(upbeat music) >> Hey welcome back everybody, Jeff Frick here with theCUBE. We're in our Palo Alto studio this morning for a CUBE Conversation talking about content marketing, attention economy, a lot of really interesting topics that should be top of mind for marketers, that we're in very interesting times on the B2C side and even more, I think, on the B2B side. So we're excited to have Randy Wootton, he's the CEO of Percolate. Randy, great to see you. >> Thanks very much for having me. A real pleasure to be here. >> Absolutely, so for those who aren't familiar, give us kind of the quick and dirty on Percolate. >> Percolate has been around for about seven years. It started as a social media marketing platform. So helping people, helping brands, build their brands on the social landscape, and integrating campaigns to deploy across the different social channels. Over the last couple of years, it's been moving more into the space called content marketing, which is really an interesting new area that marketers are coming to terms with. How do you put together content and orchestrate it across all the different channels. >> And it's interesting, a lot of vocabulary on the website around experiences and content not a lot about products. So how should marketers think and how does experience and content ultimately map back to the products and services you're trying to sell. >> Well, I do think that's a great point. And the distinction between modern brands, who are trying to create relationships with their consumers, rather than pushing products, especially if you're B2B, or technology pushing speeds and feeds. Instead, you are trying to figure out what is going to enable you to create a brand that consumers pull through versus getting pushed at. And so I think the idea around content marketing is that in some ways advertising isn't working anymore. People aren't paying attention to display ads, they're not clicking, they aren't processing the information. But, they are still buying. So the idea for marketers is, how do you get the appropriate content at the right time, to the right person, in their purchase journey. >> Right, and there's so many different examples of people doing new things. There's more conversations kind of, of the persona of the company, of the purpose, purpose driven things, really trying to appeal to their younger employees as well as a younger customer. You have just crazy off the wall things, which never fail to entertain. Like Geico, who seems to break every rule of advertising by having a different theme every time you see a Geico ad. So people are trying humor, they're trying theater, they're trying a lot of things to get through because the tough thing today is getting people's attention. >> I think so, and they talk a lot about the attention economy. That we live in a world of exponential fragmentation. All the information that we are processing across all these different devices. And a brand trying to break through, there's a couple of challenges, one is you have to create a really authentic voice, one that resonates with who you are and how you show up. And then, I think the second point is you recognize that you are co-building the brand with the consumers. It's no longer you build the Super Bowl ad and transmit it on T.V., and people experience your brand. You have this whole unfolding experience in real time. You've seen some of the airlines, for example, that have struggled with the social media downside of brand building. And so how do control, not control, but engage with consumers in a way that feels very authentic and it continues to build a relationship with your consumers. >> Yeah, it's interesting, a lot of things have changed. The other thing that has changed now is that you can have a direct relationship with that consumer whether you want it or not, via social media touches, maybe you were before, that was hidden through your distribution, or you didn't have that, that direct connect. So, you know, being able to respond to this kind of micro-segmentation, it's one thing to talk about micro-segmentation on the marketing side, it's a whole different thing with that one individual, with the relatively loud voice, is screaming "Hey, I need help." >> That's right, and I think there are a couple of things on that point. One is, I've been in technology for 20 years. I've been at Microsoft, I was at Salesforce, I was at AdReady, Avenue A, and Quantive. And now, Rocket Fuel before I came to Percolate. And I've always been wrestling with two dimensions of the digital marketing challenge. One is around consumer identity, and really understanding who the consumer is, and where they've been and what they've done. The second piece is around the context. That is, where they are in the moment, and which device they're on. And so, those are two dimensions of the triangle. The third is the content, or in advertising it's the creative. And that's always been the constraint. You never have enough creative to be able to really deliver on the promise of personalization, of getting the right message to the right person at the right time. And that now is the blockade. That now is the bottleneck, and that now is what brands are really trying to come to terms with. Is how do we create enough content so that you can create a compelling experience for each person, and then if there's someone who is engaging in a very loud voice, how do you know, and how do you engage to sort of address that, but not loose connections with all your other consumers. >> Right, it's interesting, you bring up something, in some of the research, in micro-moments. And in the old days, I controlled all of the information, you had to come for me for the information, and it was a very different world. And now, as you said, the information is out there, there's too much information. Who's my trusted conduit for the information. So by the time they actually get to me, or I'm going to try to leverage these micro-moments, it's not about, necessarily direct information exchange. What are some of these kind of micro-moments, and how are they game changers? >> Well, I think the fact that we can make decisions in near real time. And when I was at Rocket Fuel, we were making decisions in less than 20 milliseconds, processing something like 200 billion bid transactions a day, and so I just think people are not yet aware of the amount, the volume and the velocity of data that is being processed each and every day. And, to make decisions about specific moments. So the two moments I give as examples are: One, I'm sitting at home watching the Oakland Raiders with my two boys, I'm back on the couch and we're watching the game, and Disney makes an advertisement. I'm probably open to a Disney advertisement with my boys next to me, who are probably getting an advertisement at the same time by Disney. I'm a very different person in that moment, or that micro-moment than when I'm commuting in from Oakland to San Francisco on BART, reading the New York Times. I'm not open to a Disney ad at that moment, because I'm concentrating on work, I'm concentrating on the commute. And I think the thing that brands are coming to terms with is, how much am I willing to pay to engage with me sitting on the couch versus me sitting on BART. And that is where the real value comes from, is understanding which moments are the valuable ones. >> So there's so much we can learn from Ad Tech. And I don't think Ad Tech gets enough kind of credit for operating these really large, really hyper speed, really sophisticated marketplaces that are serving up I don't even know how many billions of transactions per unit time. A lot of activity going on. So, you've been in that world for a while. As you've seen them shift from kind of people driving, and buyers driving to more automation, what are some of the lessons learned, and what should learn more from a B2B side from this automated marketplace. >> Well, a couple of things, one is the machines are not our enemies, they are there to enable or enhance our capabilities. Though I do think it's going to require people to re-think work, specifically at agencies, in terms of, you don't need people to do media mixed modeling on the front end in Excel files, instead, you need capacity on the back end after the data has come out, and to really understand the insights. So there is some re-training or re-skilling that's needed. But, the machines make us smarter. It's not artificial intelligence, it's augmented intelligence. I think for B2B in particular what you're finding is, all the research shows that B2B purchasers spend something like 70 or 80% of their time in making the purchase decision before they even engage with the sales rep. And as a B2B company ourselves, we know how expensive our field reps are. And so to make sure that they are engaging with people at the right time, understanding the information that they would have had, before our sales cycle starts, super important. And I think that goes back to the content orchestration, or content marketing revolution that we are seeing now. And, you know, I that there is, when you think about it, most marketers today, use PowerPoint and Excel to have their marketing calendar and run their campaigns. And we're the only function left where you don't have an automated system, like a sales force for marketers, or a service now for marketers. Where a chief of marketing or a SVP of marketing, has, on their phone the tool of record, they system of record that they want to be able to oversee the campaigns. >> Right, although on the other hand, you're using super sophisticated A/B testing across multiple, multiple data sets, rather than doing that purchase price, right. You can test for colors, and fonts, and locations. And it's so different than trying to figure out the answer, make the investment, blast the answer, than this kind of DevOps way, test, test, test, test, test, adjust, test, test, test, test, adjust. >> You're absolutely right, and that's what, at Rocket Fuel, and any real AI powered system, they're using artificial intelligence as the higher order, underneath that you have different categories, like neural networks, deep learning and machine learning. We were using a logistic regression analysis. And we were running algorithms 27 models a day, every single day, that would test multiple features. So it wasn't just A/B testing, it was multi variant analysis happening in real time. Again, the volume and velocity of data is beyond human comprehension, and you need the machine learning to help you make sense of all that data. Otherwise, you just get overwhelmed, and you drown in the data. >> Right, so I want to talk a little bit about PNG. >> I know they're close and dear to your heart. In the old days, but more recently, I just want to bring up, they obviously wield a ton of power in the advertising spin campaign. And they recently tried to bring a little bit more discipline and said, hey we want tighter controls, tighter reporting, more independent third party reporting. There's this interesting thing going now where before, you know, you went for a big in, 'causethen you timed it by some conversion rating you had customers at the end. But now people it seems like, are so focused on the in kind of forgetting necessarily about the conversion because you can drive promoted campaigns in the social media, that now there's the specter of hmm, are we really getting, where we're getting. So again, the PNG, and the consumer side, are really leading kind of this next revolution of audit control and really closer monitoring to what's happening in these automated ad marketplaces. >> Well, I think what you're finding is, there's digital transformation happening across all functions, all industries. And, I think that in the media space in particular, you're also having an agency business model transformation. And what they used to provide for brands has to change as you move forward. PNG has really been driving that. PNG because of how much money they spend on media, has the biggest stick in the fight, and they've brought a lot of accountability. Mark Pritchard, in particular, has laid down these gauntlets the last couple of years, in terms of saying, I want more accountability, more visibility. Part of the challenge with the digital ecosystem is the propensity for fraud and lack of transparency, 'cause things are moving so quickly. So, the fact, that on one side the machines are working really well for ya, on the other side it's hard to audit it. But PNG is really bringing that level of discipline there. I think the thing that PNG is also doing really well, is they're really starting to re-think about how CPG brands can create relationships with their consumers and customers, much like we were talking about before. Primarily, before, CPG brands would work through distributors and retailers, and not really have a relationship with the end consumer. But now as they've started to build up their first party profiles, through clubs and loyalty programs, they're starting to better understand, the soccer mom. But it's not just the soccer mom, it's the soccer mom in Oakland at 4 o'clock in the afternoon, as she goes to Starbucks, when she's picked up her kids from school. All of those are features that better help PNG understand who that person is, in that context, and what's the appropriate engagement to create a compelling experience. That's really hard to do at the individual level. And when you think about the myriad of brands, that PNG has, they have to coordinate their stories and conversations across all of those brands, to drive market share. >> Yeah, it's a really interesting transformation, as we were talking earlier, I used to joke always, that we should have the underground railroad, from Cincinnati to Silicone Valley to get good product managers, right. 'Cause back in the day you still were doing PRD's and MRD's and those companies have been data driven for a long time and work with massive shares and small shifts in market percentages. But, as you said, they now, they're having to transform still data driven, but it's a completely different set of data, and much more direct set of data from the people that actually consume our products. >> And it's been a long journey, I remember when I was at Microsoft, gosh this would have been back in 2004 or 2005, we were working with PNG and they brought their brands to Microsoft. And we did digital immersions for them, to help them understand how they could engage consumers across the entire Microsoft network, and that would include X-Box, Hotmail at the time, MSN, and the brands were just coming to terms with what their digital strategy was and how they would work with Portal versus how they would work with other digital touchpoints. And I think that has just continued to evolve, with the rise of Facebook, with the rise of Twitter, and how do brands maintain relationships in that context, is something that every brand manager of today is having to do. My father, I think we were chatting a little earlier, started his career in 1968 as a brand manager for PNG. And, I remember him telling the stories about how the disciplined approach to brand building, and the structure and the framework hasn't changed, the execution has, over the last 50 years. >> So, just to bring it full circle before we close out, there's always a segment of marketing that's driven to just get me leads, right, give me leads, I need barcode scans at the conference et cetera. And then there's always been kind of the category of kind of thought leadership. Which isn't necessarily tied directly back to some campaign, but we want to be upfront, and show that we're a leading brand. Content marketing is kind of in-between, so, how much content marketing lead towards kind of thought leadership, how much lead kind of towards, actually lead conversions that I can track, and how much of it is something completely different. >> That's a great question, I think this is where people are trying to come to terms, what is content, long form, short form video. I think of content as being applied across all three dimensions of marketing. One is thought leadership, number two is demand gen, and number three is actualization or enablement in a B2B for your sales folks. And how do you have the right set of content along each of those dimensions. And I don't think they're necessarily, I fundamentally think the marketing funnel is broken. It's not you jump in at the top, and you go all the way to the bottom and you buy. You have this sort of webbed touch of experiences. So the idea is, going back to our earlier conversation, is, who is that consumer, what do you know about him, what is the context, and what's the appropriate form of content for them, where they are in their own buyer journey. So, a UGC video on YouTube may be okay for one consumer in a specific moment, but a short form video may be better for someone else, and a white paper may be better. And I think that people don't necessarily go down the funnel and purchase because they click on a search ad, they instead may be looking at a white paper at the end of the purchase, and so the big challenge, is the attribution of value, and that's one of the things that we're looking at Percolate. Is almost around thinking about it as content insight. Which content is working for whom. 'Cause right now you don't know, and I think the really interesting thing is you have a lot of people producing a lot of content. And, they don't know if it's working. And I think when we talk to marketers, that we hear their teams are producing content, and they want to know, they don't want to create content that doesn't work. They just want a better understanding of what's working, and that's the last challenge in the digital marketing transformation to solve. >> And how do you measure it? >> How do you measure, how do you define it? And categorize it, so that's one of the challenges, we were chatting a little bit before, about what you guys are doing at CUBE, and your clipper technology and how you're able to dis-aggregate videos, to these component pieces, or what in an AI world, you'd call features, that then can be loaded as unstructured data, and you can apply AI against it and really come up with interesting insights. So I think there's, as much as I say, the transformation of the internet has been huge, AI is going to transform our world more than we even can conceive of today. And I think content eventually will be impacted materially by AI. >> I still can't help but think of the original marketing quote, I've wasted half of my marketing budget, I'm just not sure which half. But, really it's not so much the waste as we have to continue to find better ways to measure the impact of all these kind of disparate non-traditional funnel things. >> I think you're right, I think it was Wanamaker who said that. I think your point is spot on, it's something we've always wrestled with, and as you move more into the branding media, they struggle more with the accountability. That's one of the reasons why direct response in the internet has been such a great mechanism, is because it's data based, you can show results. The challenge there is the attribution. But I think as we get into video, and you can get to digital video assets, and you can break it down into its component pieces, and all the different dimensions, all of that's fair game for better understanding what's working. >> Randy, really enjoyed the conversation, and thanks for taking a minute out of your busy day. >> My pleasure, always enjoy it. >> Alright, he's Randy, I'm Jeff, you're watching theCUBE from Palo Alto Studios, thanks for watching. (digital music)

>> Voiceover: Live from midtown Manhattan, it's theCUBE! Covering Big Data New York City 2017. Brought to you by, SiliconANGLE Media and as ecosystems sponsors. >> Okay, welcome back everyone, live in New York City this is theCUBE's coverage of, eighth year doing Hadoop World now, evolved into Strata Hadoop, now called Strata Data, it's had many incarnations but O'Reilly Media running their event in conjunction with Cloudera, mainly an O'Reilly media show. We do our own show called Big Data NYC here with our community with theCUBE bringing you the best interviews, the best people, entrepreneurs, thought leaders, experts, to get the data and try to project the future and help users find the value in data. My next guest is Rob Thomas, who is the General Manager of IBM Analytics, theCUBE Alumni, been on multiple times successfully executing in the San Francisco Bay area. Great to see you again. >> Yeah John, great to see you, thanks for having me. >> You know IBM is really been interesting through its own transformation and a lot of people will throw IBM in that category but you guys have been transforming okay and the scoreboard yet has to yet to show in my mind what's truly happening because if you still look at this industry, we're only eight years into what Hadoop evolved into now as a large data set but the analytics game just seems to be getting started with the cloud now coming over the top, you're starting to see a lot of cloud conversations in the air. Certainly there's a lot of AI washing, you know, AI this, but it's machine learning and deep learning at the heart of it as innovation but a lot more work on the analytics side is coming. You guys are at the center of that. What's the update? What's your view of this analytics market? >> Most enterprises struggle with complexity. That's the number one problem when it comes to analytics. It's not imagination, it's not willpower, in many cases, it's not even investment, it's just complexity. We are trying to make data really simple to use and the way I would describe it is we're moving from a world of products to platforms. Today, if you want to go solve a data governance problem you're typically integrating 10, 15 different products. And the burden then is on the client. So, we're trying to make analytics a platform game. And my view is an enterprise has to have three platforms if they're serious about analytics. They need a data manager platform for managing all types of data, public, private cloud. They need unified governance so governance of all types of data and they need a data science platform machine learning. If a client has those three platforms, they will be successful with data. And what I see now is really mixed. We've got 10 products that do that, five products that do this, but it has to be integrated in a platform. >> You as an IBM or the customer has these tools? >> Yeah, when I go see clients that's what I see is data... >> John: Disparate data log. >> Yeah, they have disparate tools and so we are unifying what we deliver from a product perspective to this platform concept. >> You guys announce an integrated analytic system, got to see my notes here, I want to get into that in a second but interesting you bring up the word platform because you know, platforms have always been kind of reserved for the big supplier but you're talking about customers having a platform, not a supplier delivering a platform per se 'cause this is where the integration thing becomes interesting. We were joking yesterday on theCUBE here, kind of just kind of ad hoc conceptually like the world has turned into a tool shed. I mean everyone has a tool shed or knows someone that has a tool shed where you have the tools in the back and they're rusty. And so, this brings up the tool conversation, there's too many tools out there that try to be platforms. >> Rob: Yes. >> And if you have too many tools, you're not really doing the platform game right. And complexity also turns into when you bought a hammer it turned into a lawn mower. Right so, a lot of these companies have been groping and trying to iterate what their tool was into something else it wasn't built for. So, as the industry evolves, that's natural Darwinism if you will, they will fall to the wayside. So talk about that dynamic because you still need tooling >> Rob: Yes. but tool will be a function of the work as Peter Burris would say, so talk about how does a customer really get that platform out there without sacrificing the tooling that they may have bought or want to get rid of. >> Well, so think about the, in enterprise today, what the data architecture looks like is, I've got this box that has this software on it, use your terms, has these types of tools on it, and it's isolated and if you want a different set of tooling, okay, move that data to this other box where we have the other tooling. So, it's very isolated in terms of how platforms have evolved or technology platforms today. When I talk about an integrated platform, we are big contributors to Kubernetes. We're making that foundational in terms of what we're doing on Private Cloud and Public Cloud is if you move to that model, suddenly what was a bunch of disparate tools are now microservices against a common architecture. And so it totally changes the nature of the data platform in an enterprise. It's a much more fluid data layer. The term I use sometimes is you have data as a service now, available to all your employees. That's totally different than I want to do this project, so step one, make room in the data center, step two, bring in a server. It's a much more flexible approach so that's what I mean when I say platform. >> So operationalizing it is a lot easier than just going down the linear path of provisioning. All right, so let's bring up the complexity issue because integrated and unified are two different concepts that kind of mean the same thing depending on how you look at it. When you look at the data integration problem, you've got all this complexity around governance, it's a lot of moving parts of data. How does a customer actually execute without compromising the integrity of their policies that they need to have in place? So in other words, what are the baby steps that someone can take, the customers take through with what you guys are dealing with them, how do they get into the game, how do they take steps towards the outcome? They might not have the big money to push it all at once, they might want to take a risk of risk management approach. >> I think there's a clear recipe for doing this right and we have experience of doing it well and doing it not so well, so over time we've gotten some, I'd say a pretty good perspective on that. My view is very simple, data governance has to start with a catalog. And the analogy I use is, you have to do for data what libraries do for books. And think about a library, the first thing you do with books, card catalog. You know where, you basically itemize everything, you know exactly where it sits. If you've got multiple copies of the same book, you can distinguish between which one is which. As books get older they go to archives, to microfilm or something like that. That's what you have to do with your data. >> On the front end. >> On the front end. And it starts with a catalog. And that reason I say that is, I see some organizations that start with, hey, let's go start ETL, I'll create a new warehouse, create a new Hadoop environment. That might be the right thing to do but without having a basis of what you have, which is the catalog, that's where I think clients need to start. >> Well, I would just add one more level of complexity just to kind of reinforce, first of all I agree with you but here's another example that would reinforce this step. Let's just say you write some machine learning and some algorithms and a new policy from the government comes down. Hey, you know, we're dealing with Bitcoin differently or whatever, some GPRS kind of thing happens where someone gets hacked and a new law comes out. How do you inject that policy? You got to rewrite the code, so I'm thinking that if you do this right, you don't have to do a lot of rewriting of applications to the library or the catalog will handle it. Is that right, am I getting that right? >> That's right 'cause then you have a baseline is what I would describe it as. It's codified in the form of a data model or in the form on ontology for how you're looking at unstructured data. You have a baseline so then as changes come, you can easily adjust to those changes. Where I see clients struggle is if you don't have that baseline then you're constantly trying to change things on the fly and that makes it really hard to get to this... >> Well, really hard, expensive, they have to rewrite apps. >> Exactly. >> Rewrite algorithms and machine learning things that were built probably by people that maybe left the company, who knows, right? So the consequences are pretty grave, I mean, pretty big. >> Yes. >> Okay, so let's back to something that you said yesterday. You were on theCUBE yesterday with Hortonworks CEO, Rob Bearden and you were commenting about AI or AI washing. You said quote, "You can't have AI without IA." A play on letters there, sequence of letters which was really an interesting comment, we kind of referenced it pretty much all day yesterday. Information architecture is the IA and AI is the artificial intelligence basically saying if you don't have some sort of architecture AI really can't work. Which really means models have to be understood, with the learning machine kind of approach. Expand more on that 'cause that was I think a fundamental thing that we're seeing at the show this week, this in New York is a model for the models. Who trains the machine learning? Machines got to learn somewhere too so there's learning for the learning machines. This is a real complex data problem and a half. If you don't set up the architecture it may not work, explain. >> So, there's two big problems enterprises have today. One is trying to operationalize data science and machine learning that scale, the other one is getting the cloud but let's focus on the first one for a minute. The reason clients struggle to operationalize this at scale is because they start a data science project and they build a model for one discreet data set. Problem is that only applies to that data set, it doesn't, you can't pick it up and move it somewhere else so this idea of data architecture just to kind of follow through, whether it's the catalog or how you're managing your data across multiple clouds becomes fundamental because ultimately you want to be able to provide machine learning across all your data because machine learning is about predictions and it's hard to do really good predictions on a subset. But that pre-req is the need for an information architecture that comprehends for the fact that you're going to build models and you want to train those models. As new data comes in, you want to keep the training process going. And that's the biggest challenge I see clients struggling with. So they'll have success with their first ML project but then the next one becomes progressively harder because now they're trying to use more data and they haven't prepared their architecture for that. >> Great point. Now, switching to data science. You spoke many times with us on theCUBE about data science, we know you're passionate about you guys doing a lot of work on that. We've observed and Jim Kobielus and I were talking yesterday, there's too much work still in the data science guys plate. There's still doing a lot of what I call, sys admin like work, not the right word, but like administrative building and wrangling. They're not doing enough data science and there's enough proof points now to show that data science actually impacts business in whether it's military having data intelligence to execute something, to selling something at the right time, or even for work or play or consume, or we use, all proof is out there. So why aren't we going faster, why aren't the data scientists more effective, what does it going to take for the data science to have a seamless environment that works for them? They're still doing a lot of wrangling and they're still getting down the weeds. Is that just the role they have or how does it get easier for them that's the big catch? >> That's not the role. So they're a victim of their architecture to some extent and that's why they end up spending 80% of their time on data prep, data cleansing, that type of thing. Look, I think we solved that. That's why when we introduced the integrated analytic system this week, that whole idea was get rid of all the data prep that you need because land the data in one place, machine learning and data science is built into that. So everything that the data scientist struggles with today goes away. We can federate to data on cloud, on any cloud, we can federate to data that's sitting inside Hortonworks so it looks like one system but machine learning is built into it from the start. So we've eliminated the need for all of that data movement, for all that data wrangling 'cause we organized the data, we built the catalog, and we've made it really simple. And so if you go back to the point I made, so one issue is clients can't apply machine learning at scale, the other one is they're struggling to get the cloud. I think we've nailed those problems 'cause now with a click of a button, you can scale this to part of the cloud. >> All right, so how does the customer get their hands on this? Sounds like it's a great tool, you're saying it's leading edge. We'll take a look at it, certainly I'll do a review on it with the team but how do I get it, how do I get a hold of this? What do I do, download it, you guys supply it to me, is it some open source, how do your customers and potential customers engage with this product? >> However they want to but I'll give you some examples. So, we have an analytic system built on Spark, you can bring the whole box into your data center and right away you're ready for data science. That's one way. Somebody like you, you're going to want to go get the containerized version, you go download it on the web and you'll be up and running instantly with a highly performing warehouse integrated with machine learning and data science built on Spark using Apache Jupyter. Any developer can go use that and get value out of it. You can also say I want to run it on my desktop. >> And that's free? >> Yes. >> Okay. >> There's a trial version out there. >> That's the open source, yeah, that's the free version. >> There's also a version on public cloud so if you don't want to download it, you want to run it outside your firewall, you can go run it on IBM cloud on the public cloud so... >> Just your cloud, Amazon? >> No, not today. >> John: Just IBM cloud, okay, I got it. >> So there's variety of ways that you can go use this and I think what you'll find... >> But you have a premium model that people can get started out so they'll download it to your data center, is that also free too? >> Yeah, absolutely. >> Okay, so all the base stuff is free. >> We also have a desktop version too so you can download... >> What URL can people look at this? >> Go to datascience.ibm.com, that's the best place to start a data science journey. >> Okay, multi-cloud, Common Cloud is what people are calling it, you guys have Common SQL engine. What is this product, how does it relate to the whole multi-cloud trend? Customers are looking for multiple clouds. >> Yeah, so Common SQL is the idea of integrating data wherever it is, whatever form it's in, ANSI SQL compliant so what you would expect for a SQL query and the type of response you get back, you get that back with Common SQL no matter where the data is. Now when you start thinking multi-cloud you introduce a whole other bunch of factors. Network, latency, all those types of things so what we talked about yesterday with the announcement of Hortonworks Dataplane which is kind of extending the YARN environment across multi-clouds, that's something we can plug in to. So, I think let's be honest, the multi-cloud world is still pretty early. >> John: Oh, really early. >> Our focus is delivery... >> I don't think it really exists actually. >> I think... >> It's multiple clouds but no one's actually moving workloads across all the clouds, I haven't found any. >> Yeah, I think it's hard for latency reasons today. We're trying to deliver an outstanding... >> But people are saying, I mean this is head room I got but people are saying, I'd love to have a preferred future of multi-cloud even though they're kind of getting their own shops in order, retrenching, and re-platforming it but that's not a bad ask. I mean, I'm a user, I want to move from if I don't like IBM's cloud or I got a better service, I can move around here. If Amazon is too expensive I want to move to IBM, you got product differentiation, I might want to to be in your cloud. So again, this is the customers mindset, right. If you have something really compelling on your cloud, do I have to go all in on IBM cloud to run my data? You shouldn't have to, right? >> I agree, yeah I don't think any enterprise will go all in on one cloud. I think it's delusional for people to think that so you're going to have this world. So the reason when we built IBM Cloud Private we did it on Kubernetes was we said, that can be a substrate if you will, that provides a level of standards across multiple cloud type environments. >> John: And it's got some traction too so it's a good bet there. >> Absolutely. >> Rob, final word, just talk about the personas who you now engage with from IBM's standpoint. I know you have a lot of great developers stuff going on, you've done some great work, you've got a free product out there but you still got to make money, you got to provide value to IBM, who are you selling to, what's the main thing, you've got multiple stakeholders, could you just clarify the stakeholders that you're serving in the marketplace? >> Yeah, I mean, the emerging stakeholder that we speak with more and more than we used to is chief marketing officers who have real budgets for data and data science and trying to change how they're performing their job. That's a major stakeholder, CTOs, CIOs, any C level, >> Chief data officer. >> Chief data officer. You know chief data officers, honestly, it's a mixed bag. Some organizations they're incredibly empowered and they're driving the strategy. Others, they're figure heads and so you got to know how the organizations do it. >> A puppet for the CFO or something. >> Yeah, exactly. >> Our ops. >> A puppet? (chuckles) So, you got to you know. >> Well, they're not really driving it, they're not changing it. It's not like we're mandated to go do something they're maybe governance police or something. >> Yeah, and in some cases that's true. In other cases, they drive the data architecture, the data strategy, and that's somebody that we can engage with right away and help them out so... >> Any events you got going up? Things happening in the marketplace that people might want to participate in? I know you guys do a lot of stuff out in the open, events they can connect with IBM, things going on? >> So we do, so we're doing a big event here in New York on November first and second where we're rolling out a lot of our new data products and cloud products so that's one coming up pretty soon. The biggest thing we've changed this year is there's such a craving for clients for education as we've started doing what we're calling Analytics University where we actually go to clients and we'll spend a day or two days, go really deep and open languages, open source. That's become kind of a new focus for us. >> A lot of re-skilling going on too with the transformation, right? >> Rob: Yes, absolutely. >> All right, Rob Thomas here, General Manager IBM Analytics inside theCUBE. CUBE alumni, breaking it down, giving his perspective. He's got two books out there, The Data Revolution was the first one. >> Big Data Revolution. >> Big Data Revolution and the new one is Every Company is a Tech Company. Love that title which is true, check it out on Amazon. Rob Thomas, Bid Data Revolution, first book and then second book is Every Company is a Tech Company. It's theCUBE live from New York. More coverage after the short break. (theCUBE jingle) (theCUBE jingle) (calm soothing music)