(upbeat music) >> Live from Las Vegas, it's theCUBE covering Informatica World 2019. Brought to you by Informatica. >> Welcome back everyone to theCUBE's live coverage of Informatica World 2019. I'm your host, Rebecca Knight, along with my co-host, John Furrier. We're joined by Steven Guggenheimer, he is the corporate vice president of AI and ISV engagement at Microsoft. Thank you so much for coming on theCUBE. >> Sure, thanks for having me. >> So one of the things that we're hearing so much at this conference is, "data needs AI but AI needs data." I'm wondering from your perspective, AI engagement, where do you come down on this? What are you hearing? what are your thoughts on that big theme? >> Um, well, data is the -- some people say the oil for AI, pick your terminology, but there is no AI without data. The reason that AI is such a hot topic right now is the combination of sort of compute storage and networking at scale, which means the access for developers and data scientists to work with large sets of data and then the actual data. If you don't have data you can't build models, if you can't build models, that's what is the definition of AI. So you need data. I always-- all the coaching I do is about sort of, BI before AI. If you can't actually get insight out of your data, let's not try to add intelligence. If you can't get insight out of your data, it means your data is not in a good-- your data state is not in order. So data first. >> A lot of architectural work is being done on data. I see a horizontally scalable cloud, gives a nice access to a lot of different you know, observational data sets. >> Yeah >> It used to be give the guy the silo, got the data, go get more data, slower. Now, data feeds the developer process because SaaS business models have been proven that data and SaaS work well together. So how do we get more-- what's the sequence of architecture to usability of data so that not only can you just have analytical systems, but where developers can start building their SaaS apps with data? >> Yeah, I mean we have this notion where we often talk about sort of, blades or feedback loops. There's sort of four or five things most companies do. You work with customers, you have employees, you have a supply chain or some type of partner chain, You run your finance and operations. So the question becomes, in each of those processes, there's data. Human-generated forms over data or pick your loop and now you getting tons and tons of data. The trick now is to make it reusable. Mostly what we've done for years, form over data, take the data, form over data. And what we do is we get all these different databases. We try and create some layer that brings it all together. We build cubes out of it to view and then we get this hopeless spaghetti. So the trick right now, we're working on something called Common Data Model, which others are well, or Common Data Service. Let's get the entities lined up from the very beginning. We've worked with Adobe and SAP on the Open Data Initiative. Let's start at the core, let's make the data layer reusable, We're you know, databases have become data warehouses have become data lakes. We're heading towards a data tidal wave, and if we don't get the data estate in order to run the line of business applications, to feed all of the things we do to use the ML and AI on top of it, we're going to drown in data and not get what we want out of it. So, architecturally I think about the Common Data Model and the Common Data Service both generically by industry, we build accelerators for that, getting the big organizations like the three I mentioned aligned around that, making it such that any, you know, organization can build from that and then building on top of that. For big companies you have to decide, what do I keep and what do I throw out? You know, what do I just give up on and start from fresh? What do I actually clean? Where do I use tools from Informatica or others to help me clean it, secure it? But you've got to put all that thought in. >> You know we were chatting before we came on camera about the internet days and the storied history that you had at Microsoft. And during the internet, search was the big application. And search on the internet actually worked really well because they didn't have a legacy. And the people that tried to crack the code on search inside an enterprise, much harder problem (Giggles). Because of the database things you mentioned. How does today's enterprise get the benefit of SaaS as if they were cloud-native SaaS with the data? So you know, the challenge we're hearing here is having a Common Data Model is all great, but I just want to be a SaaS player, I want to use my data to feed into my business value. How does a company move out of those legacy constraints? What do you see as-- >> Well there's different paths that different companies will take. I mean, the good news is that if you get your data in order to do what you said, then whether you build, buy or partner for the SaaS services, you can use that data underneath and you should be feeding it back in and making it such that it's sort of reusable and the pipeline is consistent. The truth is on all this, it's just going to end up infused anyway. When you used the internet, which is a funny analogy 'cause I remind people, you know, when the internet came out we had internet products, we had internet events, we had internet shows. We don't have any of that anymore. It's just woven into everything we do. AI is going to be the same. You have all this hype right now, you have AI shows, you have, you know, AI groups. The truth is, in 10, 15 years, AI it's just going to be woven into everything. The data is going to be set up for that. >> So what's the misconception on AI? 'Cause, first of all, I love the fact that AI is hyped up because my kids love it. Machine learning they learn because they hear about AI and they hear all this coolness. So machine learning goes hand-in-hand with AI, you feed machine learning, machine learning feeds the AI application. But a lot of people have aspirations around AI. Some of them are ungettable and so that's probably a misalignment around the hype. What's your feeling of where the reality is and what's the misconceptions, how should people approach AI? Any thoughts there. >> I think a lot about the AI journey, the first year we were having these AI conversations, we talked about AI for everybody, just go play. Now the conversation is, I call it pragmatic AI. Look, lets talk about, you know, how you want to think about AI, it's going to end up everywhere, so the question becomes, what's your differentiation as a company, and how is AI going to support it? Like any other new technology, in the beginning, people just want to play. Just because you can -- let's just say just you can build a virtual agent, doesn't mean every company should. So the question becomes, first off, BI before AI, get your data state in order. Second, in a build buy partner model, what's your differentiation as a company? Whether you want to use either your unique data or your unique skill sets to use AI against that differentiation to help you grow. Otherwise, like, expect somebody else to have infused AI into the products you buy, the SaaS services, you know, use that, then build whatever you want and then there's, you know, if you think you're going to build a new business based on your unique data or your unique AI capabilities, great, let's have that conversation, we need that too but rarely does that become the state. so, most of the conversations move from, you know, the hype to okay, let's get pragmatic which is why I always come back to data first 'cause if you not doing that, you're not setting up for the long run. Let's build for the long run, then let's just have a business conversation like, how do you differentiate yourself as a business? Okay, how is this tool going to help you? >> I want to ask about, uh about innovation, and particularly because Microsoft is a company that's now entering its middle age (giggling) and-- >> What does that say about me, oh no >> As one of famously innovative company, but how do you stay on the cutting edge? I mean, I'm wondering internally how you think about AI for Microsoft's business purposes. What are the conversations around AI? >> One of such is, core conversations around this notion of tech intensity you know, from where we focus on how we think about things we think about tech intensity against different areas, AI being one of those. Look, AI is really this interesting thing. I would say we're plumbers by trade, we build software plumbing for others. So, we do three things right, with AI. Basically, there's a layer growing on top of the core development stack, compute, storage, networking for AI. So we're building a layer, cognitive services, bot services, machine learning, set of tools for developers to infuse AI into things that they've built, so that's thing number one. Thing number two, is we infuse AI into our own products, into Windows, into Office, into Azure, into dynamics. You don't see it, we don't talk about it, we don't say Microsoft Windows Inking brought to you by Azure AI. It just works, but our inking works, our face login works, oh, you know, I can -- it's helping me write a better resume in LinkedIn, that's all AI behind the scenes. Now, the third thing you think about then is, "how do you actually use AI to run the business better"? So, how do you think about, AI assisting professionals, how do we think about the, how we do mocking better, How we forecasting sales, so AI is about plumbing, let's build a platform for others, let's use it ourselves on our own products, and then let's think about how you actually use it to run the company better. And that's how we think about it-- >> That's pragmatic >> Very pragmatic AI is kind of -- >> Yeah, that's how I think about it and we, you know, it's interesting 'cause back to the tech intensity point, we get together on an AI conversation, we searching with the senior leadership team about once every other week, and we're round robin between a research topic, the platform and one of the solutions. So it's, you're always getting constant feedback about is the platform doing what we need to build solutions? Is the research feeding the platform? So, you're getting this really nice feedback loop right now and that tech intensity. >> Quality data always has been a big part of the data modeling in the past, Cloud now allows for data marketplaces I've seen sharing of data as a dynamic, almost like sharing libraries of your developer back in the day, so data is now being merchandised in a new way. This is a trend, what's your thought on it? Because if this continues, you're going to have more data inputs, does that-- >> Err, there are places where data is aggregated and potentially can be re-used. We can -- Bing is an example, Google would be an example um, I know people who aggregate data for different industries, etcetera. It's not an easy business, the rules and rights around data, the GPR compliance, the rest of it. I think there's a deer there but you really have to be in the business for-- the trick you run into is, if you're going to be an aggregator, and then a reseller of data, where's that data coming from? What are the rights, what's the security? And then, are the people who are providing that data comfortable with their competitors getting the data? 'cause if you're really going to be a data provider marketplace, first person who's going to want on is the competitor, so, I think it's an interesting conversation, I think it's kind of growing and there's some real good work there, I don't think it's as-- >> not viable yet >> Easily to do it at scale, for as many people who think they have the data asset as believed they do. But that's Steve's view, that's not a Microsoft's statement. (laughing) >> good disclaimer >> Steve's view, so I want to hear Steve's view on the skills gap, this is a huge problem in the technology industry, as so few people to fill roles. How's Microsoft dealing-- what's your view-- >> my view is I'm glad I work at Microsoft, 'cause we spend a lot of energy on that, um, I wish there were a single solution, but we have Minecraft for education, starting with kids, how do you help, you know, Minecraft is this great tool that teachers use help kids get started, so that's a tool set we work on something called tills, which is uh, basically, our developers teach school kids remotely, junior, high school level, you know, coding. Um, we have made investments against this, we have online training, you know, we work with universities. I don't know the perfect answer, um, but I do know we invest and we work with Hadi Partovi and his group on code.org, I mean any place that there is work going on, we work with the military for people coming out of the military service. So we're heavily invested. I'm hopeful that the ease of use of some of the tools and just from a job area, it drives people but I don't know the perfect answer. Steve's view is I don't know the answer, I do know we try every trick in the book-- >> Multipronged attack >> I'm a parent of two kids, like I have my daughter, you know, working on more on the tech side and you know, it's hard to keep kids on a track for that-- >> There's no degree yet, but we had a first degree this year, graduated from the school but there's kind of like a skills portfolio of different things depending on the make-up I mean, domain expertise is critical, if you don't know what you're tryna do, that's -- >> I think we got a mix, because what you're starting to see is, the tools for subject matter experts, are getting better, we have something called the power platfrom, which allows people who aren't necessarily coders by trade, but want to be able to build, you know, sort of apps or services to be able to do that more easily and mix their subject matter expertise. And you see many more people come out of any program, take biology, with sort of computer knowledge to a decent level. AI and ML research, different area, hard skills gap right there >> Steve, great insights, thanks for spending some time with us, great insights on the skills gap and just overall >> thanks for coming on theCUBE >> We didn't talk about rugby, but okay, fine. Thanks, next time >> next time >> You're one of those ballsmen >> we'd track you down >> The ballsmen can throw >> Exactly, shout out to them >> There we go, >> thank you >> Ah, you are watching theCUBE we'd come right back with more from Informatica World I'm Rebecca Knight for John Furrier, stay tuned (upbeat music)

>> Narrator: Live from Las Vegas, it's the Cube, covering NAB 2017, brought to you by HGST. >> Hey welcome back everybody, Jeff Frick here with the Cube, we're at NAB 2017, at the Las Vegas Convention Center. A hundred thousand people that are here, have been coming for decades, it's really quite a convention. It's our first trip here, but we're really excited to be joined by an industry veteran, she's been coming for a while, coming off a pretty impressive keynote, it's Wendy Aylsworth. She's the Chief Executive Officer at Walden Pond, and a many year veteran at Warner Brothers, right? So, welcome. >> Yes I am. Thank you. >> So first impressions of the show. You've been coming for a while, it seems to have kind of a different theme every year, what do you see this year that kind of strikes you? >> A little less focus on physical devices and I think there's a growing focus on software, and how those applications help streamline production processes, distribution processes, so you're seeing the real, more heavy move to IP and software applications. >> Right, right. Which, of course, is so consistent with what we see in many other industries, right? Between, a lot of it is driven by your mobile phone and expected behavior, and basically the entire world. I say it's like your remote control for your life now. Basically everything is on your phone. But a big piece of it is Cloud. And with Cloud now, people can dial-up at a moment's notice, basically infinite amounts of compute and store, and leverage that horse power in ways that you just can't do on a local device. So I'm curious, you've been in the business for a while, how has Cloud adoption changed the game? And how does it continue to change the game as we look forward? >> Yeah exactly. I just came from this keynote by Steven Guggenheimer, of Microsoft, where he talked about it being all about bandwidth, processing and storage. And, as those increase, and become more available it kind of democratizes the ability for people to get away from having to purchase their own physical devices, and it has opened up really a wide capability for new methods of doing production that actually couldn't even be done before. As well as long distance collaboration, and more rapid distribution, and then the ability to track and understand how data is flowing, so that you might be able to better understand the consumer. It really allows a content creator to get closer to their audience. And over time I think we will continue to see that ability grow. >> Which is so interesting because the proliferation of types of content is exploding, right? >> Wendy: It is. >> Everything from your classic big houses, to new houses like Netflix. Somebody told me earlier in the week that Netflix is one of the biggest producers now of independent content, to YouTubers, with not much more than an iPhone and a microphone that can go out, and if they've got a compelling piece of content, and they relate to a specific audience, can see tremendous numbers that a lot of people would do anything for. So that democratization is a huge item, but if you don't have an audience and you're not reaching them, and you're not measuring them, pretty tough because everybody is one swipe away from something else to watch. >> Well in fact, one of the discussions, really now is about that marketing capability because, the best marketing capabilities are still in the hands of the people who have been doing it for decades and decades and know where their audiences are and how to reach them, although those are shifting. And, the ability to provide tools that help new content creators find their audience are going to become critical needs in the future. >> Right, right. And less and less we see at other places, I'm sure we'll see it here, is that marketing intuition going to be the driver of the big spin. Now it's okay, you have intuition, but what's, You know, do you have some data to back it up? And the intuition can help drive the direction and the data collection, but at the end of the day, we see it in every other industry, I'm sure we'll see it here too, where it's data-driven decisions, using automation, using software to get better results in an increasingly competitive world. >> Yeah, and getting the right results because, as we know, there's tons and tons and tons of data, but it's understanding the data and putting good intelligence to it that allows you to make the right decisions. >> Right, right. Now as you're consulting to executives, who've been in the industry a while, what are they telling you? Are they excited? Are they scared? Are they slightly caught off guard? I mean there's so much new information opportunity. I'm struck by this kind of compression, it seems like, from the outside looking in, around your release weekend, it's so competitive to have. So there's only, whatever, 52 weekends a year, so many films trying to hit that particular window, and it seems like this, such pressure to make that number in a really short period of time. At the other hand, there's all these on-demand opportunities, there's all these alternate forms of distribution. It seems like a really difficult changing environment for these houses to be in. >> It is. It's a difficult changing environment. I haven't heard anybody be disappointed or pessimistic about it. I think they recognize that throughout history things change and you must change with it. The interesting thing there is, is that it's traditional windows are shrinking, but hopefully over time it'll become more apparent where there can be other moneys to be made in later windows or in different augmented settings. So I'll use as an example virtual reality. If virtual reality becomes a type of media in its own right, then it could be that you take a title type of content and one of its offshoots is a virtual reality piece that's then sold separately and monetized separately. So I think there is pressure on the traditional windows, to make them shorter, to get more revenue faster, but there are an awful lot of new technologies bubbling up that will create new types of content in the future and the smart players will get into that and monetize it as rapidly as they can. >> The other thing of course, that's changed significantly, along with Cloud, is just the cost of all this technology infrastructure, in terms, you know, just compute, and store, and networking just continue to crash down in terms of the cost and now, with these alternative things that you might have down the road, that you may or may not even know are going to be opportunities. How is that changing looking at the asset value? Cuz before, maybe you couldn't keep dailies, or maybe storage of all this stuff was a liability, it was expensive, and once you've got the finished product out the door maybe you're less likely to keep all the derivative works. But in today's world you might have some new distribution form that you didn't even think about before. Oh I wish I had this version, or that version, or that rough cut. >> I think asset keeping is always going to be a problem. I don't think it's any different than our homes, or any closet or drawer you own with, you know, when you started in your first apartment you had limited space and every time you get a bigger house then you fill it up, and then all of a sudden you decide you want to downsize and you got a problem. And I think that's always going to be a challenge, where companies have to figure out, what is the best of these assets that I should retain, and what should I not bother to retain. Because it's frankly too expensive to keep everything. That said, in the shift from analog to digital content creation, we've seen the production step, it's just so easy to take more photos and keep them. So there's been a shift in putting the onus on the content, directly on the content creator to decide what they think is the best of their work that should be kept, because it's unmanageable now. Just like my cellphone pictures are unmanageable. >> It's funny the pictures, because before, you know, pictures were rare, and a special picture was special, because it was like open up an Easter egg, right? You took your film down, maybe it was a couple weeks after you got back from vacation, you had a couple rolls of 36, and maybe one or two great ones right, >> Maybe you got one great shot, yeah. >> where you had that treasured picture of a relative or something. Now it's almost a curse of abundance because you can just push your button down, and the hard drives are getting bigger, and everything is getting faster. Now I have thousands, I can't even find a good one, not because I didn't have a good one, because I have to wallow through 2,472 cuz the 73rd is the one that I really want. That must be amplified tremendously in this space. >> Maintenance of your storage, again, I don't care whether it's the shoes in your closet or your photographs on your phone, or for a movie production. All of the footage that they're shooting and all of the special effects, and all these different forms of content that are coming in. Management of what you're going to retain is still a problem. Maybe there's machine learning that can help us wittle that down. >> Right, right. Certainly AI and machine learning are coming. But I wonder if you're hearing much about that, not only for the standard metadata that we would want, we had someone on earlier talking about archiving and basic kind of metadata, but now we can get into the metadata at a frame level, and a lot better intelligence. I'm sure in the future will be value judgements as well, as to whether this is a good shot, or not a bad shot, or it's applicable to whatever. Are you seeing much curiosity, adoption, experimentation, what do you kind of see? >> A lot of interest, a couple of experiments, not particularly in the what to say area, but a lot of experiments in other areas of production that are monotonous and boring like, take the example of pulling great shots from a film, in order to cut together a trailer, or a teaser that's going to go on the air. Well, a machine can pick out the best shots, thereby saving the person time of going through all the shots, and pulling the right footage. And then the editor can spend their time doing what they do best, which is taking those shots, and cutting them into an interesting sequence. So, I see a lot of experimentation going on that rudimentary machine learning being applied to quality control. So every time a file gets shipped from one company to another, they check it to make sure that it's correct. Well applying a machine, that's a really boring job, applying a machine to figure out whether that pile came in correctly and didn't get corrupted, great use of machine learning. >> So when you're in the field, what do you hear as kind of the top priorities from some of the people that you're working with now, in this super crazy, evolving environment? What are they looking to your help and assistance for? >> Well, in terms of Cloud sorts of work, they're looking to reduce their capital assets and be able to aggregate and use the resources of the Cloud to lower their costs of development. >> Just kind of a classic CAPEX versus, yeah... versus OPEX. >> And in some cases, whether they can help streamline their process, and speed up their schedule, and do things more in parallel. >> It seems like a perfect match. Because movies, by their very nature, are these transient little projects that form and come together, be produced, and then they disappear. >> Wendy: And then they disappear. >> And that's like perfect kind of an application for a Cloud world, which is the same thing, it's on demand, you assemble it, use it, when it's done it goes back. So it seems like a pretty good match. >> And applications in the Cloud that are modeling themselves to offer the services, based upon the usage, as opposed to setting up a long-term contract, those are the apps that are going to win. >> Right, and that's very consistent with the way that industry has worked for a long long time, right? >> Wendy: Yeah. >> Yeah, alright, well I'll give you the last word as you're leaving the show here in a couple days, headed back to L.A. What do you thinking about for the balance of 2017 that you're taking away, that you're excited to share with some of your clients? >> I think the power of doing little steps, and getting involved into using machine learning in various methods, whether that be in the Cloud or in a local Cloud. And then looking longer range to where artificial intelligence will actually play into that. But there's initial steps that have to be done in terms of applying machine learning first, and then I think we'll get into the more interesting stuff of artificial intelligence five years down the street. >> Yeah, early days, exciting times. >> Wendy: It is very exciting. >> Alright well Wendy, well thanks for taking a few minutes out of your busy day. >> I really appreciate the time. >> Alright, Wendy Aylsworth from Walden Pond. I'm Jeff Frick, you're watching the Cube. We're at NAB 2017, from Las Vegas. We'll be right back.