>> Narrator: From San Mateo, California, it's theCUBE covering SnapLogic Innovation Day 2018. Brought to you by SnapLogic. >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're in San Mateo, California right at the crossroads. The building's called The Crossroads but it's right at the crossroads of 92 and 101. It's a really interesting intersection over the years as you watch these buildings that are on the corner continue to change names. I always think of the Seibel, his first building came up on this corner and we're here to see a good friend of SnapLogic and their brand new building. Gaurav Dhillon, Chairman and CEO, great to see you. >> Pleasure to be here. >> So how long you been in this space? >> Gosh, it's been about a year. >> Okay. >> Although it feels longer. It's a high-growth company so these are dog years. (laughs) >> That's right. and usually, you outgrow it before you all have moved in. >> The years are short but the days are long. >> And it's right next Rakuten, I have to mention it. We all see it on the Warriors' jerseys So now we know who they are and where they are exactly. >> No they're a good outfit. We had an interesting time putting a sign up and then the people who made their sign told us all kinds of back stories. >> Oh, good, good Alright. So give us an update on SnapLogic. You guys are in a great space at a really, really good time. >> You know, things been on a roll. As you know, the mission we set out to... engage with was to bring together applications and data in the enterprise. We have some of the largest customers in high technology. Folks like Qualcomm, Workday. Some of the largest customers in pharmaceuticals. Folks like Astrazeneca, Bristol-Meyers Squibb. In retail, Denny's, Wendy's, etc. And these folks are basically bringing in new cloud applications and moving data into the cloud. And it's really fun to wire that all up for them. And there's more of it every day and now that we have this very strong install-base of customers, we're able to get more customers faster. >> Right. >> In good time. >> It's a great time and the data is moving into the cloud, and the public cloud guys are really making bigger plays into the enterprise, Microsoft and, Amazon and Google. And of course, there's IBM and lots of other clouds. But integration's always been such a pain and I finally figured out what the snap in SnapLogic means after interviewing you >> (laughs) a couple of times, right. But this whole idea of, non-developer development and you're taking that into integration which is a really interesting concept, enabled by cloud, where you can now think of snapping things together, versus coding, coding, coding. >> Yeah Cloud and A.I, right We feel that this problem has grown because of the change in the platform. The compute platform's gone to the cloud. Data's going to the cloud. There was bunch of news the other day about more and more companies moving the analytics into the cloud. And as that's happening, we feel that this approach and the question we ask ourselves when we started this company, we got into building the born in the cloud platform was, what would Apple do if they were to build an integration product? And the answer was, they would make it like the iPhone, which is easy to use, but very powerful at the same time. And if you can do that, you can bring in a massive population of users who wouldn't have been able to do things like video chat. My mom was not able to do video chat, and believe me, we tried this and every other thing possible 'till facetime came along. And now she can talk to my daughter and she can do it without help, any assistance from teenage grandchildren on that side, Right? >> Right, Right >> So what we've done with SnapLogic, is by bringing in a beautiful, powerful, sleek interface, with a lot of capability in how it connects, snaps together apps and data, we've brought in a whole genre of people who need data in the enterprise so they can serve themselves data. So if your title has analyst in it, you don't have to be programmer analyst. You could be any analyst. >> Right >> You could be a compensation analyst, a commissions analyst, a finance analyst, an HR analyst. All those people can self-serve information, knock down silos, and integrate things themselves. >> It's so interesting because we talk a lot about innovation and digital transformation, and in doing thousands of these interviews, I think the answer to innovation is actually pretty simple. You give more people access to the data. You give them more access to the tools to work with the data and then you give them the power to actually do something once they figure something out. And you guys are really right in the middle of that. So before, it was kind of >> (laughs) Yeah >> democratization of the data, democratization of the tools to work with the data, but in the API economy, you got to be able to stitch this stuff together because it's not just one application, it's not just one data source. >> Correct >> You're bringing from lots and lots of different things and that's really what you guys are taking advantage of this cloud infrastructure which has everything available, so it's there to connect, >> (laughs) Versus, silo in company one and silo in company two. So are you seeing it though, in terms of, of people enabling, kind of citizen integrators if you will, versus citizen developers. >> Yeah. Heck Yeah. So I'll give you an example. One of our large customers... Adobe Systems, right here in San Jose has been amazingly successful flagship account for us. About 800 people at Adobe come to www.snaplogic.com, every week to self-serve data. We replaced legacy products like TIBCO, informatica web methods about four years ago. They first became a customer in 2014 and usage of those products was limited to Java programmers and Sequel programmers, and that was less than 50 people. And imagine that you have about 800 people doing self-service getting information do their jobs. Now, Adobe is unique in that, it's moved the cloud in a fantastic way, or it was unique in 2014. Now everybody is emulating them and the great success that they've had. With the cloud economic model, with the cloud ID model. This is working in spades. We have customers who've come on board in Q4. We're just rounding out Q1 and in less than 60, 90 days, every time I look, 50, 100, 200 people, from each large company, whether it's a cosmetics company, pharmaceuticals company, retailer, food merchandise, are coming in and using data. >> Right >> And it's proliferating, because the more successful they are, the better they are able to do in their jobs, tell their friends about it sort-of-thing, or next cubicle over, somebody wants to use that too. It's so interesting. Adobe is such a great example, cause they did transform their business. Used to be a really expensive license. You would try to find your one friend that worked there around Christmas >> (laughs) Cause you think they got two licenses a year they can buy for a grand. Like, I need an extra one I can get from you. But they moved to a subscription model. They made a big bet. >> Yes. Yes >> And they bet on the cloud, so now if you're a subscriber, which I am, I can work on my home machine, my work machine, go to machine, machine. So, it's a really great transformation story. The other piece of it though, is just this cloud application space. There's so many cloud applications that we all work with every day whether it's Basecamp, Salesforce, Hootsuite. There's a proliferation of these things and so they're there. They've got data. So the integration opportunity is unlike anything that was ever there before. Cause there isn't just one cloud. There isn't just one cloud app. There's a lot of them. >> Yes. >> How do I bring those together to be more productive? >> So here's a stat. The average enterprise has most cloud services or SAS applications, in marketing. On the average, they have 91 marketing applications or SAS applications. >> 91. That's the average. >> 96% of them are not connected together. >> Right. >> Okay. That's just one example. Now you go to HR, stock administration. You go into sales, CRM, and all the ancillary systems around CRM. And there is this sort of massive, to us, opportunity of knocking down these silos and making things work together. You mention the API economy and whilst that's true that all these SAS applications of APIs. The problem is, most companies don't have programmers to hook up those API's. >> Right. To connect them. >> Yes, in Silicon Valley we do and maybe in Manhattan they do, but in everywhere else in the world, the self-service model, the model of being able to do it to something that is simple, yet powerful. Enterprise great >> Right. Right >> and simple, beautiful is absolutely the winning formula in our perspective. So the answer is to let these 100 applications bloom, but to keep them well behaved and orchestrated, in kind of a federated model, where security, having one view of the world, etc., is managed by SnapLogic and then various people and departments can bring in a blessed, SAS applications and then snap them in and the input and the way they connect, is done through snaps. And we've found that to be a real winning model for our customers. >> So you don't have to have like 18 screens open all with different browsers and different apps. >> Swivel chair integration is gone. Swivel chair integration is gone. >> Step above sneakernet but still not-- >> Step above but still not. And again, it may make sense in very, very specific super high-speed, like Wall Street, high frequency trading and hedge funds, but it's a minuscule minority of the overall problems that there needs to be solved. >> Right. So, it's just a huge opportunity, you just are cleaning up behind the momentum in the SAS applications, the momentum of the cloud. >> Cloud data. Cloud apps. Cloud data. And in general, if a customer's not going to the cloud, they're probably not the best for us. >> Right. >> Right. Our customers' almost always going towards the cloud, have lots of data and applications on premise. And in that hybrid spot, we have the capability to straddle that kind of architecture in a way that nobody else does. Because we have a born in the cloud platform that was designed to work in the real world, which is hybrid. >> So another interesting thing, a lot of talk about big data over the years. Now it's just kind of there. But AI and machine learning. Artificial intelligence which should be automated intelligence and machine learning. There's kind of the generic, find an old, dead guy and give it a name. But we're really seeing the values that's starting to bubble up in applications. It's not, AI generically, >> Correct. >> It's how are you enabling a more efficient application, a more efficient workflow, a more efficient, get your job done, using AI. And you guys are starting to incorporate that in your integration framework. >> Yes. Yes. So we took the approach, 'doctor heal thyself.' And we're going to help our customers do better job of having AI be a game changer for them. How do we apply that to ourselves? We heard one our CIOs, CI of AstraZeneca, Dave Smoley, was handing out the Amazon Alexa Echo boxes one Christmas. About three years ago and I'm like, my gosh that's right. That was what Walt Mossberg said in his farewell column. IT is going to be everywhere and invisible at the same time. Right. >> Right. >> It'll be in the walls, so to speak. So we applied AI, starting about two years ago, actually now three, because we shipped Iris a year ago. The artificial intelligence capability inside SnapLogic has been shipping for over 12 months. Fantastic usage. But we applied to ourselves the challenge about three years ago, to use AI based on our born in the cloud platform. On the metadata that we have about people are doing. And in the sense, apply Google Autocomplete into enterprise connectivity problems. And it's been amazing. The AI as you start to snap things together, as you put one or two snaps, and you start to look for the third, it starts to get 98.7% accurate, in predicting how to connect SAS applications together. >> Right. Right. >> It's not quite autonomous integration yet but you can see where we're going with it. So it's starting to do so much value add that most of our customers, leave it on. Even the seasoned professionals who are proficient and running a center of excellence using SnapLogic, even those people choose to have sort-of this AI, on all the time helping them. And that engagement comes from the value that they're getting, as they do these things, they make less mistakes. All the choices are readily at hand and that's happening. So that's one piece of it >> Right. >> Sorry. Let me... >> It's Okay. Keep going. >> Illustrate one other thing. Napoleon famously said, "An army marches on its stomach" AI marches on data. So, what we found is the more data we've had and more customers that we've had, we move about a trillion documents for our customers worldwide, in the past 30 days. That is up from 10 million documents in 30 days, two years ago. >> Right. Right >> That more customers and more usage. In other words, they're succeeding. What we've found as we've enriched our AI with data, it's gotten better and better. And now, we're getting involved with customers' projects where they need to support data scientists, data engineering work for machine learning and that self-service intricate model is letting someone who was trying to solve a problem of, When is my Uber going to show up? So to speak. In industry X >> Right. Right. >> These kinds of hard AI problems that are predictive. That are forward changing in a sense. Those kind of problems are being solved by richer data and many of them, the projects that we're now involved in, are moving data into the cloud for data lake to then support AI machine learning efforts for our customers. >> So you jumped a little bit, I want to talk on your first point. >> Okay. Sorry >> That's okay. Which is that you're in the very fortunate position because you have all that data flow. You have the trillion documents that are changing hands every month. >> Born in the cloud platform. >> So you've got it, right? >> Got it. >> You've got the data. >> It's a virtual cycle. It's a virtual cycle. Some people call it data capitalism. I quibble with that. We're not sort-of, mining and selling people's personal data to anybody. >> Right. Right. >> But this is where, our enterprise customers' are so pleased to work with us because if we can increase productivity. If we can take the time to solution, the time to integration, forward by 10 times, we can improve the speed that by SAS application and it gets into production 10 times faster. That is such a good trade for them and for everyone else. >> Right. Right. >> And it feeds on itself. It's a virtual cycle. >> You know in the Marketo to the Salesforce integration, it's nothing. You need from company A to company B. >> I bet you somebody in this building is doing it on a different floor right now. >> Exactly. >> (laughs) >> So I think that's such an interesting thing. In the other piece that I like is how again, I like your kind of Apple analogy, is the snap packs, right. Because we live in a world, with even though there 91 on-averages, there's a number of really dominant SAS application that most people use, you can really build a group of snaps. Is snap the right noun? >> That's the right word. >> Of snaps. In a snap pack around the specific applications, then to have your AI powered by these trillion transactions that you have going through the machines, really puts you in a unique position right now. >> It does, you know. And we're very fortunate to have the kind of customer support we've had and, sort of... Customer advisory board. Big usages of our products. In which we've added so much value to our customers, that they've started collaborating with us in a sense. And are passing to us wonderful ideas about how to apply this including AI. >> Right. >> And we're not done yet. We have a vision in the future towards an autonomous integration. You should be able to say "SnapLogic, Iris, "connect my company." And it should. >> Right. Right. >> It knows what the SAS apps are by looking at your firewall, and if you're people are doing things, building pipelines, connecting your on-premise legacy applications kind of knows what they are. That day when you should be able to, in a sense, have a bot of some type powered by all this technology in a thoughtful manner. It's not that far. It's closer at hand than people might realize. >> Which is crazy science fiction compared to-- I mean, integration was always the nightmare right back in the day. >> It is. >> Integration, integration. >> But on the other hand, it is starting to have contours that are well defined. To your point, there are certain snaps that are used more. There are certain problems that are solved quite often, the quote-to-cash problem is as old as enterprise software. You do a quote in the CRM system. Your cash is in a financial system. How does that work together? These sort of problems, in a sense, are what McKinsey and others are starting to call robotic process automations. >> Right. >> In the industrial age, people... Stopped, with the industrial age, any handcrafted widget. Nuts, and bolts, and fasteners started being made on machines. You could stamp them out. You could have power driven beams, etc., etc. To make things in industrial manner. And our feeling is, some of the knowledge tasks that feel like widget manufactures. You're doing them over and over again. Or robotic, so to speak, should be automated. And integration I think, is ripe as one of those things and using the value of integration, our customers can automate a bunch of other repeatable tasks like quote-to-cash. >> Right. Right. It's interesting just when you say autonomous, I can't help but think of autonomous vehicles right, which are all the rage and also in the news. And people will say "well I like to drive "or of course we all like to drive "on Sunday down at the beach" >> Sure. Yeah. >> But we don't like to sit in traffic on the way to work. That's not driving, that's sitting in traffic on the way to work. Getting down the 101 to your exit and off again is really not that complicated, in terms of what you're trying to accomplish. >> Indeed. Indeed. >> Sets itself up. >> And there are times you don't want to. I mean one of the most pleasant headlines, most of the news is just full of bad stuff right. So and so and such and such. But one of the very pleasing headlines I saw the other day in a newspaper was, You know what's down a lot? Not bay area housing prices. >> (laughs) >> But you know what's down a lot? DUI arrests, have plummeted. Because of the benefits of Lyft and Uber. More and more people are saying, "You know, I don't have to call a black cab. "I don't need to spend a couple hundred bucks to get home. "I'm just getting a Lyft or an Uber." So the benefits of some of these are starting to appear as in plummeting DUIs. >> Right. Right >> Plummeting fatalities. From people driving while inebriated. Plunging into another car or sidewalk. >> Right. Right. >> So Yes. >> Amara's Law. He never gets enough credit. >> (laughs) >> I say it in every interview right. We overestimate in the short term and we underestimate in the long term the effects of these technologies cause we get involved-- The Gartner store. It's the hype cycle. >> Yeah, Yeah >> But I really I think Amara nailed it and over time, really significant changes start to take place. >> Indeed and we're seeing them now. >> Alright well Gaurav, great to get an update from you and a beautiful facility here. Thanks for having us on. >> Thank you, thank you. A pleasure to be here. Great to see you as well. >> Alright He's Gaurav, I'm Jeff. And you're watching theCUBE from SnapLogic's headquarters Thanks for watching. (techno music)

>> Narrator: From San Mateo, California, it's theCUBE, covering SnapLogic Innovation Day 2018. Brought to you by SnapLogic. >> Welcome back, Jeff Frick here with theCUBE. We're at the Crossroads, that's 92 and 101 in the Bay Area if you've been through it, you've had time to take a minute and look at all the buildings, 'cause traffic's usually not so great around here. But there's a lot of great software companies that come through here. It's interesting, I always think back to the Siebel Building that went up and now that's Rakuten, who we all know from the Warrior jerseys, the very popular Japanese retailer. But that's not why we're here. We're here to talk to SnapLogic. They're doing a lot of really interesting things, and they have been in data, and now they're doing a lot of interesting things in integration. And we're excited to have a many time CUBE alum. He's Greg Benson, let me get that title right, chief scientist at SnapLogic and of course a professor at University of San Francisco. Greg great to see you. >> Great to see you, Jeff. >> So I think the last time we see you was at Fleet Forward. Interesting open-source project, data, ad moves. The open-source technologies and the technologies available for you guys to use just continue to evolve at a crazy breakneck speed. >> Yeah, it is. Open source in general, as you know, has really revolutionized all of computing, starting with Linux and what that's done for the world. And, you know, in one sense it's a boon, but it introduces a challenge, because how do you choose? And then even when you do choose, do you have the expertise to harness it? You know, the early social companies really leveraged off of Hadoop and Hadoop technology to drive their business and their objectives. And now we've seen a lot of that technology be commercialized and have a lot of service around it. And SnapLogic is doing that as well. We help reduce the complexity and make a lot of this open-source technology available to our customers. >> So, I want to talk about a lot of different things. One of the things is Iris. So Iris is your guys' leverage of machine learning and artificial intelligence to help make integration easier. Did I get that right? >> That's correct, yeah. Iris is the umbrella terms for everything that we do with machine learning and how we use it to enhance the user experience. And one way to think about it is when you're interacting with our product, we've made the SnapLogic designer a web-based UI, drag-and-drop interface to construct these integration pipelines. We connect these things called Snaps. It's like building with Legos to build out these transformations on your data. And when you're doing that, when you're interacting with the designer, we would like to believe that we've made it one of the simplest interfaces to do this type of work, but even with that, there are many times we have to make decisions, like what type of transformation do you do next? How do you configure that transformation if you're talking to an Oracle database? How do you configure it? What's your credentials if you talk to SalesForce? If I'm doing a transformation on data, which fields do I need? What kind of operations do I need to apply to those fields? So as you can imagine, there's lots of situations as you're building out these data integration pipelines to make decisions. And one way to think about Iris is Iris is there to help reduce the complexity, help reduce what kind of decision you have to make at any point in time. So it's contextually aware of what you're doing at that moment in time, based on mining our thousands of existing pipelines and scenarios in which SnapLogic has been used. We leverage that to train models to help make recommendations so that you can speed through whatever task you're trying to do as quickly as possible. >> It's such an important piece of information, because if I'm doing an integration project using the tool, I don't have the experience of the vast thousands and thousands, and actually you're doing now, what, a trillion document moves last month? I just don't have that expertise. You guys have the expertise, and truth be told, as unique as I think I am, and as unique as I think my business processes are, probably, a lot of them are pretty much the same as a lot of other people that are hooking up to SalesForce to Oracle or hooking up Marketta to their CRM. So you guys have really taken advantage of that using the AI and ML to help guide me along, which is probably a pretty high-probability prediction of what my next move's going to be. >> Yeah, absolutely, and you know, back in the day, we used to consider, like, wizards or these sorts of things that would walk you through it. And really that was, it seemed intelligent, but it wasn't really intelligence or machine learning. It was really just hard-coded facts or heuristics that hopefully would be right for certain situations. The difference today is we're using real data, gigabytes of metadata that we can use to train our models. The nice thing about that it's not hard-coded it's adaptive. It's adaptive both for new customers but also for existing customers. We have customers that have hundreds of people that just use SnapLogic to get their business objectives done. And as they're building new pipelines, as they are putting in new expressions, we are learning that for them within their organization. So like their coworkers, the next day, they can come in and then they get the advantages of all the intellectual work that was done to figure something out will be learned and then will be made available through Iris. >> Right. I love this idea of operationalizing machine learning and the augmented intelligence. So how do you apply it? Don't just talk about it, don't give it a name of some dead smart person, but actually apply it to an application where you can start to see the benefit. And that's really what Iris is all about. So what's changed the most in the last year since you launched it? >> You know, one thing I'll say: The most interesting thing that we discovered when we first launched Iris, and I should say one of the first Iris technologies that we introduced was something called the integration assistant. And this was an assistant that would make, make recommendations of the next Snap as you're building out your pipeline, so the next transformation or the next connector, and before we launched it, we did lots of experimentation with different machine learning models. We did different training to get the best accuracy possible. And what we really thought was that this was going to be most useful for the new user, somebody who hasn't really used the product and it turns out, when we looked at our data, and we looked at how it got used, it turns out that yes, new users did use it, but existing or very skilled users were using it just as much if not more, 'cause it turned out that it was so good at making recommendations that it was like a shortcut. Like, even if they knew the product really well, it's still actually a little more work to go through our catalog of 400 plus Snaps and pick something out when if it's just sitting right there and saying, "Hey, the next thing you need to do," you don't even have to think. You just have to click, and it's right there. Then it just speeds up the expert user as well. That was an interesting sort of revelation about machine learning and our application of it. In terms of what's changed over the last year, we've done a number of things. Probably the operationalizing it so that instead of training off of SnapShot, we're now training on a continuous basis so that we get that adaptive learning that I was talking about earlier. The other thing that we have done, and this is kind of getting into the weeds, we were using a decision tree model, which is a type of machine learning algorithm, and we switched to neural nets now, so now we use neural nets to achieve higher accuracy, and also a more adaptive learning experience. The neural net allowed us to bring in sort of like this organizational information so that your recommendations would be more tailored to your specific organization. The other thing we're just on the cusp of releasing is, in the integration assistant, we're working on sort of a, sort of, from beginning-to-end type recommendation, where you were kind of working forward. But what we found is, in talking to people in the field, and our customers who use the product, is there's all kinds of different ways that people interact with a product. They might know know where they want the data to go, and then they might want to work backwards. Or they might know that the most important thing I need this to do is to join some data. So like when you're solving a puzzle with the family, you either work on the edges or you put some clumps in the middle and work to get to. And that puzzle solving metaphor is where we're moving integration assistance so that you can fill in the pieces that you know, and then we help you work in any direction to make the puzzle complete. That's something that we've been adding to. We recently started recommending, based on your context, the most common sources and destinations you might need, but we're also about to introduce this idea of working backwards and then also working from the inside out. >> We just had Gaurav on, and he's talking about the next iteration of the vision is to get to autonomous, to get to where the thing not only can guess what you want to do, has a pretty good idea, but it actually starts to basically do it for you, and I guess it would flag you if there's some strange thing or it needs an assistant, and really almost full autonomy in this integration effort. It's a good vision. >> I'm the one who has to make that vision a reality. The way I like to explain is that customers or users have a concept of what they want to achieve. And that concept is as a thought in their head, and the goal is how to get that concept or thought into something that is machine executable. What's the pathway to achieve that? Or if somebody's using SnapLogic for a lot of their organizational operations or for their data integration, we can start looking at what you're doing and make recommendations about other things you should or might be doing. So it's kind of like this two-way thing where we can give you some suggestions but people also know what they want to do conceptually but how do we make that realizable as something that's executable. So I'm working on a number of research projects that is getting us closer to that vision. And one that I've been very excited about is we're working a lot with NLP, Natural Language Processing, like many companies and other products are investigating. For our use in particular is in a couple of different ways. To be sort of concrete, we've been working on a research project in which, rather than, you know, having to know the name of a Snap. 'Cause right now, you get this thing called a Snap catalog, and like I said, 400 plus Snaps. To go through the whole list, it's pretty long. You can start to type a name, and yeah, it'll limit it, but you still have to know exactly what that Snap is called. What we're doing is we're applying machine learning in order to allow you to either speak or type what the intention is of what you're looking for. I want to parse a CSV file. Now, we have a file reader, and we have a CSV parser, but if you just typed, parse a CSV file, it may not find what you're looking for. But we're trying to take the human description and then connect that with the actual Snaps that you might need to complete your task. That's one thing we're working on. I have two more. The second one is a little bit more ambitious, but we have some preliminary work that demonstrates this idea of actually saying or typing what you want an entire pipeline to do. I might say I want to read data from SalesForce, I want to filter out only records from the last week, and then I want to put those records into Redshift. And if you were to just say or type what I just said, we would give you a pipeline that maybe isn't entirely complete, but working and allows you to evolve it from there. So you didn't have to go through all the steps of finding each individual Snap and connecting them together. So this is still very early on, but we have some exciting results. And then the last thing we're working on with NLP is, in SnapLogic, we have a nice view eye, and it's really good. A lot of the heavy lifting in building these pipelines, though, is in the actual manipulation of the data. And to actually manipulate the data, you need to construct expressions. And expressions in SnapLogic, we have a JavaScript expression language, so you have to write these expressions to do operations, right. One of our next goals is to use natural language to help you describe what you want those expressions to do and then generate those expressions for you. To get at that vision, we have to chisel. We have to break down the barriers on each one of these and then collectively, this will get us closer to that vision of truly autonomous integration. >> What's so cool about it, and again, you say autonomous and I can't help but think autonomous vehicles. We had a great interview, he said, if you have an accident in your car, you learn, the person you had an accident learns a little bit, and maybe the insurance adjuster learns a little bit. But when you have an accident in an autonomous vehicle, everybody learns, the whole system learns. That learning is shared orders of magnitude greater, to greater benefit of the whole. And that's really where you guys are sitting in this cloud situation. You've got all this integration going on with customers, you have all this translation and movement of data. Everybody benefits from the learning that's gained by everybody's participation. That's what is so exciting, and why it's such a great accelerator to how things used to be done before by yourself, in your little company, coding away trying to solve your problems. Very very different kind of paradigm, to leverage all that information of actual use cases, what's actually happening with the platform. So it puts you guys in a pretty good situation. >> I completely agree. Another analogy is, look, we're not going to get rid of programmers anytime soon. However, programming's a complex, human endeavor. However, the Snap pipelines are kind of like programs, and what we're doing in our domain, our space, is trying to achieve automated programming so that, you're right, as you said, learning from the experience of others, learning from the crowd, learning from mistakes and capturing that knowledge in a way that when somebody is presented with a new task, we can either make it very quick for them to achieve that or actually provide them with exactly what they need. So yeah, it's very exciting. >> So we're running out of time. Before I let you go, I wanted to tie it back to your professor job. How do you leverage that? How does that benefit what's going on here at SnapLogic? 'Cause you've obviously been doing that for a long time, it's important to you. Bill Schmarzo, great fan of theCUBE, I deemed him the dean of big data a couple of years ago, he's now starting to teach. So there's a lot of benefits to being involved in academe, so what are you doing there in academe, and how does it tie back to what you're doing here in SnapLogic? >> So yeah, I've been a professor for 20 years at the University of San Francisco. I've long done research in operating systems and distributed systems, parallel computing programming languages, and I had the opportunity to start working with SnapLogic in 2010. And it was this great experience of, okay, I've done all this academic research, I've built systems, I've written research papers, and SnapLogic provided me with an opportunity to actually put a lot of this stuff in practice and work with real-world data. I think a lot of people on both sides of the industry academia fence will tell you that a lot of the real interesting stuff in computer science happens in industry because a lot of what we do with computer science is practical. And so I started off bringing in my expertise in working on innovation and doing research projects, which I continue to do today. And at USF, we happened to have a vehicle already set up. All of our students, both undergraduates and graduates, have to do a capstone senior project or master's project in which we pair up the students with industry sponsors to work on a project. And this is a time in their careers where they don't have a lot of professional experience, but they have a lot of knowledge. And so we bring the students in, and we carve out a project idea. And the students under my mentorship and working with the engineering team work toward whatever project we set up. Those projects have resulted in numerous innovations now that are in the product. The most recent big one is Iris came out of one of these research projects. >> Oh, it did? >> It was a machine learning project about, started around three years ago. We continuously have lots of other projects in the works. On the flip side, my experience with SnapLogic has allowed me to bring sort of this industry experience back to the classroom, both in terms of explaining to students and understanding what their expectations will be when they get out into industry, but also being able to make the examples more real and relevant in the classroom. For me, it's been a great relationship that's benefited both those roles. >> Well, it's such a big and important driver to what goes on in the Bay Area. USF doesn't get enough credit. Clearly Stanford and Cal get a lot, they bring in a lot of smart people every year. They don't leave, they love the weather. It is really a significant driver. Not to mention all the innovation that happens and cool startups that come out. Well, Greg thanks for taking a few minutes out of your busy day to sit down with us. >> Thank you, Jeff. >> All right, he's Greg, I'm Jeff. You're watching theCUBE from SnapLogic in San Mateo, California. Thanks for watching.

>> Announcer: From San Mateo, California, it's theCUBE covering SnapLogic Innovation Day 2018, brought to you by SnapLogic. >> Hey, welcome back, Jeff Frick here with theCUBE. We're at the Crossroads 101 and 92. You've probably been there. You're probably stuck in traffic. Look up, you'll see the sign SnapLogic. That's where we are. We're talking digital transformation. You've probably heard us talk about digital transformation on theCUBE, but not that many people or, excuse me, companies actually have an executive who's in charge of digital transformation. And that's not the case here at SnapLogic. And we're really excited to have our next guest. She's Diletta D'Onofrio, and she's the Head of Digital Transformation for SnapLogic. Welcome. >> Thank you, thank you for inviting me. >> Absolutely, so why does SnapLogic have a Head of Digital Transformation? I've never heard that for a company, and you're not really running digital transformation inside the company. You're helping your customers' digital transformation journey. >> Yeah absolutely, because integration is at the core of many transformations that we see led by our clients. And it's not about implementing a software for the most part. There's always the people processing technology. >> Jeff: Right, right. >> So what we are trying to do is to insert ourselves in the strategic discussion so that the implementation is more solid and secure. >> Right, right. >> And, so that's the intent of our practice. >> Right, and as you said, people process technology. We hear it all the time, and we hear a lot, too, of best practices in digital transformation is you have to make a commitment to that process change. You have to make a commitment to the people change. That's actually the hardest part. >> Diletta: Yeah. >> I think integration, usually, no one really wants to talk about integration up front because that's that hard little piece that we have to worry about down the road, but let's just not pretend that we have to do that. But as you said, that's a really important piece. It's tying all these systems together. So, you've been helping people with digital transformation here and in some of your prior jobs. So when you sit down with someone who's never heard that term, what do you tell them? What is digital transformation? >> So typically, we're pretty fortunate because I think especially in high tech, here in the valley, there are many clients that have a role which is equivalent to mine and is focused internally on digital transformation. So there either the head of digital transformation, the chief of digital officer. And what we typically do with them is to try to figure out what their plans are and participate to their journey by obviously helping from an integration perspective. >> Jeff: Right. >> Both on the application and data side. >> And where do there usually report up? It's always an interesting conversation because we go to chief data officer events. We go to chief analytics officer events. So you've got kind of these new evolving roles that are really built around data and enabling data and becoming a data driven enterprise. But does it report to the CIO? Does it report to the CTO? Does it report to up through the CEO? And then now you've got this role of people kind of heading up the digital transformation. Where do you see them reporting through? And what's kind of the most effective? Maybe that's a better question. What's the more effective place for them to report through? >> It's a little bit all over the map. There is not a standard. For example, a couple of clients, at Qualcomm, our equivalent in digital transformation is head of application, and he reports to the CIO. >> Jeff: Okay. >> So that's pretty traditional. Often the CIO is chartered with digital transformation for obvious reasons. He has the skillset, he has the team, he has the capability. But, I've seen cases where he or she reports to the CEO. >> Okay. >> Which is even more interesting I think because then it put an emphasis on the importance of the program and the importance of the targets associated with this program. So another client of ours airborne in Texas is actually the CMO and head of sales who reports to the CEO and is also in charge of digital transformation. And we are helping him with some cust-- >> It has the hat of also sales and marketing? >> Diletta: Absolutely, three jobs. >> So that's pretty interesting. Which is good cause those are the things that are kind of leading edge, front edge, to the client. As opposed to digital transformation just on your back-end processes. System integrators, in both those companies, you just listed as big companies. The system integrators have been building transformation businesses for a long, long time. How do they fit? How do you work with them? How does that kind of all come together around the project? >> Yep, so Qualcomm for example, you can see pretty much any single system integrator that you can imagine of. And they all have a portion of the transformation. >> Jeff: Right. >> None of them covers the entire scope. >> Jeff: Right. >> And the interesting portion as well is that because they are all competitors, often there is not a lot of collaboration. And then we are a little bit kind of agnostic, but obviously we have an interest in penetrating the account in terms of making the use of our technology. >> Right. >> So it's in our interest in what I'm trying to do, obviously I come from the system integrator world, so I do speak their language. And what we are trying to do is to work with them to make sure that we understand, were there use cases, were there business cases, and we kind of work together across different objective to enable the client to hopefully be digitally transformed. >> Right, so it's such a big word and the CEOs are talking to the boards about it and the public companies are talking to the analysts on the earnings call. We're going to digitally transform, and these are big organizations that are complex and have many, many pieces and parts. How do you get started? What are some best practices for people that have a board edict, or have a CEO edict? We need to digitally transform, I'm afraid of the competition, I don't even know who's coming. Where should people start, how do they slice and dice this thing so their not trying to eat the whole elephant in one bite? >> Yeah, the only cases that I've seen success on are the ones where, hopefully the leader has done that before. In some kind of shape or form. If it's a brand new chief digital officer, there are more challenges. But the most important thing is kind of keep the momentum. And you tend to keep the momentum through some sort of quick-wing. So if the scope is too large, and the roadmap is to fix over three or five years given the speed of change in technology is very difficult to achieve those goals. >> Jeff: Right. >> So it's much better to have a more agile mentality and maybe plan a year ahead. We did some very tangible, deliverable in the way and mobilize everyone around this. So that the momentum is kept and it's not just a nice word that a company has because they need to talk about the digital transformation. >> Right, and then what do you look at? You obviously have a specific point of view. You have your background and you've been a system integrator, and transformation leader. But in terms of coming from the SnapLogic point of view and integration, and that opportunity, What do you look for as opportunities for those early wins? Either based on prior experience or you just know there's some really inefficient ugly things that you can make big difference on, relatively easy. What do you look for as kind of those first wins in a digital transformation project? >> Yeah, ideally we love to be involved with everything to do with customer and sales and revenue. Because obviously those are the biggest paying point for the client. >> Jeff: Right. >> But often, you need to be flexible enough to understand what the priorities are. Currently I am involved in a much more traditional close activity accounting process. You will be thinking, okay, this may cost us, but actually fixing that problem first will create a lot of credibility within the company. So I think a company like ours has to be very flexible, need to listen to the client. >> Mh-hm. >> And be very flexible in terms of what priorities to start with first. >> Right. >> To prove the technology and then progress, maybe for higher value-- >> Right. >> activities. >> So I would hope it's 2018, that people understand that they're not setting forth on a five-year SAP, ERP implementation. Are we hopefully passed that, that this is not new information. That you need to take small bites, small victories, and move quickly. >> Yeah. >> Are we there? >> Yes but, still, I've seen a lot of strategy document and business plan that are two, three years of arisen and I think the arisen is way too long. But also at the same time, is this still teaching function? So you ask to picture a vision, at least directionally. >> Right. Right. >> So I think the vision has to be generic enough to then flex with the project and the activities within. >> Right. >> Two, three months. >> Right. >> Quarterly on most occasions. >> It's so funny that we continue to find these massive inefficiencies all over the place. You'd think that most of it had been wrung out by now. Between the European PA Limitations and all the business process reengineering, I guess was the old process >> Yes. >> before digital transformation. So I just wonder if you can share some stories from the field about some of these relatively short duration projects, and the yields that they are providing on this path to a more comprehensive digital transformation. >> Yeah so, the first example that comes to mind, again, going back to Qualcomm. When they talk about human capital management or engineering, what is interesting there is that you take the entire hire to retire. And it's pretty overwhelming. From the moment you hire an employee to the moment you obviously retire their function or their role, And what they did quite interestingly, was to come up with a few applications that will make the life of the employees and their manager easier. So we are biting the process by building application that for example, enable to facilitate the on-boarding or application that help HR with analytics and inquires. And gradually trying to automate the process which today even in a large company like a Fortune 100 company can be incredibly manual. >> Right. Right. >> And then another example that comes to mind to me is if you look at the entire holder to cash cycle of a company, from the moment the client to get in contact with the company through a website, to the moment they actually purchase the product. Again, there are many touch point and they're often disconnected. And a client of ours, Airborne, what we're doing with them is to just take one small bite which is figuring out from the time a client tried to configure a product on the website to the time they want to try the product. Our experience can be more automated. So that there is not a lot of interaction necessarily with customer services which has a limited bandwidth. But it's much more self-service. >> Jeff: Right. Right. >> And then gradually tackle the rest of the holder to cash cycle. >> So both of those examples are really about automating manual processes. >> Diletta: Yeah. >> As you just described them. So then what are the KPIs that you're using to measure success? Is it total time duration? Number of steps? Calls back to a person? What are some of the metrics of success? >> Yeah, so you see on the customers side it's kind of easy because you tend to very much require feedback from the customer. So if the customer satisfaction index goes up, or revenue goes up, or less return. So those KPIs we're kind of more familiar with. >> Okay. >> But when you look at the HR award, the human capital management award, there are so many ramifications of being able to serve your employees better. But much more intangible. Like for example, turnover. Well there is good turnover and bad turnover. So if you're serving your employees better with better hours, by which they can self-service some of their activities. Does it translate in less turnover? Maybe yes, or maybe actually that's translating more turnover because maybe the employees that sneak around are the ones that are more technology savvy, so. >> Right. >> Diletta: The human capital management side is harder in terms of defining KPIs. In it's much more early stage then anything to do with customer. And then there is the other universe associated with digitalizing product. Like for example, the world of IOT. That we are involved with, with a few clients. And that is a very measurable and tangible because you actually coming up with new product and what we're doing is facilitating the ability to access data. >> Jeff: Right. >> Which is a very tangible element of the product development lifecycle. >> So of all the transformation projects that you're involved in, how would you break them down in rough numbers of kind of cost savings on an existing process, which is through automation. Versus kind of forward facing customer facing, let's just call it warpped around a customer experience so ultimately you're getting higher customer satisfaction scores and revenue. Versus the third which you just touched on, which is so, so important. Which is converting from a product based company or some of these more tangible into more of a service recurring revenue. That's probably built around that product and the example that gets thrown around all the time is, when GE starts selling miles of propulsion versus selling engines. It's a very different kind of relationship. So in the things that you work on, how would you kind of break up the percentages in those three buckets? >> Yeah, so what we see still a lot, and what I would like to see less, is the first bucket. >> Jeff: Okay. >> Which is reducing cost so I will save more than 50%. >> Jeff: Okay. >> Which is around reduce cost, drive efficiency, better reporting, eliminating application, right? Because many client have too many application to preform some of these back office processes. >> Right. Right. >> And they're very much associated with cost exercise. >> Right. >> And so over 50%, for sure. >> Okay. And that's logical cause that's obviously an easy place to start. You're not changing the company per se. >> Yeah. >> You're looking for efficiencies. Alright so, Diletta, I'll give you the last word before we sign off. If you get called in to a new project, it's a CEO, they're stressed out, they know they have to do this. What do you tell them about digital transformation? How do you kind of help them break it down so it's not just this overwhelming, giant, goal on high? But actually something that they should get excited about, something they can have some success with and something that ultimately is going to be a really good thing. >> I think there is no one recipe. It's about figuring out where the company wants to go. What is the primary objective? Is it sales? Is it new market? Is it new product? And then kind of break it down in a tangible chunck and it kind of makes sense to them. But you got to go for the first priority item. This year I'm sure we'll be able to articulate very well. >> Yes, get that quick win. Well Diletta, thanks for spending a few minutes with us. And good luck on transforming everybody. (laughs) >> Thank you. >> Alright, she's Diletta, I'm Jeff. You're watching theCUBE, from SnapLogic headquarters in San Mateo, California. Thanks for watching. (bright music)

>> Announcer: From San Mateo, California, it's theCUBE covering SnapLogic Innovation Day 2018, brought to you by SnapLogic. >> Hey, welcome back, Jeff Frick here with theCUBE. We're at the Crossroads 101 and 92. You've probably been there. You're probably stuck in traffic. Look up, you'll see the sign SnapLogic. That's where we are. We're talking digital transformation. You've probably heard us talk about digital transformation on theCUBE, but not that many people or, excuse me, companies actually have an executive who's in charge of digital transformation. And that's not the case here at SnapLogic. And we're really excited to have our next guest. She's Diletta D'Onofrio, and she's the Head of Digital Transformation for SnapLogic. Welcome. >> Thank you, thank you for inviting me. >> Absolutely, so why does SnapLogic have a Head of Digital Transformation? I've never heard that for a company, and you're not really running digital transformation inside the company. You're helping your customers' digital transformation journey. >> Yeah absolutely, because integration is at the core of many transformations that we see led by our clients. And it's not about implementing a software for the most part. There's always the people processing technology. >> Jeff: Right, right. >> So what we are trying to do is to insert ourselves in the strategic discussion so that the implementation is more solid and secure. >> Right, right. >> And, so that's the intent of our practice. >> Right, and as you said, people process technology. We hear it all the time, and we hear a lot, too, of best practices in digital transformation is you have to make a commitment to that process change. You have to make a commitment to the people change. That's actually the hardest part. >> Diletta: Yeah. >> I think integration, usually, no one really wants to talk about integration up front because that's that hard little piece that we have to worry about down the road, but let's just not pretend that we have to do that. But as you said, that's a really important piece. It's tying all these systems together. So, you've been helping people with digital transformation here and in some of your prior jobs. So when you sit down with someone who's never heard that term, what do you tell them? What is digital transformation? >> So typically, we're pretty fortunate because I think especially Nytec here in the valley, there are many clients that have a role which is equivalent to mine and is focused internally on digital transformation. So there either the head of digital transformation, the chief of digital officer. And what we typically do with them is to try to figure out what their plans are and participate to their journey by obviously helping from an integration perspective. >> Jeff: Right. >> Both on the application and data side. >> And where do there usually report at? It's always an interesting conversation because we go to chief data officer events. We go to chief analytics officer events. So you've got kind of these new evolving roles that are really built around data and enabling data and becoming a data driven enterprise. But does it report to the CIO? Does it report to the CTO? Does it report to up through the CEO? And then now you've got this role of people kind of heading up the digital transformation. Where do you see them reporting through? And what's kind of the most effective? Maybe that's a better question. What's the more effective place for them to report through? >> It's a little bit all over the map. There is not a standard. For example, a couple of clients, at Qualcomm, our equivalent in digital transformation is head of application, and he reports to the CIO. >> Jeff: Okay. >> So that's pretty traditional. Often the CIO is chartered with digital transformation for obvious reasons. He has the skillset, he has the team, he has the capability. But, I've seen cases where he or she reports to the CEO. >> Okay. >> Which is even more interesting I think because then it put an emphasis on the importance of the program and the importance of the targets associated with this program. So another client of ours airborne in Texas is actually the CMO and head of sales who reports to the CEO and is also in charge of digital transformation. And we are helping him with some cust-- >> It has the hat of also sales and marketing? >> Diletta: Absolutely, three jobs. >> So that's pretty interesting. Which is good cause those are the things that are kind of leading edge, front edge, to the client. As opposed to digital transformation just on your back-end processes. System integrators, in both those companies, you just listed as big companies. The system integrators have been building transformation businesses for a long, long time. How do they fit? How do you work with them? How does that kind of all come together around the project? >> Yep, so Qualcomm for example, you can see pretty much any single system integrator that you can imagine of. And they all have a portion of the transformation. >> Jeff: Right. >> None of them covers the entire scope. >> Jeff: Right. >> And the interesting portion as well is that because they are all competitors, often there is not a lot of collaboration. And then we are a little bit kind of agnostic, but obviously we have an interest in penetrating the account in terms of making the use of our technology. >> Right. >> So it's in our interest in what I'm trying to do, obviously I come from the system integrator ward so I do speak their language. And what we are trying to do is to work with them to make sure that we understand, were there use cases, were there business cases, and we kind of work together across different objective to enable the client to hopefully be digitally transformed. >> Right, so it's such a big word and the CEOs are talking to the boards about it and the public companies are talking to the analysts on the earnings call. We're going to digitally transform, and these are big organizations that are complex and have many, many pieces and parts. How do you get started? What are some best practices for people that have a board edict, or have a CEO edict? We need to digitally transform, I'm afraid of the competition, I don't even know who's coming. Where should people start, how do they slice and dice this thing so their not trying to eat the whole elephant in one bite? >> Yeah, the only cases that I've seen success on are the ones where, hopefully the leader has done that before. In some kind of shape or form. If it's a brand new chief digital officer, there are more challenges. But the most important thing is kind of keep the momentum. And you tend to keep the momentum through some sort of quick-wing. So if the scope is too large, and the roadmap is to fix over three or five years given the speed of change in technology is very difficult to achieve those goals. >> Jeff: Right. >> So it's much better to have a more agile mentality and maybe plan a year ahead. We did some very tangible, deliverable in the way and mobilize everyone around this. So that the momentum is kept and it's not just a nice word that a company has because they need to talk about the digital transformation. >> Right, and then what do you look at? You obviously have a specific point of view. You have your background and you've been a system integrator, and transformation leader. But in terms of coming from the SnapLogic point of view and integration, and that opportunity, What do you look for as opportunities for those early wins? Either based on prior experience or you just know there's some really inefficient ugly things that you can make big difference on, relatively easy. What do you look for as kind of those first wins in a digital transformation project? >> Yeah, ideally we love to be involved with everything to do with customer and sales and revenue. Because obviously those are the biggest paying point for the client. >> Jeff: Right. >> But often, you need to be flexible enough to understand what the priorities are. Currently I am involved in a much more traditional close activity accounting process. You will be thinking, okay, this may cost us, but actually fixing that problem first will create a lot of credibility within the company. So I think a company like ours has to be very flexible, need to listen to the client. >> Mh-hm. >> And be very flexible in terms of what priorities to start with first. >> Right. >> To prove the technology and then progress, maybe for higher value-- >> Right. >> activities. >> So I would hope it's 2018, that people understand that they're not setting forth on a five-year SAP, ERP implementation. Are we hopefully passed that, that this is not new information. That you need to take small bites, small victories, and move quickly. >> Yeah. >> Are we there? >> Yes but, still, I've seen a lot of strategy document and business plan that are two, three years of arisen and I think the arisen is way too long. But also at the same time, is this still teaching function? So you ask to picture a vision, at least directionally. >> Right. Right. >> So I think the vision has to be generic enough to then flex with the project and the activities within. >> Right. >> Two, three months. >> Right. >> Quarterly on most occasions. >> It's so funny that we continue to find these massive inefficiencies all over the place. You'd think that most of it had been wrung out by now. Between the European PA Limitations and all the business process reengineering, I guess was the old process >> Yes. >> before digital transformation. So I just wonder if you can share some stories from the field about some of these relatively short duration projects, and the yields that they are providing on this path to a more comprehensive digital transformation. >> Yeah so, the first example that comes to mind, again, going back to Qualcomm. When they talk about human capital management or engineering, what is interesting there is that you take the entire hire to retire. And it's pretty overwhelming. From the moment you hire an employee to the moment you obviously retire their function or their role, And what they did quite interestingly, was to come up with a few applications that will make the life of the employees and their manager easier. So we are biting the process by building application that for example, enable to facilitate the on-boarding or application that help HR with analytics and inquires. And gradually trying to automate the process which today even in a large company like a fortune 100 company can be incredibly manual. >> Right. Right. >> And then another example that comes to mind to me is if you look at the entire holder to cash cycle of a company, from the moment the client to get in contact with the company through a website, to the moment they actually purchase the product. Again, there are many touch point and they're often disconnected. And a client of ours, Airborne, what we're doing with them is to just take one small bite which is figuring out from the time a client tried to configure a product on the website to the time they want to try the product. Our experience can be more automated. So that there is not a lot of interaction necessarily with customer services which has a limited bandwidth. But it's much more self-service. >> Jeff: Right. Right. >> And then gradually tackle the rest of the holder to cash cycle. >> So both of those examples are really about automating manual processes. >> Diletta: Yeah. >> As you just described them. So then what are the KPIs that you're using to measure success? Is it total time duration? Number of steps? Calls back to a person? What are some of the metrics of success? >> Yeah, so you see on the customers side it's kind of easy because you tend to very much require feedback from the customer. So if the customer satisfaction index goes up, or revenue goes up, or less return. So those KPIs we're kind of more familiar with. >> Okay. >> But when you look at the HR award, the human capital management award, there are so many ramifications of being able to serve your employees better. But much more intangible. Like for example, turnover. Well there is good turnover and bad turnover. So if you're serving your employees better with better hours, by which they can self-service some of their activities. Does it translate in less turnover? Maybe yes, or maybe actually that's translating more turnover because maybe the employees that sneak around are the ones that are more technology savvy, so. >> Right. >> Diletta: The human capital management side is harder in terms of defining KPIs. In it's much more early stage then anything to do with customer. And then there is the other universe associated with digitalizing product. Like for example, the world of IOT. That we are involved with, with a few clients. And that is a very measurable and tangible because you actually coming up with new product and what we're doing is facilitating the ability to access data. >> Jeff: Right. >> Which is a very tangible element of the product development lifecycle. >> So of all the transformation projects that you're involved in, how would you break them down in rough numbers of kind of cost savings on an existing process, which is through automation. Versus kind of forward facing customer facing, let's just call it warpped around a customer experience so ultimately you're getting higher customer satisfaction scores and revenue. Versus the third which you just touched on, which is so, so important. Which is converting from a product based company or some of these more tangible into more of a service recurring revenue. That's probably built around that product and the example that gets thrown around all the time is, when GE starts selling miles of propulsion versus selling engines. It's a very different kind of relationship. So in the things that you work on, how would you kind of break up the percentages in those three buckets? >> Yeah, so what we see still a lot, and what I would like to see less, is the first bucket. >> Jeff: Okay. >> Which is reducing cost so I will save more than 50%. >> Jeff: Okay. >> Which is around reduce cost, drive efficiency, better reporting, eliminating application, right? Because many client have too many application to preform some of these back office processes. >> Right. Right. >> And they're very much associated with cost exercise. >> Right. >> And so over 50%, for sure. >> Okay. And that's logical cause that's obviously an easy place to start. You're not changing the company per se. >> Yeah. >> You're looking for efficiencies. Alright so, Diletta, I'll give you the last word before we sign off. If you get called in to a new project, it's a CEO, they're stressed out, they know they have to do this. What do you tell them about digital transformation? How do you kind of help them break it down so it's not just this overwhelming, giant, goal on high? But actually something that they should get excited about, something they can have some success with and something that ultimately is going to be a really good thing. >> I think there is no one recipe. It's about figuring out where the company wants to go. What is the primary objective? Is it sales? Is it new market? Is it new product? And then kind of break it down in a tangible chunck and it kind of makes sense to them. But you got to go for the first priority item. This year I'm sure we'll be able to articulate very well. >> Yes, get that quick win. Well Diletta, thanks for spending a few minutes with us. And good luck on transforming everybody. (laughs) >> Thank you. >> Alright, she's Diletta, I'm Jeff. You're watching theCUBE, from SnapLogic headquarters in San Mateo, California. Thanks for watching. (bright music)

>> Narrator: From San Mateo, California, it's theCUBE, covering SnapLogic Innovation Day 2018. Brought to you by SnapLogic. >> Welcome back, Jeff Frick here with theCUBE. We're at the Crossroads, that's 92 and 101 in the Bay Area if you've been through it, you've had time to take a minute and look at all the buildings, 'cause traffic's usually not so great around here. But there's a lot of great software companies that come through here. It's interesting, I always think back to the Siebel Building that went up and now that's Rakuten, who we all know from the Warrior jerseys, the very popular Japanese retailer. But that's not why we're here. We're here to talk to SnapLogic. They're doing a lot of really interesting things, and they have been in data, and now they're doing a lot of interesting things in integration. And we're excited to have a many time Cube alum. He's Greg Benson, let me get that title right, chief scientist at SnapLogic and of course a professor at University of San Francisco. Greg great to see you. >> Great to see you, Jeff. >> So I think the last time we see you was at Fleet Forward. Interesting open-source project, data, ad moves. The open-source technologies and the technologies available for you guys to use just continue to evolve at a crazy breakneck speed. >> Yeah, it is. Open source in general, as you know, has really revolutionized all of computing, starting with Linux and what that's done for the world. And, you know, in one sense it's a boon, but it introduces a challenge, because how do you choose? And then even when you do choose, do you have the expertise to harness it? You know, the early social companies really leveraged off of Hadoop and Hadoop technology to drive their business and their objectives. And now we've seen a lot of that technology be commercialized and have a lot of service around it. And SnapLogic is doing that as well. We help reduce the complexity and make a lot of this open-source technology available to our customers. >> So, I want to talk about a lot of different things. One of the things is Iris. So Iris is your guys' leverage of machine learning and artificial intelligence to help make integration easier. Did I get that right? >> That's correct, yeah. Iris is the umbrella terms for everything that we do with machine learning and how we use it to enhance the user experience. And one way to think about it is when you're interacting with our product, we've made the SnapLogic designer a web-based UI, drag-and-drop interface to construct these integration pipelines. We connect these things called Snaps. It's like building with Legos to build out these transformations on your data. And when you're doing that, when you're interacting with the designer, we would like to believe that we've made it one of the simplest interfaces to do this type of work, but even with that, there are many times we have to make decisions, like what type of transformation do you do next? How do you configure that transformation if you're talking to an Oracle database? How do you configure it? What's your credentials if you talk to SalesForce? If I'm doing a transformation on data, which fields do I need? What kind of operations do I need to apply to those fields? So as you can imagine, there's lots of situations as you're building out these data integration pipelines to make decisions. And one way to think about Iris is Iris is there to help reduce the complexity, help reduce what kind of decision you have to make at any point in time. So it's contextually aware of what you're doing at that moment in time, based on mining our thousands of existing pipelines and scenarios in which SnapLogic has been used. We leverage that to train models to help make recommendations so that you can speed through whatever task you're trying to do as quickly as possible. >> It's such an important piece of information, because if I'm doing an integration project using the tool, I don't have the experience of the vast thousands and thousands, and actually you're doing now, what, a trillion document moves last month? I just don't have that expertise. You guys have the expertise, and truth be told, as unique as I think I am, and as unique as I think my business processes are, probably, a lot of them are pretty much the same as a lot of other people that are hooking up to SalesForce to Oracle or hooking up Marketta to their CRM. So you guys have really taken advantage of that using the AI and ML to help guide me along, which is probably a pretty high-probability prediction of what my next move's going to be. >> Yeah, absolutely, and you know, back in the day, we used to consider, like, wizards or these sorts of things that would walk you through it. And really that was, it seemed intelligent, but it wasn't really intelligence or machine learning. It was really just hard-coded facts or heuristics that hopefully would be right for certain situations. The difference today is we're using real data, gigabytes of metadata that we can use to train our models. The nice thing about that it's not hard-coded it's adaptive. It's adaptive both for new customers but also for existing customers. We have customers that have hundreds of people that just use SnapLogic to get their business objectives done. And as they're building new pipelines, as they are putting in new expressions, we are learning that for them within their organization. So like their coworkers, the next day, they can come in and then they get the advantages of all the intellectual work that was done to figure something out will be learned and then will be made available through Iris. >> Right. I love this idea of operationalizing machine learning and the augmented intelligence. So how do you apply it? Don't just talk about it, don't give it a name of some dead smart person, but actually apply it to an application where you can start to see the benefit. And that's really what Iris is all about. So what's changed the most in the last year since you launched it? >> You know, one thing I'll say: The most interesting thing that we discovered when we first launched Iris, and I should say one of the first Iris technologies that we introduced was something called the integration assistant. And this was an assistant that would make, make recommendations of the next Snap as you're building out your pipeline, so the next transformation or the next connector, and before we launched it, we did lots of experimentation with different machine learning models. We did different training to get the best accuracy possible. And what we really thought was that this was going to be most useful for the new user, somebody who hasn't really used the product and it turns out, when we looked at our data, and we looked at how it got used, it turns out that yes, new users did use it, but existing or very skilled users were using it just as much if not more, 'cause it turned out that it was so good at making recommendations that it was like a shortcut. Like, even if they knew the product really well, it's still actually a little more work to go through our catalog of 400 plus Snaps and pick something out when if it's just sitting right there and saying, "Hey, the next thing you need to do," you don't even have to think. You just have to click, and it's right there. Then it just speeds up the expert user as well. That was an interesting sort of revelation about machine learning and our application of it. In terms of what's changed over the last year, we've done a number of things. Probably the operationalizing it so that instead of training off of SnapShot, we're now training on a continuous basis so that we get that adaptive learning that I was talking about earlier. The other thing that we have done, and this is kind of getting into the weeds, we were using a decision tree model, which is a type of machine learning algorithm, and we switched to neural nets now, so now we use neural nets to achieve higher accuracy, and also a more adaptive learning experience. The neural net allowed us to bring in sort of like this organizational information so that your recommendations would be more tailored to your specific organization. The other thing we're just on the cusp of releasing is, in the integration assistant, we're working on sort of a, sort of, from beginning-to-end type recommendation, where you were kind of working forward. But what we found is, in talking to people in the field, and our customers who use the product, is there's all kinds of different ways that people interact with a product. They might know know where they want the data to go, and then they might want to work backwards. Or they might know that the most important thing I need this to do is to join some data. So like when you're solving a puzzle with the family, you either work on the edges or you put some clumps in the middle and work to get to. And that puzzle solving metaphor is where we're moving integration assistance so that you can fill in the pieces that you know, and then we help you work in any direction to make the puzzle complete. That's something that we've been adding to. We recently started recommending, based on your context, the most common sources and destinations you might need, but we're also about to introduce this idea of working backwards and then also working from the inside out. >> We just had Gaurav on, and he's talking about the next iteration of the vision is to get to autonomous, to get to where the thing not only can guess what you want to do, has a pretty good idea, but it actually starts to basically do it for you, and I guess it would flag you if there's some strange thing or it needs an assistant, and really almost full autonomy in this integration effort. It's a good vision. >> I'm the one who has to make that vision a reality. The way I like to explain is that customers or users have a concept of what they want to achieve. And that concept is as a thought in their head, and the goal is how to get that concept or thought into something that is machine executable. What's the pathway to achieve that? Or if somebody's using SnapLogic for a lot of their organizational operations or for their data integration, we can start looking at what you're doing and make recommendations about other things you should or might be doing. So it's kind of like this two-way thing where we can give you some suggestions but people also know what they want to do conceptually but how do we make that realizable as something that's executable. So I'm working on a number of research projects that is getting us closer to that vision. And one that I've been very excited about is we're working a lot with NLP, Natural Language Processing, like many companies and other products are investigating. For our use in particular is in a couple of different ways. To be sort of concrete, we've been working on a research project in which, rather than, you know, having to know the name of a Snap. 'Cause right now, you get this thing called a Snap catalog, and like I said, 400 plus Snaps. To go through the whole list, it's pretty long. You can start to type a name, and yeah, it'll limit it, but you still have to know exactly what that Snap is called. What we're doing is we're applying machine learning in order to allow you to either speak or type what the intention is of what you're looking for. I want to parse a CSV file. Now, we have a file reader, and we have a CSV parser, but if you just typed, parse a CSV file, it may not find what you're looking for. But we're trying to take the human description and then connect that with the actual Snaps that you might need to complete your task. That's one thing we're working on. I have two more. The second one is a little bit more ambitious, but we have some preliminary work that demonstrates this idea of actually saying or typing what you want an entire pipeline to do. I might say I want to read data from SalesForce, I want to filter out only records from the last week, and then I want to put those records into Redshift. And if you were to just say or type what I just said, we would give you a pipeline that maybe isn't entirely complete, but working and allows you to evolve it from there. So you didn't have to go through all the steps of finding each individual Snap and connecting them together. So this is still very early on, but we have some exciting results. And then the last thing we're working on with NLP is, in SnapLogic, we have a nice view eye, and it's really good. A lot of the heavy lifting in building these pipelines, though, is in the actual manipulation of the data. And to actually manipulate the data, you need to construct expressions. And expressions in SnapLogic, we have a JavaScript expression language, so you have to write these expressions to do operations, right. One of our next goals is to use natural language to help you describe what you want those expressions to do and then generate those expressions for you. To get at that vision, we have to chisel. We have to break down the barriers on each one of these and then collectively, this will get us closer to that vision of truly autonomous integration. >> What's so cool about it, and again, you say autonomous and I can't help but think autonomous vehicles. We had a great interview, he said, if you have an accident in your car, you learn, the person you had an accident learns a little bit, and maybe the insurance adjuster learns a little bit. But when you have an accident in an autonomous vehicle, everybody learns, the whole system learns. That learning is shared orders of magnitude greater, to greater benefit of the whole. And that's really where you guys are sitting in this cloud situation. You've got all this integration going on with customers, you have all this translation and movement of data. Everybody benefits from the learning that's gained by everybody's participation. That's what is so exciting, and why it's such a great accelerator to how things used to be done before by yourself, in your little company, coding away trying to solve your problems. Very very different kind of paradigm, to leverage all that information of actual use cases, what's actually happening with the platform. So it puts you guys in a pretty good situation. >> I completely agree. Another analogy is, look, we're not going to get rid of programmers anytime soon. However, programming's a complex, human endeavor. However, the Snap pipelines are kind of like programs, and what we're doing in our domain, our space, is trying to achieve automated programming so that, you're right, as you said, learning from the experience of others, learning from the crowd, learning from mistakes and capturing that knowledge in a way that when somebody is presented with a new task, we can either make it very quick for them to achieve that or actually provide them with exactly what they need. So yeah, it's very exciting. >> So we're running out of time. Before I let you go, I wanted to tie it back to your professor job. How do you leverage that? How does that benefit what's going on here at SnapLogic? 'Cause you've obviously been doing that for a long time, it's important to you. Bill Schmarzo, great fan of theCUBE, I deemed him the dean of big data a couple of years ago, he's now starting to teach. So there's a lot of benefits to being involved in academe, so what are you doing there in academe, and how does it tie back to what you're doing here in SnapLogic? >> So yeah, I've been a professor for 20 years at the University of San Francisco. I've long done research in operating systems and distributed systems, parallel computing programming languages, and I had the opportunity to start working with SnapLogic in 2010. And it was this great experience of, okay, I've done all this academic research, I've built systems, I've written research papers, and SnapLogic provided me with an opportunity to actually put a lot of this stuff in practice and work with real-world data. I think a lot of people on both sides of the industry academia fence will tell you that a lot of the real interesting stuff in computer science happens in industry because a lot of what we do with computer science is practical. And so I started off bringing in my expertise in working on innovation and doing research projects, which I continue to do today. And at USF, we happened to have a vehicle already set up. All of our students, both undergraduates and graduates, have to do a capstone senior project or master's project in which we pair up the students with industry sponsors to work on a project. And this is a time in their careers where they don't have a lot of professional experience, but they have a lot of knowledge. And so we bring the students in, and we carve out a project idea. And the students under my mentorship and working with the engineering team work toward whatever project we set up. Those projects have resulted in numerous innovations now that are in the product. The most recent big one is Iris came out of one of these research projects. >> Oh, it did? >> It was a machine learning project about, started around three years ago. We continuously have lots of other projects in the works. On the flip side, my experience with SnapLogic has allowed me to bring sort of this industry experience back to the classroom, both in terms of explaining to students and understanding what their expectations will be when they get out into industry, but also being able to make the examples more real and relevant in the classroom. For me, it's been a great relationship that's benefited both those roles. >> Well, it's such a big and important driver to what goes on in the Bay Area. USF doesn't get enough credit. Clearly Stanford and Cal get a lot, they bring in a lot of smart people every year. They don't leave, they love the weather. It is really a significant driver. Not to mention all the innovation that happens and cool startups that come out. Well, Greg thanks for taking a few minutes out of your busy day to sit down with us. >> Thank you, Jeff. >> All right, he's Greg, I'm Jeff. You're watching theCUBE from SnapLogic in San Mateo, California. Thanks for watching.

>> Narrator: From San Mateo, California, it's theCUBE covering SnapLogic Innovation Day 2018. Brought to you by SnapLogic. >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're in San Mateo, California right at the crossroads. The building's called The Crossroads but it's right at the crossroads of 92 and 101. It's a really interesting intersection over the years as you watch these buildings that are on the corner continue to change names. I always think of the Seville, his first building came up on this corner and we're here to see a good friend of SnapLogic and their brand new building. Gaurav Dhillon, Chairman and CEO, great to see you. >> Pleasure to be here. >> So how long you been in this space? >> Gosh, it's been about a year. >> Okay. >> Although it feels longer. It's a high-growth company so these are dog years. (laughs) >> That's right. and usually, you outgrow it before you all have moved in. >> The years are short but the days are long. >> And it's right next Rakuten, I have to mention it. We all see it on the Warriors' jerseys So now we know who they are and where they are exactly. >> No they're a good outfit. We had an interesting time putting a sign up and then the people who made their sign told us all kinds of back stories. >> Oh, good, good Alright. So give us an update on SnapLogic. You guys are in a great space at a really, really good time. >> You know, things been on a roll. As you know, the mission we set out to... engage with was to bring together applications and data in the enterprise. We have some of the largest customers in high technology. Folks like Qualcomm, Workday. Some of the largest customers in pharmaceuticals. Folks like Astrazeneca, Bristol-Meyers Squibb. In retail, Denny's, Wendy's, etc. And these folks are basically bringing in new cloud applications and moving data into the cloud. And it's really fun to wire that all up for them. And there's more of it every day and now that we have this very strong install-base of customers, we're able to get more customers faster. >> Right. >> In good time. >> It's a great time and the data is moving into the cloud, and the public cloud guys are really making bigger plays into the enterprise, Microsoft and, Amazon and Google. And of course, there's IBM and lots of other clouds. But integration's always been such a pain and I finally figured out what the snap in SnapLogic means after interviewing you >> (laughs) a couple of times, right. But this whole idea of, non-developer development and you're taking that into integration which is a really interesting concept, enabled by cloud, where you can now think of snapping things together, versus coding, coding, coding. >> Yeah Cloud and A.I, right We feel that this problem has grown because of the change in the platform. The compute platform's gone to the cloud. Data's going to the cloud. There was bunch of news the other day about more and more companies moving the analytics into the cloud. And as that's happening, we feel that this approach and the question we ask ourselves when we started this company, we got into building the born in the cloud platform was, what would Apple do if they were to build an integration product? And the answer was, they would make it like the iPhone, which is easy to use, but very powerful at the same time. And if you can do that, you can bring in a massive population of users who wouldn't have been able to do things like video chat. My mom was not able to do video chat, and believe me, we tried this and every other thing possible 'till facetime came along. And now she can talk to my daughter and she can do it without help, any assistance from teenage grandchildren on that side, Right? >> Right, Right >> So what we've done with SnapLogic, is by bringing in a beautiful, powerful, sleek interface, with a lot of capability in how it connects, snaps together apps and data, we've brought in a whole genre of people who need data in the enterprise so they can serve themselves data. So if your title has analyst in it, you don't have to be programmer analyst. You could be any analyst. >> Right >> You could be a compensation analyst, a commissions analyst, a finance analyst, an HR analyst. All those people can self-serve information, knock down silos, and integrate things themselves. >> It's so interesting because we talk a lot about innovation and digital transformation, and in doing thousands of these interviews, I think the answer to innovation is actually pretty simple. You give more people access to the data. You give them more access to the tools to work with the data and then you give them the power to actually do something once they figure something out. And you guys are really right in the middle of that. So before, it was kind of >> (laughs) Yeah >> democratization of the data, democratization of the tools to work with the data, but in the API economy, you got to be able to stitch this stuff together because it's not just one application, it's not just one data source. >> Correct >> You're bringing from lots and lots of different things and that's really what you guys are taking advantage of this cloud infrastructure which has everything available, so it's there to connect, >> (laughs) Versus, silo in company one and silo in company two. So are you seeing it though, in terms of, of people enabling, kind of citizen integrators if you will, versus citizen developers. >> Yeah. Heck Yeah. So I'll give you an example. One of our large customers... Adobe Systems, right here in San Jose has been amazingly successful flagship account for us. About 800 people at Adobe come to www.snaplogic.com, every week to self-serve data. We replaced legacy products like DIBCO, informatica web methods about four years ago. They first became a customer in 2014 and usage of those products was limited to Java programmers and Sequel programmers, and that was less than 50 people. And imagine that you have about 800 people doing self-service getting information do their jobs. Now, Adobe is unique in that, it's moved the cloud in a fantastic way, or it was unique in 2014. Now everybody is emulating them and the great success that they've had. With the cloud economic model, with the cloud ID model. This is working in spades. We have customers who've come on board in Q4. We're just rounding out Q1 and in less than 60, 90 days, every time I look, 50, 100, 200 people, from each large company, whether it's a cosmetics company, pharmaceuticals company, retailer, food merchandise, are coming in and using data. >> Right >> And it's proliferating, because the more successful they are, the better they are able to do in their jobs, tell their friends about it sort-of-thing, or next cubicle over, somebody wants to use that too. It's so interesting. Adobe is such a great example, cause they did transform their business. Used to be a really expensive license. You would try to find your one friend that worked there around Christmas >> (laughs) Cause you think they got two licenses a year they can buy for a grand. Like, I need an extra one I can get from you. But they moved to a subscription model. They made a big bet. >> Yes. Yes >> And they bet on the cloud, so now if you're a subscriber, which I am, I can work on my home machine, my work machine, go to machine, machine. So, it's a really great transformation story. The other piece of it though, is just this cloud application space. There's so many cloud applications that we all work with every day whether it's Basecamp, Salesforce, Hootsuite. There's a proliferation of these things and so they're there. They've got data. So the integration opportunity is unlike anything that was ever there before. Cause there isn't just one cloud. There isn't just one cloud app. There's a lot of them. >> Yes. >> How do I bring those together to be more productive? >> So here's a stat. The average enterprise has most cloud services or SAS applications, in marketing. On the average, they have 91 marketing applications or SAS applications. >> 91. That's the average. >> 96% of them are not connected together. >> Right. >> Okay. That's just one example. Now you go to HR, stock administration. You go into sales, CRM, and all the ancillary systems around CRM. And there is this sort of massive, to us, opportunity of knocking down these silos and making things work together. You mention the API economy and whilst that's true that all these SAS applications of APIs. The problem is, most companies don't have programmers to hook up those API's. >> Right. To connect them. >> Yes, in Silicon Valley we do and maybe in Manhattan they do, but in everywhere else in the world, the self-service model, the model of being able to do it to something that is simple, yet powerful. Enterprise great >> Right. Right >> and simple, beautiful is absolutely the winning formula in our perspective. So the answer is to let these 100 applications bloom, but to keep them well behaved and orchestrated, in kind of a federated model, where security, having one view of the world, etc., is managed by SnapLogic and then various people and departments can bring in a blessed, SAS applications and then snap them in and the input and the way they connect, is done through snaps. And we've found that to be a real winning model for our customers. >> So you don't have to have like 18 screens open all with different browsers and different apps. >> Swivel chair integration is gone. Swivel chair integration is gone. >> Step above sneakernet but still not-- >> Step above but still not. And again, it may make sense in very, very specific super high-speed, like Wall Street, high frequency trading and hedge funds, but it's a minuscule minority of the overall problems that there needs to be solved. >> Right. So, it's just a huge opportunity, you just are cleaning up behind the momentum in the SAS applications, the momentum of the cloud. >> Cloud data. Cloud apps. Cloud data. And in general, if a customer's not going to the cloud, they're probably not the best for us. >> Right. >> Right. Our customers' almost always going towards the cloud, have lots of data and applications on premise. And in that hybrid spot, we have the capability to straddle that kind of architecture in a way that nobody else does. Because we have a born in the cloud platform that was designed to work in the real world, which is hybrid. So another interesting thing, a lot of talk about big data over the years. Now it's just kind of there. But AI and machine learning. Artificial intelligence which should be automated intelligence and machine learning. There's kind of the generic, find an old, dead guy and give it a name. But we're really seeing the values that's starting to bubble up in applications. It's not, AI generically, >> Correct. >> It's how are you enabling a more efficient application, a more efficient workflow, a more efficient, get your job done, using AI. And you guys are starting to incorporate that in your integration framework. >> Yes. Yes. So we took the approach, 'doctor heal thyself.' And we're going to help our customers do better job of having AI be a game changer for them. How do we apply that to ourselves? We heard one our CIOs, CI of AstraZeneca, Dave Smoley, was handing out the Amazon Alexa Echo boxes one Christmas. About three years ago and I'm like, my gosh that's right. That was what Walt Mossberg said in his farewell column. IT is going to be everywhere and invisible at the same time. Right. >> Right. >> It'll be in the walls, so to speak. So we applied AI, starting about two years ago, actually now three, because we shipped iris a year ago. The artificial intelligence capability inside SnapLogic has been shipping for over 12 months. Fantastic usage. But we applied to ourselves the challenge about three years ago, to use AI based on our born in the cloud platform. On the metadata that we have about people are doing. And in the sense, apply Google Autocomplete into enterprise connectivity problems. And it's been amazing. The AI as you start to snap things together, as you put one or two snaps, and you start to look for the third, it starts to get 98.7% accurate, in predicting how to connect SAS applications together. >> Right. Right. >> It's not quite autonomous integration yet but you can see where we're going with it. So it's starting to do so much value add that most of our customers, leave it on. Even the seasoned professionals who are proficient and running a center of excellence using SnapLogic, even those people choose to have sort-of this AI, on all the time helping them. And that engagement comes from the value that they're getting, as they do these things, they make less mistakes. All the choices are readily at hand and that's happening. So that's one piece of it >> Right. >> Sorry. Let me... >> It's Okay. Keep going. >> Illustrate one other thing. Napoleon famously said, "An army marches on it's stomach" AI marches on data. So, what we found is the more data we've had and more customers that we've had, we move about a trillion documents for our customers worldwide, in the past 30 days. That is up from 10 million documents in 30 days, two years ago. >> Right. Right >> That more customers and more usage. In other words, they're succeeding. What we've found as we've enriched our AI with data, it's gotten better and better. And now, we're getting involved with customers' projects where they need to support data scientists, data engineering work for machine learning and that self-service intricate model is letting someone who was trying to solve a problem of, When is my Uber going to show up? So to speak. In industry X >> Right. Right. >> These kinds of hard AI problems that are predictive. That are forward changing in a sense. Those kind of problems are being solved by richer data and many of them, the projects that we're now involved in, are moving data into the cloud for data lake to then support AI machine learning efforts for our customers. >> So you jumped a little bit, I want to talk on your first point. >> Okay. Sorry >> That's okay. Which is that you're in the very fortunate position because you have all that data flow. You have the trillion documents that are changing hands every month. >> Born in the cloud platform. >> So you've got it, right? >> Got it. >> You've got the data. >> It's a virtual cycle. It's a virtual cycle. Some people call it data capitalism. I quibble with that. We're not sort-of, mining and selling people's personal data to anybody. >> Right. Right. >> But this is where, our enterprise customers' are so pleased to work with us because if we can increase productivity. If we can take the time to solution, the time to integration, forward by 10 times, we can improve the speed that by SAS application and it gets into production 10 times faster. That is such a good trade for them and for everyone else. >> Right. Right. >> And it feeds on itself. It's a virtual cycle. >> You know in the Marketo to the Salesforce integration, it's nothing. You need from company A to company B. >> I bet you somebody in this building is doing it on a different floor right now. >> Exactly. >> (laughs) >> So I think that's such an interesting thing. In the other piece that I like is how again, I like your kind of Apple analogy, is the snap packs, right. Because we live in a world, with even though there 91 on-averages, there's a number of really dominant SAS application that most people use, you can really build a group of snaps. Is snap the right noun? >> That's the right word. >> Of snaps. In a snap pack around the specific applications, then to have your AI powered by these trillion transactions that you have going through the machines, really puts you in a unique position right now. >> It does, you know. And we're very fortunate to have the kind of customer support we've had and, sort of... Customer advisory board. Big usages of our products. In which we've added so much value to our customers, that they've started collaborating with us in a sense. And are passing to us wonderful ideas about how to apply this including AI. >> Right. >> And we're not done yet. We have a vision in the future towards an autonomous integration. You should be able to say "SnapLogic, Iris, "connect my company." And it should. >> Right. Right. >> It knows what the SAS apps are by looking at your firewall, and if you're people are doing things, building pipelines, connecting your on-premise legacy applications kind of knows what they are. That day when you should be able to, in a sense, have a bot of some type powered by all this technology in a thoughtful manner. It's not that far. It's closer at hand than people might realize. >> Which is crazy science fiction compared to-- I mean, integration was always the nightmare right back in the day. >> It is. >> Integration, integration. >> But on the other hand, it is starting to have contours that are well defined. To your point, there are certain snaps that are used more. There are certain problems that are solved quite often, the quote-to-cash problem is as old as enterprise software. You do a quote in the CRM system. Your cash is in a financial system. How does that work together? These sort of problems, in a sense, are what McKinsey and others are starting to call robotic process automations. >> Right. >> In the industrial age, people... Stopped, with the industrial age, any handcrafted widget. Nuts, and bolts, and fasteners started being made on machines. You could stamp them out. You could have power driven beams, etc., etc. To make things in industrial manner. And our feeling is, some of the knowledge tasks that feel like widget manufactures. You're doing them over and over again. Or robotic, so to speak, should be automated. And integration I think, is ripe as one of those things and using the value of integration, our customers can automate a bunch of other repeatable tasks like quote-to-cash. >> Right. Right. It's interesting just when you say autonomous, I can't help but think of autonomous vehicles right, which are all the rage and also in the news. And people will say "well I like to drive "or of course we all like to drive "on Sunday down at the beach" >> Sure. Yeah. >> But we don't like to sit in traffic on the way to work. That's not driving, that's sitting in traffic on the way to work. Getting down the 101 to your exit and off again is really not that complicated, in terms of what you're trying to accomplish. >> Indeed. Indeed. >> Sets itself up. >> And there are times you don't want to. I mean one of the most pleasant headlines, most of the news is just full of bad stuff right. So and so and such and such. But one of the very pleasing headlines I saw the other day in a newspaper was, You know what's down a lot? Not bay area housing prices. >> (laughs) >> But you know what's down a lot? DUI arrests, have plummeted. Because of the benefits of Lyft and Uber. More and more people are saying, "You know, I don't have to call a black cab. "I don't need to spend a couple hundred bucks to get home. "I'm just getting a Lyft or an Uber." So the benefits of some of these are starting to appear as in plummeting DUIs. >> Right. Right >> Plummeting fatalities. From people driving while inebriated. Plunging into another car or sidewalk. >> Right. Right. >> So Yes. >> Amara's Law. He never gets enough credit. >> (laughs) >> I say it in every interview right. We overestimate in the short term and we underestimate in the long term the effects of these technologies cause we get involved-- The Gartner store. It's the hype cycle. >> Yeah, Yeah >> But I really I think Amara nailed it and over time, really significant changes start to take place. >> Indeed and we're seeing them now. >> Alright well Gaurav, great to get an update from you and a beautiful facility here. Thanks for having us on. >> Thank you, thank you. A pleasure to be here. Great to see you as well. >> Alright He's Gaurav, I'm Jeff. And you're watching theCUBE from SnapLogic's headquarters Thanks for watching. (techno music)

live from San Francisco California it's the queue at vmworld 2014 brought to you by vmware cisco EMC HP and nutanix now here are your hosts John furrier and Stu minimum okay welcome back around here live in San Francisco for VMware 2014 this is our fifth year with the cube extracting the city from the noise at vmworld always a pleasure and we have the chief marketing officer Robin Matlock here inside the queue of my Coast stupid minute for this segment Robin welcome back to the cube thank you great keynote this morning you opened it up in front of a packed house for Pat Gelsinger and delivered an amazing keynote before we get some icky knows what some of the stats with the show here obviously vmworld it just keeps getting bigger and bigger and bigger every year well you know it's amazing the energy is fantastic here this year we're going strong we have well over twenty two thousand attendees the solutions exchange is packed there's about 250 companies that are they're exhibiting we have all kinds of breakout sessions and content I mean if you just walk around here the energy is just really thrive and the theme is no limit so I got to get some a back story on the theme I'll see no limits breaking through this is the transformation market the sign is just break it was a quick taste of wow how this all came together yeah what's the meaning behind the pictures are they're all on the hall you know it's really fun the themes that every year actually put just tremendous effort into them they can really be stressful but at the end when you land or right when it feels so good this whole notion of concrete you know in breaking through and that there's something on the other side that is truly infinite for us that just really spoke to our business it spoke to what our customers are going through and it truly spoke to the potential of this incredible you know this incredible industry you know i was when i think of the No Limits I think about the space jump the Red Bull I think about some of the things with it within the cloud that developers are doing you know Pat mentioned uber they have no asses of mass evaluation of hurts and to cumbies combined this is the kind of dream that entrepreneurs think about is like this is this inflection point stuff right so is that was that some of the vibe you guys were thinking absolutely and I think when we look at where we are in our journey relative to cloud relative to a software-defined world we're really passionate that you know the customers and the attendees of this conference are very well positioned to truly break through some of the silos that have been holding us back for a long time and we are at Crossroads um you know we believe vehemently that the data center is destined to be software-defined and that many of these attendees are well positioned to take us on that journey so I got to ask you because I see you're involved in the brain trust and all the formulation of the strategy the company and out of how to communicate it's always a challenge when it's like a moving train of innovation but you have some new things going on this year first of all nothing new on strategy it's the same marching orders with with Pats cadence hybrid cloud you know March to that cadence ops ii server defined data center but now AirWatch comes on over the top how did that affect things for you or did it it's just more of more the same so actually they bring in there some of that security and the apps piece of the business did that change some of the thinking and all I know it's an interesting question but I think at the end of the day the three strategic priorities for VMware have been very consistent now for multiple years you know largely under Pat's leadership it's about a software-defined world that's the software-defined data center it's about extending that to the hybrid cloud and it's always been about end-user computing I think the air watch acquisition just took it up a couple notches really the world of mobility we're big advocates and believers that the mobile workforce is exploding but there's a really strong connective value between what's happening at the infrastructure layer and what we can do to enable that mobile workforce so I think it was very consistent with the strategy but I do think the air guac acquisition is changing the game it's certainly producing Pat was giving us a little taste on the cube talk about the steams of the show today we had Pat had bill father's Carl up sure do a little Q&A a little little cube action almost on stage with Bill and what's what's tomorrow did you guys bring it up by thieves share with the folks out here Shey lay the land here what's the what's the contracts for tomorrow so today what we try to do is really telex the expanse of entire story what's going on holistically and you know the Karl part of it was a lot about getting our customers to really talk about what's working for them I think that's really important because we laid out a vision for VMware um you know a couple years ago and it's important to make that tangible and real and I hope the customers were able to bring that to life for people tomorrow is all about the technical under the hood let's get you know inside and really understand how the technologies are delivering against that vision and we're going to go through the whole thing it's going to cover the infrastructure it's going to talk about the hybrid cloud and we're going to talk a lot about mobility well the geeks want under the hood I mean it gets a gig show the end of the day it's very content rich at vmworld as we know it super busy a lot of parties going off as Deb going on certainly the business transactions are happening but it's still a geek show you guys have preserved that here right you know if we ask ourselves every year you know how how and should or shouldn't we evolve vmworld and i tell you we're really resolved at the end of the day this is largely a practitioner show they come for technological information education certifications and we have no desire to take a square pose and put in a round hole I mean it works so well for this audience let's just give this crowd what they need and I want to do more of it year after year yeah and we can always tell how good the conferences are in terms of content based upon how much Twitter activity there is in terms of like if people are just talking a lot on Twitter and not say anything that means it's kind of a boring show when there's not a lot of Twitter activity mostly it's text sessions people have too busy running around between between the events I mean are you guys seeing the sessions packet but we haven't had a chance to go out there what's happening yeah well to be really honest I haven't at a moment to scan too much but from what I'm hearing they are overflowing and frankly they were booked you know even before we showed up today because we do give people the schedule builder and a chance to book their sessions so I know that they are all full we're doing repeats we're trying to get you know more breakouts so people can deal with Wednesday and Thursday as things settle down but all the reports I'm getting so far is that we are pretty much over sold and oversubscribed yeah so buds do you Robin I was just gonna say you know is my fifth year now coming to vmworld it's all we impressive just the passion of the people in the virtualization community it's such a good community everybody gives back I really like what you guys did with the charity event that's going I mean what's a destination give by 25,000 with 250 oh not twenty five thousand two hundred and hundred and fifty thousand dollars that that's fantastic you know I got to talk to the hands-on lab guys today and things were running so smooth and so many people do it because as John said the geeks really love to geek out here I noticed it looked like on the badge it had you know the show spread out beyond just the north south and the West you brought the analysts kind of off to off to a hotel because they don't need to be in the center of all the geeks and everything the show floor is cranking as usual so you know it sounds like you still have the core and just pieces add on to it yeah i mean the core of the program if you were to look at breakout sessions keynotes labs that's going to stay right here in moscone but the reality is we're bursting out of the scenes and we love San Francisco we loved the venue but we have to take advantage of all the hotel space around so we got things at the w we got things at the westin we got things at the marriott we got things at the Intercontinental I mean we're or everywhere frankly but you're right we are having to kind of spread out a little bit so I got to ask you about the 10-year anniversary because that was a pretty epic event and you mentioned you made a comment on stage where'd that world go and i love the Golden Gate Bridge metaphor you put together what's changed for you over the past year it seems to be like it seems like seven years ago internet years it seems like a decade ago almost from last year yeah a lots changed and you share your perspective yeah I think a lot has changed I think on though um to be almost all for the good in my view I think you know VMware had built such a business on kind of one core platform which was compute virtualization and over the last several years we've really broadened our wings right and we are now dealing with networking and storage and security and automation and cloud and mobility and I think the diversity that that brings um from a customer perspective from an ecosystem perspective from our routes to market perspective I mean certainly it is definitely a charge because there's just so much tremendous diversity it also means we got a lot of things to cover so you know I think with that comes a responsibility to make sure our customers can understand all these different diverse you know offerings what's your objective for the show what's your preferred outcomes you can look back and just fast forward to thursday evening friday morning you know you're in a hot tub relaxing maybe it's saturday or monday morning what do you want to have happen what's your ideal outcome for vmworld beyond the fact that i like my feet attached to my body because right now i'm afraid they might fall off but let's say personal attributes aside you know i really hope that these attendees you know 22,000 plus people get on those airplanes fly home and feel like they had one of the most invigorating educational inspirational experiences professionally that they're going to have all year I hope that they got to the content that was relevant for them that they were able to navigate and you know really spend time in the areas of focus for them and I hope that people met dozens and dozens of new people that will only help them broaden their career so I have this little prop I brought because I was attended the VIP event you guys had an amazing event mark injuries since the NBC was broadcasting there Joe Tucci was there and then you know opening up your new facility which could have been around for a while so we've got some new new areas got these hot pens there so I'm going to ask you about the culture and the brand future brand for vmware I mean it's an amazing campus eco-friendly beautiful design high quality is this the brand of VMware that you seeing vision for me and you what's your vision for the brand I mean it's evolving in in real time for the company it is evolving but at the same time I think our brand and what we stand for as a company is also very stable it's great that you came to that event and saw the final unveiling of the last building as we finished it up and certainly it's a beautiful campus and it's green you know it's very you know natural woods and doing all kinds of things to protect the environment I think at the core of VMware there's you know five key values and those values are sustaining the test of time you know it's about innovation it's about community it's about people it's about integrity and it's about our customers and I think really no matter what products and services and solutions we wrap around our company I think we still stand for the same core values and I hope that never changes so I got to ask you out the community I think it's one of those things and you know something to pat about how doctor is implemented community aspect of the open source of their product and made them success you guys have had great community over the years really part of the backbone of vmware versus other companies some don't even have a heartbeat to a community you guys have a great thriving ecosystem how do you maintain that as we get more connected with the crowdsourcing with the Twitter expansion and all the people talking and it's not just forums anymore it's and more it's it's it's a virtual event every day it's like vmworld every day out there how do you handle that what's your vision and how you going to get your arms around that going forward well it's yeah I think it's really critical first of all just like anything whether you're talking about technologies you're talking about engaging with customers you have to evolve you can't use the same techniques that you use last year really to propel you next year so I think it's all about making sure you understand how our customers choosing to engage and then embrace that for us our social channels are really important our communities are really important and we're all about enabling facilitation and engagement and I think we're really that's kind of philosophically how we go about our whole social strategy it's all about enablement so that's a personal question for you to you always loved your eye for you know detail remember the first VMware we did you had pointed out the vmware stickers which ended up being perfect camera location ibly I like her I like this Robin woman she's awesome but what are you excited about now I mean what are you personally motivated upon right now what gets you really excited about the tech industry about what you what you're involved in what's the what's the one thing that get you so excited you know frankly I'm extremely proud to be the CMO of VMware I think there was a great company and I think we're part of something truly meaningful I think there was a time when maybe we weren't going to be as relevant we and by we I don't mean to see him or I mean this this whole thing that maybe we weren't going to be as relevant in the next decade but we collectively as a mystery are making bold moves we're doubling down on software we're pushing the boundaries of the data center we're getting out beyond compute we're going to storage or going to networking we're looking at security we're layering in automation and I think we are really securing our future as an industry that we are relevant and we need a seat at the table a strategic seat at the table and I'm thrilled to be a part and you certainly the global footprint the virtualization has been a great part of enabling that that mindset great to have you on the cube any other tidbits about the show you'd like to share the folks you know I think the main thing is just get involved and try some things that are different push your own personal boundaries explore there's so much content there's so many networking opportunities there's breakouts and I think definitely sampling a little bit of everything and making sure that you go home exhausted and then I'll be happy but certainly is exhausting show but Pat brought up the whole brave concept that's really about bold moves writing that's about that's kind of the whole theme here right yeah I think you know the notion of bravery is in the sense that given that things are changing so rapidly and the world is so dynamic and fluid as a business climate it's going to take some calculated risk you're going to have to really decide where are you partnering where are you betting what kind of steps are you going to take and I think action is key and the one thing it probably isn't going to work is status quo Robin Matlock the chief marketing officer for VMware keynote speech this morning set the table for Pat Gelsinger great jobs at the big picture laid out everything out the holistic vision of VMware continues to thrive thanks for coming down the cube always great to have you it's the Cubist retin from the noise we'll be right back with our next guest after the short break great thanks John you