>> Live from Las Vegas. It's the queue covering Adobe Summit twenty nineteen brought to you by Adobe. >> Hey, welcome back, everyone. Cube live coverage here in Las Vegas for Adobe sum of twenty nineteen. I'm John for which have Frick. Where he with a cube alumni that had job for three years. And you'LL Bhambri, Vice president of Platform Engineering at Adobe. Great to see you. Thanks for coming by. >> Thank you. >> Let's talk. Engineering. That was your line on the keynote. Great Kino today, by the way, super impressed with content. I'm washing that slides you're presenting, like were to cloud company. I'm failing my Amazon reinvent here. You guys built a really cool platform. Take us through. This was your mission. That's true. So take us through your journey. So how'd we get here? How did you get this beautiful platform? >> So, you know, we've been at it for a few years, and as you know, we've seen CEOs and see emos late. That their focus is to really deliver, you know, delightful experiences to their customers. And not just once, but throughout the journey off the customer. Right? Delight your customer. Every step of the way is what you'LL hear from Adobe from our customers. And we are really helping them to do that. And obviously, in order to do that, there is on, as you well know, that data is behind everything to do with experiences as well. There is a lot ofthe interaction of data and bringing it all together to really understand that holistic view of the customer is super important. And, you know, as you've been this realist, you know, the holistic view of the customer. It's not that you just ended once, and you forget about it, right? You have to build this in real time because the interactions that customers are having with brands are to wear through mobile devices to the apse that they're using off the those brands. And the businesses have to understand that whole journey off the customers and understand what their preferences are. Write what? You know what they like, what they don't like and be able to keeping like that context really during the journey. Whether they're coming to their Web site for the first time are they are repeat, customers be able to give them the right experience at every touch point. And that's where you need all of this data, which is a lot of data. So so you know, We've been on this big data journey on me personally, even, you know, for a long time. But the scale that I've seen here I had not seen before >> our IBM conscious when you weren't IBM prior from Hadoop World, you had your eye on this big data trend. Now, at Adobe, when you have really data coming in with apple cases out in the market place to put a platform together. Hard task. But I want to ask you specific question around that. Looking at the architecture slide you have and analytics cloud and add Cloud a marketing cloud in the commerce cloud. They all have Marcus that they have to address and be highly effective as almost appear placed in alone. But now, integrating across each other now with the journey that you guys were put together is difficult. I know that from a computer science background. How does how did you guys look at that? Architecturally, what were some of the guiding principles around building that? Because you don't want to compromise the capabilities of those functional elements. So you decompose and I get that. How did you put it all together? What was the key guiding principle around. >> Yeah, so that's a really good question, because I mean, Adobe has bean delivering applications, right? Like you said, whether it's around analytics, our marketing cloud or advertising. And now we obviously just acquired the commerce cloud on DH. When you look at the common stuff around all of this, it's data, right? Data being captured, two different channels, data that needs to be curated, you know, having a common data dictionary so that, you know, things mean the same on DH, even though they're captured two different channels. So gathering this data curating this data, organizing it for that holistic view of the customer organizing it so that you can do B I, and reporting on that data is all something that we pull together in the platform there. Now it becomes that whether it is you're doing analytics on this right, which could be a B I and the putting all your doing I and Melander is to do your next best action. All your targeting these customers with personalized content. You're doing it on that single version of the truth, which is the real time customer profile that powers all of these different clouds. So that it's not like when you do reporting you have one view ofthe a customer. But when you're trying to show them personalized content, half the view is lost because the data was siloed. So we've gone past all of that. There's no data silos now, right? >> Real time customer profile is literally being updated all the time. That's the key in great, exciting part about it is a curious >> kind of philosophically. And execution is like you've been in this space for a long time, and one of the jokes I left shares, you know, we used to make decisions based on a sampling of something that happened in the past. Now you know, we can make decisions based on all of the data that's happening now, but at the same time, your challenges, that source's heir changing all the time. The speed of the input is changing all the time, and the expected return on your reaction is shortening all the time. So from from just a date, a professional and I'm sure it's super exciting and super scary to move that paradigm shift to you got to deliver the right thing right now >> and you know, one of the key things field is that as all of this data was being gathered, right, obviously this data has to be gathered with these events are occurring. So if you look at glands, their customers are global. They are transacting browsing, whether it's on where mobile devices with that land globally around the world. That means data has to be collected from these globally distributed edges. And it has to be brought in processed in real time pending that profile. And as the data keeps coming, the profile is updated right? And and you can't have stained a dying, they're right, because otherwise, you know you are action ing based on something that happened five minutes ago. You know how we've seen that you buy something and you're still getting ads off that same product that you buy even a day or two days late? >> Already bought ten anymore. Ten. >> So that's because that bland has a stale profile off you, right? But if they had the real time customer profile, then there's no way that they would be delivering our action ing based on that stale information. So just like the data was being gathered from edges even when we have to deliver the experiences right. This is where edge computing comes into the picture, right? So we are also taking. So when you look at the whole architecture of the platform, yes, it's based on the cloud and you know it's a big data stack. It's completely assassin offering. But there is also a big edge computing part of the platform, which is where all the hard data is collected. Process and action and to your point, trade, like as we build, say, predictive models on Ex Best action on the data that's on the cloud. The scoring off the models has to happen on the edges where the events are crying. So this is a complicated engineering problem. But that's why I guess we love it. >> Big smile. So the data is critical. So about how adobes changed over the past few years because you guys did clown. I heard the nuance. I heard that keynote, you know, reading through the names of the lines. Is that it? It's hard to get data right at the beginning. Yeah, get cloud right now. You got data rights. Take us through that point because this is where I think the key to success is how to make that data work. Because if you're gonna have open AP eyes and open data integrity, that data right database, it's a time Siri's aircraft dated. A lot of different applications might choose certain technology. Yes, you have to deal with that. How, how important is the texture on that? >> So So that's why that's a great question that, you know, from a platform standpoint, our goal is that we have to be able to answer the questions with the right laden see or speed as well as relevancy, right? So when we talk really time, it's about it's Leighton sees. You know, when you talk to engineers, they only talk agency. But it's not that right. It's needn't see and relevancy. So in order to depending on. Like if it's more like B I r. Reporting kind off questions or queries, you need to organize the data certainly for, you know, single lookups off customers, right? You have to organize the data differently, and that's where our I'd be comes into the picture that how do we partition and organize this data to meet the needs ofthe both operational as well as the more, you know, like analytical kind ofthe workloads. So we support both and to your point, also that, you know, then we need a sequel database where there's no sequel database are a graft database. I mean, those are choices we make, but on top, they're providing FBI's. So we're abstracting all of that from the user. And you know how where we direct question, that's all R ight, but their applications are not going to break because they're writing to the FBI's. So as technologies advance underneath, we make those choices, but again so that they're getting the right agency and relevancy. >> So in the cloud game, we used to talk about this when you when you're on the Cuban way, an IBM the devil's movement was full tilt and they use the term infrastructure is code. Uh, so you're kind of getting out. I want to get your reaction to this Is that if applications and workloads are the use, cases are gonna determine the date of structures, data architecture and Leighton see relevance equation isn't. Then there's a new kind of infrastructures code emerging. Is that data as code? So, or maybe it's this should that workloads dictate what type of data diversity and Leighton see relevance is needed Or is that come from the network again? The question is, workloads are kind of in charge, I guess. What? I'm trying to get out. So >> I Yeah, I would say that, you know, as a platform, you have to support all of these workloads, right? So which means that from an architecture standpoint, we have to make sure that whether it's analytical, kindof a question or workload like B. I reporting whether it is, you know, more like an operational kind ofthe question around, You know that you want to just do a quick question around. You know, what did this customer by or what John's action happened? The underneath data structures and databases we have to pick the right ones so that way are able to support both >> the expectations, the expected yes, the expectations of the workload. >> It is. >> You're running commerce. Leighton Seon Relevance. Low latent. She's going to be in the milliseconds or >> gut ache >> and relevance. Gus, have a high bar there, too. Analytics query for a B. I tool might be, if every second so again, this is a huge Delta in terms of capabilities, and I think that will happen on the flies hard. Yes. How do you guys do that was sauce. >> Yeah, so that's That's the, you know, underlying technology that you know the way we are bending, that is, so that you can support both of those and wait with the customers were sticking to that. They wants equal access to the data they're getting. That's equal access now, depending on the kind ofthe queries, whether they, Paula's B I and reporting are more like transactional kind of things in nature. That's the that. Those are the right technical choices that we're making behind the scenes so that the user, those on our lab print right, because they can really focus on the insights that they're getting and really making decisions based on that inside and not get caught into how to bend all of these different pieces so that they can support both of these work clothes. The other thing is that you know a lot off the time that has Bean spent an I T. Has Bean to figure out all of this so that the CEO can support the line of business like the CMO now by, you know, Adobe taking. Get off this all this. It's heavy lifting. That idea had to do. I think that, you know it will be able to meet the requirements of the line of business much faster. And there's going to be, you know, the agility that is needed to support the business. I think that's really our goal in how we support the CEOs so that they don't worry about all this technology, all the data management, how to collect all this data from globally distributed edges. I mean, that's the partnership that we are, you know, bending with the CEOs so that we help them in their journey off, really helping their line of business deliver the best experiences >> on Jewel. Great to see you having so much fun, Toby. Thank you. What's it like there? Tell us, what's it like working in a job? You got a platform? Certainly. There's a lot of hard problems to solve. So you got that on the engineering side, tell us what the cultures like they're >> doing is a fantastic company. I mean, I just love every bit every every minute that I spend here is fantastic. It's, you know, great people open culture open to new ideas on DH. You know, I guess, uh, >> all the >> creative cloud you know has got the straight of it. Eve itches in fused in people. So it's just it's it's just being a blast and and, you know, people recognize them. Barton's off how data is so critical to delivering those delightful experiences, and it's very rewarding to just see how focused everybody is in the company to really help businesses delight their customers. So it's zygo >> system is great, but the developer ecosystem What's your reaction to that of the >> I mean Adobe Io is I don't know. I feel, you know, Yeah, So that's so if you think of all the creators that work with Adobe products and build their applications, I mean, the ecosystem is very rich. So combined creatives on the data and I t I mean >> so we should call the marketing native like cloud native accomplice of developers, developers. It's coming together >> on DH because >> cats living together I mean, this is >> called wait. Call them that experience maker's late. So we are really bringing experience makers, developers, data, scientists all together >> It's a whole new level for a >> whole new level. It's thanks >> for coming on. Sharing the insights. Cube coverage live here, and it will be some in Las Vegas. I'm John for your jefe. Rick, Stay with us. We're here for two days. We're in day one of wall to wall coverage at Adobe Summit. We write back.

okay welcome back to IBM's information on demand live in Las Vegas this is the cube SiliconANGLE movie bonds flagship program we go out to the events it's check the student from the noise talk to the thought leaders get all the data share that with you and you go to SiliconANGLE com or Wikibon or to get all the footage and we're if you want to participate with us we're rolling out our new innovative crowd activated innovation application called crowd chat go to crouch at net / IBM iod just login with your twitter handle or your linkedin and participate and share your voice is going to be on the record transcript of the cube conversations I'm John furrier with silicon items with my co-host hi buddy I'm Dave vellante Wikibon dork thanks for watching aren't you Oh bhambri is here she's the vice president of big data and analytics at IBM many time cube guests as you welcome back good to see you again thank you so we were both down at New York City last week for the hadoop world really amazing to see how that industry has evolved I mean you guys I've said the number of times today and I said this to you before you superglued your your big data or your analytics business to the Big Data meme and really created a new category I don't know if that was by design or you know or not but it certainly happened suddenly by design well congratulations then because because I think that you know again even a year a year and a half ago those two terms big data and analytics were sort of separate now it's really considered as one right yeah yeah I think because initially as people our businesses started getting really flooded with big data right dealing with the large volumes dealing with structured semi-structured or unstructured data they were looking at that you know how do you store and manage this data in a cost-effective manner but you know if you're just only storing this data that's useless and now obviously it's people realize that they need and there is insights from this data that has to be gleaned and there's technology that is available to do that so so customers are moving very quickly to that it's not just about cost savings in terms of handling this data but getting insights from it so so big data and analytics you know is becoming it's it's becoming synonymous heroes interesting to me on Jules is you know just following this business it's all it's like there's a zillion different nails out there and and and everybody has a hammer and they're hitting the nail with their unique camera but I've it's like IBM as a lot of different hammers so we could talk about that a little bit you've got a very diverse portfolio you don't try to force one particular solution on the client you it sort of an it's the Pens sort of answer we could talk about that a little bit yeah sure so in the context of big data when we look at just let's start with transactional data right that continues to be the number one source where there is very valuable insights to be gleaned from it so the volumes are growing that you know we have retailers that are handling now 2.5 million transactions per hour a telco industry handling 10 billion call data detailed records every day so when you look at that level that volume of transactions obviously you need to be you need engines that can handle that that can process analyze and gain insights from this that you can get you can do ad hoc analytics on this run queries and get information out of this at the same speed at which this data is getting generated so you know we we announced the blu acceleration rate witches are in memory columnstore which gives you the power to handle these kinds of volumes and be able to really query and get value out of this very quickly so but now when you look at you know you go beyond the structured data or beyond transactional data there is semi structured unstructured data that's where which is still data at rest is where you know we have big insights which leverages Apache Hadoop open source but we've built lots of capabilities on top of that where we get we give the customers the best of open source plus at the same time the ability to analyze this data so you know we have text analytics capabilities we provide machine learning algorithms we have provided integration with that that customers can do predictive modeling on this data using SPSS using open source languages like our and in terms of visualization they can visualize this data using cognos they can visualize this data using MicroStrategy so we are giving customers like you said it's not just you know there's one hammer and they have to use that for every nail the other aspect has been around real time and we heard that a lot at strada right in the like I've been going to start us since the beginning and those that time even though we were talking about real time but nobody else true nobody was talking nobody was back in the hadoop world days ago one big bats job yeah so in real time is now the hotbed of the conversation a journalist storm he's new technologies coming out with him with yarn has done it's been interesting yeah you seen the same thing yeah so so and and of course you know we have a very mature technology in that space you know InfoSphere streams for a real-time analytics has been around for a long time it was you know developed initially for the US government and so we've been you know in the space for more than anybody else and we have deployments in the telco space where you know these tens of billions of call detail records are being processed analyzed in real time and you know these telcos are using it to predict customer churn to prevent customer churn gaining all kinds of insights and extremely high you know very low latency so so it's good to see that you know other companies are recognizing the need for it and are you know bringing other offerings out in this space yes every time before somebody says oh I want to go you know low latency and I want to use spark you say okay no problem we could do that and streets is interesting because if I understand it you're basically acting on the data producing analytics prior to persisting the data on in memory it's all in memory and but yet at the same time is it of my question is is it evolving where you now can blend that sort of real-time yeah activity with maybe some some batch data and and talk about how that's evolving yeah absolutely so so streams is for for you know where as data is coming in it can be processed filtered patterns can be seen in streams of data by correlating connecting different streams of data and based on a certain events occurring actions can be taken now it is possible that you know all of this data doesn't need to be persisted but there may be some aspects or some attributes of this data that need to be persisted you could persist this data in a database that is use it as a way to populate your warehouse you could persist it in a Hadoop based offering like BigInsights where you can you know bring in other kinds of data and enrich the data it's it's like data loans from data and a different picture emerges Jeff Jonas's puzzle right so that's that that's very valid and so so when we look at the real time it is about taking action in real time but there is data that can be persisted from that in both the warehouse as well as on something like the insides are too I want to throw a term at you and see what what what this means to you we actually doing some crowd chats with with IBM on this topic data economy was going to SS you have no date economy what does the data economy mean to you what our customers you know doing with the data economy yes okay so so my take on this is that there are there are two aspects of this one is that the cost of storing the data and analyzing the data processing the data has gone down substantially the but the value in this data because you can now process analyze petabytes of this data you can bring in not just structured but semi-structured and unstructured data you can glean information from different types of data and a different picture emerges so the value that is in this data has gone up substantially I previously a lot of this data was probably discarded people without people knowing that there is useful information in this so to the business the value in the data has gone up what they can do with this data in terms of making business decisions in terms of you know making their customers and consumers more satisfied giving them the right products and services and how they can monetize that data has gone up but the cost of storing and analyzing and processing has gone down rich which i think is fantastic right so it's a huge win win for businesses it's a huge win win for the consumers because they are getting now products and services from you know the businesses which they were not before so that that to me is the economy of data so this is why I John I think IBM is really going to kill it in this in this business because they've got such a huge portfolio they've got if you look at where I OD has evolved data management information management data governance all the stuff on privacy these were all cost items before people looked at him on I gotta deal with all this data and now it's there's been a bit flip uh-huh IBM is just in this wonderful position to take advantage of it of course Ginny's trying to turn that you know the the battleship and try to get everybody aligned but the moons and stars are aligning and really there's a there's a tailwind yeah we have a question on domains where we have a question on Twitter from Jim Lundy analyst former Gartner analyst says own firm now shout out to Jim Jim thanks for for watching as always I know you're a cube cube alum and also avid watcher and now now a loyal member of the crowd chat community the question is blu acceleration is helps drive more data into actionable analytics and dashboards mm-hmm can I BM drive new more new deals with it I've sued so can you expound it answers yes yes yes and can you elaborate on that for Jim yeah I you know with blu acceleration you know we have had customers that have evaluated blue and against sa bihana and have found that what blue can provide is is they ahead of what SI p hana can provide so we have a number of accounts where you know people are going with the performance the throughput you know what blue provides is is very unique and it's very head of what anybody else has in the market in solving SI p including SI p and and you know it's ultimately its value to the business right and that's what we are trying to do that how do we let our customers the right technology so that they can deal with all of this data get their arms around it get value from this data quickly that's that's really of a sense here wonderful part of Jim's question is yes the driving new deals for sure a new product new deals me to drive new footprints is that maybe what he's asking right in other words you traditional IBM accounts are doing doing deals are you able to drive new footprints yeah yeah we you know there are there are customers that you know I'm not gonna take any names here but which have come to us which are new to IBM right so it's a it's that to us and that's happening that new business that's Nate new business and that's happening with us for all our big data offerings because you know the richness that is there in the portfolio it's not that we have like you were saying Dave it's not that we have one hammer and we are going to use it for every nail that is out there you know as people are looking at blue big insights for her to streams for real time and with all this comes the whole lifecycle management and governance right so security privacy all those things don't don't go away so all the stuff that was relevant for the relational data now we are able to bring that to big data very quickly and which is I think of huge value to customers and as people are moving very quickly in this big data space there's nobody else who can just bring all of these assets together from and and you know provide an integrated platform what use cases to Jim's point I don't you know I know you don't want to name names but can you name you how about some use cases that that these customers are using with blue like but use cases and they solving so you know I from from a use case a standpoint it is really like you know people are seeing performance which is you know 30 32 times faster than what they had seen when they were not using and in-memory columnstore you know so eight to twenty five thirty two times per men's gains is is you know something that is huge and is getting more and more people attracted to this so let's take an industry take financial services for example so the big the big ones in financial services are a risk people want to know you know are they credit risk yeah there's obviously marketing serving up serving up ads a fraud detection you would think is another one that in more real time are these these you know these will be the segments and of course you know retail where again you know there is like i was saying right that the number of transactions that are being handled is is growing phenomenally i gave one example which was around 2.5 million transactions per hour which was unheard of before and the information that has to be gleaned from it which is you know to leverage this for demand forecasting to leverage this for gaining insights in terms of giving the customers the right kind of coupons to make sure that those coupons are getting you know are being used so it was you know before the world used to be you get the coupons in your email in your mail then the world changed to that you get coupons after you've done the transaction now where we are seeing customers is that when a customer walks in the store that's where they get the coupons based on which i layer in so it's a combination of the transactional data the location data right and we are able to bring all of this together so so it's blue combined with you know what things like streams and big insights can do that makes the use cases even more powerful and unique so I like this new format of the crowd chatting emily is a one hour crowd chat where it's kind of like thought leaders just going to pounding away but this is more like reddit AMA but much better question coming in from grant case is one of the themes to you is one of the themes we've heard about in Makino was the lack of analytical talent what is going on to contribute more value for an organization skilling up the work for or implementing better software tools for knowledge workers so in terms so skills is definitely an issue that has been a been a challenge in the in the industry with and it got pretty compound with big data and the new technology is coming in from the standpoint of you know what we are doing for the data scientists which is you know the people who are leveraging data to to gain new insights to explore and and and discover what other attributes they should be adding to their predictive models to improve the accuracy of those models so there is there's a very rich set of tools which are used for exploration and discovery so we have which is both from you know Cognos has such such such capabilities we have such capabilities with our data Explorer absolutely basically tooling for the predictive on the modeling sister right now the efforts them on the modeling and for the predictive and descriptive analytics right I mean there's a lot of when you look at that Windows petabytes of data before people even get to predictive there's a lot of value to be gleaned from descriptive analytics and being able to do it at scale at petabytes of data was difficult before and and now that's possible with extra excellent visualization right so that it's it's taking things too that it the analytics is becoming interactive it's not just that you know you you you are able to do this in real time ask the questions get the right answers because the the models running on petabytes of data and the results coming from that is now possible so so interactive analytics is where this is going so another question is Jim was asking i was one of ibm's going around doing blue accelerator upgrades with all its existing clients loan origination is a no brainer upgrade I don't even know that was the kind of follow-up that I had asked is that new accounts is a new footprint or is it just sort of you it is spending existing it's it's boat it's boat what is the characteristic of a company that is successfully or characteristics of a company that is successfully leveraging data yeah so companies are thinking about now that you know their existing edw which is that enterprise data warehouse needs to be expanded so you know before if they were only dealing with warehouses which one handling just structure data they are augmenting that so this is from a technology standpoint right there augmenting that and building their logical data warehouse which takes care of not just the structure data but also semi-structured and unstructured data are bringing augmenting the warehouses with Hadoop based offerings like big insights with real-time offerings like streams so that from an IT standpoint they are ready to deal with all kinds of data and be able to analyze and gain information from all kinds of data now from the standpoint of you know how do you start the Big Data journey it the platform that at least you know we provide is a plug-and-play so there are different starting points for for businesses they may have started with warehouses they bring in a poly structured store with big inside / Hadoop they are building social profiles from social and public data which was not being done before matching that with the enterprise data which may be in CRM systems master data management systems inside the enterprise and which creates quadrants of comparisons and they are gaining more insights about the customer based on master data management based on social profiles that they are building so so this is one big trend that we are seeing you know to take this journey they have to you know take smaller smaller bites digests that get value out of it and you know eat it in chunks rather than try to you know eat the whole pie in one chunk so a lot of companies starting with exploration proof of concepts implementing certain use cases in four to six weeks getting value and then continuing to add more and more data sources and more and more applications so there are those who would say those existing edw so many people man some people would say they should be retired you would disagree with that no no I yeah I I think we very much need that experience and expertise businesses need that experience and expertise because it's not an either/or it's not that that goes away and there comes a different kind of a warehouse it's an evolution right but there's a tension there though wouldn't you say there's an organizational tension between the sort of newbies and the existing you know edw crowd i would say that maybe you know three years ago that was there was a little bit of that but there is i mean i talked to a lot of customers and there is i don't see that anymore so people are people are you know they they understand they know what's happening they are moving with the times and they know that this evolution is where the market is going where the business is going and where the technology you know they're going to be made obsolete if they don't embrace it right yeah yeah so so as we get on time I want to ask you a personal question what's going on with you these days with within IBM asli you're in a hot area you are at just in New York last week tell us what's going on in your life these days I mean things going well I mean what things you're looking at what are you paying attention to what's on your radar when you wake up and get to work before you get to work what's what are you thinking about what's the big picture so so obviously you know big data has been really fascinating right lots of lots of different kinds of applications in different industries so working with the customers in telco and healthcare banking financial sector has been very educational right so a lot of learning and that's very exciting and what's on my radar is we are obviously now seeing that we've done a lot of work in terms of helping customers develop and their Big Data Platform on-premise now we are seeing more and more a trend where people want to put this on the cloud so that's something that we have now a lot of I mean it's not like we haven't paid attention to the cloud but you know in the in the coming months you are going to see more from us are where you know how do we build cus how do we help customers build both private and and and public cloud offerings are and and you know where they can provide analytics as a service two different lines of business by setting up the clouds soso cloud is certainly on my mind software acquisition that was a hole in the portfolio and that filled it you guys got to drive that so so both software and then of course OpenStack right from an infrastructure standpoint for what's happening in the open source so we are you know leveraging both of those and like I said you'll hear more about that OpenStack is key as I say for you guys because you have you have street cred when it comes to open source I mean what you did in Linux and made a you know great business out of that so everybody will point it you know whether it's Oracle or IBM and HP say oh they just want to sell us our stack you've got to demonstrate and that you're open and OpenStack it's great way to do that and other initiatives as well so like I say that's a V excited about that yeah yeah okay I sure well thanks very much for coming on the cube it's always a pleasure to thank you see you yeah same here great having you back thank you very much okay we'll be right back live here inside the cube here and IV IBM information on demand hashtag IBM iod go to crouch at net / IBM iod and join the conversation where we're going to have a on the record crowd chat conversation with the folks out the who aren't here on-site or on-site Worth's we're here alive in Las Vegas I'm Java with Dave on to write back the q

>> Live from Las Vegas, it's theCUBE. Covering Adobe Summit 2019. Brought to you by Adobe. >> Welcome back everyone, live CUBE coverage here in Las Vegas for Adobe Summit 2019 I'm John Furrier. With Jeff Frick. Our next guest is Jeff Allen, Senior Director Product Marketing, Adobe. Jeff, welcome to theCUBE, thanks for joining us. >> Thank you. Nice to be here. >> So day one is kind of winding down, big, great keynote, laid out the platform product's working together, lot of data, lots of data conversations. >> Yeah, exciting day. Excited to have Adobe Analytics in the mix with that, you saw the four clouds we talked about, Analytics Cloud is one of them and really kind of core to everything we do at Adobe, right? In fact, even in the Creative Cloud side, Document Cloud side, our customers have to be able to measure what they're doing and so, data is obviously key to that. >> Tapping the data across the different applications and now clouds - It's interesting - it's a whole new grail, people have been trying to do for how many years? >> Forever, from the beginning. >> And it's always been that holy grail, where is it? Now some visibility is starting to get to see into the benefits of horizontal scale, diverse data, contextual workloads, >> Absolutely, yeah. >> This is a big deal. >> It is a big deal. >> Explain why it's impacting. >> It's funny. Our culture now expects data right? We measure everything. Our kids are taught to measure things, even something as simple as likes on, my kids, they argue about whether the picture mom posted of them or the other one got more likes, right? So we kind of have hardwired our society around measurement, and now of course, marketing has always been a measurement-heavy discipline, and so, it's just absolutely core to what we're doing. >> And we had a historic moment, we've been doing theCUBE, it's our 10th season, a lot of events. >> Congratulations. >> And we had a guest come on here, that we've never had before, the title was Marketing CIO, it was one of your customers at MetLife >> Interesting, yeah. >> But this brings the question of, of the confluence of you know, the factions coming together. IT, creative, marketing, where the tech, measurement, data. >> Yeah, totally. >> Data processing, information systems, kind of an IT concept now being driven and married in with the business side. >> Absolutely. >> This is really the fundamental thing. >> I started my career marketing to CIOs, in fact, I've spent most of my career marketing to the CIO organization, right, and about 7 years ago, I came over to Adobe to market to marketing, right? And I used to say, "You know I kind of like marketing to this guy, I understand him better," right? Because I know how marketers think a lot better than CIOs, I had to go learn how they thought. But it's amazing how the tech explosion has happened in MarTech and AdTech, all of these vendors here at this event, this is just a piece of our industry, right? There's thousands of companies serving marketing organizations, and so, all of a sudden, the tech stack looks more crazy than even what many CIOs manage, and so it doesn't surprise me at all that organizations, you're talking to organizations that have a CIO/CMO hybrid role. >> Jeff, I'm curious how the landscape is changing, because all the talk here is about experiences, right? And the transaction is part of the experience, but it's not the end game, in fact, it's just a marker on a journey that hopefully lasts a long time. How does that change kind of the way that you look at data, the way customers are looking at data, you know, how the KPIs are changing, and what they're measuring, and the value of the different buckets of data as it's no longer about getting to that transaction, boom, ship the product, and we're done. >> Yeah, so I look after Adobe Analytics, and Adobe Analytics was the first component we acquired in this business, right? Experience Cloud, started with the acquisition of a company called Omniture back in 2009, was an analytics company, primarily web and mobile app analytics, and it has grown since then, to measure many more things. And we've seen our category with analytics that we've addressed move from web analytics to a broader view of digital analytics, right? The digital parts of marketing to all of marketing, the rest of marketing said, "Hey, we need measurements too. We need tools." And then it clicked out another broader click to this idea of experience, right? Because everybody has a stake in experience, and experience is all wrapped around people and how people move through experiences with your brand, so that's where we sit today, is really helping organizations measure experiences, and that spans every person in the organization. >> Talk about the dynamic between how the old way of thinking was shifting to this new way, and specifically, the old way was "I'm a database guy. I've got operational databases and analytical databases," you know, and that was it. You know, relational, unstructured, you know, kind of quadrants. Now, it's kind of, you have (laughs) it's not about databases, it's about data. So you have operational data, which is the analytical data now >> Yeah. >> So you have now, this new dynamic, it's not about the databases anymore >> Absolutely. >> It's about the data itself. >> It's not about, I would say, it's not about the stores of data, right? It's about really getting the insights out of the data, and you know, for the longest time, in my career, uh, you went to CIO, the CIO organization and there was a BI team there, and you would ask them for data, and they could go to the main frame, they could go to these big IT systems, and you know, in 30 days, they could email you back a .csv file, or even before that meeting, give you a .zip file or something with the .csv file on it. And then you got to go see if you could even get it to open on your laptop and get it into Excel and start to manipulate it. And those days don't work. >> And then you go get your root canal right after. It's a painful process. >> What if the data - today that data is trying to understand, "Hey I got a guy that just checked into the hotel. He's standing in front of me, I need to know if he had a bad experience the last time he checked in with us, so I know if I need to give him an upgrade. And you can't go down to I.T. real quick and ask them to take 30 days to get that data and then crunch the data all to find out. Customers need to know, and in the experience business, immediately this person just walked into the hotel and we need to give them a good experience, we blew it last time for them. That's what the experience business wants out of data. >> One of the questions we had with Anjul, who runs engineering on the platform side, was around the rise of prominence of streaming data, how is that impacting the analytics piece, because, you know, if you want the flow, this is a key part of probably your side of the business. Can you comment, what's your reaction to that - streaming trend? >> We've been talking about streaming for a while. CIO, this isn't a new thing, we were streaming applications, right, 10 years ago, 15 years ago, but really in the story I just shared, right? The idea of going down and waiting in this asynchronous process with data, the experience business can't handle that, so streaming data is really implying that, as it's coming in, we're processing it, and learning from it, and getting that out into the systems and the people that can take action, instantaneously. >> Talk about the dynamic that customers have around, traditional silos within their organization, you know, that guy runs the database and data for that department, that person runs the data over there, and if this vision is to be, is to be, is to come true, you have to address all the data, you got to know what's out there you got to have data about the data, you got to know in real time, and these are important concepts. How does a company get through that struggle, to break down those kind of existing organizational structures? >> It's a cultural shift, I mean, who has a desktop publishing team anymore in their organization, right? Everyone does desktop publishing, that is how data is too. Everyone's got to be comfortable with data, they have to be conversing around data, and everyone needs access to data. So, that's, you know, that's what is happening in our industry, the analytics industry, is that we're democratizing that data, and getting it everybody's hands, but it's not enough to give them charts and graphs, they have to be able to manipulate that and make it apply to their part of the business, so they can make a decision, and go, and so, that shift in how people think about data, as it's not part of your - it's part of everyone's job, as opposed to being a specialized, siloed job. >> I'm just curious to get your take, a lot of conversations here about you know, Adobe, using their own products, eating your own dog food, drinking your own champagne, whatever analogy (laughs) you like to use. And when you see the DDOM, right, the Data-Driven Operating Model, on the screen, in the keynote, with the CEO, and he says, "Basically everyone at this company is running their business off of these dashboards, that's got to be pretty, pretty, uh, profound for a guy like you who is helping feed those things. >> It's cool. I like to talk about what I call the modern measurement team. The modern measurement team is no longer that centralized data team, right, or that centralized BI team, but every single function, right, under CIO. Every one of the CEO's directs, has their own data team. You go look around and you see that in every single function, there is a sophisticated data team. They have the best tools in the industry, they have the smartest people they can find, they have PhDs on staff, and that's not enough. So, these teams now have to get that out to every constituent in their organization. And that's what we're trying to do at Adobe, that's what we're seeing our best customers do as well, is trying to inform every decision anybody makes. >> And that's where machine learning really shines. You get high quality data on the front end, with the semantic data pipeline capability, get that into the machine learning, help advance, automate, that seems to be the trend. >> Yeah. Yeah, look the insights that you can get from the data, the ability to predict with rich data, it sounds - prediction sounds like - invention used to sound like this novel thing, right, and then you realize, we're inventing things all the time, that's not so - that's just creativity. Well, the same thing is happening with AI and ML, is we're able to predict things with good statistical modeling, with pretty strong, uh, reliability around those models. >> The keynote had great content, I liked how you guys did a lot things really well, you had the architectural slides, platform was a home run, how you guys evolved as a business, see you laid that out nicely, but one of the things I liked, not that obvious, unless you go to a lot of events like we do, everyone says "The journey of the customer", I mean, it's a, it's become a cliche, you guys actually mapped specific things to the journey piece that fit directly into the Adobe set of products and technologies, and the platform. It's interesting, so the word journey has become, actually something you can look at, see some product, see some - a pathway to get some value. >> There's definitely a risk if the word journey, becomes like "Big Data" and all these cliche terms, you know, that means everything, so it comes to mean nothing. But for us, journey, and as marketers especially, journey is just naturally understanding where did I interact with this person, and what did that lead to along the way, right? And so, customer journey, is absolutely core to data analytics. >> All the hype markets, cloud washing, until Amazon shows them how it's done, everyone else kind of follows, you guys are doing it here with journey, one of the things that came out was a journey IQ. I didn't really catch that. Can you take a minute to explain? >> So we have a couple of things. We have something called Segment IQ, Attribution IQ, and now we have even introduced Journey IQ. And when you see that IQ moniker on one of our, kind of our super umbrella features - that means that we're applying AI and ML, right, and Sensei is involved. So we're using powerful data techniques, and we're also wrapping it with a really simple user experience. So Journey IQ starts to break down the customer journey in terms that a normal person, without a PhD, without knowing statistical methods, or advanced mathematics, can leverage those techniques to get really powerful insights. And that's specifically around the customer journey. >> So the IQ is a marker that you guys use to indicate some extra intelligence coming out of the Adobe, from the platform. >> Yeah, yeah, if we're going to democratize data, right, we have to democratize data science as well, right? And so, a big part of what we're doing at Adobe Analytics is really simplifying the user experience, right? So I don't say, Do you want to run a regression model against this to answer your question? We just say Click this button to analyze. Right? So it's a simple user experience, behind the scenes, we can run these powerful models for the customer, and give them back valuable insights. So, Journey IQ is specifically taking things like cohorts, and introducing cohort analysis into the experience, making it simple to do powerful things with cohorts. >> What's the pitch to a customer when you go to one and talk about all this complicated tech and kind of new, operationalized business models around the way you guys are rolling it out, when they just want to ask you, "Hey Jeff, I care about customer experiences." So, bottom line me. What's the pitch? >> How can you possibly address your customer's needs if you don't know what they think. Right? What they need? So, at the end of the day, the great thing about working with customers, like most businesses do, is customers are happy to tell you where you're getting it right, and where you're getting it wrong, right? And that's all over the data. So all you have to do is develop a culture of using data to make decisions, and 9 times out of 10, if you have the right data, and people are using the data to make decisions, they are going to make the right calls and get it right for your customer. And when they don't, they're using opinions and they're going to get it wrong all the time. >> Or, bad data, could be hearsay. >> Or you course correct, or that wasn't - you know, make an adjustment. Right? Again, based on the data. >> Exactly, yeah. >> You're in product marketing, which is a unique position, because you have to look back into the engineering organization, and look out to the customers, so you're, you're in a unique position. What's the customer trend look like right now? What are some of the things you're hearing from the market basket of customers that you talk to? Generally, their orientation towards data? Where are they on the progress bar? What is the state of the market on the landscape of the customer, what patterns are you seeing? >> Good question. So there's a lot of - there's a lot of, um, anxiety around where do I have pockets of data that I'm not able to leverage, and how do I bring that together, so when we tell a platform story, like you heard us tell today, customers are really excited about that, because they know, they've known forever. I mean, this isn't a new problem, like, data silos have been around as long as data has. So, the idea of being able to bring this data into a central place, and do powerful things with it, that's a big point of stress for our customers. And they know, like, "Hey, I have dark spots in my customer experience, that I lose the customer." For example, if I'm heavily oriented around digital, let's say, um, I'm a retailer, and I see a customer, I acquire them through advertising channels, they come through an experience on my website, and they buy the product. Success. I ship the product to them, and then they return it in the retail store. The digital team might not see that return. >> So they might think it was successful. >> They think it was successful. So what do they do? They go take more money and spend it in the ad channel, where that person originated. When in reality, if they could look at the data over time, and incorporate this other channel data, of in-store returns, the picture might look very different. >> So basically, basically. >> It's those dark spots that customers are really needing. >> So getting access to more diverse data, gives you better visibility into what's happening contextually, to open up those blind spots. >> Exactly. Yup. It's just that, adding resolution to a photo. >> Love this conversation, obviously we're data-driven as well on theCUBE, we're sharing the data out there. This interview is data as well. >> Fantastic. >> Jeff, final question for you - for the folks that couldn't make it here, what's the - how would you summarize the show this year, what's the vibe, what's the top story here, what's the big story that needs to be told from Adobe Summit? >> We're just a day in, there a lot, there's a lot to do still, right? We still have two more solid days of this show. But you know, the big themes are going to be around data, they are going to be optimizing the experience for your customers, and what's really amazing is how many customers are here, telling their stories. That's the thing, I wish everybody in your audience could experience by coming here, because there is 300 breakout sessions that feature our customers talking. All of our sessions on main stage, we bring customers out, and we learn from them. That's the best part of my job, is seeing how customers do that. >> Some of the best marketing, you let the customers do the talking, and they're doing innovative things. They're not just your standard, typical, testimonials, they're actually doing - I mean, Best Buy, what a great example that was. >> Cool brand - we work with some of the coolest brands in the world, so, fascinating, brilliant people. >> Marketing, at scale, with data. Good job, Jeff, thanks for coming on, appreciate it. >> Thank you. >> Jeff Allen, here inside theCUBE with Adobe. I'm John Furrier with Jeff Frick. Stay with us for more Day 1 coverage after this short break. Stay with us.

>> Live from Las Vegas, it's theCUBE! Covering Adobe Summit, 2019. Brought to you by Adobe. Welcome back everyone to the Cube's coverage, here in Las Vegas for Adobe Summit 2019. I'm John Furrier with Jeff Frick, our next guest is Ronell Hugh, head of product strategy and marketing for Adobe and Adobe Cloud Experience, which was announced available today, welcome to theCUBE, thanks for joining us. >> Hey, thank you John, thanks for having us. >> So the Experience Cloud Platform, is game changer for Adobe. >> Yes. Could you describe what is it? Like, where'd it come from, how'd it all start? >> Yeah I can definitely do that. So, the Experience Platform, Adobe Experience Platform, the genesis of it came from, data is such an important part, I think you've had lots of people on here talking about data and what it can do. And really it's like, when you have data that is dispersed across an enterprise, how do you actually, what do you do with that, right? A lot of customers are out there, and I, terminology I came across the other day was data swamps, you know, data lakes, data warehouse, we're all aware of those ideas. But how do you take that data and actually do something meaningful? The idea came from, we have siloed repositories for our data, sitting across all of our solutions, how do we bring that together and rationalize and standardize that data, so that it's more useful for a customer, so they actually can do something that's truly meaningful with it? And that's really around driving these real time personalized experiences with customers, right? And so I think that's where it started. And as we've evolved that, what you heard today is kind of what you're seeing about how do we then take that to the next level? How do you apply machine learning? How do you provide a data model that standardizes the taxonomy across the ecosystem? How do you then leverage that and how do you have it being open? To now, you give customers, developers an opportunity to start to develop new applications that advances what they're trying to do in their environment. >> What I think, what I found super impressive was, you guys really cracked the code on what I call cloud scale architecture, >> Yeah. >> While not, missing out on the opportunities to innovate at the user level. You have the creativity, the applications, and then the data almost is like this DevOps kind of mindset where it's like the data's being available in a diverse way for the use cases that matter at the right time, so. That's a hard nut to crack. >> Yeah it is a hard nut to crack, I think. But at the core, again, it's like, it's the data that's important. Once you have that centralized, you've created some rules around that, you're governing it so that you can now leverage, depending on what you're trying to use it for, it's really then down to the use cases. To your point, like, what are the specific use cases a customer has, that they're trying to solve? There could be industry ones that we could apply them to, we've identified a few of those that we think are important for customers, some of those around the real-time customer data platform and how Experience Platform from along with Audience Manager helps to solve that use case for a customer. But there's others around, how do you enable customers, from a development standpoint? Applications, they're really trying to figure out, hey, I need an open system, but I can start to develop something rich and new, right? And drive advancements in their organizations. And so there's a lot that we've had, there's kind of four that we've identified from a use case standpoint. But that's not limited to those four. Every customer is going to apply either one or all of those in a unique way within their environment. >> When you say four, you mean clouds, like analytical cloud, ad cloud-- >> No, no, I mean, so the use cases that we've identified. >> Oh okay. >> So we have, real-time customer data platform, we have one around, application, customer experience application development, customer journey intelligence is all around how do you take and leverage AI ML tools, to help enrich data? And then we have one around how you take and deliver across multiple applications. What's the channel execution looks like, now that you have data standardized in one place? What does that mean for your channels that you're now trying to execute across your ecosystem? >> Well you guys did the product development on this and the product marketing and all the stuff that goes in to building a platform, you got to go out an talk to customers, right? So what was the, when you guys talked to customers, what was their initial feedback to you guys? And when you 'em the platform now, where are they, I mean, what's the reaction? Can you share some either anecdotal or, specific? >> Yeah, anecdotally, I mean we started talking about a platform and the idea and a vision of a platform, I think, three or four years ago. Last year we then laid the groundwork around, there's three areas to this, a profile, the data side too and a content side, what you're seeing now is a data piece of this, like, how does data then really drive a lot of the interactions there? And as we've progressed, the reception has been great. Customers are like, we understand this. And it's really around the notion of real-time. Real-time is really built on the knowledge that, hey, you're taking data, you're not just doing batch any more. I know batch is predominantly what customers like to use. But real-time means getting data in, that's current. That therefore you can then action upon. Which really is the relevant data that you need. And I think that started to resonate really well. >> How do they define real-time? 'Cause it could depend based upon the application. If you're a doctor you need real-time now. >> If you're an investor, >> Yeah, you need it now! >> You need it now! If you're a BI application on a query, it could be a little slower. I mean real-time is a relative term, can you just unpack the customer's expectation of real-time? >> Yeah I mean, you look across multiple verticals, right? So, depending which vertical you're in, to your point, it could vary, right? But if you're a brand that's delivering consumer experiences, real-time is like, are you interacting with them with the right data to help inform that interaction with that customer, right? And that is real-time. So it varies by industry of course, right? Hospitality, you think of that, when you walk into a hotel, getting a notification that your room is ready. Me recently coming here on a plane trip, having to check my luggage, notified that the bag was check in, and also now that it's being delivered now for me to pick up. Those are all, that's real-time, right? And it varies, I think, by industry. And I think that's where it starts to get really exciting, is like how do you apply it? What does it mean for real-time for each company that's starting to apply Experience Platform to their infrastructure? >> That's my favorite definition of that, real-time is in time to do something about it. (all laughing) Which depending on what the situation is, could be a short period of time or a longer period of time. But Ronall I'm curious, 'cause we've always had the transactional data and real-time's always been a focus on the transactional data, but on the behavioral data to then pull back in to transactional activity, that's a little bit more recent. Especially with so many sources of data that are coming in and changing all the time. How are people dealing with that data flow challenge and as you said, aggregating it and coalescing it into a single platform that now you can take action on it? >> Yeah, I mean the behavioral data's a core to Adobe it's definitely a part of our bread and butter. And I think it's combining it with all the other data sources that will make it even more richer for our customers, right? You think about a customer, if the real Holy Grail, in a way, of our Experience Platform is that real-time customer profile. There're so many different data points that help to build that. When you isolate it just behavioral, that's great. We know the interactions that a customer is having with the brand, but there's other parts that, transactional, POS, social, that helps to build out the view of that customer. And then, think of then at that point, for a customer, any of our customers are using this today, some that were heard today as part of our keynote. How they're then taking that to the next level of how they then build experiences for their customers. It's because it's a culmination of all of that, right? I think behavioral is a huge part of it. Because it's not static data or stagnant data, it's kind of like that data that we have that's been gathered over the last several years of a customer, and how they're currently interacting with a brand. But then it's, again, bringing it all together. Harnessing that, and then building that real-time customer profile, it really is a powerful piece of the platform. >> You know when I looked at the slide on the keynote, it was clear that this'll have a lot of data chops within Adobe. Because you had the data pipelining piece after data input sources, and then the other side of the chart was the piece around the applications, ISVs, ecosystem, and then you had your real-time profile, which I get is the centerpiece. But before that you had something that was around semantic data pipelining, >> Semantic data pipelining yeah. >> Data pipelining and semantics. >> Yeah. >> What is that piece? Is that really where the transformations are happening? Is that the input into the, you're smiling, wow. >> Yeah this is great, I love talking about this. >> You're nerding out. Okay. >> So, pipeline and semantics is all around, so pipeline is the thought process around, we have connectors that we built, right? That's really where the data comes in. When we see at the beginning of the diagram is the bit that said streaming, it's the connectors that allow that streaming to happen but it also gives customers the option of saying, now you can batch it, right? You can batch it, which is what you've been doing, but streaming is really what we're pushing. 80% of customers still think that batching is the only way to manage their data right? And then really it's more about, hey, if you want to action in real-time, where is that data currently at? So that's what we say that happens with the data in the pipeline part of it. Additionally you have things like Adobe Experience Platform Launch and Auditor, Launch is all around data collection as well. But it's also about deployment of tags. When you deploy a tag you're also connecting information that can feed back into the system as well, and then the last piece of that is we have a feature of Platform that's called Auditor, and really it's about auditing your environment to make sure that it's being implemented correctly, right? Semantics is all about governance and control of the data. Standardizing the data, so we have something we call Experience Data Model, they talked a little bit about that, or ExDM, Experience Data Model is all around, it's an open source initiative to help standardize taxonomy of your data. I grew up in Germany, first language is German, and when I moved to the US if I were to walk into a room and started speaking German, no one would've understood me, right? It would've been stares and everything. But if I had switched my language, luckily I speak English too, so I was able to share and speak English, it's the same with data. You can't have it labeled differently for it to communicate. And that's what really happens in semantics and the data pipeline piece we did. >> And it's important too, I want to unpack it a little bit >> It's great to know. >> because semantics also feeds into contextual awareness. And one of the things we've observed doing these CUBE interviews with a lot of experts is, we've heard diverged data and flow, creates more visibility into potential blind spots. Just in data science parlance. Talk about that streaming piece, I think that's something that I see, the people who get data right, will stream as much as they could to get some flow going, to get data sources coming in, to have more diverse data. Talk about that dynamic of diverse data. >> Diverse data, I mean, a part of that diagram you saw, on the left of that when Anjul was speaking, was around data sources, data inputs, right? And so we talked about behavioral, transactional, third party, POS, and it's the variety of data, and that coming in consistently that helps you create that picture of a customer. So you need a variety of data. I think just having our data gives you, again, like we talked about before, the behavioral components of that, but consistently bringing in multiple pieces of data helps to take that further. Now one thing you talked about was AI, and I want to take you there just a little bit 'cause that piece of then how you can manipulate the data, and enrich with new insights, is key. Again, lots more data, standardized, controlled, now being governed in the right way to meet different regulations and policies that are out there. And then now adding AI models to that, ML models to that, to take your organization further. I think that's where we see the power of that data, and having lots of data. Open and extensible is one of the key things that we've been talking about with the platform. >> And clean data feeds clean machine learning. >> Yeah. >> Dirty data gives dirty machine learning. >> Yeah, dirty insights, right? (both laughing) And we always want it to be clean, right? But that's so important, we sit here and think about it, customers want that. They're desiring to have that so they can innovate within their infrastructure and their organizations to take their businesses further. >> And that's where we see the machine, that's why data's so core for you guys in this piece. Alright, so what is the customer environment like? Are they all tuned in to what you just said? I can see some progress in the big companies and maybe, cloud native folks getting, jazzed up on that but, are the big companies tuning in to this? In your mind, where are they on the progress bar? >> Yeah, so John and Jeff, the big companies that we have talked to, are typically further along, that are cloud native, they're more pushing the boundaries of innovation and when we looked at this by industry, you tend to see more of the typical companies by industry that are kind of leaning into this. You know, hospitality, automotive, you have entertainment, media, you also have retail, you know. There's been a lot of interest from those from healthcare and financial services as well because they see the implications of what it means to them in terms of managing their data and executing that data to drive more engagement with their customers. >> They get an edge too, if they can nail the customer experience with data, they'll have a competitive advantage, I mean, if I had to choose between a hotel that was going to take care of me on my app, versus one that doesn't, I think I'm going to go with the one with the app every time. >> Definitely. >> If the price is, all things being equal. >> A key part to that though, and Shantanu I think, and Anjul, multiple people mentioned today, was that customer journey, right? Depending on where you are, data plays a key role in all aspects of that customer journey. And how do you activate it then in each part of the customer journey? To drive those experiences in real-time. So I think it's a key part to how we see it working. And I think that the AI and ML, it explodes even further, to your point, that cleanliness of the data then just makes that more potent in terms of what it can deliver. >> Well one of the things that you guys have is Adobe products, your customers have other things besides Adobe. So one of the things Anjul said in her keynote was open data open APIs. So how do you bring that other stuff in, when, first party data is getting harder and harder to get with all the stuff we're seeing online these days with privacy and regulations? First party data's great, if you can get it. >> Yeah. >> So how is this all impacting, outside the Adobe realm from a customer standpoint because they want to have a platform that can be easily tied together? How do you guys look at that changing landscape? It's changing pretty radically. >> It's high priority for our customers, right? They've always had a challenge with isolated vendors, right? And how do you then bring that data together? One of the things that we'd readily notice when I talked to customers is that, this excites them. The opportunity that they have now, to have a platform, regardless of which whether it's first party or third party, to bring that together, is something that they deem as necessary for their organizations to be successful, right? And so now it's all about, we've built now the tools to help them do that. We actually have third party connectors, right? So you can bring in data or we have ETL partners that we can work with to bring that data through that source-- >> And developers can develop on it, right? >> And developers can develop on it. >> Is there a developer program for the Experience Platform yet, or is that still ongoing? >> There is, a big component of what we're doing is the developer betas for this so now developers can go to adobe.com, adobe.io actually, and find a lot of the APIs that are there, available for them, and documentation to help them build an application on top of Platform. >> So they can do that today? >> They can do that today. >> Awesome. >> They can go check that out today, and that, but you're pointing out something that's really important. A platform that is open and extensible, now makes itself available to customers who have, large developer teams. Many CTOs have an organization engineers area, chomping at the bit to build new applications for their organizations. They also have big data science teams too, that are, wanting this take. Data science teams have always been about massaging data, they've been managing it, that gets old for them. They don't want to do that, they want to build something that's unique, innovative and actually inspire their organizations. >> High quality data, real-time and relevant, fast and cool, that's what it's all about. >> Yeah. >> And you guys got a platform, so final question for you. To get a platform right, we've observed, you got to enable success. You've got to be an enabling technology. What's the big secret sauce for this platform? >> The secret sauce. I think it comes down to something that may seem simple. But I think there's a couple pieces that are a secret sauce to it, the ultimate secret sauce that is powered by those other areas, is that real-time customer profile. And that's only the secret sauce because of, what we do from out data connector standpoint of bringing in data in real-time, and standardizing that with the right taxonomy to then inform that real-time customer profile. It's the power of what the platform can do. And then after that, how you use query to develop more data inputs from that, or how you then deliver that, through decisioning or other triggers that you might have available, that's really the secret sauce of what we have within the platform. >> Awesome, Ronall, thanks for coming on. >> Thank you. >> Appreciate the insights we'll follow up, love the streaming, love the real-time profiling, love the data. Adobe's Experience Platform, hitting the market. It's theCUBE, live coverage, day one of two days, of wall to wall coverage. We'll be right back after this short break. (electronic music)