>>Hi and welcome to another Cube conversation today were BMC Felix's Immersion Days and the Senate Clara Marry on Santa Clara, California We're having a great series of conversations about the convergence of digital service's and operations management on one of the most important features of that is How do you realise Analytics Analytics is on? The tip of everybody's tongue is these days, but it's being applied marketing and sales >>kind of the >>surely cobbler's children that aren't getting the same treatment or, in fact, the IittIe organization. So what we're gonna do in this next few conversation is learn more about how I t analytics is beginning to transform I t. And facilitating this convergence of digital service is in operations management. And to do that, we've got Gore over Bari. Who's the president's or co founder on CEO of numeric fi. Welcome to the Cube. >>Thank you, Peter. Pleasure to be here. >>So, Gaurav, tell us a little about new verify. Let's start there. >>Sure. Yeah, I know. I liked, you know, in your opening statement, he talked about I t in terms of its own use of analytics being a little like, you know, a situation where the cobbler's children don't have any shoes because I t stood up pretty powerful analytical applications for the CMO, the CFO, VP of sales, et cetera, but not for writing itself. And so we think. Yes, it's ironic, but it's also untenable. And it's untenable because in the age of digital transformation, where you're opening up digital channels for revenue generation and the like and customer engagement, I t. Is really moving up from the basement to the boardroom, right? So you have CEOs who worry now about things like availability, about sort of a speed of innovation with quality and things like that. And so to be able to have a decision support system ah, system of intelligence, if you will, that across rank and file off i t across the plan bill run life cycle across the entire idea. State from infrastructure to ABS to Business Service's gives you recommendations and intelligent insights on how to, you know, improve the quality of your work, the health of your systems to reduce the risk of your systems that we felt it was an idea whose time had come on. So that's why we got started with the Mer if I and we rolled out a bunch of targeted analytical applications across areas like Project Analytics develops analytic service and lyrics, Asset Analytics and the like. And so it's sort of a string of purse that you can deploy across your I T organization and its interconnected s so you can ask cross getting questions as well. So that's in a nutshell. The story in America, Fi in its vision. >>So, Garv, I've been within a proximate to i t You're in I t for a long time now. And it's not that we didn't have reporting because I t was always doing reporting. We have poured on no stop lights projects wherever they were. But I think what you're saying is something a little bit more fundamental. It's really Can we do a better job of really capturing the resources that are creating value for the business, understand how to deploy them or successfully We're not just talking about the infrastructure. We're talking a lot about people. I got that right. >>You hit that nail on the head there. Ultimately, you know it is a business, and you have to if you want to face sort of the epic challenges and opportunities off tomorrow that I t alone can really take on. You have to understand the people process, project and product dimensions of the I t business. And so what that means is, if you want to drive down your iron oh, costs from, you know, roughly 72% of I t budget, which is what it was. The average today to 50% is the gold standard. That's half a trillion dollars for the G two K, right? And you want to take that savings and reinvest it in agility in foster app. David. Higher quality, right? How do you do that without tapping into things like automation and the use of analytics to drive down your ire no cost rationally and increase your dev your development velocity intelligently? Right? So that's where analytics has a huge role to play. >>But also it's got to be fucking interrupt you. It's gotta be that you have to have. You have to start with visibility. Yep, into what resource is are generating the greatest return? Yeah. Why air they generating that return? Why are other resources not generating return? Yeah, and seeing how all that gets connected across the range of activities that a nightie organization is performing on behalf of the business. >>Yeah, I know exactly. I think the how is really about getting that visibility across sources, and it's a non trivial problem to do that when you have a plethora of sources that were never built to talk to one another. You may want to, for example, with an I t. Understand. You know, the total open work on each person's plate, right? So they may have a bunch of incident resolution work that they're doing, and the data and the signal from that comes from a B, M, C or a service now and yet they may be pulled into apt of work, which the signal is coming from Ajira or a C A. How do you pull that together into a single dashboard that gives you that view of what everyone's working on? And then you can make decisions like goodness with so much unplanned work that's gone Fred's way, there is no way that the epic that he's involved with is going to, you know, be completed on time. So I have Project Chris. I have released risk as a result, I may even have attrition risk. And so the ability to pull together data into a single model answer the visibility question, too. You're to the point you make and then go the next step off predicting likely outcomes. That's the magic. And that's the use of analytics to sort of trance for my tea into, um, you know, operating in a far more intelligent paradigm than it has thus far. >>Other tools have attempted to do this, but they attempted to basically be the soup to nuts tool. So they forced users Thio install agents everywhere that there was a single process model that was expected to be employed to administer all kinds of different resources. There are very significant limits on how you considered application development application management, For example, Why is numeric five different? >>Yeah, what we've tried to do is really take ah leaf from the page on books of those who have set up succeeded in this endeavor before. So if you look at you know the solutions that a CMO might have it at her fingertips or a CFO might have right fundamentally, it's about pulling data into existing systems, not requiring a change of behavior but pulling data from existing systems into a canonical model into a standard sort of analytical data model that runs on surfing. Ah, a dedicated stack on. Then you basically have this layer off descriptive, prescriptive and predictive analytics sort of folded in on. That's the approach we've taken where we say, Hey, look, we want There is such a thing as a change management system that doesn't go away. We would like to mind the accumulated history of all the changes you've ever put into production by tapping into your service management system and then your upstream Devon test system. Because change is often a piece of court, it began its life somewhere in a in a death cycle. So how many times was that piece of court rollback tested? How many times that it failed the testing cycle? Who worked on it? What's been their success rate thus far? And then, with respect to the change itself in the past, how often has a change like this failed? You know, if changes were done on a weekend through a combination of an unsure in offshore team, is that implicated in a failed changes in the past and then downstream of the change in the past, you know, Was it a decline in performance or usage availability as gleaned from your monitoring tools? So we pull all data from all these sources without requiring you to re instrument them into a standard model. And then, for every upcoming change, we tell you Hey, this one is a risky change. Go look at it. Send it back for further testing. Hey, this one is a lower exchange pusher to production directly and so inherently thehe bility toe pull data from multiple existing sources into a standard data model and have best practice reports and insights sort of layer on top. That's the approach taken. >>Well, look, I really like this. Uh, let me let me see if I can summarize something you just said So Numeric fei is not immediately antagonistic to anything that anybody has with the shop. That it starts from a proposition. That look what you're doing is working or not, But let's start from across from a premise. It you're doing something now. Let's learn more about it. Let's then asked Can we do it better? Yes or no? You have the intelligence to do that. And if it should be replaced, can you actually get to the point of that? You can actually indicate or suggest how and when to replace something. >>Yeah, that's a fabulous question. I think you know, increasingly what we're seeing is that our customers are pulling us in the direction off, making active recommendations on decisions that they could potentially make such as, you know, you may want to consider consolidating a certain class of applications because, you know, given its revenue and usage, the amount of support button associated with it is too high here. You might want to take a more refined and data driven, inside driven approach to asset retirement because you know this whole, >>you know, >>everything that Lenovo, in five years old Moscow is too blunt an instrument, you know, retired those assets that are the most error prone and keep alive those assets that still have useful life >>And that process, maybe itself be extremely expensive, very limited returns >>precisely precisely. So the ability to transcend now from just visibility on dashboards to providing active recommendations for every action along the way, you know, project race release risk, patrician risk, change, risk service quality risk, et cetera. We see that as as the as the vision for us. You know, it's the use of a I not just for automation, you know, sort of Ah, which clearly the ops field is investing in, but also the use of a i for decision support for providing you with intelligent recommendations across the full sphere of activities that I t undertakes. >>Grove Ari from the from the verify. Thanks very much for being on the Cube. >>My pleasure. Thank you. >>Once again, this has been a cute conversation from BMC. Helix is immersion days and the we look forward to seeing you again. Thanks for listening.

>> 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. >> 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)

>> Announcer: Live from Las Vegas, it's The Cube! Covering ServiceNow, Knowledge 2018. Brought to you by ServiceNow. >> Welcome back everyone to The Cube's live coverage of ServiceNow Knowledge 18 in Las Vegas. I'm your host, Rebecca Knight. We're joined by Gaurav Dutt Uniyal, who is the North American practice head of service management for Infosys. Thanks so much for joining us, Gaurav. >> Thanks, Rebecca for hosting me. >> So first, this is your second time on The Cube, so I should say welcome back. >> Yes, thank you. >> So tell us about what you do at Infosys and what, sort of what the strategy is in this space. >> Sure, sure. So I lead our practice for North America, and Infosys is our technology services company. And we help our organizations go through their digital transformation journey. And with ServiceNow I've been working for the last 10 years, so we have seen this platform evolving from a basic ticketing tool to a platform that can be used by IT, and now it has reached a point where it's being adopted by, across the organizations, right? All the business owners, be it HR or customer services or program managers, portfolio managers, and so on and so forth. And interestingly, you know, the theme which we have adopted for this year, is how do we help our clients accelerate their journey for HR, CSM, item? So basically how do we help our organization adopt ServiceNow, be on IT, and take it to the enterprise. >> So that's, so that's the strategy. How do we accelerate customers in their journey. And is the strategy led by customers themselves? Did they say, "Look, Infosys, we have this problem, "we're going too slow," or what would you say, where was sort of the impetus for this? >> I think it's a mix of both. You know, we do get, obviously, inputs from the clients, but the value that Infosys brings in is the diverse experience from multiple engagements, right? And to give you some more views of how we are approaching this space, so on a very high level what we see as the key things or strategies in this space. The first of all, any control system that we have with the client, the first and foremost topic is about user experience. That, you know, as we implement SerivceNow, how do we enhance the user experience for the internalized as for the regular customers? That's one. Second strategy or thing that we see is that, you know, while ServiceNow has been matured and implemented for a larger part of IT organization, but how do we make sure that the similar level of maturity can be achieved for HR managers, right? How program managers, portfolio managers, the security organizations, the facilities team, how they can adopt the platform. So that's the second strategy. The third strategy that we work on is bringing in domain expertise as part of ServiceNow implementation, right? Now, for example, if we are implementing ServiceNow for retail, so how do we bring in experience of store's management, for example? If we are implementing it for a foreign organization, you know how do we bring in that domain expertise, and integrate that with SerivceNow. That's the third strategy. And the fourth thing that we are focusing on is some of the newer things, which possibly, not revenue generating engines for us yet, >> Rebecca: Yet. (laughs) >> But down the line, you're 12 months, 13 months down the line, we expect more revenue to come. So things like, IOT, and, I shouldn't even say AI because AI is something we're already implementing for some of the clients. >> So, that's interesting. You're exploring this area, this area is so hot, what are some of the, how do you see some of the potential use cases? >> So the use cases that we are seeing is so ITSM, I think in our viewpoint, it has matured. A lot of organizations have adopted ITSM, the basic capabilities on incident, problem, team management, asset management, CMDB, is already out there, right? An obvious thing, clients taking those foundational capabilities and taking it to other parts of organization, right? So in case of HR, we are seeing organizations adopting it for case management. Helping onboarding, offboarding all their employees. Managing their payroll systems. That's one set of use cases that we are seeing. The second set of use cases we are seeing are around automating your business processes. So, there are a few clients where they identified a set of business use cases or workflows and they are leveraging the power of ServiceNow to automate those. >> So how are you, how are you and your customers measuring the return on the investment here? What are they seeing? >> So what we do is, when we work on these engagements, so at the beginning of the engagements we do identify certain outcomes that we are going to deliver for our clients, right? And one simple example is if we are implementing ServiceNow for help desk, so one of the key outcomes that we would measure is that by implementing ServiceNow, how many tickets that we have reduced? Or how many calls to service desk have been reduced by implementing SerivceNow? Which, you know, actually has reduced the cost of operations for the client. So that's just one example. What we do is across the organization we identify those use cases and the kind of outcomes that it would deliver, and we also identify the set of metrics, right, which we jointly review during the engagement. At what kind of outcomes that have been delivered with this implementation. >> When you think about all of the, the solutions that you helped customers come to, what are you most excited about? >> Yeah, so I think if you look at the different types of solutions that are out there, right, and how customers are adopting it, I think in my view, or the way we see it, the solutions around the specific domain or industry, right? Because so far what we have seen is that service management or ITSM, it's like a whole giant lair, it is not really tied to a specific domain, right? But more and more we are seeing that clients are asking for solutions which are relevant to their business. Which can help make some difference to their business outcomes. And that's where we are seeing turned around building solutions for retail organizations, building solutions for insurance organization, money factoring, finance, and so on and so forth. So that's one interesting turn that we are seeing in the market right now. >> Last question, how many Knowledges have you been to? >> I have been to, I have been coming right from the beginning. >> So you've seen the conference above, why do you keep coming back? >> I think so first of all, this is a great product. A lot of organizations are adopting it. But interestingly I think it's an equal system. If you look at this conference on 18,000, 20,000 people attending it last year it was only 15,000, and if you look at so many partners and customers out there, I think there's a big big family and bigger ecosystem out there. So yeah, excited to be part of it. >> Gaurav, thanks so much for coming on The Cube, it's been great. >> Thank you. >> I'm Recebba Knight, we will have more tomorrow from ServiceNow Knowledge 18. Until then, good night!

(switch clicking) >> Hey, welcome back, everybody. Jeff Frick, here with theCUBE. We're at the Mission Bay Conference Center in downtown San Francisco at Node Summit 2017. TheCUBE's been coming here for a number of years. In fact, Ryan Dahl's one of our most popular interviews in the history of the show, talking about Node. And, the community's growing, the performance is going up and there's a lot of good energy here, so we're excited to be here and there's a lot of big companies that maybe you would or wouldn't expect to be involved. And, we're excited to have Gaurav Seth. He is the Product Manager for Several Things JavaScript. I think that's the first time we've ever had that title on. He's from Microsoft. Thanks for stopping by. >> Yeah, hey, Jeff, nice to be here. Thanks for having me over. >> Absolutely, >> Yes. >> so let's just jump right into it. What is Microsoft doing here in such a big way? >> So, one of the things that Microsoft is, like, I think we really are, now, committed and, you know, we have the mantra that we are trying to follow which is any app, any developer, any platform. You know, Node actually is a great growing community and we've been getting soaked more and more and trying to help the community and build the community and play along and contribute and that's the reason that brings us here, like, it's great to see the energy, the passion with people around here. It's great to get those connections going, have those conversations, hear from the customers as to what they really need, hear from developers about their needs and then having, you know, a close set of collaboration with the Core community members to see how we can even evolve the project further. >> Right, right, and specifically on Azure, which is interesting. You know, it's been interesting to watch Microsoft really go full bore into cloud, via Azure. >> Right. >> I just talked to somebody the other day, I was talking about 365 being >> Uh huh. >> such a game-changer in terms of cloud implementation, as a big company. There was a report that came out about, you know, the path at 20 billion, >> Right. >> so, clearly, Microsoft is not only all-in, but really successfully >> Right. >> executing on that strategy >> Yeah, I mean-- >> and you're a big piece of that. >> Yes, I mean, I think one of the big, big, big pieces, really, is as the developer paradigms are changing, as the app paradigms are changing, you know, how do you really help make developers this transition to a cloud-native world? >> Right, right. >> How do you make sure that the app platforms, the underlying infrastructure, the cloud, the tools that developer use, how do you combine all of them and make sure that you're making it a much easier experience for developers to move on >> Right. >> from their existing paradigms to these new cloud-native paradigms? You know, one of the things we've been doing on the Azure side of the house and when, especially when we look at Node.js as a platform, we've been working on making sure that Node.js has a great story across all the different compute models that we support on Azure, starting from, like, hey, if you you want to do server list of functions, if you want to do BasS, if you want to go the container way, if you want to just use WEAMS, and, in fact, we just announced the Azure container instances, today, >> Right. >> so it's, one of the work, some of the work we are doing is really focused on making sure that the developer experiences as you migrate your workloads from old traditional, monolithic apps are also getting ready to move to this cloud native era. >> Right, so it's an interesting point of view from Microsoft 'cause some people, again, people in-the-know already know, but a lot of people maybe don't know, kind of, Microsoft's heritage in open source. We think, you know, that I used to buy my Office CD, >> Right. >> and my Outlook CD >> Right. >> you know, it's different, especially as you guys go more heavily into cloud, >> Right. >> you need to be more open to the various tools of the developer community. >> That's absolutely true and one of the focus areas for us, really, has been, you know, as we think through the cloud-native transition, what are the big pieces, the main open source tools, the frameworks that are available and how do we provide great experiences for those on Azure? >> Right, right. >> Right, because, at times, people come with the notion that, hey, Azure probably might just be good for dot NET or might just be good for Windows, but, you know, the actual fact, today, is really that Azure has great supporting story for Linux, Azure has great story for a lot of these open source tools and we are continuing to grow our story in that perspective. >> Right. >> So, we really want to make sure that open source developers who come and work on our platform are successful. >> And then, specifically for Node, and you're actually on the Board, so you've got >> Right. >> a leadership position, >> Yep. >> when you look at Node.js within the ecosystem of opensource projects and the growth that we keep hearing about in the sessions, >> Yep. >> you know, how are you, and you specifically and Microsoft generally, kind of helping to guide the growth of this community and the development of this community as it gets bigger and bigger and bigger? >> Right, I think that's a great question. I think from my perspective, and also Microsoft's perspective, there are a bunch of things we are actually doing to engage with the community, so I'll kind of list out three or four things that we are doing. I think the first and foremost is, you know, we are a participant in the Node.js Foundation. >> Right. >> You know, that's where like, hey, we kind of look at the administrative stuff. We are a sponsor of, you know, at the needed levels, et cetera, so that's just the initial monetary support, but then it gets to really being a part of the Node Core Committee, like, as we work on some of the Core pieces, as we evolve Node, how can we actually bring more perspectives, more value, into the actual project? So, that's, you know, we have many set of engineers who are, right now, working across different working groups with Node and helping evolve Node. You know, you might have heard about the NAPI effort. We are working with the Diagnostics Working Group, we are working with the Benchmarking Working Group and, you know, bringing the thing. The third thing that we did, a while back, was we also did this integration of bringing Chakra which is the JavaScript Runtime from Microsoft that powers Microsoft Edge. We made Node work with Chakra because we wanted to bring the power of Node to this new platform called Windows IoT >> Right, right. >> and, you know, the existing Node could not get there because some of the platform limitations. So, those are like some of the few examples that we've, and how we've been actually communicating and contributing. And then, I think the biggest and the foremost for me, really, are the two pillars, like when I think about Microsoft's contribution, it's really, like, you know, the big story or the big pivot for us is, we kind of go create developer tools and help make developer live's easier by giving them the right set of tools to achieve what they want to achieve in less time, be more productive >> Right, right. >> and the second thing is, really, like the cloud platforms, as things are moving. I think across both of those areas, our focus really had been to make sure that Node as a language, Node as a platform has great first-class experiences that we can help define. >> Right. Well, you guys are so fortunate. You have such a huge install base of developers, >> Right. >> but, again, traditionally, it wasn't necessarily cloud application developers and that's been changing >> Yep. >> over time >> Yep. >> and there's such a fierce competition for that guy, >> Yep. >> or gal, who wakes up >> Yep. >> in the morning or not, maybe, the morning, at 10:00, >> Yep. >> has a cup of coffee >> Yep. >> and has to figure out what they're going to develop today >> Right. >> and there's so many options >> Right. >> and it's a fierce competition, >> Right. >> so you need to have an easy solution, you need to have a nice environment, you need to have everything that they want, so they're coding on your stuff and not on somebody else's. >> That's true, I mean I, you know, somehow, I kind of instead of calling it competition, I have started using this term coopetition because between a lot of the companies and vendors that we talk about, right, it's more about, for all of us, it's working together to grow the community. >> Right. >> It's working together to grow the pie. You know, with open source, it's not really one over the other. It's like the more players you have and the more players who engage with great ideas, I think better things come out of that, so it's all about that coopetition, >> rather than competition, >> Right. >> I would say. >> Well, certainly, around and open source project, here, >> Yes, exactly. >> and we see a lot of big names, >> Exactly. >> but I can tell you, I've been to a lot of big shows where they are desperately trying to attract >> Right, right, yes. >> the developer ecosystem. "Come develop on our platforms." >> Yes, yes. >> So, you're in a fortunate spot, you started, >> Yes, I mean that-- >> not from zero, but, but open source is different >> Yes. >> and it's an important ethos because it is much more community >> Exactly, exactly. >> and people look at the name, they don't necessarily look at the title >> Exactly. >> or even the company >> Yep, exactly. >> that people work for. >> Exactly, and I think having more players involved also means, like, it's going to be great for the developer ecosystem, right, because everybody's going to keep pushing for making it better and better, >> Right. >> so, you know, as we grow from a smaller stage to, like, hey, there's actually a lot of enterprised option of these use case scenarios that people are coming up with, et cetera, it's always great to have more parties involved and more people involved. >> Gaurav, thank you very much >> Yeah. >> and, again, congratulations on your work here in Node. Keep this community strong. >> Sure. >> It looks like you guys are well on your way. >> Yeah. Thanks, Jeff. >> All right. >> Thanks for your time, take care, yeah. >> Guarav Seth, he's a Project Lead at Microsoft. I'm Jeff Frick. You're watching theCUBE from Node Summit 2017. Thanks for watching. (upbeat synthpop music)

>> Announcer: Live from Orlando, Florida, it's theCUBE. Covering ServiceNow Knowledge '17. Brought to you by ServiceNow. >> Welcome back to Orlando, everybody. This is theCUBE, the leader in live tech coverage, and we are covering three days wall to wall coverage of ServiceNow Knowledge 2017. I'm Dave Volante with my co-host Jeff Frick. When we first started doing Knowledge in 2013, you'd walk around the show floor, and the names that you'd see weren't the brand names. Well, Infosys is here and Gaurav Uniyal, who's the industry principal of North America for the practice lead at ITSM for the ServiceNow practice with Infosys, you're seeing the big SIs join the community and really start to add value. Gaurav, welcome to theCUBE, thanks so much. >> Thank you. >> How'd you guys get into this? Like you say, four or five years ago, you guys might have been kicking the can, and now, you're all in. What's the journey been like? >> Sure, sure. We have been a partner with ServiceNow for almost last eight years, and as I look back to the journey, I can categorize the journey into four parts. Initially we saw 2010 to 2012 is basically about ITSM, how do you get the foundation capabilities in? Once that was there, we saw for the next couple of years it was all about how do you integrate services together, the service integration management as a concept. The third wave we saw is where concepts like ITOM, mobility, there's a lot of focus on user experience. And now, here we are in 2017, and as we look at the trends, what we are anticipating for the next two to three years, on a very high level, there are three trends which we believe are going to shape the journey of ServiceNow. First one is AI, obviously, how do you bring in concepts of machine learning, chat bars, predictive analytics, and how would that help organization do things faster, more efficiently, and in a cost-optimizing manner? AI is definitely one. Second trend that we are seeing is now organizations are looking for solutions that are relevant to their business. Solutions which are specific to retail industry, to CBGs, to finance, to healthcare, and so on, so forth. We are seeing a lot of traction there. And third is the natural expansion of ServiceNow into newer areas like obviously CSM, HR and so on, so forth. These are the three trends on the high level that we see, AI, going vertical, and on going horizontal by expanding these capabilities. >> Big factor when you talk to customers is sometimes it's not simple to implement ServiceNow. They need a partner like yours, so where do you start? I mean, when we first started following ServiceNow, a lot of folks weren't adopting CMDB and going too hard on the service catalog. To take advantage of these trends, the AI and other things that you talked about, do they need to be there on the majority curve? I wonder if you could talk about that a little bit. >> Sure, sure. What we see is that obviously there are a set of foundational capabilities that are required. There's definitely a push required from the management to be able to drive the initiator. But more and more we are seeing our clients implementing the solution in a standardized manner. If I look back four or five years back, a lot of customization, everybody have their own processes. But when I talk with clients now, they're looking for something which is ready-made, which can be deployed in a very, very faster manner. >> Gaurav, why Infosys? Talk about what you bring to the table versus maybe some of the other suppliers out there, and what do you consider your sweet spot? >> I think I would, a couple of things. One is Infosys we do a lot of work outside of ServiceNow. We have our practices for cloud, we have practices for HR, and so on, so forth. One thing that have been to our table is the domain expertise. If you're implementing HR, it requires not only ServiceNow skills, but as well as domain skills to be able to configure the processes. That's one differentiator that we have. The second differentiator we have is delivering ServiceNow as a service, so clients are also looking for turnkey projects where one render can bring in the platform, bring in consulting, implementation services, and also be able to manage the platform end-to-end, so that's the second thing. And third thing is basically being ahead of the curve. What we have done, we have invested last, I would say, last eight to 10 months in building a product that we brand as ESM Cafe, Enterprise Service Management Cafe, and it's what we call as a gold image of ServiceNow, and that helps you deploy ServiceNow faster and in efficient manner. >> So, Gaurav, what did you see eight years ago, 'cause clearly ServiceNow isn't where it is today, that gave you guys the confidence to make the investment? >> And before ServiceNow, we used to work with other products as well. What we saw new with ServiceNow was a huge focus on user experience. How do you make it easy for the users, how do you deploy an intuitive solution? And in our view, that has been the key, a focus on user experience, bring simplistic workflows, and be able to drive user behavior. >> Maybe some of those other domains, you mentioned HR, where else do you see Infosys as really strong? >> What we are seeing is ITOM is definitely one area that we are focusing on. HR, CSM, these are two big stack we have. And then, we are also focusing a lot on building vertical solutions. As I said, having specific solutions for retail industry, for our healthcare clients, or manufacturing clients. That has been a focus for us. >> We're out of time, Gaurav, but I'd like to leave you with the last word. Knowledge 2017, what does it mean to you, your customers, and Infosys and your presence here? Give us the bumper sticker. >> So I think, if I have to summarize everything in one word, I will say it's all about diversity. We see so many partners, so many clients, everybody they have their own perspective. But how do you bring in all that diverse experience and gel it together to be able to deliver the experience for the users? >> Great, well, Gaurav, thanks very much for coming on theCUBE, we appreciate it. >> Yep, it has been pleasure. >> Okay, well, keep it right there, everybody. We'll be back with our next guest right after this short break. This is theCUBE, we're live from ServiceNow Knowledge '17. Be right back. (electronic keyboard music)

>> Hey, welcome back everybody. Jeff Rick here with the Cube. We are live in downtown San Jose at the historic Pagoda Lounge, part of Big Data SV, which is part of Strata + Hadoop Conference, which is part of Big Data Week because everything big data is pretty much in San Jose this week. So we're excited to be here. We're here with George Gilbert, our big data analyst from Wikibon, and a great guest, Gaurav Dhillon, Chairman and CEO of SnapLogic. Gaurav, great to see you. >> Pleasure to be here, Jeff. Thank you for having me. George, good to see you. >> You guys have been very busy since we last saw you about a year ago. >> We have. We had a pretty epic year. >> Yeah, give us an update, funding, and customers, and you guys have a little momentum. >> It's a good thing. It's a good thing, you know. A friend and a real mentor to us, Dan Wormenhoven, the Founder and CEO of NetApp for a very long time, longtime CEO of NetApp, he always likes to joke that growth cures all startup problems. And you know what, that's the truth. >> Jeff: Yes. >> So we had a scorching year, you know. 2016 was a year of continuing to strengthen our products, getting a bunch more customers. We got about 300 new customers. >> Jeff: 300 new customers? >> Yes, and as you know, we don't sell to small business. We sell to the enterprise. >> Right, right. >> So, this is the who's who of pharmaceuticals, continued strength in high-tech, continued strength in retail. You know, all the way from Subway Sandwich to folks like AstraZeneca and Amgen and Bristol-Myers Squibb. >> Right. >> So, some phenomenal growth for the company. But, you know, we look at it very simply. We want to double our company every year. We want to do it in a responsible way. In other words, we are growing our business in such a way that we can sail over to cash flow break-even at anytime. So responsibly doubling your business is a wonderful thing. >> So when you look at it, obviously, you guys are executing, you've got good products, people are buying. But what are some of the macro-trends that you're seeing talking to all these customers that are really helping push you guys along? >> Right, right. So what we see is, and it used to be the majority of our business. It's now getting to be 50/50. But still I would say, historically, the primary driver for 2016 of our business was a digital transformation at a boardroom level causing a rethinking of the appscape and people bringing in cloud applications like Workday. So, one of the big drivers of our growth is helping fit Workday into the new fabric in many enterprises: Vassar College, into Capital One, into finance and various other sectors. Where people bring in Workday, they want to make that work with what they have and what they're going to buy in the future, whether it's more applications or new types of data strategies. And that is the primary driver for growth. In the past, it was probably a secondary driver, this new world of data warehousing. We like to think of it as a post-modern era in the use of data and the use of analytics. But this year, it's trending to be probably 50/50 between apps and data. And that is a shift towards people deploying in the same way that they moved from on-premise apps to SAS apps, a move towards looking at data platforms in the cloud for all the benefits of racking and stacking and having the capability rather than being in the air-conditioning, HVAC, and power consumption business. And that has been phenomenal. We've seen great growth with some of the work from Microsoft Azure with the Insights products, AWS's Redshift is a fantastic growth area for us. And these sorts of technologies, we think are going to be of significant impact to the everyday, the work clothing types of analytics. Maybe the more exotic stuff will stay on prem, but a lot of the regular business-like stuff, you know, stuff in suits and ties is moving into the cloud at a rapid pace. >> And we just came off the Google Next show last week. And Google really is helping continue to push kind of ML and AI out front. And so, maybe it's not the blue suit analytics. >> Gaurav: Indeed, yes. >> But it does drive expectations. And you know, the expectations of what we can get, what we should get, what we should be moving towards is rapidly changing. >> Rapidly changing, for example, we saw at The New York Times, which as many of Google's flagship enterprise customers are media-related. >> Jeff: Right. >> No accident, they're so proficient themselves being in the consumer internet space. So as we encountered in places like The New York Times, is there's a shift away from a legacy data warehouse, which people like me and others in the last century, back in my time in Informatica, might have sold them towards a cloud-first strategy of using, in their case, Google products, Bigtable, et cetera. And also, they're doing that because they aspirationally want to get at consumer prices without having to have a campus and the expense of Google's big brain. They want to benefit from some of those things like TensorFlow, et cetera, through the machine learning and other developer capabilities that are now coming along with that in the cloud. And by the way, Microsoft has amazing machine learning capability in its Azure for Microsoft Research as well. >> So Gaurav, it's interesting to hear sort of the two drivers. We know PeopleSoft took off starting with HR first and then would add on financials and stumble a little bit with manufacturing. So, when someone wants to bring in Workday, is it purely an efficiency value prop? And then, how are you helping them tie into the existing fabric of applications? >> Look, I think you have to ask Dave or Aneel or ask them together more about that dynamic. What I know, as a friend of the firm and as somebody we collaborate with, and, you know, this is an interesting statistic, 20 percent of Workday's financial customers are using SnapLogic, 20 percent. Now, it's a nascent business for them and you and I were around in the last century of ERP. We saw the evolution of functional winners. Some made it into suites and some didn't. Siebel never did. PeopleSoft at least made a significant impact on a variety of other things. Yes, there was Bonn and other things that prevented their domination of manufacturing and, of course, the small company in Walldorf did a very good job on it too. But that said, what we find is it's very typical, in a sense, how people using TIBCO and Informatica in the last century are looking at SnapLogic. And it's no accident because we saw Workdays go to market motion, and in a sense, are following, trying to do the same thing Dave and Aneel have done, but we're trying to do the same thing, being a bunch of ex-Informatica guys. So here's what it is. When you look at your legacy installation, and you want to modernize it, what are your choices? You can do a big old upgrade because it's on-premise software. Or you can say, "You know what? "For 20% more, I could just get the new thing." And guess what? A lot of people want to get the new thing. And that's what you're going to see all the time. And that's what's happening with companies like SnapLogic and Workday is, you know, someone. Right here locally, Adobe, it's an icon in technology and certainly in San Jose that logo is very big. A few years ago, they decided to make the jump from legacy middleware, TIBCO, Informatica, WebMethods, and they've replaced everything globally with SnapLogic. So in that same way, instead of trying to upgrade this version and that version and what about what we do in Japan, what do we do in Sweden, why don't you just find a platform as a service that lets you elevate your success and go towards a better product, more of a self-service better UX, millennial-friendly type of product? So that's what's happening out there. >> But even that three-letter company from Walldorf was on-stage last week. You can now get SAP on the Google Cloud Platform which I thought was pretty amazing. And the other piece I just love but there's still a few doubters out there on the SAS platform is now there's a really visual representation. >> Gaurav: There is. >> Of the dominance of that style going up in downtown San Francisco. It's 60 stories high, and it's taken over the landscape. So if there's ever any a doubt of enterprise adaptation of SAS, and if anything, I would wonder if kind of the proliferation of apps now within the SAS environment inside the enterprise starts to become a problem in and of its own self. Because now you have so many different apps that you're working on and working. God help if the internet goes down, right? >> It's true, and you know, and how do you make e pluribus unim, out of many one, right? So it's hilarious. It is almost at proliferation at this point. You know, our CFO tapped me the other day. He said, "Hey, you've got to check this out." "They're using a SAS application which they got "from a law firm to track stock options "inside the company." I'm like, "Wow, that is a job title and a vertical." So only high growth private venture backed companies need this, and typically it's high tech. And you have very capable SAS, even in the small grid squares in the enterprise. >> Jeff: Right, right. >> So, a sign, and I think that's probably another way to think about the work that we do at SnapLogic and others. >> Jeff: Right, right. >> Other people in the marketplace like us. What we do essentially is we give you the ERP of one. Because if you could choose things that make sense for you and they could work together in a very good way to give you very good fabric for your purposes, you've essentially bought a bespoke suit at rack prices. Right? Without that nine times multiplier of the last century of having to have just consultants without end, darkened the sky with consultants to make that happen. You know? So that, yes, SAS proliferation is happening. That is the opportunity, also the problem. For us, it's an opportunity where that glass is half-full we come in with SnapLogic and knit it together for you to give you fabric back. And people love that because the businesses can buy what they want, and the enterprise gets a comprehensive solution. >> Jeff: Right, right. >> Well, at the risk of taking a very short tangent, that comment about darkening the skies, if I recall, was the battle of the Persians threatening the 300 Greeks at the battle of Thermopylae. >> Gaurav: Yes. >> And they said, "We'll darken the skies with our arrows." And so the Greek. >> Gaurav: Come and get 'em. >> No, no. >> The famous line was, he said, "Give us your weapons." And the guy says, "Come and get 'em." (laughs) >> We got to that point, the Greek general says, "Well, we'll fight in the shade." (all laughing) But I wanted to ask you. >> This is the movie 300 as well, right? >> Yes. >> The famous line is, "Give us your weapons." He said, "Come and get 'em." (all laughing) >> But I'm thinking also of the use case where a customer brings in Workday and you help essentially instrument it so it can be a good citizen. So what does that make, or connect it so it can be a good citizen. How much easier does that mean or does that make fitting in other SAS apps or any other app into the fabric, application fabric? >> Right, right. Look, George. As you and I know, we both had some wonderful runs in the last century, and here we are doing version 2.0 in many ways, again, very similar to the Workday management. The enterprise is hip to the fact that there is a Switzerland nature to making things work together. So they want amazing products like Workday. They want amazing products like the SAP Cloud Suite, now with Concur, SuccessFactors in there. Some very cool things happening in the analytics world which you'll see at Sapphire and so on. So some very, very capable products coming from, I mean, Oracle's bought 80 SAS companies or 87 SAS companies. And so, what you're seeing is the enterprise understands that there's going to be red versus blue and a couple other stripes and colors and that they want their businesspeople to buy whatever works for them. But they want to make them work together. All right? So there is a natural sort of geographic or structural nature to this business where there is a need for Switzerland and there is a need for amazing technology, some of which can only come from large companies with big balance sheets and vertical understanding and a legacy of success. But if a customer like an AstraZeneca where you have a CIO like Dave Smoley who transformed Flextronics, is now doing the same thing at AstraZeneca bringing cloud apps, is able to use companies like SnapLogic and then deploy Workday appropriately, SAP appropriately, have his own custom development, some domestic, some overseas, all over the world, then you've got the ability again to get something very custom, and you can do that at a fraction of the cost of overconsulting or darkening the skies in the way that things were done in the last century. >> So, then tell us about maybe the convergence of the new age data warehousing, the data science pipeline, and then this bespoke collection of applications, not bespoke the way Oracle tried it 20 years ago where you had to upgrade every app tied into every other app on prem, but perhaps the integration, more from many to one because they're in the cloud. There's only one version of each. How do you tie those two worlds together? >> You know, it's like that old bromide, "Know when to hold 'em. "Know when to fold them." There is a tendency when programming becomes more approachable, you have more millennials who are able to pick up technology in a way. I mean, it's astounding what my children can do. So what you want to do is as a enterprise, you want to very carefully build those things that you want to build, make sure you don't overbuild. Or, say, if you have a development capability, then every problem looks like a development nail and you have a hammer called development. "Let's hire more Java programmers." That's not the answer. Conversely, you don't want to lose sight of the fact that to really be successful in this millennium, you have to have a core competence around technology. So you want to carefully assemble and build your capability. Now, nobody should ever outsource management. That's a bad idea. (chuckles) But what you want to do is you want to think about those things that you want to buy as a package. Is that a core competence? So, there are excellent products for finance, for human capital management, for travel expense management. Coupa just announced today their for managing your spend. Some of the work at Ariba, now the Ariba Cloud at SAP, are excellent products to help you do certain job titles really well. So you really shouldn't be building those things. But what you should be doing is doing the right element of build and buy. So now, what does that mean for the world of analytics? In my view, people building data platforms or using a lot of open source and a lot of DevOps labor and virtualization engineering and all that stuff may be less valuable over time because where the puck is going is where a lot of people should skate to is there is a nature of developing certain machine language and certain kind of AI capabilities that I think are going to be transformational for almost every industry. It is hard to imagine anything in a more mechanized back office, moving paper, manufacturing, that cannot go through a quantum of improvement through AI. There are obviously moral and certain humanity dystopia issues around that to be dealt with. But what people should be doing is I think building out the AI capabilities because those are very custom to that business. Those have to do with the business's core competence, its milieu of markets and competitors. But there should be, in a sense, stroking a purchase order in the direction of a SAS provider, a cloud data provider like Microsoft Azure or Redshift, and shrinking down their lift-and-shift bill and their data center bill by doing that. >> It's fascinating how long it took enterprises to figure out that. Just like they've been leveraging ADP for God knows how many years, you know, there's a lot of other SAS applications you can use to do your non-differentiated heavy lifting, but they're clearly all in now. So Gaurav, we're running low on time. I just want to say, when we get you here next year, what's top of your plate? What's top of priorities for 2017? Cause obviously you guys are knocking down things left and right. >> Thank you, Jeff. Look, priority for us is growth. We're a growth company. We grow responsibly. We've seen a return to quality on the part of investors, on the part of public and private investors. And you know, you'll see us continue to sort of go at that growth opportunity in a manner consistent with our core values of building product with incredible success. 99% of our customers are new to our products last quarter. >> Jeff: Ninety-nine percent? >> Yes sir. >> That says it all. >> And in the world of enterprise software where there's a lot of snake oil, I'm proud to say that we are building new product with old-fashioned values, and that's what you see from us. >> Well 99% customer retention, you can't beat that. >> Gaurav: Hard to beat! There's no way but down from there, right? (laughing) >> Exactly. Alright Gaurav, well, thanks. >> Pleasure. >> For taking a few minutes out of your busy day. >> Thank you, Jeff. >> And I really appreciate the time. >> Thank you, Jeff, thank you, George. >> Alright, he's George Gilbert. I'm Jeff Rick. You're watching the Cube from the historic Pagoda Lounge in downtown San Jose. Thanks for watching.

>> 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. >> 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.

>> Voiceover: Live from San Francisco it's theCUBE. Covering AWS Summit 2017. Brought to you by Amazon Web Services. (upbeat music) >> Hey welcome back to theCUBE live at the Moscone Center at the Amazon Web Services Summit San Francisco. Very excited to be here, my co-host Jeff Rick. We're now talking to the Chief Scientist and professor at University of San Francisco, Greg Benson of SnapLogic. Greg, welcome to theCUBE, this is your first time here we're excited to have you. >> Thanks for having me. >> Lisa: So talk to us about what SnapLogic is, what do you do, and what did announce recently, today, with Amazon Web Services? >> Greg: Sure, so SnapLogic is a data integration company. We deliver a cloud-native product that allows companies to easily connect their different data sources and cloud applications to enrich their business processes and really make some of their business processes a lot easier. We have a very easy-to-use what we call self-service interface. So previously a lot of the things that people would have to do is hire programmers and do lots of manual programming to achieve some of the same things that they can do with our product. And we have a nice drag-and-drop. We call it digital programming interface to achieve this. And along those lines, I've been working for the last two years on ways to make that experience even easier than it already is. And because we're Cloud-based, because we have access to all of the types of problems that our customers run into, and the solutions that they solve with our product, we can now leverage that, and use it to harness machine-learning. We call this technology Iris, is what we're calling it. And so we've built out this entire meta-data framework that allows us to do data science on all of our meta-data in a very iterative and rapid fashion. And then we look for patterns, we look for historical data that we can learn from. And then what we do is we use that to train machinery and algorithms, in order to improve the customer experience in some way. When they're trying to achieve a task, specifically the first product feature that is based on the Iris technology is called the Integration Assistant. And the Integration Assistant is a very practical tool that is involved in the process of actually building out these pipelines. We call, when you build a pipeline it consists of these things called snaps, right? Snaps encapsulate functionality and then you can connect these snaps together. Now, it's often challenging when you have a problem to figure, OK, it's like a puzzle what snaps do I put together, and when do I put them together? Well, now that we've been doing this for a little while and we have quite a few customers with quite a few pipelines, we have a lot of knowledge about how people have solved those puzzles in the past. So, what we've done with Iris, is we've learned from all of those past solutions and now we give you automatic suggestions on where you might want to head next. And, we're getting pretty good accuracy for what we're predicting. So, we're basically, and this integration system is, a recommendation engine for connecting snaps into your pipelines as they're developing. So it's a real-time assistant. >> Jeff: So if I'm getting this right, it's really the intelligence of the crowd and the fact that you have so many customers that are executing many of the similar, same processes that you use as the basis to start to build the machine-learning to learn the best practices to make suggestions as people are going through this on their own. >> Greg: That's absolutely right. And furthermore, not only can we generalize from all of our customers to help new customers take advantage of this past knowledge, but what we can also do is tailor the suggestions for specific companies. So as you, as a company, as you start to build out more solutions that are specific to your problems, your different integration problems... >> Jeff: Right. >> The algorithms can now be, can learn from those specific things. So we both generalize and then we also make the work that you're doing easier within your company. >> And what's the specific impact? Are there any samples, stories you can share of what is the result of this type of activity? >> Greg: We're just, we're releasing it in May. >> Jeff: Oh OK. >> So it's going to be generally available to customers. >> Couple weeks still. >> Greg: Yeah. So... So... And... So... So we've done internal tests, so we've dove both through sort of the data science, so the experimentation to see, to feed it and get the feedback around how accurately it works. But we've also done user studies and what the user studies, not only did the science show but the user studies show that it can improve the time to completion of these pipelines, as you're building them. >> Lisa: So talk to us a little bit about who your target audience is. We're AWS, as we said. They really started 10 years ago in the start of space and have grown tremendous at getting to enterprise. Who is the target audience for SnapLogic that you're going after to help them really significantly improve their infrastructure get to the cloud, and beyond? >> Greg: So, so, so basically, we work with, largely with IT organizations within enterprises, who are, you know, larger companies are tasked with having sort of a common fabric for connecting, you know, which in an organization is lots of different databases for different purposes, ERP systems, you know, now, increasingly, lots of cloud applications and that's where part of our target is, we work with a lot of companies that still have policies where of course their data must be behind their firewall and maybe even on their premise, so our technology, while we're... we're hosted and run in the cloud, and we get the advantage of the SAS, a SAS platform, we also have the ability to run behind a firewall, and execute these data pipelines in the security domains of the customers themselves. So, they get the advantage of SAS, they get the advantage of things like Iris, and the Integration Assistant, right, because we can leverage all of the knowledge, but they get to adhere to any, you know, any regulatory or security policies that they have. And we don't have to see their data or touch their data. >> Lisa: So helping a customer that was, you know, using a service-oriented architecture or an ETL, modernize their infrastructure? >> Greg: Oh it's completely about modernization. Yeah, I mean, we, you know, our CEO, Gaurav Dhillon has been in the space for a while. He was formerly the CEO of Informatica. And so he has a lot of experience. And when he set out to start SnapLogic he wanted to look, you know, embrace the technologies of the time, right? So we're web-focused, right? We're HTTP and REST and JSON data. And we've centered the core technologies around these modern principles. So that makes us work very well with all the modern applications that you see today. >> Jeff: Look Greg, I want to shift gears a little bit. >> Greg: Yeah. >> You're also a professor. >> Greg: Correct. >> At University of San Francisco and UC Davis. I'd just love to get your perspective from the academic side of the house on what's happening at schools, around this new opportunity with big data, machine-learning, and AI and how that world is kind of changing? And then you are sitting in this great position where you kind of cross-over both... How does that really benefit, you know, to have some of that fresh, young blood, and learning, and then really take that back over, back into the other side of the house? >> Greg: Yeah, so a couple of things. Yeah, professor at University of San Francisco for 19 years. I did my PhD at UC Davis in computer science. And... My background is research in operating systems, parallel and distributed computing, in recent years, big data frameworks, big data processing. And University of San Francisco, itself, we have a, what we call the Senior and Masters Project Programs. Where, we've been doing this for, ever since I've been at USF, where what we do is we partner groups of students with outside sponsors, who are looking for opportunities to explore a research area. Maybe one that they can't allocate, you know, they can't justify allocating funds for, because it's a little bit outside of the main product, right? And so... It's a great win, 'cause our students get experience with a San Francisco, Silicon Valley company, right? So it helps their resume. It enhances their university experience, right? And because, you know, a lot of research happens in academia and computer science but a lot of research is also happening in industry, which is a really fascinating thing, if you look at what has come out of some of the bigger companies around here. And we feel like we're doing the same thing at SnapLogic and at the University of San Francisco. So just to kind of close that loop, students are great because they're not constrained by, maybe, some of us who have been in the industry for a while, about maybe what is possible and what's no so possible. And it's great to have somebody come and look at a problem and say, "You know, I think we could approach this differently." And, in fact, really, the impetus for the Integration Assistant came out of one of these projects where I pitched to our students, and I said "OK, we're going to explore SnapLogic meta-data and we're going to look at ways we can leverage machine-learning in the product on this data." But I left it kind of vague, kind of open. This fantastic student of mine from Thailand, his name is Jump, he kind of, he spent some time looking at the data and he actually said, "You know I'm seeing some patterns here. I'm seeing that, you know, we've got this great repository of these," like I described, "of these solved puzzles. And I think we could use that to train some algorithms." And so we spent, in the project phase, as part of his coursework, he worked on this technology. Then we demoed it at the company. The company said, "Wow, this is great technology. Let's put this into production." And then, there was kind of this transition from sort of this more academic, sort of experimental project into, going with engineers and making it a real feature. >> Lisa: What a great opportunity though, not just for the student to get more real-world applicability, like you're saying, taking it from that very experimental, investigational, academic approach and seeing all of the components within a business, that student probably gets so much more out of just an experiment. But your other point is very valid of having that younger talent that maybe doesn't have a lot of the biases and the pre-conceived notions that those of us that have been in the industry for a while. That's a great pipeline, no pun intended... >> Greg: Sure. >> For SnapLogic, is that something that you helped bring into the company by nature of being a professor? Just sort of a nice by-product? >> Well, so a couple of things there. One is that, like I said, University of San Francisco we were running this project class for a while, and... I got involved, you know, I had been at USF for a long time before I got involved with SnapLogic. I was introduced to Gaurav and there was this opportunity. And initially, right, initially, I was looking to apply some of my research to the technology, their product and their technology. But then it became clear that hey, you know we have this infrastructure in place at the university, they go through the academic training, our students are, it's a very rigorous program, back to your point about what they are exposed to, we have, you know, we're very modern, around big data, machine-learning, and then all of the core computer science that you would expect from a program. And so, yeah, it's been... It's been a great mutually beneficial relationship with SnapLogic and the students. But many other companies also come and pitch projects and those students also do similar types of projects at other companies. I would like to say that I started it at USF but I didn't. It was in existence. But I helped carry it forward. >> Jeff: That's great. >> Lisa: That is fantastic. >> And even before we got started, I mean you said your kind of attitude was to be the iPhone in this space. >> Greg: Of integration, yeah. >> Jeff: So again, taking a very different approach a really modern approach, to the expected behavior of things is very different. And you know, the consumerization of IT in terms of the expected behavior of how we interact with stuff has been such a powerful driver in the development of all these different applications. It's pretty amazing. >> Greg: And I think, you know, just like maybe, now you couldn't imagine most sort-of consumer-facing products not having a mobile application of some sort, increasingly what you're seeing is applications will require machine-learning, right, will require some amount of augmented intelligence. And I would go as far to say that the technology that we're doing at SnapLogic with self-service integration is also going to be a requirement. That, you just can't think of self-service integration without having it powered by a machine-learning framework helping you, right? It almost, like, in a few years we won't imagine it any other way. >> Lisa: And I like the analogy that Jeff, you just brought up, Greg, the being the iPhone of data integration. The simplicity message, something that was very prevalent today at the keynote, about making things simpler, faster, enabling more. And it sounds like that's what you're leveraging computer science to do. So, Greg Benson, Chief Scientist at SnapLogic. Thank you so much for being on theCUBE, you're now CUBE alumni, so that's fantastic. >> Alright. >> Lisa: We appreciate you being here and we appreciate you watching. For my co-host Jeff Rick, I'm Lisa Martin, again we are live from the AWS Summit in San Francisco. Stick around, we'll be right back. (upbeat music)

>> Narrator: From San Jose, in the heart of Silicon Valley, it's The Cube, covering Big Data SV 2016. Now your hosts, John Furrier and Peter Burris. >> Okay, welcome back everyone. We are here live in Silicon Valley for Big Data Week, Big Data SV Strata Hadoop. This is The Cube, SiliconANGLE's flagship program. We go out to the events and extract the signals from the noise. I'm John Furrier, my co-host is Peter Burris. Our next guest is Ritika Gunnar, VP of Data and Analytics at IBM and David Richards is the CEO of WANdisco. Welcome to The Cube, welcome back. >> Thank you. >> It's a pleasure to be here. >> So, okay, IBM and WANdisco, why are you guys here? What are you guys talking about? Obviously, partnership. What's the story? >> So, you know what WANdisco does, right? Data replication, active-active replication of data. For the past twelve months, we've been realigning our products to a market that we could see rapidly evolving. So if you had asked me twelve months ago what we did, we were talking about replicating just Hadoop, but we think the market is going to be a lot more than that. I think Mike Olson famously said that this Hadoop was going to disappear and he was kind of right because the ecosystem is evolving to be a much greater stack that involves applications, cloud, completely heterogeneous storage environment, and as that happens the partnerships that we would need have to move on from just being, you know, the sort of Hadoop-specific distribution vendors to actually something that can deliver a complete solution to the marketplace. And very clearly, IBM has a massive advantage in the number of people, the services, ecosystem, infrastructure, in order to deliver a complete solution to customers, so that's really why we're here. >> If you could talk about the stack comment, because this is something that we're seeing. Mike Olson's kind of being political when he says make it invisible, but the reality is there is more to big data than Hadoop. There's a lot of other stuff going on. Call it stack, call it ecosystem. A lot of great things are growing, we just had Gaurav on from SnapLogic said, "everyone's winning." I mean, I just love that's totally true, but it's not just Hadoop. >> It's about Alldata and it's about all insight on that data. So when you think about Alldata, Alldata is a very powerful thing. If you look at what clients have been trying to do thus far, they've actually been confined to the data that may be in their operational systems. With the advent of Hadoop, they're starting to bring in some structured and unstructured data, but with the advent of IOT systems, systems of engagement, systems of records and trying to make sense of all of that, Alldata is a pretty powerful thing. When I think of Alldata, I think of three things. I think of data that is not only on premises, which is where a lot of data resides today, but data that's in the cloud, where data is being generated today and where a majority of the growth is. When I think of Alldata, I think of structured data, that is in your traditional operational systems, unstructured and semi-structured data from IOT systems et cetera, and when I think of Alldata, I think of not just data that's on premises for a lot of our clients, but actually external data. Data where we can correlate data with, for example, an acquisition that we just did within IBM with The Weather Company or augmenting with partnerships like Twitter, et cetera, to be able to extract insight from not just the data that resides within the walls of your organization, but external data as well. >> The old expression is if you want to go fast, do it alone, if you want to go deeper and broader and more comprehensive, do it as a team. >> That's right. >> That expression can be applied to data. And you look at The Weather data, you think, hmmm, that's an outlier type acquisition, but when you think about the diversity of data, that becomes a really big deal. And the question I want to ask you guys is, and Ritika, we'll start with you, there's always a few pressure points we've seen in big data. When that pressure is relieved, you've seen growth, and one was big data analytics kind of stalled a little bit, the winds kind of shifted, eye of the storm, whatever you want to call it, then cloud comes in. Cloud is kind of enabling that to go faster. Now, a new pressure point that we're seeing is go faster with digital transformation. So Alldata kind of brings us to all digital. And I know IBM is all about digitizing everything and that's kind of the vision. So you now have the pressure of I want all digital, I need data driven at the center of it, and I've got the cloud resource, so kind of the perfect storm. What's your thoughts on that? Do you see that similar picture? And then does that put the pressure on, say, WANdisco, say hey, I need replication, so now you're under the hood? Is that kind of where this is coming together? >> Absolutely. When I think about it, it's about giving trusted data and insights to everyone within the organization, at the speed in which they need it. So when you think about that last comment of, "At the speed in which they need it," that is the pressure point of what it means to have a digitally transformed business. That means being able to make insights and decisions immediately and when we look at what our objective is from an IBM perspective, it's to be able to enable our clients to be able to generate those immediate insights, to be able to transform their business models and to be able to provide the tooling and the skills necessary, whether we have it organically, inorganically, or through partnerships, like with WANdisco to be able to do that. And so with WANdisco, we believe we really wanted to be able to activate where that data resides. When I talk about Alldata and activation of that data, WANdisco provided to us complementary capabilities to be able to activate that data where it resides with a lot of the capabilities that they're providing through their fusion. So, being able to have and enable our end-users to have that digitally infused set of reactive type of applications is absolutely something... >> It's like David, we talk about, and maybe I'm oversimplifying your value proposition, but I always look at WANdisco as kind of the five nines of data, right? You guys make stuff work, and that's the theme here this year, people just want it to work, right? They don't want to have it down, right? >> Yeah, we're seeing, certainly, an uptick in understanding about what high availability, what continuous availability means in the context of Hadoop, and I'm sure we'll be announcing some pretty big deals moving forward. But we've only just got going with IBM. I would, the market should expect a number of announcements moving forward as we get going with this, but here's the very interesting question associated with cloud. And just to give you a couple of quick examples, we are seeing an increasing number of Global 1,000 companies, Fortune 100 companies move to cloud. And that's really important. If you would have asked me 12 months ago, how is the market going to shape up, I'd have said, well, most CIO's want to move to cloud. It's already happening. So, FINRA, the major financial regulator in the United States is moving to cloud, publicly announced it. The FCA in the UK publicly announced they are moving 100% to cloud. So this creates kind of a microcosm of a problem that we solve, which is how do you move transactional data from on-premise to cloud and create a sort of hybrid environment. Because with the migration, you have to build a hybrid cloud in order to do that anyway. So, if it's just archive systems, you can package it on a disk drive and post it, right? If we're talking about transactional data, i.e, stuff that you want to use, so for example, a big travel company can't stop booking flights while they move their data into the cloud, right? They would take six months to move petabyte scale data into cloud. We solve that problem. We enable companies to move transactional data from on-premise into cloud, without any interruption to services. >> So not six months? >> No, not six months. >> Six hours? >> And you can keep on using the data while it is in transit. So we've been looking for a really simplistic problem, right, to explain this really complex algorithm that we've got that you know does this active-active replication stuff. That's it, right? It's so simple, and nobody else can do it. >> So no downtime, no disruption to their business? >> No, and you can use the cloud or you can use the on-prem applications while the data is in transit. >> So when you say all cloud, now we're on a theme, Alldata, all digital, all cloud, there's a nuance there because most, and we had Gaurav from SnapLogic talk about it, there's always going to be an on-prem component. I mean, probably not going to see 100% everyone move to the cloud, public cloud, but cloud, you mean hybrid cloud essentially, with some on-prem component. I'm sure you guys see that with Bluemix as well, that you've got some dabbling in the public cloud, but ultimately, it's one resource pool. That's essentially what you're saying. >> Yeah, exactly. >> And I think it's really important. One of the things that's very attractive e about the WANdisco solution is that it does provide that hybridness from the on-premises to cloud and that being able to activate that data where it resides, but being able to do that in a heterogeneous fashion. Architectures are very different in the cloud than they are on premises. When you look at it, your data like may be as simple as Swift object store or as S3, and you may be using elements of Hadoop in there, but the architectures are changing. So the notion of being able to handle hybrid solutions both on-premises and cloud with the heterogeneous capability in a non-invasive way that provides continuous data is something that is not easily achieved, but it's something that every enterprise needs to take into account. >> So Ritika, talk about the why the WANdisco partnership, and specifically, what are some of the conversations you have with customers? Because, obviously there's, it sounds like, the need to go faster and have some of this replication active-active and kind of, five nines if you will, of making stuff not go down or non-disruptive operations or whatever the buzzword is, but you know, what's the motivation from your standpoint? Because IBM is very customer-centric. What are some of the conversations and then how does WANdisco fit into those conversations? >> So when you look at the top three use cases that most clients use for even Hadoop environments or just what's going on in the market today, the top three use cases are you know, can I build a logical data warehouse? Can I build areas for discovery or analytical discovery? Can I build areas to be able to have data archiving? And those top three solutions in a hybrid heterogeneous environment, you need to be able to have active-active access to the data where that data resides. And therefore, we believe, from an IBM perspective, that we want to be able to provide the best of breed regardless of where that resides. And so we believe from a WANdisco perspective, that WANdisco has those capabilities that are very complementary to what we need for that broader skills and tooling ecosystem and hence why we have formed this partnership. >> Unbelievably, in the market, we're also seeing and it feels like the Hadoop market's just got going, but we're seeing migrations from distributions like Cloudera into cloud. So you know, those sort of lab environments, the small clusters that were being set up. I know this is slightly controversial, and I'll probably get darts thrown at me by Mike Olson, but we are seeing pretty large-scale migration from those sort of labs that were set up initially. And as they progress, and as it becomes mission-critical, they're going to go to companies like IBM, really, aren't they, in order to scale up their infrastructure? They're going to move the data into cloud to get hyperscale. For some of these cases that Ritika was just talking about so we are seeing a lot of those migrations. >> So basically, Hadoop, there's some silo deployments of POC's that need to be integrated in. Is that what you're referring to? I mean, why would someone do that? They would say okay, probably integration costs, probably other solutions, data. >> If you do a roll-your-own approach, where you go and get some open-source software, you've got to go and buy servers, you've got to go and train staff. We've just seen one of our customers, a big bank, two years later get servers. Two years to get servers, to get server infrastructure. That's a pretty big barrier, a practical barrier to entry. Versus, you know, I can throw something up in Bluemix in 30 minutes. >> David, you bring up a good point, and I want to just expand on that because you have a unique history. We know each other, we go way back. You were on The Cube when, I think we first started seven years ago at Hadoop World. You've seen the evolution and heck, you had your own distribution at one point. So you know, you've successfully navigated the waters of this ecosystem and you had gray IP and then you kind of found your swim lanes and you guys are doing great, but I want to get your perspective on this because you mentioned Cloudera. You've seen how it's evolving as it goes mainstream, as you know, Peter says, "The big guys are coming in and with power." I mean, IBM's got a huge spark investment and it's not just you know, lip service, they're actually donating a ton of code and actually building stuff so, you've got an evolutionary change happening within the industry. What's your take on the upstarts like Cloudera and Hortonworks and the Dishrow game? Because that now becomes an interesting dynamic because it has to integrate well. >> I think there will always be a market for the distribution of opensource software. As that sort of, that layer in the stack, you know, certainly Cloudera, Hortonworks, et cetera, are doing a pretty decent job of providing a distribution. The Hadoop marketplace, and Ritika laid this on pretty thick as well, is not Hadoop. Hadoop is a component of it, but in cloud we talk about object store technology, we talk about Swift, we talk about S3. We talk about Spark, which can be run stand-alone, you don't necessarily need Hadoop underneath it. So the marketplace is being stretched to such a point that if you were to look at the percentage of the revenue that's generated from Hadoop, it's probably less than one percent. I talked 12 months ago with you about the whale season, the whales are coming. >> Yeah, they're here. >> And they're here right now, I mean... >> (laughs) They're mating out in the water, deals are getting done. >> I'm not going to deal with that visual right now, but you're quite right. And I love the Peter Drucker quote which is, "Strategy is a commodity, execution is an art." We're now moving into the execution phase. You need a big company in order to do that. You can't be a five hundred or a thousand person... >> Is Cloudera holding onto dogma with Hadoop or do they realize that the ecosystem is building around them? >> I think they do because they're focused on the application layer, but there's a lot of competition in the application layer. There's a little company called IBM, there's a little company called Microsoft and the little company called Amazon that are kind of focused on that as well, so that's a pretty competitive environment and your ability to execute is really determined by the size of the organization to be quite frank. >> Awesome, well, so we have Hadoop Summit coming up in Dublin. We're going to be in Ireland next month for Hadoop Summit with more and more coverage there. Guys, thanks for the insight. Congratulations on the relationship and again, WANdisco, we know you guys and know what you guys have done. This seems like a prime time for you right now. And IBM, we just covered you guys at InterConnect. Great event. Love The Weather Company data, as a weather geek, but also the Apple announcement was really significant. Having Apple up on stage with IBM, I think that is really, really compelling. And that was just not a Barney deal, that was real. And the fact that Apple was on stage was a real testament to the direction you guys are going, so congratulations. This is The Cube, bringing you all the action, here live in Silicon Valley here for Big Data Week, BigData SV, and Strata Hadoop. We'll be right back with more after this short break.