 Live from Las Vegas, it's theCUBE, covering Adobe Summit 2019, brought to you by Adobe. Welcome back everyone to theCUBE's coverage here in Las Vegas for Adobe Summit 2019. I'm John Furrier, Jeff Frick. Our next guest is Ronel Hu, Head of Product Strategy and Marketing for Adobe and Adobe Cloud Experience, which was announced available today. Welcome to theCUBE. Thanks for joining us. 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 did it come from, how did 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 would tie some people on here talking about data and what it can do. And really it's like, when you have data that's kind of dispersed across an enterprise, how do you actually, what do you do with that? Right, I mean a lot of customers are out there and a terminology I came across the other day was data swamps. You know, data lakes, data warehouse, we're all aware of those ideas, but like how do you take that data and actually do something meaningful? So 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 that they actually can do something that's truly meaningful with it, really. And that's really around driving these real-time, personalized experiences with customers, right? 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, you know, missing out on the opportunity 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 yeah, 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 know, you've created some rules around that. You're governing it so that you can be now leveraged 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, you know, that we could kind of apply them to. We've identified a few of those that we think are important for customers. You know, some of those around the real-time customer data platform and how experienced platform 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, like, they're really trying to figure out, hey, I need an open system that I can start to develop something rich and new 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 a unique way within their environment. We say four, usually you mean clouds, like a little cloud ad. No, no, I mean, so the use cases that we've identified. Oh, we have, yeah, so we have, you know, 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, you know, MI, sorry, AI, ML tools to help enrich data. And then we have a one-around how you take and then, you know, take it 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 executing across your business? When you guys did the product development on this and the product marketing, all the stuff that goes into building a platform, you got to go out and talk to customers, right? And so what was the, when you guys talk to customers, what was their initial kind of feedback to you guys? And when you show them 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 kind of the idea and a vision of a platform, I think, three or four years ago. Last year, we kind of then laid the groundwork around, there's three areas to this, a profile, you know, the data side to 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 progress, 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 anymore. I know batch is predominantly what customers like to use, but it's real time means getting data in that's current, that you 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? It could depend based upon the application. If you're a doctor, you need a real time now. You need a Tesla, you need it now. If you're a BI application on a query, it could be a little bit slower. But I mean, real time is a relative term. Can you just unpack the customer's expectation of real time? Yeah, I mean, it depends on, you look across multiple oracles, right? So depending on which vertical peer 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 like when you walk into a tell, get in 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 checked 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, really, 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. Which depending on what the situation is could be a short period of time or a longer period of time. But now I'm curious, because we've always had the transactional data and real time's always been a focus on the transactional data. But on the behavioral data, and then pull back into 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 kind of dealing with that data flow challenge? And as you said, kind of aggregating it and coalescing it into a single kind of platform that now you can take action on it. Yeah, I mean the behavioral data is a core to Adobe, right? It's definitely a part of our bread and butter. I think it's combining it with all the other data sources that 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's so many different data points that help to build that. When you isolate it just to 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 kind of build out the view of that customer. And then think of then at that point for a customer, like any of our customers are using this today, some of 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 the brand. But then it's again, bringing it all together, harnessing that and then building that real time customer profile that really is a powerful piece of the platform. You know, when I looked at the slide on the keynote, it was clear that there's 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 to centerpiece. But before that, you had something that was around semantic data pipelining. Yeah, semantic and pipeline. Yeah, data pipelining and semantics. What is that piece? Is that really what transformations are happening? Is that the input into the... You smile like what? Yeah, this is great. I love talking about this. Okay, so pipeline and semantics is all around. So pipeline is kind of the thought process around we have connectors that we built, right? That's really where the data comes in. When we say at the beginning of the diagram, as I've 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 kind of manage their data, right? And it really is all about, hey, if you want to action in real time, where is that data currently at? So that's what we see that happens within 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 conducting information that can feed back into the system as well. And then the last bit 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, like standardizing the data. So we have something we call Experience Data Model. They talked a little bit about that, or XTM. Experience Data Model is all around, it's an open source initiative to help standardize the taxonomy of your data. I grew up in Germany, first language was German. When I moved to the US, if I were to walk into a room and started speaking German, no one would have understood me, right? It would have been like stairs and everything. But if I had to switch my language, luckily I speak English too, so I was able to shift and speak English. It's the same way 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 pieces. And it's important too, I want to unpack it a little bit, 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 diverse data and flow creates more visibility into potential blind spots. Just kind of in data, kind of in data science, kind of parlance. Talk about that streaming pieces. 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 the 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 to create that picture of a customer. So you need a variety of data. I think just having our data gives you that, again, I'll be talking about four, the behavioral components of that, but consistently bringing in multiple pieces of data helps to take that further. And one thing you talked about was like AI, and I want to take you there just a little bit because 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 kind of one of the key things that we've been talking about. And clean data feeds clean machine learning. Yeah, dirty data gets dirty. Yeah, you know dirty data, right? We always wanted it to be clean, right? Like, I mean, that's so important. I mean, we hear and think about it, like customers want that. They're designed 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 is so core for you guys in this piece. All right, so what is the customer environment like? Do they all, they all tuned into what you just said? I mean, I can see some progress of the big companies and maybe cloud native folks getting, you know, jazzed up on that, but are the big companies tuning into this, in your mind? Where are they on the progress bar? Yeah, so John, the big companies that we've talked to are, you know, they're typically furthers along that are, you know, cloud natives. They're more pushing the boundaries of innovation, right, 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 know, 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 turn of managing their data and executing that data to kind of drive more engagement with the customer. They got an edge too. I mean, thinking, they can nail the customer experience with data. Yeah. They have a competitive advantage. I mean, who, if I had a shooting hotel that was going to take care of me on my app, which is one that doesn't, I think I'm going to go with the one with the app every time. Definitely. The price is all, well, things being equal. A key part to that, though, and Shantun, I think, and honestly, I want to be able to mention today was that customer journey, right? Like, 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 get this kind of explodes even further to your point, that cleanliness of the data then just kind of 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 our keynote was open data, open APIs. Yep. 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 is 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 could be easily tied together. How do you guys look at that changing landscape, changing it pretty radically? It's high priority for our customers, right? They've always had a challenge with isolated vendors, right? And how do you bring that data together? One of the things that we'd readily notice when I talk to customers is that this excites them. The opportunity that they have now to have a platform regardless of which first-party or third-party to bring that together is something that they deem as necessary for the organization 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 experienced platform yet, or is that still ongoing? There is a big component of what we're doing is the developer piece of it. So now developers can go to Adobe.com and Adobe.io, actually, and find a lot of the APIs that are there available for them and documentation to help them build applications on top of platform. So they can do that today. They can do that today. Awesome. They can go check that out today. But you're pointing out something that's really important. A platform that is opening sensible now makes itself available to customers who have large developer teams. Many CTOs have an organization of engineers that are chomping at the bits to build new applications for their organizations. They also have big data science teams too, that are one of those take, you know, data science teams have always been massaging data. They've been managing it. That gets old for them. They don't want to do that. They want to actually do, they want to build something that's unique, innovative, and actually inspire their organization. High quality data, real time and relevant, fast and, you know, cool. That's what it's all about. Yeah. And you guys got a platform. So the final question for you, to get a platform right we've observed, you got to enable success. You got to be an enabling technology. Yeah. What's the big secret sauce for this platform? The secret sauce, yeah. I think it comes down to something that may seem simple, but I think there's a couple pieces that are the 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 our 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 kind of, you know, 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. Ronald, 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.