, the consumer insight people, the people, the salespeople. The people who spend time with the customers day in and day out. Somehow they're left out of the room. They're consulted, but they're not a stakeholder. >> Can I actually >> Yeah, yeah please. >> Can I actually give a quick example? So for example, we at Galvanize train the executives and the managers. And then the technical people, the data scientists and the analysts. But in order to actually see all of the RY behind the data, you also have to have a creative fluid conversation between non technical and technical people. And this is a major trend now. And there's a major gap. And we need to increase awareness and kind of like create a new, kind of like environment where technical people also talks seamlessly with non technical ones. >> [Tricia] We call-- >> That's one of the things that we see a lot. Is one of the trends in-- >> A major trend. >> data science training is it's not just for the data science technical experts. It's not just for one type of person. So a lot of the training we do is sort of data engineers. People who are more on the software engineering side learning more about the stats of math. And then people who are sort of traditionally on the stat side learning more about the engineering. And then managers and people who are data analysts learning about both. >> Michael, I think you said something that was of interest too because I think we can look at IBM Watson as an example. And working in healthcare. The human component. Because often times we talk about machine learning and AI, and data and you get worried that you still need that human component. Especially in the world of healthcare. And I think that's a very strong point when it comes to the data analysis side. Is there any particular example you can speak to of that? >> So I think that there was this really excellent paper a while ago talking about all the neuro net stuff and trained on textual data. So looking at sort of different corpuses. And they found that these models were highly, highly sexist. They would read these corpuses and it's not because neuro nets themselves are sexist. It's because they're reading the things that we write. And it turns out that we write kind of sexist things. And they would sort of find all these patterns in there that were sort of latent, that had a lot of sort of things that maybe we would cringe at if we sort of saw. And I think that's one of the really important aspects of the human element, right? It's being able to come in and sort of say like, okay, I know what the biases of the system are, I know what the biases of the tools are. I need to figure out how to use that to make the tools, make the world a better place. And like another area where this comes up all the time is lending, right? So the federal government has said, and we have a lot of clients in the financial services space, so they're constantly under these kind of rules that they can't make discriminatory lending practices based on a whole set of protected categories. Race, sex, gender, things like that. But, it's very easy when you train a model on credit scores to pick that up. And then to have a model that's inadvertently sexist or racist. And that's where you need the human element to come back in and say okay, look, you're using the classic example would be zip code, you're using zip code as a variable. But when you look at it, zip codes actually highly correlated with race. And you can't do that. So you may inadvertently by sort of following the math and being a little naive about the problem, inadvertently introduce something really horrible into a model and that's where you need a human element to sort of step in and say, okay hold on. Slow things down. This isn't the right way to go. >> And the people who have -- >> I feel like, I can feel her ready to respond. >> Yes, I'm ready. >> She's like let me have at it. >> And the people here it is. And the people who are really great at providing that human intelligence are social scientists. We are trained to look for bias and to understand bias in data. Whether it's quantitative or qualitative. And I really think that we're going to have less of these kind of problems if we had more integrated teams. If it was a mandate from leadership to say no data science team should be without a social scientist, ethnographer, or qualitative researcher of some kind, to be able to help see these biases. >> The talent piece is actually the most crucial-- >> Yeah. >> one here. If you look about how to enable machine intelligence in organization there are the pillars that I have in my head which is the culture, the talent and the technology infrastructure. And I believe and I saw in working very closely with the Fortune 100 and 200 companies that the talent piece is actually the most important crucial hard to get. >> [Tricia] I totally agree. >> It's absolutely true. Yeah, no I mean I think that's sort of like how we came up with our business model. Companies were basically saying hey, I can't hire data scientists. And so we have a fellowship where we get 2,000 applicants each quarter. We take the top 2% and then we sort of train them up. And we work with hiring companies who then want to hire from that population. And so we're sort of helping them solve that problem. And the other half of it is really around training. Cause with a lot of industries, especially if you're sort of in a more regulated industry, there's a lot of nuances to what you're doing. And the fastest way to develop that data science or AI talent may not necessarily be to hire folks who are coming out of a PhD program. It may be to take folks internally who have a lot of that domain knowledge that you have and get them trained up on those data science techniques. So we've had large insurance companies come to us and say hey look, we hire three or four folks from you a quarter. That doesn't move the needle for us. What we really need is take the thousand actuaries and statisticians that we have and get all of them trained up to become a data scientist and become data literate in this new open source world. >> [Katie] Go ahead. >> All right, ladies first. >> Go ahead. >> Are you sure? >> No please, fight first. >> Go ahead. >> Go ahead Nir. >> So this is actually a trend that we have been seeing in the past year or so that companies kind of like start to look how to upscale and look for talent within the organization. So they can actually move them to become more literate and navigate 'em from analyst to data scientist. And from data scientist to machine learner. So this is actually a trend that is happening already for a year or so. >> Yeah, but I also find that after they've gone through that training in getting people skilled up in data science, the next problem that I get is executives coming to say we've invested in all of this. We're still not moving the needle. We've already invested in the right tools. We've gotten the right skills. We have enough scale of people who have these skills. Why are we not moving the needle? And what I explain to them is look, you're still making decisions in the same way. And you're still not involving enough of the non technical people. Especially from marketing, which is now, the CMO's are much more responsible for driving growth in their companies now. But often times it's so hard to change the old way of marketing, which is still like very segmentation. You know, demographic variable based, and we're trying to move people to say no, you have to understand the complexity of customers and not put them in boxes. >> And I think underlying a lot of this discussion is this question of culture, right? >> Yes. >> Absolutely. >> How do you build a data driven culture? And I think that that culture question, one of the ways that comes up quite often in especially in large, Fortune 500 enterprises, is that they are very, they're not very comfortable with sort of example, open source architecture. Open source tools. And there is some sort of residual bias that that's somehow dangerous. So security vulnerability. And I think that that's part of the cultural challenge that they often have in terms of how do I build a more data driven organization? Well a lot of the talent really wants to use these kind of tools. And I mean, just to give you an example, we are partnering with one of the major cloud providers to sort of help make open source tools more user friendly on their platform. So trying to help them attract the best technologists to use their platform because they want and they understand the value of having that kind of open source technology work seamlessly on their platforms. So I think that just sort of goes to show you how important open source is in this movement. And how much large companies and Fortune 500 companies and a lot of the ones we work with have to embrace that. >> Yeah, and I'm seeing it in our work. Even when we're working with Fortune 500 companies, is that they've already gone through the first phase of data science work. Where I explain it was all about the tools and getting the right tools and architecture in place. And then companies started moving into getting the right skill set in place. Getting the right talent. And what you're talking about with culture is really where I think we're talking about the third phase of data science, which is looking at communication of these technical frameworks so that we can get non technical people really comfortable in the same room with data scientists. That is going to be the phase, that's really where I see the pain point. And that's why at Sudden Compass, we're really dedicated to working with each other to figure out how do we solve this problem now? >> And I think that communication between the technical stakeholders and management and leadership. That's a very critical piece of this. You can't have a successful data science organization without that. >> Absolutely. >> And I think that actually some of the most popular trainings we've had recently are from managers and executives who are looking to say, how do I become more data savvy? How do I figure out what is this data science thing and how do I communicate with my data scientists? >> You guys made this way too easy. I was just going to get some popcorn and watch it play out. >> Nir, last 30 seconds. I want to leave you with an opportunity to, anything you want to add to this conversation? >> I think one thing to conclude is to say that companies that are not data driven is about time to hit refresh and figure how they transition the organization to become data driven. To become agile and nimble so they can actually see what opportunities from this important industrial revolution. Otherwise, unfortunately they will have hard time to survive. >> [Katie] All agreed? >> [Tricia] Absolutely, you're right. >> Michael, Trish, Nir, thank you so much. Fascinating discussion. And thank you guys again for joining us. We will be right back with another great demo. Right after this. >> Thank you Katie. >> Once again, thank you for an excellent discussion. Weren't they great guys? And thank you for everyone who's tuning in on the live webcast. As you can hear, we have an amazing studio audience here. And we're going to keep things moving. I'm now joined by Daniel Hernandez and Siva Anne. And we're going to turn our attention to how you can deliver on what they're talking about using data science experience to do data science faster. >> Thank you Katie. Siva and I are going to spend the next 10 minutes showing you how you can deliver on what they were saying using the IBM Data Science Experience to do data science faster. We'll demonstrate through new features we introduced this week how teams can work together more effectively across the entire analytics life cycle. How you can take advantage of any and all data no matter where it is and what it is. How you could use your favorite tools from open source. And finally how you could build models anywhere and employ them close to where your data is. Remember the financial adviser app Rob showed you? To build an app like that, we needed a team of data scientists, developers, data engineers, and IT staff to collaborate. We do this in the Data Science Experience through a concept we call projects. When I create a new project, I can now use the new Github integration feature. We're doing for data science what we've been doing for developers for years. Distributed teams can work together on analytics projects. And take advantage of Github's version management and change management features. This is a huge deal. Let's explore the project we created for the financial adviser app. As you can see, our data engineer Joane, our developer Rob, and others are collaborating this project. Joane got things started by bringing together the trusted data sources we need to build the app. Taking a closer look at the data, we see that our customer and profile data is stored on our recently announced IBM Integrated Analytics System, which runs safely behind our firewall. We also needed macro economic data, which she was able to find in the Federal Reserve. And she stored it in our Db2 Warehouse on Cloud. And finally, she selected stock news data from NASDAQ.com and landed that in a Hadoop cluster, which happens to be powered by Hortonworks. We added a new feature to the Data Science Experience so that when it's installed with Hortonworks, it automatically uses a need of security and governance controls within the cluster so your data is always secure and safe. Now we want to show you the news data we stored in the Hortonworks cluster. This is the mean administrative console. It's powered by an open source project called Ambari. And here's the news data. It's in parquet files stored in HDFS, which happens to be a distributive file system. To get the data from NASDAQ into our cluster, we used IBM's BigIntegrate and BigQuality to create automatic data pipelines that acquire, cleanse, and ingest that news data. Once the data's available, we use IBM's Big SQL to query that data using SQL statements that are much like the ones we would use for any relation of data, including the data that we have in the Integrated Analytics System and Db2 Warehouse on Cloud. This and the federation capabilities that Big SQL offers dramatically simplifies data acquisition. Now we want to show you how we support a brand new tool that we're excited about. Since we launched last summer, the Data Science Experience has supported Jupyter and R for data analysis and visualization. In this week's update, we deeply integrated another great open source project called Apache Zeppelin. It's known for having great visualization support, advanced collaboration features, and is growing in popularity amongst the data science community. This is an example of Apache Zeppelin and the notebook we created through it to explore some of our data. Notice how wonderful and easy the data visualizations are. Now we want to walk you through the Jupyter notebook we created to explore our customer preference for stocks. We use notebooks to understand and explore data. To identify the features that have some predictive power. Ultimately, we're trying to assess what ultimately is driving customer stock preference. Here we did the analysis to identify the attributes of customers that are likely to purchase auto stocks. We used this understanding to build our machine learning model. For building machine learning models, we've always had tools integrated into the Data Science Experience. But sometimes you need to use tools you already invested in. Like our very own SPSS as well as SAS. Through new import feature, you can easily import those models created with those tools. This helps you avoid vendor lock-in, and simplify the development, training, deployment, and management of all your models. To build the models we used in app, we could have coded, but we prefer a visual experience. We used our customer profile data in the Integrated Analytic System. Used the Auto Data Preparation to cleanse our data. Choose the binary classification algorithms. Let the Data Science Experience evaluate between logistic regression and gradient boosted tree. It's doing the heavy work for us. As you can see here, the Data Science Experience generated performance metrics that show us that the gradient boosted tree is the best performing algorithm for the data we gave it. Once we save this model, it's automatically deployed and available for developers to use. Any application developer can take this endpoint and consume it like they would any other API inside of the apps they built. We've made training and creating machine learning models super simple. But what about the operations? A lot of companies are struggling to ensure their model performance remains high over time. In our financial adviser app, we know that customer data changes constantly, so we need to always monitor model performance and ensure that our models are retrained as is necessary. This is a dashboard that shows the performance of our models and lets our teams monitor and retrain those models so that they're always performing to our standards. So far we've been showing you the Data Science Experience available behind the firewall that we're using to build and train models. Through a new publish feature, you can build models and deploy them anywhere. In another environment, private, public, or anywhere else with just a few clicks. So here we're publishing our model to the Watson machine learning service. It happens to be in the IBM cloud. And also deeply integrated with our Data Science Experience. After publishing and switching to the Watson machine learning service, you can see that our stock affinity and model that we just published is there and ready for use. So this is incredibly important. I just want to say it again. The Data Science Experience allows you to train models behind your own firewall, take advantage of your proprietary and sensitive data, and then deploy those models wherever you want with ease. So summarize what we just showed you. First, IBM's Data Science Experience supports all teams. You saw how our data engineer populated our project with trusted data sets. Our data scientists developed, trained, and tested a machine learning model. Our developers used APIs to integrate machine learning into their apps. And how IT can use our Integrated Model Management dashboard to monitor and manage model performance. Second, we support all data. On premises, in the cloud, structured, unstructured, inside of your firewall, and outside of it. We help you bring analytics and governance to where your data is. Third, we support all tools. The data science tools that you depend on are readily available and deeply integrated. This includes capabilities from great partners like Hortonworks. And powerful tools like our very own IBM SPSS. And fourth, and finally, we support all deployments. You can build your models anywhere, and deploy them right next to where your data is. Whether that's in the public cloud, private cloud, or even on the world's most reliable transaction platform, IBM z. So see for yourself. Go to the Data Science Experience website, take us for a spin. And if you happen to be ready right now, our recently created Data Science Elite Team can help you get started and run experiments alongside you with no charge. Thank you very much. >> Thank you very much Daniel. It seems like a great time to get started. And thanks to Siva for taking us through it. Rob and I will be back in just a moment to add some perspective right after this. All right, once again joined by Rob Thomas. And Rob obviously we got a lot of information here. >> Yes, we've covered a lot of ground. >> This is intense. You got to break it down for me cause I think we zoom out and see the big picture. What better data science can deliver to a business? Why is this so important? I mean we've heard it through and through. >> Yeah, well, I heard it a couple times. But it starts with businesses have to embrace a data driven culture. And it is a change. And we need to make data accessible with the right tools in a collaborative culture because we've got diverse skill sets in every organization. But data driven companies succeed when data science tools are in the hands of everyone. And I think that's a new thought. I think most companies think just get your data scientist some tools, you'll be fine. This is about tools in the hands of everyone. I think the panel did a great job of describing about how we get to data science for all. Building a data culture, making it a part of your everyday operations, and the highlights of what Daniel just showed us, that's some pretty cool features for how organizations can get to this, which is you can see IBM's Data Science Experience, how that supports all teams. You saw data analysts, data scientists, application developer, IT staff, all working together. Second, you saw how we support all tools. And your choice of tools. So the most popular data science libraries integrated into one platform. And we saw some new capabilities that help companies avoid lock-in, where you can import existing models created from specialist tools like SPSS or others. And then deploy them and manage them inside of Data Science Experience. That's pretty interesting. And lastly, you see we continue to build on this best of open tools. Partnering with companies like H2O, Hortonworks, and others. Third, you can see how you use all data no matter where it lives. That's a key challenge every organization's going to face. Private, public, federating all data sources. We announced new integration with the Hortonworks data platform where we deploy machine learning models where your data resides. That's been a key theme. Analytics where the data is. And lastly, supporting all types of deployments. Deploy them in your Hadoop cluster. Deploy them in your Integrated Analytic System. Or deploy them in z, just to name a few. A lot of different options here. But look, don't believe anything I say. Go try it for yourself. Data Science Experience, anybody can use it. Go to datascience.ibm.com and look, if you want to start right now, we just created a team that we call Data Science Elite. These are the best data scientists in the world that will come sit down with you and co-create solutions, models, and prove out a proof of concept. >> Good stuff. Thank you Rob. So you might be asking what does an organization look like that embraces data science for all? And how could it transform your role? I'm going to head back to the office and check it out. Let's start with the perspective of the line of business. What's changed? Well, now you're starting to explore new business models. You've uncovered opportunities for new revenue sources and all that hidden data. And being disrupted is no longer keeping you up at night. As a data science leader, you're beginning to collaborate with a line of business to better understand and translate the objectives into the models that are being built. Your data scientists are also starting to collaborate with the less technical team members and analysts who are working closest to the business problem. And as a data scientist, you stop feeling like you're falling behind. Open source tools are keeping you current. You're also starting to operationalize the work that you do. And you get to do more of what you love. Explore data, build models, put your models into production, and create business impact. All in all, it's not a bad scenario. Thanks. All right. We are back and coming up next, oh this is a special time right now. Cause we got a great guest speaker. New York Magazine called him the spreadsheet psychic and number crunching prodigy who went from correctly forecasting baseball games to correctly forecasting presidential elections. He even invented a proprietary algorithm called PECOTA for predicting future performance by baseball players and teams. And his New York Times bestselling book, The Signal and the Noise was named by Amazon.com as the number one best non-fiction book of 2012. He's currently the Editor in Chief of the award winning website, FiveThirtyEight and appears on ESPN as an on air commentator. Big round of applause. My pleasure to welcome Nate Silver. >> Thank you. We met backstage. >> Yes. >> It feels weird to re-shake your hand, but you know, for the audience. >> I had to give the intense firm grip. >> Definitely. >> The ninja grip. So you and I have crossed paths kind of digitally in the past, which it really interesting, is I started my career at ESPN. And I started as a production assistant, then later back on air for sports technology. And I go to you to talk about sports because-- >> Yeah. >> Wow, has ESPN upped their game in terms of understanding the importance of data and analytics. And what it brings. Not just to MLB, but across the board. >> No, it's really infused into the way they present the broadcast. You'll have win probability on the bottom line. And they'll incorporate FiveThirtyEight metrics into how they cover college football for example. So, ESPN ... Sports is maybe the perfect, if you're a data scientist, like the perfect kind of test case. And the reason being that sports consists of problems that have rules. And have structure. And when problems have rules and structure, then it's a lot easier to work with. So it's a great way to kind of improve your skills as a data scientist. Of course, there are also important real world problems that are more open ended, and those present different types of challenges. But it's such a natural fit. The teams. Think about the teams playing the World Series tonight. The Dodgers and the Astros are both like very data driven, especially Houston. Golden State Warriors, the NBA Champions, extremely data driven. New England Patriots, relative to an NFL team, it's shifted a little bit, the NFL bar is lower. But the Patriots are certainly very analytical in how they make decisions. So, you can't talk about sports without talking about analytics. >> And I was going to save the baseball question for later. Cause we are moments away from game seven. >> Yeah. >> Is everyone else watching game seven? It's been an incredible series. Probably one of the best of all time. >> Yeah, I mean-- >> You have a prediction here? >> You can mention that too. So I don't have a prediction. FiveThirtyEight has the Dodgers with a 60% chance of winning. >> [Katie] LA Fans. >> So you have two teams that are about equal. But the Dodgers pitching staff is in better shape at the moment. The end of a seven game series. And they're at home. >> But the statistics behind the two teams is pretty incredible. >> Yeah. It's like the first World Series in I think 56 years or something where you have two 100 win teams facing one another. There have been a lot of parity in baseball for a lot of years. Not that many offensive overall juggernauts. But this year, and last year with the Cubs and the Indians too really. But this year, you have really spectacular teams in the World Series. It kind of is a showcase of modern baseball. Lots of home runs. Lots of strikeouts. >> [Katie] Lots of extra innings. >> Lots of extra innings. Good defense. Lots of pitching changes. So if you love the modern baseball game, it's been about the best example that you've had. If you like a little bit more contact, and fewer strikeouts, maybe not so much. But it's been a spectacular and very exciting World Series. It's amazing to talk. MLB is huge with analysis. I mean, hands down. But across the board, if you can provide a few examples. Because there's so many teams in front offices putting such an, just a heavy intensity on the analysis side. And where the teams are going. And if you could provide any specific examples of teams that have really blown your mind. Especially over the last year or two. Because every year it gets more exciting if you will. I mean, so a big thing in baseball is defensive shifts. So if you watch tonight, you'll probably see a couple of plays where if you're used to watching baseball, a guy makes really solid contact. And there's a fielder there that you don't think should be there. But that's really very data driven where you analyze where's this guy hit the ball. That part's not so hard. But also there's game theory involved. Because you have to adjust for the fact that he knows where you're positioning the defenders. He's trying therefore to make adjustments to his own swing and so that's been a major innovation in how baseball is played. You know, how bullpens are used too. Where teams have realized that actually having a guy, across all sports pretty much, realizing the importance of rest. And of fatigue. And that you can be the best pitcher in the world, but guess what? After four or five innings, you're probably not as good as a guy who has a fresh arm necessarily. So I mean, it really is like, these are not subtle things anymore. It's not just oh, on base percentage is valuable. It really effects kind of every strategic decision in baseball. The NBA, if you watch an NBA game tonight, see how many three point shots are taken. That's in part because of data. And teams realizing hey, three points is worth more than two, once you're more than about five feet from the basket, the shooting percentage gets really flat. And so it's revolutionary, right? Like teams that will shoot almost half their shots from the three point range nowadays. Larry Bird, who wound up being one of the greatest three point shooters of all time, took only eight three pointers his first year in the NBA. It's quite noticeable if you watch baseball or basketball in particular. >> Not to focus too much on sports. One final question. In terms of Major League Soccer, and now in NFL, we're having the analysis and having wearables where it can now showcase if they wanted to on screen, heart rate and breathing and how much exertion. How much data is too much data? And when does it ruin the sport? >> So, I don't think, I mean, again, it goes sport by sport a little bit. I think in basketball you actually have a more exciting game. I think the game is more open now. You have more three pointers. You have guys getting higher assist totals. But you know, I don't know. I'm not one of those people who thinks look, if you love baseball or basketball, and you go in to work for the Astros, the Yankees or the Knicks, they probably need some help, right? You really have to be passionate about that sport. Because it's all based on what questions am I asking? As I'm a fan or I guess an employee of the team. Or a player watching the game. And there isn't really any substitute I don't think for the insight and intuition that a curious human has to kind of ask the right questions. So we can talk at great length about what tools do you then apply when you have those questions, but that still comes from people. I don't think machine learning could help with what questions do I want to ask of the data. It might help you get the answers. >> If you have a mid-fielder in a soccer game though, not exerting, only 80%, and you're seeing that on a screen as a fan, and you're saying could that person get fired at the end of the day? One day, with the data? >> So we found that actually some in soccer in particular, some of the better players are actually more still. So Leo Messi, maybe the best player in the world, doesn't move as much as other soccer players do. And the reason being that A) he kind of knows how to position himself in the first place. B) he realizes that you make a run, and you're out of position. That's quite fatiguing. And particularly soccer, like basketball, is a sport where it's incredibly fatiguing. And so, sometimes the guys who conserve their energy, that kind of old school mentality, you have to hustle at every moment. That is not helpful to the team if you're hustling on an irrelevant play. And therefore, on a critical play, can't get back on defense, for example. >> Sports, but also data is moving exponentially as we're just speaking about today. Tech, healthcare, every different industry. Is there any particular that's a favorite of yours to cover? And I imagine they're all different as well. >> I mean, I do like sports. We cover a lot of politics too. Which is different. I mean in politics I think people aren't intuitively as data driven as they might be in sports for example. It's impressive to follow the breakthroughs in artificial intelligence. It started out just as kind of playing games and playing chess and poker and Go and things like that. But you really have seen a lot of breakthroughs in the last couple of years. But yeah, it's kind of infused into everything really. >> You're known for your work in politics though. Especially presidential campaigns. >> Yeah. >> This year, in particular. Was it insanely challenging? What was the most notable thing that came out of any of your predictions? >> I mean, in some ways, looking at the polling was the easiest lens to look at it. So I think there's kind of a myth that last year's result was a big shock and it wasn't really. If you did the modeling in the right way, then you realized that number one, polls have a margin of error. And so when a candidate has a three point lead, that's not particularly safe. Number two, the outcome between different states is correlated. Meaning that it's not that much of a surprise that Clinton lost Wisconsin and Michigan and Pennsylvania and Ohio. You know I'm from Michigan. Have friends from all those states. Kind of the same types of people in those states. Those outcomes are all correlated. So what people thought was a big upset for the polls I think was an example of how data science done carefully and correctly where you understand probabilities, understand correlations. Our model gave Trump a 30% chance of winning. Others models gave him a 1% chance. And so that was interesting in that it showed that number one, that modeling strategies and skill do matter quite a lot. When you have someone saying 30% versus 1%. I mean, that's a very very big spread. And number two, that these aren't like solved problems necessarily. Although again, the problem with elections is that you only have one election every four years. So I can be very confident that I have a better model. Even one year of data doesn't really prove very much. Even five or 10 years doesn't really prove very much. And so, being aware of the limitations to some extent intrinsically in elections when you only get one kind of new training example every four years, there's not really any way around that. There are ways to be more robust to sparce data environments. But if you're identifying different types of business problems to solve, figuring out what's a solvable problem where I can add value with data science is a really key part of what you're doing. >> You're such a leader in this space. In data and analysis. It would be interesting to kind of peek back the curtain, understand how you operate but also how large is your team? How you're putting together information. How quickly you're putting it out. Cause I think in this right now world where everybody wants things instantly-- >> Yeah. >> There's also, you want to be first too in the world of journalism. But you don't want to be inaccurate because that's your credibility. >> We talked about this before, right? I think on average, speed is a little bit overrated in journalism. >> [Katie] I think it's a big problem in journalism. >> Yeah. >> Especially in the tech world. You have to be first. You have to be first. And it's just pumping out, pumping out. And there's got to be more time spent on stories if I can speak subjectively. >> Yeah, for sure. But at the same time, we are reacting to the news. And so we have people that come in, we hire most of our people actually from journalism. >> [Katie] How many people do you have on your team? >> About 35. But, if you get someone who comes in from an academic track for example, they might be surprised at how fast journalism is. That even though we might be slower than the average website, the fact that there's a tragic event in New York, are there things we have to say about that? A candidate drops out of the presidential race, are things we have to say about that. In periods ranging from minutes to days as opposed to kind of weeks to months to years in the academic world. The corporate world moves faster. What is a little different about journalism is that you are expected to have more precision where people notice when you make a mistake. In corporations, you have maybe less transparency. If you make 10 investments and seven of them turn out well, then you'll get a lot of profit from that, right? In journalism, it's a little different. If you make kind of seven predictions or say seven things, and seven of them are very accurate and three of them aren't, you'll still get criticized a lot for the three. Just because that's kind of the way that journalism is. And so the kind of combination of needing, not having that much tolerance for mistakes, but also needing to be fast. That is tricky. And I criticize other journalists sometimes including for not being data driven enough, but the best excuse any journalist has, this is happening really fast and it's my job to kind of figure out in real time what's going on and provide useful information to the readers. And that's really difficult. Especially in a world where literally, I'll probably get off the stage and check my phone and who knows what President Trump will have tweeted or what things will have happened. But it really is a kind of 24/7. >> Well because it's 24/7 with FiveThirtyEight, one of the most well known sites for data, are you feeling micromanagey on your people? Because you do have to hit this balance. You can't have something come out four or five days later. >> Yeah, I'm not -- >> Are you overseeing everything? >> I'm not by nature a micromanager. And so you try to hire well. You try and let people make mistakes. And the flip side of this is that if a news organization that never had any mistakes, never had any corrections, that's raw, right? You have to have some tolerance for error because you are trying to decide things in real time. And figure things out. I think transparency's a big part of that. Say here's what we think, and here's why we think it. If we have a model to say it's not just the final number, here's a lot of detail about how that's calculated. In some case we release the code and the raw data. Sometimes we don't because there's a proprietary advantage. But quite often we're saying we want you to trust us and it's so important that you trust us, here's the model. Go play around with it yourself. Here's the data. And that's also I think an important value. >> That speaks to open source. And your perspective on that in general. >> Yeah, I mean, look, I'm a big fan of open source. I worry that I think sometimes the trends are a little bit away from open source. But by the way, one thing that happens when you share your data or you share your thinking at least in lieu of the data, and you can definitely do both is that readers will catch embarrassing mistakes that you made. By the way, even having open sourceness within your team, I mean we have editors and copy editors who often save you from really embarrassing mistakes. And by the way, it's not necessarily people who have a training in data science. I would guess that of our 35 people, maybe only five to 10 have a kind of formal background in what you would call data science. >> [Katie] I think that speaks to the theme here. >> Yeah. >> [Katie] That everybody's kind of got to be data literate. >> But yeah, it is like you have a good intuition. You have a good BS detector basically. And you have a good intuition for hey, this looks a little bit out of line to me. And sometimes that can be based on domain knowledge, right? We have one of our copy editors, she's a big college football fan. And we had an algorithm we released that tries to predict what the human being selection committee will do, and she was like, why is LSU rated so high? Cause I know that LSU sucks this year. And we looked at it, and she was right. There was a bug where it had forgotten to account for their last game where they lost to Troy or something and so -- >> That also speaks to the human element as well. >> It does. In general as a rule, if you're designing a kind of regression based model, it's different in machine learning where you have more, when you kind of build in the tolerance for error. But if you're trying to do something more precise, then so much of it is just debugging. It's saying that looks wrong to me. And I'm going to investigate that. And sometimes it's not wrong. Sometimes your model actually has an insight that you didn't have yourself. But fairly often, it is. And I think kind of what you learn is like, hey if there's something that bothers me, I want to go investigate that now and debug that now. Because the last thing you want is where all of a sudden, the answer you're putting out there in the world hinges on a mistake that you made. Cause you never know if you have so to speak, 1,000 lines of code and they all perform something differently. You never know when you get in a weird edge case where this one decision you made winds up being the difference between your having a good forecast and a bad one. In a defensible position and a indefensible one. So we definitely are quite diligent and careful. But it's also kind of knowing like, hey, where is an approximation good enough and where do I need more precision? Cause you could also drive yourself crazy in the other direction where you know, it doesn't matter if the answer is 91.2 versus 90. And so you can kind of go 91.2, three, four and it's like kind of A) false precision and B) not a good use of your time. So that's where I do still spend a lot of time is thinking about which problems are "solvable" or approachable with data and which ones aren't. And when they're not by the way, you're still allowed to report on them. We are a news organization so we do traditional reporting as well. And then kind of figuring out when do you need precision versus when is being pointed in the right direction good enough? >> I would love to get inside your brain and see how you operate on just like an everyday walking to Walgreens movement. It's like oh, if I cross the street in .2-- >> It's not, I mean-- >> Is it like maddening in there? >> No, not really. I mean, I'm like-- >> This is an honest question. >> If I'm looking for airfares, I'm a little more careful. But no, part of it's like you don't want to waste time on unimportant decisions, right? I will sometimes, if I can't decide what to eat at a restaurant, I'll flip a coin. If the chicken and the pasta both sound really good-- >> That's not high tech Nate. We want better. >> But that's the point, right? It's like both the chicken and the pasta are going to be really darn good, right? So I'm not going to waste my time trying to figure it out. I'm just going to have an arbitrary way to decide. >> Serious and business, how organizations in the last three to five years have just evolved with this data boom. How are you seeing it as from a consultant point of view? Do you think it's an exciting time? Do you think it's a you must act now time? >> I mean, we do know that you definitely see a lot of talent among the younger generation now. That so FiveThirtyEight has been at ESPN for four years now. And man, the quality of the interns we get has improved so much in four years. The quality of the kind of young hires that we make straight out of college has improved so much in four years. So you definitely do see a younger generation for which this is just part of their bloodstream and part of their DNA. And also, particular fields that we're interested in. So we're interested in people who have both a data and a journalism background. We're interested in people who have a visualization and a coding background. A lot of what we do is very much interactive graphics and so forth. And so we do see those skill sets coming into play a lot more. And so the kind of shortage of talent that had I think frankly been a problem for a long time, I'm optimistic based on the young people in our office, it's a little anecdotal but you can tell that there are so many more programs that are kind of teaching students the right set of skills that maybe weren't taught as much a few years ago. >> But when you're seeing these big organizations, ESPN as perfect example, moving more towards data and analytics than ever before. >> Yeah. >> You would say that's obviously true. >> Oh for sure. >> If you're not moving that direction, you're going to fall behind quickly. >> Yeah and the thing is, if you read my book or I guess people have a copy of the book. In some ways it's saying hey, there are lot of ways to screw up when you're using data. And we've built bad models. We've had models that were bad and got good results. Good models that got bad results and everything else. But the point is that the reason to be out in front of the problem is so you give yourself more runway to make errors and mistakes. And to learn kind of what works and what doesn't and which people to put on the problem. I sometimes do worry that a company says oh we need data. And everyone kind of agrees on that now. We need data science. Then they have some big test case. And they have a failure. And they maybe have a failure because they didn't know really how to use it well enough. But learning from that and iterating on that. And so by the time that you're on the third generation of kind of a problem that you're trying to solve, and you're watching everyone else make the mistake that you made five years ago, I mean, that's really powerful. But that doesn't mean that getting invested in it now, getting invested both in technology and the human capital side is important. >> Final question for you as we run out of time. 2018 beyond, what is your biggest project in terms of data gathering that you're working on? >> There's a midterm election coming up. That's a big thing for us. We're also doing a lot of work with NBA data. So for four years now, the NBA has been collecting player tracking data. So they have 3D cameras in every arena. So they can actually kind of quantify for example how fast a fast break is, for example. Or literally where a player is and where the ball is. For every NBA game now for the past four or five years. And there hasn't really been an overall metric of player value that's taken advantage of that. The teams do it. But in the NBA, the teams are a little bit ahead of journalists and analysts. So we're trying to have a really truly next generation stat. It's a lot of data. Sometimes I now more oversee things than I once did myself. And so you're parsing through many, many, many lines of code. But yeah, so we hope to have that out at some point in the next few months. >> Anything you've personally been passionate about that you've wanted to work on and kind of solve? >> I mean, the NBA thing, I am a pretty big basketball fan. >> You can do better than that. Come on, I want something real personal that you're like I got to crunch the numbers. >> You know, we tried to figure out where the best burrito in America was a few years ago. >> I'm going to end it there. >> Okay. >> Nate, thank you so much for joining us. It's been an absolute pleasure. Thank you. >> Cool, thank you. >> I thought we were going to chat World Series, you know. Burritos, important. I want to thank everybody here in our audience. Let's give him a big round of applause. >> [Nate] Thank you everyone. >> Perfect way to end the day. And for a replay of today's program, just head on over to ibm.com/dsforall. I'm Katie Linendoll. And this has been Data Science for All: It's a Whole New Game. Test one, two. One, two, three. Hi guys, I just want to quickly let you know as you're exiting. A few heads up. Downstairs right now there's going to be a meet and greet with Nate. And we're going to be doing that with clients and customers who are interested. So I would recommend before the game starts, and you lose Nate, head on downstairs. And also the gallery is open until eight p.m. with demos and activations. And tomorrow, make sure to come back too. Because we have exciting stuff. I'll be joining you as your host. And we're kicking off at nine a.m. So bye everybody, thank you so much. >> [Announcer] Ladies and gentlemen, thank you for attending this evening's webcast. If you are not attending all cloud and cognitive summit tomorrow, we ask that you recycle your name badge at the registration desk. Thank you. Also, please note there are two exits on the back of the room on either side of the room. Have a good evening. Ladies and gentlemen, the meet and greet will be on stage. Thank you.

>> Announcer: Live from Washington D.C., it's the CUBE, covering AWS Public Sector Summit 2017, brought to you by Amazon Web Services and its partner, Ecosystem. >> Well, welcome back to the CUBE here. We are live in Washington D.C., day two of our coverage here at the AWS Public Sector Summit 2017. Again, in Washington D.C., just about a mile and a half or so, about a mile from the White House, conveniently located here in our nation's capitol with John Furrier. I'm John Walls. John, good morning to you, sir. >> Good morning, great day yesterday. A lot of great interviews, thought leaders, inspirational, very informational. And again, the CUBE just doing its thing, our inaugural event, here at Amazon Web Services Public Sector Summit. Our first time here, this is the seventh year of the show. Started out as just a kind of gathering, people coming together. >> Kind of a hope for a gathering too, right? We heard yesterday, guys, "Boy, I hope somebody shows up." Well, we have 10,000 showing up now, so. >> It's still small, but that's a huge number. Some big companies don't even get that many for their annual user customer conference in general. So, 10,000 is certainly a good number. I expect Amazon to continue to blow away their performance and the numbers. I expect this show to be, again, the Amazon re:Invent, which is their big show, as a company. Amazon, which was a re:Invent, which is held in Las Vegas every year and overseas. But this going to be the public sector version: education, government, health, all these different public sector opportunities are ripe for the cloud. And that's really the big story. >> You know, and I think we saw that on the keynotes this morning with Theresa Carlson who's the Vice President of Worldwide Public Sector here for AWS. But she brought out a number of guests, John Edwards being the most prominent, the CIO of the CIA, but also Representative for the Australian Tax Office, Representative for the Ocean Conservancy. She talked about state and local governments. So you hit the nail on the head. We think public sector, I think maybe the presumption is go right to big government. But there are a lot of tentacles, if you will, out there or a lot of segments out there. 22 thousand non-profits, for example, that AWS is now working with. State, local, and federal governments. So they've cast a wide net, and they've caught a lot of fish. >> I mean, yeah, I mean to me this is an interesting time in our lives. What's the famous quote? We live in interesting times? We are living in interesting times, certainly in Washington D.C. here we are feeling it. Obviously, coming from California, I always love to come into D.C. to feel what it's like into the boiling water with Trump in the office and all the disarray in the government. There's a shooting of a Congressman this morning, 50 shots fired at a softball practice. It's insane. And so, there's also change going on at the technology level, but that's changing government and also roles of education and whatnot. So you have this really kind of weird environment like all the evidence of the frog in boiling water. At some point, it doesn't know it's being boiled to death, but that's been the public sector for generations. Really, I think the seminal changeover was mainframes and minicomputers really kind of powered the government, and I think it's been incremental changes. And you've seen IT become what we've seen in the enterprise: an incremental improvement and bolting on some support. Here, we've got wireless. And so, it's kind of moving the ball down the field yard by yard, no major long ball throws to the endzone as we say in football. But now with the cloud, you have an opportunity to take the domain expertise of all the different agencies because they want to do a good job. Their world is changing. You can look no farther than education, higher ed, and even K through 12. I mean, they're dealing with an audience that's grown up with cell phones, mobile phones, smartphones. I mean, they're not phones anymore. They're computers that happen to make phone calls, and half the kids don't even make phone calls anymore, so. >> That's right, half are you kidding? >> It's not even a phone anymore. It's a computer, a camera. >> It's a texting device. >> User experiences are driving this, and it's a forcing function. So all this disarray, all this opportunity, the perfect storm of innovation happening. And I think the cloud enables that. And I think that's part of the reason why Amazon Web Services is, again, feeling the love here because the growth is right there in front of them. >> Now, we're going to have Theresa Carlson on a little bit later on, but I want to just get your take on her. She's taken this from an infancy stage and has just walked it, absolutely, she's amazing. >> Theresa Carlson, we'll have her on. She's been on the CUBE multiple times. We always joke with her when she comes on the CUBE when we're at re:Invent and other places we see her when she comes on. "Hey, we should come to your conference." And so, we're here. But the thing about Theresa Carlson is, she's loved by all of the customers today, and she's very customer-focused. But she's tenacious. She is smart. She's beautiful. She's a hustler. She's great. So she is a great leader, and she's been knocking on doors in this town for years when cloud wasn't cloud yet. And you know, when you're an innovator, pioneer, the door slams in your face, right? So, you know, you've got to have that kind of tenacity to stay on it, and that's what she's done. She's been amazing. I'm a real big fan of hers. I mean, I think she's got some work to do, areas I think that she's got to really expand and go faster with Ecosystem. Some of the case studies are out there to be had. We know for a fact, I mean, and they talked on stage, but there's a lot of smart cities, things going on. There's a ton of transformative Amazon Web Services deals happening, so you want to see more of those, want to see more of them faster. I want to see more peer review. I want to see more case studies. So to me, that's where I think she's going to have to really keep the hustle going and then get her team cut out, set the bar high and continue to innovate. >> You know, we talked about that seminal moment with the CIA deal four years ago when the CIA made the move, went to AWS, chose them over IBM. John Edwards was talking about that mindshift at the agency today, saying, it was our goal as we looked at all of our partners, instead of making you or them become like us, we wanted to become like them. We wanted to be faster. We wanted to be more agile. We wanted to be more nimble. We wanted to be more open in a way or at least open to new ideas. And so, it was a transformational shift in their paradigm that really sent them on a great course. He couldn't have been more positive on that stage today talking about AWS and the relationship with the CIA and what they have done for the agency, what it's done for the agency. >> Look, there's a frustration in public sector. It's the elephant in the room, so to speak. And that is, they want to do more with less because that's always been their role. Now, some kind of say, "Oh yeah, "a bolted contractor kind of bids." And you know, the procurement process which old school was, you know, the $45 bolt that the joke in D.C. is for these big government and, you know, Army contracts. But they still get scrutinized on costs. So, you know, there's been a way of doing things that are changing, right? So how you procure technology, how you deploy it, is really different now. And the opportunity is to get this in the hands of people who want to move fast. They want to actually deliver a good product. There's a lot of great people in public sector who love their job, and if they don't give them the tools, you're going to see what I call a brain drain go on in public service. And you're seeing that going on, obviously with Trump and the government here. There are a lot of smart people saying, "Hey, I'm out of here." Right? It always kind of happens during political changeovers, but no more than the passion of the people working. Just give them the tools for the job, alright? That's kind of the cloud mojo. It's like give them, move fast, give them the technology they need. And a lot of stuff we're hearing from friends is one of our guests yesterday, they need some of the basic stuff automated away. I want the compliance. I want the security. I want to make sure that I can run the operations at scale. And that's really the table stakes. And that's going to be the tipping point, when all those details around compliance can just be programmed in once and just work. That's when you're going to start to see some real acceleration, new apps, new developers, new environments for whether it's students, federal workers, or practitioners in health and human services, you're going to start to see those things happen. >> Well, it's all about stability, right? It's the stability and certainty and knowing that what I'm doing is okay. Right? That I'm staying within the confines, the regulations, you know, this town knows regulations. >> All of these markets, you know, what's going on in those worlds? And a lot of people ask questions. People in the industry, they know what's going on, and they want better, faster, cheaper, now. And I think that's Amazon's ethos. I mean, Jeff Bezos, the CEO, is living large right now, stock prices at thousands and his personal cash to send people to space and build up Mars, for instance. That's his moon shot. It's not his moon shot, it's his Mars shot. So, he's got a grand vision. He loves space. But he's always said the ethos of Amazon, which Amazon Web Services is part of Amazon, is lower prices for customers, constantly deliver lower prices, push the prices lower, and ship product faster. >> That's true. >> Get it in the hands faster on the delivery side. So you could apply that ethos to anything. It's really a timeless ethos. It's not pegged on one division. Andy Jassy and Theresa Carlson, they picked that up. They're trying to drive the prices down. CIA talks about that. And delivering faster means speed. I want faster drives. I want lower prices. And they've delivered that. Amazon has consistently delivered better product at a lower price and working on shipping software faster, better performance. You know, delivering here is packets. So, there it is. That's really why Amazon is winning. That's the key to their success. >> Well, it's been a winning formula, for sure, and we'll be talking about that much more today as we continue our coverage here from Washington D.C. We are live here on the CUBE. We continue with more from AWS Public Sector Summit 2017 right after this.

>> Announcer: Live from Las Vegas, it's theCUBE, covering Dell EMC World 2017. Brought to you by Dell EMC. >> Welcome back, here at Las Vegas at The Venetian. As theCUBE continues our coverage of Dell EMC World 2017. Along with our co-host, Paul Gillin, I'm John Walls, good to have you with us. As we, I guess were coming down the home stretch. >> Paul: We are. >> Day one. >> Paul: End of the day. >> Here at Dell EMC World 2017. With us now is Chhandomay Mandal, who is product marketing director at Dell EMC. Double dipping on us, we just had you on a few moments ago. This is nice, we get two shots. >> Thanks for having me. >> Good to have you back with us, yeah, it's good. >> Chhandomay: Thanks for having me. >> So before we were talking about XtremeIO, what you were doing in the healthcare space. Moving over now to copy data management, different part of your portfolio, and kind of what's up in that world with you. So just give us a little rundown, an overview of what you're up to right now. >> Sure, so let's start with what exactly is copy data problem. Why it's a problem, and why we need to be solving it? If you think of any business application, it comes with its production data. But for every bit of production data, you have many different copies. For example, when you're developing applications, you need copies for your development and testing. You need copies for your backup. You need copies for running your analytics environment. It's for every single production database, typically, we see five to 12 copies of that data. And in fact, I did see estimates. The copy data sprawl is like 40 billion dollar market, and 60% of all the data that exists are on copies. Now, our mission, especially with Dell EMC XtremIO, is to solve that copy data problem, giving the customers back a lot in terms of the storage efficiencies, and not only is the storage TCO, but transforming the business workflows. We did copy data management so that they can realize storage and infrastructure settings, but also the business impact from transforming the application workflows and bringing new production market in a much quicker way. >> So you said a 40 billion dollar market, I mean, what are the costs here? Is it just storage cost? Is it bandwidth, is it errors? Lack of data being out of sync? >> So the cost here has multiple components, right? First of all, there is the cost of the lost storage where you need to put the data on. But then, there comes the cost of managing the storage. How do you figure out where you backup copies are, if you need to restore, where are you going to get the data from? It's a cost of inefficiency meaning, like if your developer who is the highly paid, highly productive guy, supposed to be, right? He is waiting for the DBA or the storage admin to give the copy that he needs, then, that's just enough money, right? It's not just the infrastructure cost, but also the soft cost of, like your ability to bring the product to the market in a quicker way, addressing your customer needs in a quicker way. That acts up and those are the components in, like, how I value this intermarket. >> I guess what I'm hearing here, if you got five to 12 copy sets of data, I mean, massive amounts of waste in some cases, right? And maybe some of your clients, they like to know where everything is, but do they lose track of it, and so it's taking up space, taking up money, taking up time. Is that, are these the problems they're facing? >> Chhandomay: Yes, yes. >> Alright, so then, what's the answer then in terms of this better identification? There's X2, get to the heart of that, and help them in terms of better efficiencies? >> How do we achieve that efficiencies? Now, one thing is, the way, first of all, like, if you can consolidate your copies into one single platform. And copies are duplicate bits, right? So, first of all, the first thing in the process is you eliminate all the duplication that exists in your storage. You have your production data base, and you have your copies, which are, if not unique, then basically should not take up any extra space. Now, you take those copies, and make it like a repair plus one. So for example, your Dell part can run tests on it. So when the rights are coming in, only the changes that are happening, that should go into the storage. So that's Part A. Part B is, when you are running production environment, as well as what works on your copies, you need huge performance with consistently low latency. Because you cannot impact your production SLS. You have to meet that. >> You can't tell it, "Hang on, I've got something "else going on over here, right?" >> So you need a platform that can handle consistently high performance with low latency no matter what workload you are running. And then the copies themselves need to be very efficient. They should not take any extra space, unless there is something unique. And they should be able to perform just as well as in a production value. The hard part of this is, you need to orchestrate the inner process, right? I mean, you as in oracle admin. You really do not need to worry about how and where the storage is going to be saved for your copies. You click on a button, and it should do all the steps necessary right from your application console down to the storage. So this is the application orchestration that we in-built with AppSync and XtremIO. And then we have APIs that our customers can use to provide their own service catalog. So using these pillars, we consolidated all the copies, on the same platform, running different applications, with the same SLS, okay? And that kind of helps the customers to bring product faster, and address the copy needs. >> Now, this is a very hot market right now. And I'm thinking there's some startups, I'm thinking of Actifio and Catalogic in particular, that say that you shouldn't have many copies. You should have one copy, and then you should have pointers to that. What's your opinion of that? What are the pros and cons of that approach versus yours? >> So our approach, essentially, I mean, since you mentioned, right, there are copy data management vendors. What they're doing is, you have your production, then you make a gold copy off your production, and from that gold copy you run off different applications on those copies, right? So here you are introducing another element, another software, and another appliance, so to speak, to manage the copies. What we are doing, is kind of like you don't need that extra copy that your analytic part provider can provide. And then there are performance implications with the integrated copy data management that we are referring. The reason we can do it is, all of our metadata is in memory. It does not consume any extra space for storage. And no matter what the workload is, we can offer consistently high latency because everything is, the metadata is operating from the memory itself. So the way the third parties are doing, we do it the same way, even better, and at the production level. >> Another thing, and forgive my technical ignorance here, but David Fleur at Worky-Ban, has talked a lot about the benefits of flash storage. In that you don't have to create copies, you can create a single copy in flash, and then multiple users or applications can work from that. Do I have that straight? He says that's a game changer. >> Yes, that will be that game changer, and that's really like what we do. The caveat to that is, when you are creating the copies, and you want to run applications on the copies, your production should not be impacted, and the copies should also be able to deliver the same performance. And that part has been the challenge with other solutions in terms of providing the same performance, the same data services on the copies themselves. That's the idiot we solve will our intelligent content error in memory metadata architecture with XtemIO. >> You're talking about the integrated data management just a little bit ago. I mean, from a real life perspective, can you give us an idea about maybe a success story, somebody that you can point to and say, "This is how they incorporated that "into their process, I see it work for them, "and we can make it work for you too?" >> So, I'll give you an interesting statistic. We have 3,000 plus customers running XtremIO in production, and we get all the phone home data at our end, and we can see what they're doing. Now, for XtremIO customers, 56% of the copies that they're making, they are running workloads on them. They are not just for local data production. And, all the IOs, XtremIos that is out there in the field we'll see, 40% of the IOs are because of the copies. So we see across the board on the customers. I have many examples. For the sake of time, I'll just speak one. We all know Moen, they are the leading, not American manufacturers of the faucets, right? It's a big shop, and they have like, a lot of SLP landscapes in there. Before XtremIO, they could not keep up with the backups and the copies that they needed. After moving to XtremIO, now they can actually take the copies of their production SLP landscapes twice a day. They are quietly running reports. They are actually running like 90% shorter, and in fact, we were talking with Harvey H., literally, like before this segment, right? He was also talking about how efficient their copies are. I was talking with Scripps Health, who are also going to be presenting in here. They run like 3,000 copies in their environment, with XtremIO and AppSync, and like it's all working great. No impact on the performance, and they are meeting their SLS. >> Well, your performance on theCUBE has been outstanding. Back-to-back saves, we appreciate the time. Chhandomay, thanks for hanging with us. Best of luck down the road, and continued success here at the show as well. >> Thank you, it was a pleasure. >> We will continue with more from theCUBE here in Las Vegas. We are live at Dell EMC World 2017.

>> Narrator: Live from Austin, Texas, it's The Cube covering South by Southwest 2017, brought to you by Intel. Now, here's John Furrier. >> Hey, welcome everyone back for day two of live coverage of South by Southwest. This is the cube, our flagship program from Silicon Angle. We go out to the events and extract the (mumbles). We're at the Intel AI Lounge, people are rolling in, it's an amazing vibe here, South by Southwest. The themes are AI, virtual reality, augmented reality, technology. They got great booths here, free beers, free drinks, and of course great sessions and great conversations here with the Cube. My first guest of the day here is Ben Parr, a friend of the Cube. He's been an entrepreneur, he's been a social media maven, he's been a journalist, all around great guy. Ben, thanks for joining us today. >> Thank you for having me again. >> So you're a veteran with South by Southwest, you know the social scene, you've seen the evolution from Web 2.0 all the way to today, had Scobel on yesterday, Brian Fanzo, really the vibe is all about that next level, of social to connecting and you got a startup you're working on that you founded, co-founded called AI? >> Ben: Octane AI. >> Octane AI, that's in the heart of this new social fabric that's developing. Where AI is starting to do stuff, keep learning, analytics but, ultimately, it's just a connection. Talk about your company. What is Octane AI? Tell us a little bit about the company. >> So Octane AI is a platform that lets you build an audience on Facebook Messenger and then through a bot. And so, what we do is allow you to create a presence on Messenger because if I told you there was a social app that had a billion users every month, bigger than Snapchat plus Twitter plus Instagram combined you'd want to figure out a strategy for how to engage with those people right? And that social app is Facebook Messenger. And yet no one ever thinks, oh could I build an audience on a messaging app? Could I build an audience on Messenger or WeChat or any of the others. But you can through a bot. And you can not just build an audience but you can create really engaging content through conversation. So what we've done is, we've made it really easy to make a bot on messenger but more importantly, a real reason for people to, actually, come to your bot and engage with it and make it really easy to create content for it. In the same way you create content for a blog or create content for YouTube Channel. Maroon 5, Aerosmith, KISS, Lindsay Lohan, 30 seconds to MARS, Jason Derulo and a whole bunch more use us to build an audience and engage their fans on Messenger. >> So let me get your thoughts on a couple of trends around this. Cause this is really kind of, to me, a key part that chat bots illustrate the big trends that are going on. Chat bots were the hype. People were talking about, oh chat bots. It's a good mental model for people to see AI but it also has been, kind of, I won't say a pest, if you will, for users. It's been like a notification. A notification of the economy we're living in. Now you're taking it to the next level. This is what we're seeing. The deep learnings and the analytics around turning notifications which can be noisy after a while, into real content and connections. >> Into something useful, absolutely. Like look, the last year of bots. The Facebook platform is not even a year old. We've been in that fart apps stage of bots. Remember the first year of mobile apps? You had the fart app and that made $50,000 a day and that was annoying as hell. We're at that stage now, the experimentation stage. And we've seen different companies going in different, really cool directions. Our direction is, how do you create compelling content so you're not spamming people but you have content that you can share, not just in your bot but as a link on your social media to your followers, to your fans, on Twitter, everywhere else and have a scalable conversation about whatever you want. Maroon 5 has conversations with their audience about their upcoming tours or they even released an exclusive preview of their new song, Cold, through our bots. You could do almost anything with our bots or with any bot. We're just learning right now, as an industry, what are the best practices. >> So where do bots go for the next level? Because you and I have known each other for almost over 10 years, we've seen the whole movement and now we're living in a fake news era. But social media is evolving where content now is super important that glues people together, communities together. In a way, you're taking AI or bots, if you will. Which is a first, I mean, .5 version of where AI is going. Where content, now, is being blended into notifications. How important is content in community? >> Content in community are essential to any product. And I feel like when you hear the word bot, you don't think community and that you could build a community with it because it's a bot, it's supposed to be automated. But you, actually, can if you do it in the right way and it can be a very, very powerful experience. We're building features that allow you to build more community in your bot and have people who are talking with your bot communicate with each other. There's a lot of that. What I feel like is, we're at the zero point one or zero point two of the long scale of AI. What we need to do right now is showcase all the use cases that really work for AI, bots, machine learning. Over time, we will be adding more other great technologies from Intel and others that will make all these technologies and everything we do better, more social and most of all, more personalized. I think that's one of the big benefits of AI. >> Do you see bot technology or what bots can turn into being embedded into things like autonomous vehicles, AR, is there a stack developing, if you will, around bots? What you're talking about is a progression of bots. What's your vision on where this goes down the road? >> I see a bunch of companies, now, building the technological stack for AI. I see a bunch of companies building the consumer interface, bots is one of those consumer interfaces. Not just chat bots but voice bots. And then I see another layer that's more enterprise that's helping make more efficient things like recruiting or all sorts of automation or driving. That are being built as well. But you need each of those stacks to work really well to make this all work. >> So are there bots here at South by Southwest? Is there a bot explosion, is there bots that tell you where the best parties are? What's the scene here at Southby? Where are the bots and if there were bots, what would they be doing to help people figure out what to do? >> The Southby bot is, actually, not a bad bot. They launched their bot just before South by Southwest. It has a good party recommendations and things. But it the standard bot. I feel like what we're seeing is the best use, there's a lot of good bot people. What I'm seeing right now is that people are still flushing out the best use cases for their bots. There's no bot yet that can predict all the parties you want to go to. We got to have our expectations set. That will happen but we're still a few years away from really deep AI bots. But there are clearly ones where you can communicate faster with your friends. There's clearly ones that help you connect with your favorite artist. There's clearly ones that help you build an audience and communicate at scale. And I feel like the next step is the usefulness. >> Talk about the user interface. Robert Scobel and I were talking yesterday, we have some guests coming on today that had user experience background. With AI, with virtual reality, with bots, with deep learning, all this collective intelligence going on, what's your vision of the user interface as it changes, as people's expectations? What are some of those things that you might see developing pretty quickly as deep learning, analytics, more data stats come online? What is the user interface? Cause bots will intersect with that as an assistant or a value add for the user. What's your vision on? >> I'll tell you what I see in the near term and then I'll tell you a really crazy idea of how I see the long term. In the near term, I think what you're going to see is bots have become more predictive. That, based on your conversations, are more personalized and maybe not a necessarily need as much input from you to be really intelligent. And so voice, text, standard interfaces that we're used to. I think the bigger, longer run is neurological. Is the ability to interface without having to speak. Is AI as a companion to help us in everything we do. I feel like, in 30 years, we won't even, it's, kind of like, do your remember the world when it had no internet? It's hard, it feels so much different. There will be a point in about 20 years we will not understand what the world was before AI. Before AI assistance where assisting us mentally, automatically and through every interface. And so good AI's, in the long run, don't just run on one bot or one thing, they follow you wherever you go. Right now it might be on your phone. When you get home, it may be on your home, it may be in your car but it should be the same sets of AI's that you use daily. >> Doctor Nevine Rou, yesterday, called the AI the bulldozer for data. What bulldozers where in the real world, AI's going to do that for data. Cause you want to service more data and make things more usable for users. >> Yes, the data really helps AI become more personalized and that's a really big benefit to the user to every individual. The more personalized the experience, the less you have to do. >> Alright, so what's the most amazing thing you've seen so far this year at Southby? What's going on out there that's pretty amazing? That's popping out of the wood work? In terms of either trend, content, product, demos, what are some of the cool things you're seeing. >> So, as it is only Saturday, I feel like the coolest thing will still come to me. But outside of AI, there have been some really cool mixed reality, augmented reality demos. I can't remember the name. There's a product with butterflies flying around me. All sorts of really breaking edge technologies that, really, create another new interface honestly where AI may interact with us through the augmented reality of our world. I mean, that's Robert Scogul's thing exactly. But there's a lot of really cool things that are being built on that front. I think those are the obvious, coolest ones. I'm curious to see which ones are going to be the big winners. >> Okay, so I want to ask you a personal question. So you were doing some venture investing around AI and some other things. What caused you to put that pause button on that mission to start the chat bot AI company? >> So I was an investor for a couple of years. I invested in ubean, the wireless electricity company and Shots with Justin Bieber which is always fun. And I love investing and I love working with companies. But I got into Silicone Valley and I got into startups because I wanted to build companies. I wanted to build ideas. This happened, in part, because of my co-founders. My co-founder Matt, who is the first head of product at Ustream and twice into the Forbes 30 under 30. One of the king makers of the bot industry. The opportunity to be a part of building the future of AI was irresistible to me. I needed to be a part of that. >> Okay, can you tell any stories about Justin Bieber for us, while we're here inside the Cube? (laughs) >> I wonder how many of those I can, actually, tell? Okay, so look. Justin Bieber is an investor in a company I'm an investor in called Shots. Which is now a super studio that represents everyone from Lele Pons to Mike Tyson on digital online and they're doing really, really well. One of Justin's best friends is the founder, John Shahidi. And so it's just really random. Sitting with John, who I invested in and just getting random FaceTime's. Be like, oh it's Justin Bieber, say hi to Justin. As if it was nothing. As if it was a normal, it's a normal day in his life. >> Could you just have him retweet one of my Tweets. He's got like a zillion followers. What's his follower count at now? >> You don't want that. He's done that to me before. When Justin retweets you or even John retweets you, thousands of not tens of thousands of Justin Bieber fans, bots and not bots, start messaging you, asking you to follow them, talking to you all the time. I still get the tweets all the time from all the Justin fans. >> Okay don't tweet me then. I'm nice and happy with 21,000 followers. Alright, so next level for you in terms of this venture. Obviously, they got some rock stars in there. What's the next step for you guys right now? Give us a little inside baseball in the venture status where you guys are at. What's the next step? >> We launched the company publicly in November, we started in May. We raised 1.6 million from general catalyst, from Sherpa Ventures, a couple of others. When we launched our new feature, Convos, which allows you to create shareable bots, shareable conversations with the way you share blog posts. And that came out with all those launch partners I mentioned before like Maroon 5. We're working on perfecting the experience and, mostly, trying to make a really, really compelling experience with the user with bots because if we can't do that, then there's no use to doing anything. >> So you provide the octane for the explosive conversations? (laughs) >> Yes, there you go, thank you, thank you. And we make it really easy. So we're just trying to make it easier to do this. This is a product that your mom could use, that an artist could use, any social media team could use. Writing a convo is like writing a blog post on media. >> Are moms really getting the chat bot scene? I, honestly, get the Hollywood. I'm going to go back to Hollywood in a second but being a general, middle America kind of tech/genre, what are they like? Are they grokking the whole bot thing? What's the feedback from middle America tech? >> But think of it this way. There are a billion people on Messenger and it's a, really, part of the question, they all use Facebook Messenger. And so, they may be communicating with a bot without knowing it. Or they might want to communicate with their fans. It's not about the technology as much as this is like connecting with who you really care about. If I really care about a Maroon 5 or Rachel Ray, I can now have that option. And it doesn't really matter what the technology is as much as it is that personal connection, that experience is good. >> John: Is it one-one-one or group? Cause it sounds like it's town hall, perfect for a town hall situation. >> It's one-on-one, it's scale. So you could have a conversation with a bot while each of the audience members is having a conversation one-on-one. When you can choose different options and it could be a different conversation for each person. >> Alright, so I got to ask about the Hollywood scene. You mentioned Justin Bieber. I wanted to go down that because Hollywood really has adopted social media pretty heavily because they can go direct to the audience. We're seeing that. Obviously, with the election, Trump was on Twitter. He bypasses all the press but Hollywood has done very well with social. How are they using the bots? They are a tell sign of where it's going. Can you share some antidotal stories or data around how Maroon 5, Justin, these guys are leveraging this and what's some of the impact? >> Sure, so about a month 1/2, 2 months before Maroon 5 launched their new song, new single, Cold. They came to us and wanted to build a distribution. They wanted to reach their audience in a more direct personal way. And so we helped them make a bot. It didn't take long. We helped them write convos. And so what they did was they wrote convos about things like exclusive behind the scenes photos from their recent tour or their top moments of 2016 or things that their fans really care about. And they shared em. They got a URL just like you would get, a blog poster URL. They shared it out with their 39 million Facebook fans, they shared it with their Twitter followers, they shared it across their social media. And 10's of thousand's of people started talking with their bot each time they did this. About 24 hours before the bot, before their new single release, they exclusively released a 10 second clip of Cold through their bot. And when they did that, within 24 hours, the size of their bot doubled because it went viral within the Maroon 5 community. There's a share function in our convos and people shared the convo with their friends and with their friends friends and it kept on spreading. We saw this viral graph happen. And the next day when they released the single, 1000's of people bought the song because of the bot alone. And now the bot is a core of their social strategy. They share a convo every single week and it's not just them but now Lohan and a whole bunch of others are doing the same thing. >> John: Lindsay Lohan. >> Lindsay Lohan is one of our most popular bots. Her fans are really dedicated. >> And so you can almost see it's, almost connecting with CGI, looking at what CGI's doing in film making. You could almost have a CGI component built-in. So it's all this stuff coming together. >> Ben: Multimedia matters. >> So what do you think about the Intel booth here? The AI experience? They got some Kinetic photo experience, amazing non-profit activities in deep loading (mumbles), missing children, what do you think? >> This is some of the best use cases for AI which is, people think of AI as just like the direct consumer interface which is what we do but AI is an underlying layer to everything we do. And if it can help even 1% or 1,000% identify and find missing children or increase the efficiency of our technology stacks so that we save energy. Or we figure out new ways to save energy. This is where AI can really make an impact. It is just a fundamental layer of everything. In the same way the internet is just a fundamental layer of everything. So I've seen some very cool things here. >> Alright, Ben Parr, great guest, in venture capitalist now founder of a great company Octane AI. High octane, explosive conversations looking forward to adopting. We're going to, definitely, take advantage of the chat bot and maybe we can get some back stage passes to Maroon 5. (laughs) >> (laughs) There will be some fun times in the future, I know it. >> Alright Ben Parr. >> Ben: Justin Bieber. >> Justin Bieber inside the Cube right here and Ben Parr. Thanks for watching. It's the Intel AI Lounge. A lot of great stuff. A lot of great people here. Thanks for joining us. Our next guest will be up after this short break. (lively music)

>>we're back. >>This is Dave Volante. I'm with Wiki bond dot Oregon. This is Silicon angle's the cube where we extract the signal from the noise. We go into the events, we're bringing you the best guests that we can find. And we're here at the AWS summit. Amazon is taking the cloud world by storm. He was on, invented the cloud in 2006. They've popularized it very popular of course with developers. Everybody knows that story. Uh, Amazon appealing to the web startups, but what's most impressive is the degree to which Amazon is beginning to enter the enterprise markets. I'm here with my cohost Jeff Frick and Jeff, we heard Andy Jassy this morning just laying out the sort of marketing messaging and progress and strategies of AWS. One of the things that was most impressive was the pace at which they put forth innovations. We talked about that earlier, but also the pace at which they proactively reduce prices. Uh, that's different than what you'd see in the normal sort of enterprise space. Talk about that a little bit. >>Yeah. Again, I think it really speaks to their strategy to lock up the customer. It's really a lifetime value of the customer and making sure that they don't have a really an opportunity or a reason to go anywhere else. So as we discussed a little bit earlier, they leverage, you know, kind of the pure hardware economics of, of decreasing a computing power, decreasing storage, decreasing bandwidth, but then they also get all the benefits of scale. And I think what's in one of the interesting things that Andy talked about and kind of his six key messages was that it's actually cheaper to rent from them because of the scale than it is to buy yourself. And I know that's a pretty common knock between kind of a build or buy, um, kind of process you go through and usually you would think renting at some scale becomes less economical than if you just did it yourself. But because their scale is so massive because of the flexibility that you can bring, uh, computing resources to bear based on what you're trying to accomplish really kind of breaks down the, uh, the old age old thought that, you know, at scale we need to do it ourselves. >>Well, and that's the premise. Um, I think, and, uh, let's Brits break down a little bit about that, that analysis and, and Andy's keynote. So he put forth some data from IDC which showed that, uh, the Amazon cloud is cheaper than the, uh, a, a so-called private cloud or an in house on premise installation. You know, I certainly, there's, it's, it's a, it's an, it's depends, right? It really depends on the workload. That's somewhat of an apples to orange is going on here and the types of workloads that are going down in the AWS cloud, granted he's right and that they're running Oracle, they're running SAP, but the real mission critical workloads, what he calls mission critical aren't the same as what, you know, Citi would call mission critical. Right? So to replicate that level of mission criticality, uh, would probably almost most certainly be more expensive rental versus owning the real Achilles heel of, of, of any cloud, not just Amazon. >>Cloud really is getting data out. Um, moving data, right? Amazon's going to charge you not to get data in. They're gonna charge you to store it there to exercise, you know, compute. Uh, and then, but they're also gonna charge it if you wanted to take it out. That's expensive. The bandwidth costs and the extrication costs are expensive. Uh, the other issue with cloud again is data movement. It takes a long time to move a terabyte, let alone multiple terabytes. So those are sort of the two sort of Achilles heels of, of cloud. But that's not specific to Amazon's cloud. That's any cloud. Yeah. So we've got a great lineup today. Um, let's see. We've got Ariel Kelman coming on, uh, and I believe he's in the house. So we're going to take a quick break. Quick break. Right now we right back with Ariel Kelman, who's the head of marketing at AWS. Keep right there. This is the cube right back. >>we lift out all the programs out there and identified a gap in tech news coverage. Those shows are just the tip of the iceberg and we're here for the deep dive, the market beg for our program to fill that void. We're not just touting off headlines. We also want to analyze the big picture and ask the questions that no one else is asking. We work with analysts who know the industry from the inside out. So what do you think was the source of this missing? So you mentioned briefly there are, that's the case then why does the world need another song? We're creating a fundamental change in news coverage, laying the foundation and setting the standard, and this is just the beginning. We looked on all the programs out there and identified a gap in tech news coverage. There are plenty of tech shows that provide new gadgets and talk about the latest in gaming, but those shows aren't just the tip of the iceberg. And we're here for the deep dive. >>Okay, >>Dave Olanta. I'm with Wiki bond.org and this is Silicon angle's the cube where we extract signal from the noise. We bring you the best guest that we can find. We go into events like ESPN goes into sporting events, we go into tech events, we find the tech athletes and bring to you their knowledge and share with you our community. We're here at Moscone in San Francisco at the AWS summit. We're here with Arielle Kellman who's the head of worldwide marketing for AWS. Arielle, welcome to the cube. Thanks for having me, Dave. Yeah, our pleasure. I really appreciate you guys having us here. Great venue. Uh, let's see. What's the numbers? It looks like you know, many, many thousands, well over 5,000 people here by four or 5,000 people here. We're doing a about a dozen of these around the world, one to 4,000 people to help educate our customers about all the new things we're doing, all the new partners that are available to help them thrive in the AWS cloud. >>It's mind boggling the amount of stuff that you guys are doing. We just heard NG Jesse's keynote, for those of you who saw Andy's keynote at reinvent, a lot of similar themes with some, some new stuff in there, but one of the most impressive, he said, he said, other than security, one of the things that we're most proud of is the pace at which we introduce new services. And he talked about this fly wheel effect. Can you talk about that a little bit? Sure. Well, there's kind of two different things going on. The pace of innovation is we're really trying to be nimble and customer centric and ultimately we're trying to give our customers a complete set of services to run virtually any workload in the cloud. So you see us expanding a broader would additional services. And then as we get feedback we add more and more features. >>Yeah. So we're obviously seeing a big enterprise push. Uh, Andy was, was very, I thought, politically correct. He said, look, there's one model which is to keep charging people as much as you possibly can. And then there's our model, which is we proactively cut prices and we passed that on to customers. Um, and, and he also stressed that that's not something that's not a gimmick. It's not a sort of a onetime thing. Can you talk about that in terms of your philosophy and your DNA? It's just our philosophy. It's actually a lot less dramatic than is often portrayed in the press. Just the way we look at things as we're constantly trying to drive efficiencies out of our operations. And as we lower our cost structure, we have a choice. We can either pocket those savings as extra margin or we can pass those savings along to our customers in the form of lower prices. >>And we feel that the ladder is the approach that customers like and we want to make our customers happy. So this event, uh, we were talking off camera, you said you've been doing these now for about two years. You do re-invent once a year. That's your big conference out in Vegas and it's a very, very large event, very well attended. And you do these regionally and in and around the world, right. Talk about that a little bit. We do about a dozen of these a year. Um, we did, uh, New York a couple of weeks ago, London, Australia and Sydney. I'm going to go to India and Tokyo, really about a dozen cities in the world and it's a little tactic. I'm not going to beat all of them, but you know, the focus is to really, uh, deliver educational content. Uh, we'll do about maybe 12 to 16 technical breakout sessions all for free, uh, for, for customers and people who want to learn about AWS for the first time. >>And the, and the audience here is largely practitioners and partners, right? Can it talk about the makeup a little bit? Sure. It's a pretty diverse set of people. Um, we have a technical executives like CEOs and architects and we have lots of developers and then lots of people from our, our partner ecosystem of integrators wanting to, um, you know, brush up on the latest technologies and skills and a lot of people who just want to learn about the cloud and learn about AWS. I think there are a lot of misconceptions about AWS and I'd like to just tackle some of those with you if I may. So let me just sort of, let's list them off and you can respond. Yeah, we'll let our audience to sort of decide. So the first is that AWS has only tested dev workloads. Can you talk about that a little bit? >>Sure. Um, well test and dev local workloads are very popular. We saw, we covered that in the keynote. Um, and it's often a place where it organizations will start out with AWS, but it is by no means the most popular or most dominant workload. We have a lot of people migrating, uh, enterprise apps to the cloud. Um, if you look at, uh, in New York, uh, in our summit we talked about Bristol Myers Squibb, uh, running all of their, um, clinical trial simulations and reducing the amount of time it takes to run a simulation by 98%. Uh, if people are running Oracle, SharePoint, SAP, pretty much any workload in the cloud. And then another popular use is building brand new applications, uh, for the cloud. You can miss, some people call them cloud native applications. A good example is the Washington post who built an app called the social reader that delivers their content to Facebook and now as more people viewing their content, their than with their print magazines and they just couldn't have done that, uh, on premises. >>So, uh, the other one I want to talk about, we're going to do some serious double clicking on security so we don't have to go crazy on it, but, but there's a sort of common perception that the cloud is not secure. What do you guys say about that? Yeah, so, um, really our number one priority is security. You're looking at a security, operational performance, uh, and then our pace of innovation. But with security, um, what we want to do is to give enterprises everything they need to understand how our security works and to evaluate it and how it meets with their requirements for their projects. So it really all starts with our, our physical security, um, our network security, the access of our people. They're all the similar types of technologies that our customers are familiar with. And then they also tend to look at all the certifications and accreditations, SAS 70 type two SOC one SIS trust. >>I ATAR for our government customers. And then I think it was something a lot of people don't understand is how much work we've put into the security features. It's not just is the cloud secure, but can I interact and integrate, uh, your security functionality with all of my existing systems so we can integrate with people's identity and access systems. You could have a private dedicated connection from your enterprise to AWS with direct connect to, I really encourage anyone who has interest in digging into our security features to go to the security center and our website. It's got tons of information. So I'm putting on the spot. Um, what percent of data centers in the world have security that are, that is as good or better than AWS. It'd be an interesting thing for us to do a survey on. But if you think about security at the infrastructure layer down is what we take care of. >>Now when you build your application, you can build a secure app or non-secure app. So the customer has some responsibility there. But in terms of that cloud infrastructure, um, for a vast majority of our customers, they're getting a pretty substantial upgrade in their security. And here's something to think about is that, um, we run a multitenant service, so we have lots and lots of customers sharing that infrastructure and we get feedback from some of the most security conscious companies in the world and government agencies. So when our customers are giving us a enhancement request, and let's say it is, uh, an oil company like shell or financial services company like NASDAQ, and we implement that improvement because there's always new requirements. We implement that all of our hundreds of thousands of customers get those improvements. So it's very hard for a lot of companies to match that internally, to stay up to speed with all the latest, um, requirements that people need. >>Yeah. Okay. So, uh, and you touched on this as well as the compliance piece of it, but when you think of things like, like HIPAA compliance for example, I think a lot of people don't realize that you guys are a lead in that regard. Can you talk about that a little bit more? Yeah. So, uh, we have a lot of customers running HIPAA compliant, uh, workloads. Um, there's, there's one company or the, the Schumacher group, which does emergency room staffing out of Lafayette, Louisiana. And we, companies like that are going through the process. They have to follow their internal compliance guidelines for implementing a HIPAA compliant plan app. It's actually, it's more about how you implement and manage the application than the infrastructure, which is part of it. But we, we satisfy that for our customers. Let's talk a little bit about SLA. That didn't come up at least today in Andy's keynote, but it didn't reinvent and he made a statement at reinvent. >>He said, we've never lost a piece of business because of SLS. And that caught my attention and I said, okay, interesting. Um, talk about, uh, the criticisms of the SLA. So a lot of people say, wow, SLA, not just of Amazon's cloud, but any public cloud. I mean, SLA is a really a, in essence, a, an indication of the risk that you're able to take and willing to take. What are your customers tell you about SLS? The first thing is we don't hear a lot of questions about SLS from our customers. Some customers, it's very important that we have SLA is for most of our services, but what they're usually judging us on is the operational track record that we provide and doing testing and seeing how we operate and how we perform. Uh, and, uh, we had an analyst from IDC recently do a survey of a bunch of our customers and they found that on average the average app that runs on AWS had 80% less downtime than similar apps that are running on premises. >>So we have a lot of anecdotal evidence to suggest that our customers are seeing a reliability improvement by migrating their apps to AWS. You're saying don't judge us on the paper, judge us on our actual activities in production and in the field. Typically what most of our customers are asking for is they want to dig into the actual operational features and, and a track record. Now the other thing I want to address is the so called, you know, uh, uh, exit tax, right? It's no charge to get my data in there. I keep my data in there. You, you, you charged me for storing it for exercise and compute activity, but it's expensive to get it out. Um, how do you address that criticism? Well, our pricing is different for every service and we really model it around our customers to both really to really satisfy a broad set of use cases. >>So one example I think you may be talking about is I would Amazon glacier archive service, which is one penny per gigabyte per month. And for an archive service, we figured that most people want to keep their data in there for a long period of time so that we want to make it as cheap as possible for people to put it in. And if you actually needed to pull it out, the reason is because you may have had some disaster or you accidentally deleted something and that you are going to be, uh, you're going to be retrieving data on a far less frequent basis. So on an overall basis for most customers it makes sense that we could have done is made the retrieval costs lower and then made the storage costs higher. But the feedback we got from customers is, you know, archiving a majority of customers may never even retrieve that data at all. >>So it ended up being cheaper for a vast majority of our customers. I mean that's the point of glacier. If you put it there, you kind of hope you never have to go back and get it. Um, the other thing I wanted to ask you about is some of the innovations that we've seen lately in the industry, like a red shift, right? The data warehouse, you mentioned glacier. It was interesting. Andy said that glacier is the fastest growing service in terms of customers. Red shift was the fastest growing service, I guess overall at NAWS. So Redshift is an interesting move for you guys. Uh, that whole big data and analytics space. What if you could talk about that a little bit? If you talk to it, executives in the enterprise and even startups now, they have to analyze lots of data. Building a big data warehouse is, is one of the best examples of how much the pain of hardware and software infrastructure gets in the way of people. >>And there's also a gatekeeping aspect to it. If you're working in a big company and you want to run, you have a question and a hypothesis, you want to run queries against terabytes and petabytes of data, you pretty often have to go and ask for permission. Can I borrow some time from the data warehouse? No, no, no, no. You're not as important. Well, what are customers going to go, Hey, I'm going to go load the data, load a petabyte of data, run a bunch of analysis, and shut it down and only pay for a few hours. So it's not just about making a cheaper, it's about making use of technology possible where it was just not possible in feasible and cost prohibited before. Yeah, so that's an important point. I mean, it's not, it's not just about sort of moving workloads to the cloud, you know, the old saying a my mess for less. >>It's about enabling new business processes and new procedures and deeper business integration. Um, can you talk about that a little bit more? Add a little color to that notion of adding value beyond just moving workloads out of, you know, on premise into the cloud to cut costs, cut op ex, but enabling new business capabilities. When you remove the infrastructure burden between your ideas and what you want to do, you enable new things to be possible. I think innovation is a big aspect of this where if you think about if you reduce the cost of failure for technology projects so much that approaches zero, you change the whole risk taking culture in a company and more people can try out new ideas and companies can Greenlight more ideas because if they fail it doesn't cost you that much. You haven't built up all this infrastructure. So if you have more ideas that are, that are cultivated, you end up with more innovation. >>Whereas before people are too afraid to try new things. So I'm a reader of of Jeffrey's a annual letters. I mean I think they're great. They're Warren buffet like in that regard. One of the exact emphasizes, you know this year was the customer focus. You guys are a customer focused organization, not a competitive focused organization. And again, you got to recognize that both models can work, right? Can you talk about that a little bit? Just the church of the culture. Yeah, I mean when, you know, starts out with how we build our products. Anyone who has a new idea for a product, first thing they got to do is write the press release. So what our customers are going to see is it valuable to them. And then we get come get products out quickly and then we iterate with customers. We don't spend five years building the first version of something. >>We get it out quickly. Uh, sort of the, the, the lean startup, if you heard of the minimum viable product approach, get it out there and get feedback from customers. Uh, and iterate. We don't spend a lot of time looking at what our competitors are doing cause they're not the ones that pay our bill. They're not the ones that can hire and fire us. It's the customers. So I'm you've seen this thing come, you know, quite a ways. I mean, you were at Salesforce, right? Um, which I guess started at all in 99. You could sell that, look at that as the modern cloud sort of movement was, wasn't called cloud. And then you guys in 2006 actually announced what we now know is, you know, the cloud, where are we in terms of, you know, the cloud, you know, what ending is it? To use the sports analogy, I don't know what ending is it, but you know, it's an amazing time where there's such a massive amount of momentum of adoption of the cloud from every type of company, every type of government agency. >>But yet still, when you look at the percentage of it spend or you go talk to a large company and you say, even with all these projects, what percentage of your total projects, there's still tremendous growth ahead of us. Yeah. So, um, there's always that conversation about the pie charts. 70% of our, our effort is spent on keeping the lights on. 30% is spent on, on innovation. And I don't know where that number came from but, but I think generally anecdotally it feels about right. Um, talk about that shift. Yeah. Well I mean your customer base, you talk to any CIO, they don't like the idea of having 80% of their staff and budget being focused on keeping the lights on and the infrastructure would they like to do is to really shift the mix of what people are working on within their organization. It's not about getting rid of it, it's about giving it tools so that every ounce of effort they're doing is geared towards delivering things to the business. >>And that, that, that's what gets CIO is excited about the cloud is really shifting that and having a majority of their people building and iterating with their end users and with their customers. So we talked about the competition a little bit. I want to ask you a question in general, general terms, you guys have laid out sort of the playbook and there's a lot more coming. We know that, uh, but you know this industry quite well. You know, it's very competitive. People S people see what leaders are doing and they all sort of go after it. Why do you feel confident that AWS will be able to maintain its lead and Kennedy even extend its lead in why? Well, there's a couple things that we sort of suggest for customers to look at. I think first of all is the track record and experience of when you're looking at a cloud provider, have they been in this business for a long time? >>Do they have a services mentality where they've had customers trust them for their, for applications that really they trust their business on? Um, and then I think secondly, is there a commitment to innovation? Is there a pace of new features and new technologies as requirements change? And I think the other, the other piece that our customers really give us a lot of feedback on is that they can count on us Lauren prices, they can count on a real partnership as we get better at this and we're always learning as we get better and we reduce our cost structure, they're going to get to benefit and lower their costs as well. So I think those are kind of big things. The other thing is, is the customer ecosystem I think is a big part of it where, um, you know, this is technology. Uh, people need advice, they need, uh, best practices. >>They often need help. And I'm in a kind of analogy I make is if I have a problem with my phone, with my iPhone, I can probably close my eyes and throw it, I'm going to hit someone who also has an iPhone. I can ask them for help. Well, if you're a startup in San Francisco or London or if you're an enterprise in New York or Sydney, odds are that your colleagues, if they're doing cloud, they're doing it with AWS and you have a lot of people to help you out. A lot of people to share best practices with. And that's a subtle but important point is as, as industry participants begin to aggregate within your cloud, there's a data angle there, right? Because there's data that potentially those organizations could share if they so choose to a, that is a, that is a value. And as you say, the best practice sharing as well. >>I have two last questions for you. Sure. First is, is what gets you excited in this whole field? I think it's like seeing what customers are doing. I mean, that's the cool thing about, uh, offering cloud infrastructure is that anything is possible. Like we met Ryan, uh, who spoke from atomic fiction. These guys are the world's first digital effects agency that's 100% in the cloud. And to see that they made a movie and all the effects like the Robertson mech, his flight film without owning a single server, um, it's just, it's amazing. And to see what these guys can do, how happy they are to have a group of 30, 40 artists that, um, can say yes when the director says I want it to do differently. I want to add, go from 150 to 300 shots and to see how happy and excited they are. >>I mean that, that's what motivates me. Yeah. Okay. And then my last question, Ariel, is, um, you know, what keeps you up at night? What worries you? Well, I think, you know, the most important thing that we can't forget is to really keep our fingers on the pulse of the customers and what they want, and also helping them to figure out what they want next. Because if we don't keep moving, then we're not going to keep pace with what the customers want to use the cloud for. All right, Ariel Kelman thanks very much. Congratulations on the Mason's progress and we'll be watching and, and really appreciate, again, you having us here. Appreciate your time coming on. Good luck with the rest of the tour. I hope you don't have to do every city. It sounds like you don't, but, uh, but if it sounds like you've enjoyed them, so, uh, congratulations again. Great. All right. This is Dave Milan to keep it right there. This is the cube. We'll be back with our next guest right after this word.