>> 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)