>> Announcer: From San Mateo, California, it's theCUBE, covering SnapLogic Innovation Day 2018. Brought to you by SnapLogic. >> Welcome back everybody, Jeff Frick here with theCUBE. We're at the crossroads, it's 101 and 92 in San Mateo, California. Lot of software companies have developed here. It's got a long history, at one point it was really kind of the all the software in Silicon Valley was based here versus chips in the south new media in the north. It's not quite the same anymore, that's really the roots of the area, you're probably stuck in traffic if your here, so look up, you'll see the SnapLogic sign, that's where we are, at their new headquarters. And we're excited to have practitioner, we love getting customers on, it's Omar Nawaz, he's the global head of digital transformation and a CISO, so not a small responsibility at Quantum. Great to see you. >> Well thank you for inviting me, I'm happy to be here. >> Absolutely. So you are one of these, could be the new unicorn, the head of digital transformation. So you were brought in for that role, you've been at the company a little over six months, less than a year. Why did they bring you in and where do you get started? >> Well, it's a very interesting role. Digital transformation is about change and we all know that that's hard, and that's why I specifically brought into the company, to help change the operating model and the business model for the company. So what I really do there is work with the leadership of the company and understand what their ambitions are. And then the exciting part starts, where my team and I actually help convert an ambition into reality. And so that we can create a measurable way to understand the reality we are creating for the ambition that we want to achieve is it really meaningful for us or not. >> And who do you report to? Who brought you in? >> So I actually report to the CFO of the company which >> CFO >> So you see the sort of different places where these roles fit in, but in our organization it made a lot of sense because as we're going through the transformation, it was important for us to sort of be close to the money, because it is investment required and you want to manage the cost as well, so that's where I'm at. >> And it's also very interesting that you're a CISO as well, Chief Information Security Officer, for those not following me on the acronym world. So security is a really important piece that is not an insignificant job, so how much of your time is transformation and how much of your time is CISO. >> I think most of my time is to transformation and it's part of when we look at security, we look at security as part of the transformation because as we evolve the company to a new model, it has ramification on how do we secure the new environment as well, so there's a split, I have more than one full time job, I guess you can say that. >> Welcome to Silicon Valley right? >> But yeah, I spend most of my time focused around digital transformation but security is a very important aspect of my role and we want to make sure the environment continues to be safe. >> So there's somebody out here watching this video, they're sitting in their office they just got the edict that they're now in charge of digital transformation at their company and they're pulling their hair out looking for CUBE interviews to help them out. So where do they go, how do they get started, what sort of resources should they be asking for, should they be leveraging, should they expect to give them some sort of success in this very very difficult role? >> So I think there's a lot of places where companies can start and I think of the things you have to understand is how digitally mature you as a company are. One of the key things in this industry is that we all see is that the speed and the rate of innovation is so tremendous and we see these waves of disruptive technology that comes in and there are companies that are adopting and embracing those technologies. And think about mobile or cloud or analytics or social, and those companies that adopt those technologies they can gain a certain level of proficiency and performance improvement, but the cycle is very very fast and now we are seeing yet another wave of technology innovation around IOT, API, artificial intelligence and so if you can quickly jump to that next round of technology and innovation then you can continue to build those efficiencies within the company and gain that competitive advantage or maintain that competitive advantage, and I think it's important for the companies to realize that they have to engage in this very very quickly and it's not a one time process either, it's never going to end, the transformation is never going to end, so you have to continually invest in it and where you start with it and where you go is to make sure that you understand where the company wants to go. >> Right. >> And how the technology can help you get there. That's sort of the hardest part of my job is to really convince the leadership and say this is where we will gain some significant benefit and so when I go to my CEO or CFO or the Board what I'm trying to help them understand is that by investing in technology A, B, C, whichever it is, this is what we achieve or this is sort of the picture, part of the puzzle we're trying to build. >> I love this concept, digital maturity, I've never heard anyone say that before, so it almost begs the question, is there some type of a checklist that you have to have made a minimum, either acknowledgement, I don't know if commitment is the right word, obviously you have to be 100 percent on cloud, but it does beg, is there some sort of, have you adopted some cloud, have you adopted some of this, some of that, some of this, to demonstrate A, that you're digitally mature or you're heading in that direction, and B, these are kind of necessary conditions to execute the digital transformation that I'm trying to put in place. >> Yeah, I don't have a specific measuring stick of where you measure your digital maturity but the things that you talked about, for example, if your organization is still dealing with sort of maintaining some of their own data centers and you're investing resources to that, you have not adopted cloud, mobile applications, you know your applications cannot be accessed remotely, then you're certainly not very digitally mature. Right. How much self service is available for your users internally or for your customers. Those are other signs of digital immaturity, another area to look at is, you know, you have a lot of data within the organization. How are you using that data? Is the data sitting in silos? Or is the data being integrated and now you can, you have analytics running on top of it. That's another measure of your maturity and as you look across the companies, you will see that there are companies who are sitting there in sort of that old traditional model of we're going to build these long term strategic plans and that's also a sign of accepting or adopting these technologies because they're hoping, they're waiting to really fully understand what the technology is going to be when they get there and they need to know all of those how and what it will look like when they get there and I think also to me that's also a sign of digital maturity of a company is do they understand what waves of disruption or technology is coming out. >> Right. So it's interesting, you said that you're biggest challenge is going to the Board and and the C suite and telling them how this is going to work. The other hand, they brought you in, not that long ago, with this very specific objective, so clearly you've got some great executive support. So how do you convince them and what are some of the things that you found just work, what are the right stories, what are the right examples, what are the right use cases, that even the digitally immature, finally are like ah now I get it. >> Yeah, so, I mean it helped that they were already thinking about it before they brought me in so that helps a lot, no doubt, I think the things that when I came in and I looked at the company, so there's many places where you can start, some of the areas you can think about is how do you improve the customer service, that's a very important aspect of how you become a better organization. So another area is process improvement and the third area is business model improvement, so I came in and I talked more about before we actually start looking at modifying or enhancing our business models, we need to get to a better, higher performance level within the organization and therefore I'm initially more focused on how do we improve our processes internally, right, and for us, based on our situation, and it varies for different companies, for us the first step in that was really to make sure that the people, systems, and the data are more interconnected. So even within that first step for me for the first phase for us was really to make sure that the people are connected, so do we have the right set of collaboration and communication tools, right, do we have the right set of analytics to sit on top of it, so we just finished that phase, we want to make sure that these are tangible, small steps, because you need to show some wins very very quickly so for us the first step was lets get the people connected. So we just did that, now the next step for us is to get our systems connected. So again, as I mentioned earlier, there is a lot of data that's sitting there, it has to be integrated. There's tremendous value that you can gain from that. So that's what we're getting into, this is our second phase of how do we connect the data together so this way we can start to get the next level of efficiency out of the company. >> So I am guessing after sitting here all day that the integration of your data, obviously we are at SnapLogic, is going to be easier than getting the people to change their processes and the connected people. What were some of the tricks to get people to adopt these new tools before we even start talking about the data? >> So I think there is, you have to show them the value obviously, if you talk about communication and collaboration tools I think the first thing is really about awareness. Right, there's a little bit of sort of top down, sort of mandate, or you may want to call sponsorship, that I think that that helps. >> Or stick >> Or stick, you know, so that helps. Because for some companies and for Quantum it was true that we did not have a corporate communication tool. There were multiple, right, so within the groups they were fine because they were able to communicate but between groups they were not able to, so we had to standardize on that, so I think that you kind of have to show these, there's always skepticism, because everything when people are used to certain things it seems to work for them right? >> I've always done it this way. >> Exactly right, so you have to show them new things and you have to create the awareness and then they start to see the value. It's not a one time thing, it's continuous effort, so we do lunch and learns, we do webinars, we do support sessions and things like this so this way people are more comfortable taking on the new technology. >> But it's so important right because your probability of success if you don't get the buy in from the participant is not very high, so the fact that you started there on the people before you really dove into the technology I think is pretty insightful and will probably increase your probability of success on the next phase tremendously, versus if you just integrated all the data and integrated all the apps and you still don't have people talking together, probably not going to be very successful. >> Exactly, because the data is in all these different business units and different groups and if they're not talking to each other, connecting the data has little or no value. So to me it's really about creating that connectivity so for us when you ask me, sort of, how do we start, so we start with connecting, connection is the first sort of phase of it and then the second is to empower people you know to create more self service and create more sort of autonomous units so that they can start to create value for themselves and for the company. So it's really about enabling the whole organization, sort of the ground swell type of approach, but you're going to first sort of bring the people to that sort of common place where it's easy for them to work, you bring the data along with it and then you standardize the environment or simplify it if you can and therefore it's easy for them to start taking on the services themselves. >> Right, so you finished the first phase and now the next phase is you're going to start integrating all the systems. >> Correct. >> So obviously, we're sitting here at SnapLogic, it's a big piece of what they do, so why did you decide to go with them and how are they helping you in this process? >> So for us, for this phase of digital transformation, you know there were two things that were really really important for us. One was really about how do we connect these systems together in a simple standardized way, so that was one criteria for us. And I believe SnapLogic does a great job and we're going to build it out at sort of the back core of our network. And then the second piece was really can we take this platform and make it available to our end users. So that they can create the connections or access the data that they want, right, and that's again where SnapLogic was able to demonstrate that this is very easy for them to use. So those were the two sort of very pivotal things for us as part of this phase of our digital transformation as to why we picked SnapLogic. >> Yeah it was funny 'cause you used the word self-service in your first phase so I think kind of this thing where your over and over and over it's so important to drive innovation in big companies is demarketerization demarketerization of the data, demarketerization of the tools and then let people find out things and then actually be able to execute. >> Exactly, because you know IT, there's a constant pressure on IT to cut costs, you know, so we cannot serve the whole company for all the things that needs to happen and the technology and the business is changing at such a rapid pace that unless we have experts who really understand that business unit function that well we are not the best people to build those things for them, they are the ones, but then you have a technology learning barrier or learning curve of do you need to put developers in there, so that's why to us this SnapLogic technology helps us that we believe that we can extend this ability to those users who really know their business, they can make the changes as they come, and the IT can help make sure that the right sort of infrastructure exists and the right sort of, level of connectivity exists. >> So I'm just curious, I know you're still early days in this project, but are there any Luddites that have kind of come around since you've been on this journey that suddenly just woke up and said oh okay now I get it now I see the value, now I kind of understand where we're trying to go, who maybe didn't think that way at the beginning. Or they all just know that they got to go. (laughs) >> No I think we are constantly learning along the way, I think that one of the key things that we learned just recently and SnapLogic is going to help us with that particular aspect of it is that we saw that there were a lot of systems that work fine, we don't use them, it's not a daily use type of thing, they get used quarterly, or annually, but we realized that if we can just bring more automation into those processes and we can tie it back to longer more historical data, then we can build more insights around it, so I think that when we show this to the users and especially the CFO now you all of a sudden sort of the lightbulbs go on and it's like oh this is great. Right, that I don't have to rely on only a small window of information, now I have a much broader window. >> Alright then, Omar thank you for spending a few minutes with us and sharing your story with us. I wish you nothing but success on this. >> Thank you very much. >> I'm sure it will be long and exciting with twists and turns and highs and lows. So good luck. >> We're looking forward to that. >> Alright, he's Omar, I'm Jeff Frick. We're at SnapLogic in San Mateo, California. Thanks for watching. (bright music)

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

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

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

>> Announcer: From San Mateo, California, it's theCUBE! Covering SnapLogic, Innovation Day, 2018. Brought to you by SnapLogic. >> Hey welcome back everybody, Jeff Frick here with theCUBE. We are in San Mateo, at what they call the crossroads, it's 92 and 101. If you're coming by and probably sitting in a traffic, look up and you'll see SnapLogic. It's their new offices. We're really excited to be here for Innovation Day. We're excited to have this CTO, James Markarian. James, great to see you and I guess, we we last talked was a couple years ago in New York City. >> Yeah that's right, and why was I there? It was like a big data show. >> That's right. >> And we we are two years later talking about big data. >> Big data, big data is fading a little bit, because now big data is really an engine, that's powering this new thing that's so exciting, which is all about analytics, and machine learning, and we're going to eventually stop saying artificial intelligence and say augmented intelligence, 'cause there's really nothing artificial about it. >> Yeah and we might stop saying big data and just talk about data because it's becoming so ubiquitous. >> Jeff: Right. >> I know that big data, it's not necessarily going away but it's sort of how we're thinking about handling it is, like kind of evolved over time, especially in the last couple of years. >> Right. >> That's what we're kind of seeing from our customers. >> 'Cause there's kind of an ingredient now, right? It's no longer this new shiny object now. It's just part of the infrastructure that helps you get everything else done. >> Yeah, and I think when you think about it, from like, an enterprise point of view, that that shift is going from experimentation to operationalizing. I think that the things you look for in experimentation, there's like, one set of things here looking for proving out the overall value, regardless maybe of cost and uptime and other things and as you operationalize you start thinking about other considerations that obviously Enterprise IT has to think about. >> Right, so if you think back to like, Hadoop Summit and Hadoop World who were first cracking their teeth, like in 2010 or around that time frame, one of the big discussions that always comes up and that was before kind of the rise of public cloud, you know which has really taken off over the last several years, there's this kind of ongoing debate between, do you move the data to the compute or do you move the compute to the data? There was always like, this monster data gravity issue which was almost insurmountable and many would say, oh, you're never going to get all your data into the cloud. It's just way too hard and way too expensive. But, now Amazon has Snowball and Snowball isn't big enough. They actually had a diesel truck that'll come and help you come move your data. Amazon rolled that thing across the stage a couple of years ago. The data gravity thing seems to be less and if you think of a world with infinite compute, infinite stored, infinite networking asyndetically approaching zero, not necessarily good news for some vendors out there but that's a world that we're eventually getting to that changes the way that you organize all this stuff. >> Yeah, I think so and so much has changed. I was fortunate to be one of the early speakers, like I used to do Worlds and everything, and I was adamantly proclaiming you know, the destiny of Hadoop as bright and shiny and there's this question about what really happened. I think that there's a kind of a few different variables that kind of shifted at the same time. One, is of course, this like glut of computing in the cloud happened and there are so many variables moving at once. It's like, How much time do you have Jeff? >> Ask them to get a couple more drinks for us. >> Seeing our lovely new headquarters here and one of the things is that there is no big data center. We have a little closet with some of the servers we keep around but mostly, everything we do is on Amazon. You're even looking at things like, commercial real estate is changing because I don't need all the cooling and the power and the space for my data center that I once had. >> Jeff: Right, right. >> I become a lot more space efficient than I used to be and so the cloud is really kind of changing everything. On the data side, you mention this like, interesting philosophical shift, going from I couldn't possibly do it in the cloud to why in the world would we not do things in the cloud. Maybe the one stall word in there being some fears about security. Obviously there's been a lot of breaches. I think that there's still a lot of introspection everyone needs to do about, are my on premise systems actually more secure than some of these cloud providers? It's really not clear that we know the answer to that. In fact, we suspect that some of the cloud providers are actually more secure because they are professionals about it and they have the best practice. >> And a whole lot of money. >> The other thing that happened that you didn't mention, that's approaching infinity and we're not quite there yet, is interconnect speeds. So it used to be the case that I have a bunch of mainframes and I have a tier rating system and I have a high speed interconnect that puts the two together. Now with fiber networks and just in general, you can run super high speed, like WAN. Especially if you don't care quite as much about latency. So if 500 millisecond latency is still okay with you. >> Great. >> You can do a heck of a lot and move a lot to the cloud. In fact, it's so good, that we went from worrying, could I do this in the cloud at all to well, why wouldn't I do somethings in Amazon and some things in Microsoft and some things in Google? Even if it meant replicating my data across all these environments. The backdrop for some of that is, we had a lot of customers and I was thinking that people would approach it this way, they would install on premise Hadoop, whether it's like Apache or Cloud Air or the other vendors and I would hire a bunch of folks that are the administrators and retire terra data and I'm going to put all my ETL jobs on there, etc. It turned out to be a great theory and the practice is real for some folks but it turned out to be moving a lot of things to kind of shifting sands because Hadoop was evolving at the time. A lot of customers were putting a lot of pressure on it, operational pressure. Again, moving from experimentation phase over to like, operational phase. >> Jeff: Right, right. >> When you don't have the uptime guarantee and I can't just hire somebody off the street to administer this, it has to be a very sharp, knowledgeable person that's very expensive, people start saying, what am I really getting from this and can I just dump it all in S3 and apply a bunch of technology there and let Amazon worry about keeping this thing up and running? People start to say, I used to reject that idea and now it's sounding like a very smart idea. >> It's so funny we talk about people processing tech all the time, right? But they call them tech shows, they don't call them people in process shows. >> Right. >> At least not the ones we go to but time and time again I remember talking to some people about the Hadoop situation and there's just like, no Hadoop people. Sometimes technology all day long. There just aren't enough people with the skills to actually implement it. It's probably changed now but I remember that was such a big problem. It's funny you talk about security and cloud security. You know, at AWS, on Tuesday night of Reinvent, they have a special, kind of a technical keynote speak and like, James Hamilton would go. In the amount of resources, and I just remember one talk he gave just on their cabling across the ocean, and the amount of resources that he can bring to bear, relative to any individual company, is so different; much less a mid-tier company or a small company. I mean, you can bring so much more resources, expertise and knowledge. >> Yeah, the economy is a scale, their just there. >> They're just crazy. >> That's right and that why you know, you sort of assume that the cloud sort of, eventually eats everything. >> Right, right. >> So there's no reason to believe this won't be one of those cases. >> So you guys are getting Extreme. So what is Snaplogic Extreme? >> Well, Snaplogic Extreme is kind of like a response to this trend of data moving from on premise to the cloud and there are some interesting dynamics of that movement. First of all, you need to get data into the cloud, first of all and we've been doing that for years. Connect to everything, dump it in S3, ADLS, etc. No problem. The thing we're seeing with cloud computing is like, there's another interesting shift. Not only is it kind of like mess for less, and let Amazon manage all this, and I probably refer to Amazon more than other vendors would appreciate. >> Right, right. They're the leaders so let's call a spade a spade. >> Yeah. >> Certainly Google and Microsoft are out there as well so those are the top three and we've acknowledged that. >> One of the interesting things about it is that you couldn't really adequately achieve on premises is the burstiness of your compute. I run at a steady state where I need, you know, 10 servers or a 100 servers, but every once in a while, I need like, 1,000 or 10,000 servers to apply to something. So what's the on premise model? Rack and stack, 10,000 machines, and it's like waiting for the great pumpkin, waiting for that workload to come that I've been waiting months and months for and maybe it never comes but I've been paying for it. I paid for a software license for the thing that I need to run there. I'm paying for the cabling and the racking and everything and the person administering. Make sure the disks are all operating in the case where it gets used. Now, all of a sudden, we are taking Amazon and they're saying, hey, pay us for what you're using. You can use reserved pricing and pay a lower rate for the things you might actually care about on a consistent basis but then I'm going to allow you to spike, and I'll just run the meter. So this has caused software vendors like us, to look at the way we charge and the way that we deploy our resources and say, hey, that's a very good model. We want to follow that and so we introduced Snaplogic Extreme, which has a few different components. Basically, it enables us to operate in these elastic environments, shift our thinking in pricing so that we don't think about like, node based or god forbid, core based pricing and say like, hey, basically pay us for what you do with your data and don't worry about how many servers it's running on. Let Snaplogic worry about spinning up and spinning down these machines because a lot of these workloads are data integration or application workloads that we know lots about. >> Right. >> So first of all, we manage these ephemeral, what we call ephemeral or elastic clusters. Second of all, the way that we distribute our workload is by generating Spark code currently. We use the same graphic environment that you use for everything but instead of running on our engines, we kind of spit out Spark code on the end that takes advantage of the massive scale out potential for these ephemeral environments. >> Right. >> We've also kind of built this in such a way that it's Spark today but it could be like, Native or some other engine like Flank or other things that come up. We really don't care like what back end engine actually is as long as it can run certain types of data oriented jobs. It's actually like lots of things in one. We combine out data acquisition and distribution capability with this like, massive elastic scale out capability. >> Yeah, it's unbelievable how you can spin that up and then of course, most people forget you need to spin it down after the event. >> James: Yeah, that's right. >> We talked to a great vendor who talked about, you know, my customer spends no money with me on the weekend, zero. >> James: Right. >> And I'm thrilled because they're not using me. When they do use me, then they're buying stuff. I think what's really interesting is how that changes. Also, your relationship with your customer. If you have a recurring revenue model, you have to continue to deliver a value. You have to stay close to your customer. You have to stay engaged because it's not a one time pop and then you send them the 15% or 20% maintenance bill. It's really this ongoing relationship and they're actually gaining value from your products each and every time you use that. It's a very different way. >> Yeah, that's right. I think it creates better relationships because you feel like, what we do is unproportionate to what they do and vise versa, so it has this fundamental fairness about it, if you will. >> Right, it's a good relationship but I want to go down another path before you turn the cameras on. Talk a little bit about the race always between the need for compute and the compute. It used to be personified best with Microsoft and Intel until we come out with a new chip and then Microsoft OS would eat up all the extra capacity and then they'd come up with a new chip and it was an ongoing thing. You made an interesting comment that, especially in the cloud world where the scale of these things is much, much bigger, that ran a world now where the compute and the storage have kind of, outpaced the applications, if you will, and there's an opportunity for the application to catch up. Oh by the way, we have this cool new thing called machine learning and augmented intelligence. I wonder if you could, is that what's going to fill or kind of rebalance the consumption pattern? >> Yeah, it seems that way and I always think about kind of like, compute and software spiraling around each other like a helix. >> Like at one point, one is leading the other and they sort of just, one eventually surpasses the other and then you need innovation on the other side. I think for a while, like if you turn the clock way back to like, when the Pentium was introduced and everyone was like, how are we ever going to use all of the compute power. >> Windows 95, whoo! >> You know, power of like the Pentium. Do I really need to run my spreadsheets 100% faster? There's no business value whatsoever in transacting faster, or like general user interface or like graphical user interfaces or rendering web pages. Then you start seeing this new glut, often led by like researchers first. Like, software applications coming up that use all of this power because in academia you can start saying, what if I did have infinite compute? What would I do differently? You see things, you know like VR and advanced gaming, come up on the consumer side. Then I think the real answer on the business side is AI and ML. The general trend I start thinking of is something I used to talk about, back in the old days, which is conversion of like, having machines work for us instead of us working for machines. The only way we're ever going to get there is by having higher and higher intelligence on the application side so that it kind of intuits more based on what it's seen before and what it knows about you, etc., in terms of the task that needs to get done. Then there's this whole new breed of person that you need in order to wield all that power because like Hadoop, it's not just natural. You don't just have people floating around like, hey, you know, I'm going to be an Uzi expert or a yarn expert. You don't run into people everyday that's like, oh, yeah, I know neural nets well. I'm a gradient descent expert or whatever you're model is. It's really going to drive like, lots of changes I think. >> Right, well hopefully it does and especially like we were talking about earlier, you know, within core curriculums at schools and stuff. We were with Grace Hopper and Brenda Wilkerson, the new head of the Anita Borg organization, was at this Chicago public school district and they're actually starting to make CS a requirement, along with biology and and physics and chemistry and some of these other things. >> Right. >> So we do have a huge, a huge dearth of that but I want to just close out on one last concept before I let you go and you guys are way on top of this. Greg talked about what you just talked about, which is making the computers work for us versus the other way around. That's where the democratization of the power that we heard a lot about the democratization of big data and the tools and now you guys you guys are talking about the democratization of the integration, especially when you have a bunch of cloud based applications that everybody has access to and maybe, needs to stitch together a different way. But when you look at this whole concept of democratization of that power, how do you see that kind of playing out over the next several years? >> Yeah, that's a very big- >> Sorry I didn't bring you a couple of beer before I brought that up. >> Oh no, I got you covered. So it's a very big, interesting question because I think that you know, first of all, it's one of these, god knows, we can't predict with a lot of accuracy how exactly that's going to look because we're sort of juxtaposing two things. One is, part of the initial move to the cloud was the failure to properly democratize data inside the enterprise, for whatever reason, and we didn't do it. Now we have the computer resources and the central, kind of web based access to everything. Great. Now we have Cambridge Analytica and like, Facebook and people really thinking about data privacy and the fact that we want ubiquitous safe access. I think we know how to make things ubiquitous. The question is, do we know how to make it safe and fair so that the right people are using the right data and the right way? It's a little bit like, you know, there's all these cautionary tales out there like, beware of AI and robotics and everything and nobody really thinks about the danger of the data that's there. It's a much more immediate problem and yet it's sort of like the silent killer until some scandal comes up. We start thinking about these different ways we can tackle it. Obviously there's great solutions for tokenization and encryption and everything at the data level but even if you have the access to it, the question is, how do you control that wildfire that could happen as soon as the horse leaves the barn. Maybe not in it's current form, but when you look at things like Blockchain, there's been a lot of predictions about how Blockchain can be used around like, data. I think that this privacy and this curation and tracking of who has the data, who has access to it and can we control it, I think you are looking at even more like, centralized and guarded access to this private data. >> Great, interesting times. >> Yeah, yeah Jeff, for sure. >> Alright James, well thanks for taking a couple of minutes with us. I really enjoyed the conversation. >> Yeah, it's always great. Thanks for having me Jeff. >> It's James on Jeff and you're watching theCUBE We're at the Snaplogic headquarters in San Mateo, California and thanks for watching. (electronic music)

>> Announcer: 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 are in San Mateo, at what they call the crossroads, it's 92 and 101. If you're coming by and probably sitting in a traffic, look up and you'll see SnapLogic. It's their new offices. We're really excited to be here for Innovation Day. We're excited to have this CTO, James Markarian. James, great to see you and I guess, we we last talked was a couple years ago in New York City. >> Yeah that's right, and why was I there? It was like a big data show. >> That's right. >> And we we are two years later talking about big data. >> Big data, big data is fading a little bit, because now big data is really an engine, that's powering this new thing that's so exciting, which is all about analytics, and machine learning, and we're going to eventually stop saying artificial intelligence and say augmented intelligence, 'cause there's really nothing artificial about it. >> Yeah and we might stop saying big data and just talk about data because it's becoming so ubiquitous. >> Jeff: Right. >> I know that big data, it's not necessarily going away but it's sort of how we're thinking about handling it is, like kind of evolved over time, especially in the last couple of years. >> Right. >> That's what we're kind of seeing from our customers. >> 'Cause there's kind of an ingredient now, right? It's no longer this new shiny object now. It's just part of the infrastructure that helps you get everything else done. >> Yeah, and I think when you think about it, from like, an enterprise point of view, that that shift is going from experimentation to operationalizing. I think that the things you look for in experimentation, there's like, one set of things here looking for proving out the overall value, regardless maybe of cost and uptime and other things and as you operationalize you start thinking about other considerations that obviously Enterprise IT has to think about. >> Right, so if you think back to like, Hadoop Summit and Hadoop World who were first cracking their teeth, like in 2010 or around that time frame, one of the big discussions that always comes up and that was before kind of the rise of public cloud, you know which has really taken off over the last several years, there's this kind of ongoing debate between, do you move the data to the compute or do you move the compute to the data? There was always like, this monster data gravity issue which was almost insurmountable and many would say, oh, you're never going to get all your data into the cloud. It's just way too hard and way too expensive. But, now Amazon has Snowball and Snowball isn't big enough. They actually had a diesel truck that'll come and help you come move your data. Amazon rolled that thing across the stage a couple of years ago. The data gravity thing seems to be less and if you think of a world with infinite compute, infinite stored, infinite networking asyndetically approaching zero, not necessarily good news for some vendors out there but that's a world that we're eventually getting to that changes the way that you organize all this stuff. >> Yeah, I think so and so much has changed. I was fortunate to be one of the early speakers, like I used to do Worlds and everything, and I was adamantly proclaiming you know, the destiny of Hadoop as bright and shiny and there's this question about what really happened. I think that there's a kind of a few different variables that kind of shifted at the same time. One, is of course, this like glut of computing in the cloud happened and there are so many variables moving at once. It's like, How much time do you have Jeff? >> Ask them to get a couple more drinks for us. >> Seeing our lovely new headquarters here and one of the things is that there is no big data center. We have a little closet with some of the servers we keep around but mostly, everything we do is on Amazon. You're even looking at things like, commercial real estate is changing because I don't need all the cooling and the power and the space for my data center that I once had. >> Jeff: Right, right. >> I become a lot more space efficient than I used to be and so the cloud is really kind of changing everything. On the data side, you mention this like, interesting philosophical shift, going from I couldn't possibly do it in the cloud to why in the world would we not do things in the cloud. Maybe the one stall word in there being some fears about security. Obviously there's been a lot of breaches. I think that there's still a lot of introspection everyone needs to do about, are my on premise systems actually more secure than some of these cloud providers? It's really not clear that we know the answer to that. In fact, we suspect that some of the cloud providers are actually more secure because they are professionals about it and they have the best practice. >> And a whole lot of money. >> The other thing that happened that you didn't mention, that's approaching infinity and we're not quite there yet, is interconnect speeds. So it used to be the case that I have a bunch of mainframes and I have a tier rating system and I have a high speed interconnect that puts the two together. Now with fiber networks and just in general, you can run super high speed, like WAN. Especially if you don't care quite as much about latency. So if 500 millisecond latency is still okay with you. >> Great. >> You can do a heck of a lot and move a lot to the cloud. In fact, it's so good, that we went from worrying, could I do this in the cloud at all to well, why wouldn't I do somethings in Amazon and some things in Microsoft and some things in Google? Even if it meant replicating my data across all these environments. The backdrop for some of that is, we had a lot of customers and I was thinking that people would approach it this way, they would install on premise Hadoop, whether it's like Apache or Cloud Air or the other vendors and I would hire a bunch of folks that are the administrators and retire terra data and I'm going to put all my ETL jobs on there, etc. It turned out to be a great theory and the practice is real for some folks but it turned out to be moving a lot of things to kind of shifting sands because Hadoop was evolving at the time. A lot of customers were putting a lot of pressure on it, operational pressure. Again, moving from experimentation phase over to like, operational phase. >> Jeff: Right, right. >> When you don't have the uptime guarantee and I can't just hire somebody off the street to administer this, it has to be a very sharp, knowledgeable person that's very expensive, people start saying, what am I really getting from this and can I just dump it all in S3 and apply a bunch of technology there and let Amazon worry about keeping this thing up and running? People start to say, I used to reject that idea and now it's sounding like a very smart idea. >> It's so funny we talk about people processing tech all the time, right? But they call them tech shows, they don't call them people in process shows. >> Right. >> At least not the ones we go to but time and time again I remember talking to some people about the Hadoop situation and there's just like, no Hadoop people. Sometimes technology all day long. There just aren't enough people with the skills to actually implement it. It's probably changed now but I remember that was such a big problem. It's funny you talk about security and cloud security. You know, at AWS, on Tuesday night of Reinvent, they have a special, kind of a technical keynote speak and like, James Hamilton would go. In the amount of resources, and I just remember one talk he gave just on their cabling across the ocean, and the amount of resources that he can bring to bear, relative to any individual company, is so different; much less a mid-tier company or a small company. I mean, you can bring so much more resources, expertise and knowledge. >> Yeah, the economy is a scale, their just there. >> They're just crazy. >> That's right and that why you know, you sort of assume that the cloud sort of, eventually eats everything. >> Right, right. >> So there's no reason to believe this won't be one of those cases. >> So you guys are getting Extreme. So what is Snaplogic Extreme? >> Well, Snaplogic Extreme is kind of like a response to this trend of data moving from on premise to the cloud and there are some interesting dynamics of that movement. First of all, you need to get data into the cloud, first of all and we've been doing that for years. Connect to everything, dump it in S3, ADLS, etc. No problem. The thing we're seeing with cloud computing is like, there's another interesting shift. Not only is it kind of like mess for less, and let Amazon manage all this, and I probably refer to Amazon more than other vendors would appreciate. >> Right, right. They're the leaders so let's call a spade a spade. >> Yeah. >> Certainly Google and Microsoft are out there as well so those are the top three and we've acknowledged that. >> One of the interesting things about it is that you couldn't really adequately achieve on premises is the burstiness of your compute. I run at a steady state where I need, you know, 10 servers or a 100 servers, but every once in a while, I need like, 1,000 or 10,000 servers to apply to something. So what's the on premise model? Rack and stack, 10,000 machines, and it's like waiting for the great pumpkin, waiting for that workload to come that I've been waiting months and months for and maybe it never comes but I've been paying for it. I paid for a software license for the thing that I need to run there. I'm paying for the cabling and the racking and everything and the person administering. Make sure the disks are all operating in the case where it gets used. Now, all of a sudden, we are taking Amazon and they're saying, hey, pay us for what you're using. You can use reserved pricing and pay a lower rate for the things you might actually care about on a consistent basis but then I'm going to allow you to spike, and I'll just run the meter. So this has caused software vendors like us, to look at the way we charge and the way that we deploy our resources and say, hey, that's a very good model. We want to follow that and so we introduced Snaplogic Extreme, which has a few different components. Basically, it enables us to operate in these elastic environments, shift our thinking in pricing so that we don't think about like, node based or god forbid, core based pricing and say like, hey, basically pay us for what you do with your data and don't worry about how many servers it's running on. Let Snaplogic worry about spinning up and spinning down these machines because a lot of these workloads are data integration or application workloads that we know lots about. >> Right. >> So first of all, we manage these ephemeral, what we call ephemeral or elastic clusters. Second of all, the way that we distribute our workload is by generating Spark code currently. We use the same graphic environment that you use for everything but instead of running on our engines, we kind of spit out Spark code on the end that takes advantage of the massive scale out potential for these ephemeral environments. >> Right. >> We've also kind of built this in such a way that it's Spark today but it could be like, Native or some other engine like Flank or other things that come up. We really don't care like what back end engine actually is as long as it can run certain types of data oriented jobs. It's actually like lots of things in one. We combine out data acquisition and distribution capability with this like, massive elastic scale out capability. >> Yeah, it's unbelievable how you can spin that up and then of course, most people forget you need to spin it down after the event. >> James: Yeah, that's right. >> We talked to a great vendor who talked about, you know, my customer spends no money with me on the weekend, zero. >> James: Right. >> And I'm thrilled because they're not using me. When they do use me, then they're buying stuff. I think what's really interesting is how that changes. Also, your relationship with your customer. If you have a recurring revenue model, you have to continue to deliver a value. You have to stay close to your customer. You have to stay engaged because it's not a one time pop and then you send them the 15% or 20% maintenance bill. It's really this ongoing relationship and they're actually gaining value from your products each and every time you use that. It's a very different way. >> Yeah, that's right. I think it creates better relationships because you feel like, what we do is unproportionate to what they do and vise versa, so it has this fundamental fairness about it, if you will. >> Right, it's a good relationship but I want to go down another path before you turn the cameras on. Talk a little bit about the race always between the need for compute and the compute. It used to be personified best with Microsoft and Intel until we come out with a new chip and then Microsoft OS would eat up all the extra capacity and then they'd come up with a new chip and it was an ongoing thing. You made an interesting comment that, especially in the cloud world where the scale of these things is much, much bigger, that ran a world now where the compute and the storage have kind of, outpaced the applications, if you will, and there's an opportunity for the application to catch up. Oh by the way, we have this cool new thing called machine learning and augmented intelligence. I wonder if you could, is that what's going to fill or kind of rebalance the consumption pattern? >> Yeah, it seems that way and I always think about kind of like, compute and software spiraling around each other like a helix. >> Like at one point, one is leading the other and they sort of just, one eventually surpasses the other and then you need innovation on the other side. I think for a while, like if you turn the clock way back to like, when the Pentium was introduced and everyone was like, how are we ever going to use all of the compute power. >> Windows 95, whoo! >> You know, power of like the Pentium. Do I really need to run my spreadsheets 100% faster? There's no business value whatsoever in transacting faster, or like general user interface or like graphical user interfaces or rendering web pages. Then you start seeing this new glut, often led by like researchers first. Like, software applications coming up that use all of this power because in academia you can start saying, what if I did have infinite compute? What would I do differently? You see things, you know like VR and advanced gaming, come up on the consumer side. Then I think the real answer on the business side is AI and ML. The general trend I start thinking of is something I used to talk about, back in the old days, which is conversion of like, having machines work for us instead of us working for machines. The only way we're ever going to get there is by having higher and higher intelligence on the application side so that it kind of intuits more based on what it's seen before and what it knows about you, etc., in terms of the task that needs to get done. Then there's this whole new breed of person that you need in order to wield all that power because like Hadoop, it's not just natural. You don't just have people floating around like, hey, you know, I'm going to be an Uzi expert or a yarn expert. You don't run into people everyday that's like, oh, yeah, I know neural nets well. I'm a gradient descent expert or whatever you're model is. It's really going to drive like, lots of changes I think. >> Right, well hopefully it does and especially like we were talking about earlier, you know, within core curriculums at schools and stuff. We were with Grace Hopper and Brenda Wilkerson, the new head of the Anita Borg organization, was at this Chicago public school district and they're actually starting to make CS a requirement, along with biology and and physics and chemistry and some of these other things. >> Right. >> So we do have a huge, a huge dearth of that but I want to just close out on one last concept before I let you go and you guys are way on top of this. Greg talked about what you just talked about, which is making the computers work for us versus the other way around. That's where the democratization of the power that we heard a lot about the democratization of big data and the tools and now you guys you guys are talking about the democratization of the integration, especially when you have a bunch of cloud based applications that everybody has access to and maybe, needs to stitch together a different way. But when you look at this whole concept of democratization of that power, how do you see that kind of playing out over the next several years? >> Yeah, that's a very big- >> Sorry I didn't bring you a couple of beer before I brought that up. >> Oh no, I got you covered. So it's a very big, interesting question because I think that you know, first of all, it's one of these, god knows, we can't predict with a lot of accuracy how exactly that's going to look because we're sort of juxtaposing two things. One is, part of the initial move to the cloud was the failure to properly democratize data inside the enterprise, for whatever reason, and we didn't do it. Now we have the computer resources and the central, kind of web based access to everything. Great. Now we have Cambridge Analytica and like, Facebook and people really thinking about data privacy and the fact that we want ubiquitous safe access. I think we know how to make things ubiquitous. The question is, do we know how to make it safe and fair so that the right people are using the right data and the right way? It's a little bit like, you know, there's all these cautionary tales out there like, beware of AI and robotics and everything and nobody really thinks about the danger of the data that's there. It's a much more immediate problem and yet it's sort of like the silent killer until some scandal comes up. We start thinking about these different ways we can tackle it. Obviously there's great solutions for tokenization and encryption and everything at the data level but even if you have the access to it, the question is, how do you control that wildfire that could happen as soon as the horse leaves the barn. Maybe not in it's current form, but when you look at things like Blockchain, there's been a lot of predictions about how Blockchain can be used around like, data. I think that this privacy and this curation and tracking of who has the data, who has access to it and can we control it, I think you are looking at even more like, centralized and guarded access to this private data. >> Great, interesting times. >> Yeah, yeah Jeff, for sure. >> Alright James, well thanks for taking a couple of minutes with us. I really enjoyed the conversation. >> Yeah, it's always great. Thanks for having me Jeff. >> It's James on Jeff and you're watching theCUBE We're at the Snaplogic headquarters in San Mateo, California and thanks for watching. (electronic music)

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

>> Announcer: From San Mateo, California, it's theCUBE, covering SnapLogic Innovation Day 2018. Brought to you by SnapLogic. >> Welcome back everybody, Jeff Frick here with theCUBE. We're at the crossroads, it's 101 and 92 in San Mateo, California. Lot of software companies have developed here. It's got a long history, at one point it was really kind of the all the software in Silicon Valley was based here versus chips in the south new media in the north. It's not quite the same anymore, that's really the roots of the area, you're probably stuck in traffic if your here, so look up, you'll see the SnapLogic sign, that's where we are, at their new headquarters. And we're excited to have practitioner, we love getting customers on, it's Omar Nowaz, he's the global head of digital transformation and a CISO, so not a small responsibility at Quantum. Great to see you. >> Well thank you for inviting me, I'm happy to be here. >> Absolutely. So you are one of these, could be the new unicorn, the head of digital transformation. So you were brought in for that role, you've been at the company a little over six months, less than a year. Why did they bring you in and where do you get started? >> Well, it's a very interesting role. Digital transformation is about change and we all know that that's hard, and that's why I specifically brought into the company, to help change the operating model and the business model for the company. So what I really do there is work with the leadership of the company and understand what their ambitions are. And then the exciting part starts, where my team and I actually help convert an ambition into reality. And so that we can create a measurable way to understand the reality we are creating for the ambition that we want to achieve is it really meaningful for us or not. >> And who do you report to? Who brought you in? >> So I actually report to the CFO of the company which >> CFO >> So you see the sort of different places where these roles fit in, but in our organization it made a lot of sense because as we're going through the transformation, it was important for us to sort of be close to the money, because it is investment required and you want to manage the cost as well, so that's where I'm at. >> And it's also very interesting that you're a CISO as well, Chief Information Security Officer, for those not following me on the acronym world. So security is a really important piece that is not an insignificant job, so how much of your time is transformation and how much of your time is CISO. >> I think most of my time is to transformation and it's part of when we look at security, we look at security as part of the transformation because as we evolve the company to a new model, it has ramification on how do we secure the new environment as well, so there's a split, I have more than one full time job, I guess you can say that. >> Welcome to Silicon Valley right? >> But yeah, I spend most of my time focused around digital transformation but security is a very important aspect of my role and we want to make sure the environment continues to be safe. >> So there's somebody out here watching this video, they're sitting in their office they just got the edict that they're now in charge of digital transformation at their company and they're pulling their hair out looking for CUBE interviews to help them out. So where do they go, how do they get started, what sort of resources should they be asking for, should they be leveraging, should they expect to give them some sort of success in this very very difficult role? >> So I think there's a lot of places where companies can start and I think of the things you have to understand is how digitally mature you as a company are. One of the key things in this industry is that we all see is that the speed and the rate of innovation is so tremendous and we see these waves of disruptive technology that comes in and there are companies that are adopting and embracing those technologies. And think about mobile or cloud or analytics or social, and those companies that adopt those technologies they can gain a certain level of proficiency and performance improvement, but the cycle is very very fast and now we are seeing yet another wave of technology innovation around IOT, API, artificial intelligence and so if you can quickly jump to that next round of technology and innovation then you can continue to build those efficiencies within the company and gain that competitive advantage or maintain that competitive advantage, and I think it's important for the companies to realize that they have to engage in this very very quickly and it's not a one time process either, it's never going to end, the transformation is never going to end, so you have to continually invest in it and where you start with it and where you go is to make sure that you understand where the company wants to go. >> Right. >> And how the technology can help you get there. That's sort of the hardest part of my job is to really convince the leadership and say this is where we will gain some significant benefit and so when I go to my CEO or CFO or the Board what I'm trying to help them understand is that by investing in technology A, B, C, whichever it is, this is what we achieve or this is sort of the picture, part of the puzzle we're trying to build. >> I love this concept, digital maturity, I've never heard anyone say that before, so it almost begs the question, is there some type of a checklist that you have to have made a minimum, either acknowledgement, I don't know if commitment is the right word, obviously you have to be 100 percent on cloud, but it does beg, is there some sort of, have you adopted some cloud, have you adopted some of this, some of that, some of this, to demonstrate A, that you're digitally mature or you're heading in that direction, and B, these are kind of necessary conditions to execute the digital transformation that I'm trying to put in place. >> Yeah, I don't have a specific measuring stick of where you measure your digital maturity but the things that you talked about, for example, if your organization is still dealing with sort of maintaining some of their own data centers and you're investing resources to that, you have not adopted cloud, mobile applications, you know your applications cannot be accessed remotely, then you're certainly not very digitally mature. Right. How much self service is available for your users internally or for your customers. Those are other signs of digital immaturity, another area to look at is, you know, you have a lot of data within the organization. How are you using that data? Is the data sitting in silos? Or is the data being integrated and now you can, you have analytics running on top of it. That's another measure of your maturity and as you look across the companies, you will see that there are companies who are sitting there in sort of that old traditional model of we're going to build these long term strategic plans and that's also a sign of accepting or adopting these technologies because they're hoping, they're waiting to really fully understand what the technology is going to be when they get there and they need to know all of those how and what it will look like when they get there and I think also to me that's also a sign of digital maturity of a company is do they understand what waves of disruption or technology is coming out. >> Right. So it's interesting, you said that you're biggest challenge is going to the Board and and the C suite and telling them how this is going to work. The other hand, they brought you in, not that long ago, with this very specific objective, so clearly you've got some great executive support. So how do you convince them and what are some of the things that you found just work, what are the right stories, what are the right examples, what are the right use cases, that even the digitally immature, finally are like ah now I get it. >> Yeah, so, I mean it helped that they were already thinking about it before they brought me in so that helps a lot, no doubt, I think the things that when I came in and I looked at the company, so there's many places where you can start, some of the areas you can think about is how do you improve the customer service, that's a very important aspect of how you become a better organization. So another area is process improvement and the third area is business model improvement, so I came in and I talked more about before we actually start looking at modifying or enhancing our business models, we need to get to a better, higher performance level within the organization and therefore I'm initially more focused on how do we improve our processes internally, right, and for us, based on our situation, and it varies for different companies, for us the first step in that was really to make sure that the people, systems, and the data are more interconnected. So even within that first step for me for the first phase for us was really to make sure that the people are connected, so do we have the right set of collaboration and communication tools, right, do we have the right set of analytics to sit on top of it, so we just finished that phase, we want to make sure that these are tangible, small steps, because you need to show some wins very very quickly so for us the first step was lets get the people connected. So we just did that, now the next step for us is to get our systems connected. So again, as I mentioned earlier, there is a lot of data that's sitting there, it has to be integrated. There's tremendous value that you can gain from that. So that's what we're getting into, this is our second phase of how do we connect the data together so this way we can start to get the next level of efficiency out of the company. >> So I am guessing after sitting here all day that the integration of your data, obviously we are at SnapLogic, is going to be easier than getting the people to change their processes and the connected people. What were some of the tricks to get people to adopt these new tools before we even start talking about the data? >> So I think there is, you have to show them the value obviously, if you talk about communication and collaboration tools I think the first thing is really about awareness. Right, there's a little bit of sort of top down, sort of mandate, or you may want to call sponsorship, that I think that that helps. >> Or stick >> Or stick, you know, so that helps. Because for some companies and for Quantum it was true that we did not have a corporate communication tool. There were multiple, right, so within the groups they were fine because they were able to communicate but between groups they were not able to, so we had to standardize on that, so I think that you kind of have to show these, there's always skepticism, because everything when people are used to certain things it seems to work for them right? >> I've always done it this way. >> Exactly right, so you have to show them new things and you have to create the awareness and then they start to see the value. It's not a one time thing, it's continuous effort, so we do lunch and learns, we do webinars, we do support sessions and things like this so this way people are more comfortable taking on the new technology. >> But it's so important right because your probability of success if you don't get the buy in from the participant is not very high, so the fact that you started there on the people before you really dove into the technology I think is pretty insightful and will probably increase your probability of success on the next phase tremendously, versus if you just integrated all the data and integrated all the apps and you still don't have people talking together, probably not going to be very successful. >> Exactly, because the data is in all these different business units and different groups and if they're not talking to each other, connecting the data has little or no value. So to me it's really about creating that connectivity so for us when you ask me, sort of, how do we start, so we start with connecting, connection is the first sort of phase of it and then the second is to empower people you know to create more self service and create more sort of autonomous units so that they can start to create value for themselves and for the company. So it's really about enabling the whole organization, sort of the ground swell type of approach, but you're going to first sort of bring the people to that sort of common place where it's easy for them to work, you bring the data along with it and then you standardize the environment or simplify it if you can and therefore it's easy for them to start taking on the services themselves. >> Right, so you finished the first phase and now the next phase is you're going to start integrating all the systems. >> Correct. >> So obviously, we're sitting here at SnapLogic, it's a big piece of what they do, so why did you decide to go with them and how are they helping you in this process? >> So for us, for this phase of digital transformation, you know there were two things that were really really important for us. One was really about how do we connect these systems together in a simple standardized way, so that was one criteria for us. And I believe SnapLogic does a great job and we're going to build it out at sort of the back core of our network. And then the second piece was really can we take this platform and make it available to our end users. So that they can create the connections or access the data that they want, right, and that's again where SnapLogic was able to demonstrate that this is very easy for them to use. So those were the two sort of very pivotal things for us as part of this phase of our digital transformation as to why we picked SnapLogic. >> Yeah it was funny 'cause you used the word self-service in your first phase so I think kind of this thing where your over and over and over it's so important to drive innovation in big companies is demarketerization demarketerization of the data, demarketerization of the tools and then let people find out things and then actually be able to execute. >> Exactly, because you know IT, there's a constant pressure on IT to cut costs, you know, so we cannot serve the whole company for all the things that needs to happen and the technology and the business is changing at such a rapid pace that unless we have experts who really understand that business unit function that well we are not the best people to build those things for them, they are the ones, but then you have a technology learning barrier or learning curve of do you need to put developers in there, so that's why to us this SnapLogic technology helps us that we believe that we can extend this ability to those users who really know their business, they can make the changes as they come, and the IT can help make sure that the right sort of infrastructure exists and the right sort of, level of connectivity exists. >> So I'm just curious, I know you're still early days in this project, but are there any Luddites that have kind of come around since you've been on this journey that suddenly just woke up and said oh okay now I get it now I see the value, now I kind of understand where we're trying to go, who maybe didn't think that way at the beginning. Or they all just know that they got to go. (laughs) >> No I think we are constantly learning along the way, I think that one of the key things that we learned just recently and SnapLogic is going to help us with that particular aspect of it is that we saw that there were a lot of systems that work fine, we don't use them, it's not a daily use type of thing, they get used quarterly, or annually, but we realized that if we can just bring more automation into those processes and we can tie it back to longer more historical data, then we can build more insights around it, so I think that when we show this to the users and especially the CFO now you all of a sudden sort of the lightbulbs go on and it's like oh this is great. Right, that I don't have to rely on only a small window of information, now I have a much broader window. >> Alright then, Omar thank you for spending a few minutes with us and sharing your story with us. I wish you nothing but success on this. >> Thank you very much. >> I'm sure it will be long and exciting with twists and turns and highs and lows. So good luck. >> We're looking forward to that. >> Alright, he's Omar, I'm Jeff Frick. We're at SnapLogic in San Mateo, California. Thanks for watching. (bright music)

>> Narrator: From San Mateo, California, it's theCUBE, covering SnapLogic Innovation Day 2018. Brought to you by SnapLogic. >> Hey, welcome back here, Jeff Frick here with theCUBE. We're at the crossroads, it's 101 and 92 in San Mateo, California. A lot of popular software companies actually started here, I can always think of the Siebel sign going up and we used to talk about the movement of Silicon Valley from the chips down in the South Bay and Sunnyvale, and intel, really to a lot of software here in the middle of the peninsula. We're excited to be here at SnapLogic's headquarters for Innovation Day, and our next guest is Craig Stewart, he's the VP of product management. Craig, great to see you. >> Thank you very much. Welcome. >> Absolutely So, we're talking about API's, and we go to a lot of tech shows and the API economy is something that's talked about all the time. But really that has evolved for a couple reasons. One, is the proliferation of Cloud services, and the proliferation of applications in the Cloud services. We all know if you go to Google Cloud Next or Amazon re:Invent, the logo slide of absent services available for these things is tremendous. Give us kind of an update, you've been involved in this space for a long time, how its evolving what you guys are are working on here at SnapLogic. >> What we've seen change of late, is that not only is there a requirement for our customers to build API's, but also to then allow those API's to be consumed by their partners and networks out there. As a part of that, they may need to have more management of those API's, then we provide. We're very good at creating API's with inbound and outbound payload, parameters, all of those things, so we can create those data services via our API's, but customers then need to have a requirement now to add some functionality around. What about when I have a thousand users of these, and I need to be able to throttle them and those kinds of things. What we've seen happening is there's been this space of the full lifecycle API management technologies, which have been available for some time, and amongst those we've had Google Apigee kind of being the benchmark of those with the Apigee Edge platform, and in fact what we've done in this latest release is we've provided engineered integration into that Apigee Edge platform so that the API's that we create, we can push those directly into the Apigee Edge platform for them to do the advanced authentication, the monetization, the developer platform around it to develop a portal, all of those kind of things. In addition to that, we've also added the functionality to generate the open API specification, Swagger, as it's known, and to be able to take that Swagger definition to having generated it, we can then actually drop it into the API gateways provided by all of the different Cloud vendors. Whether it's Amazon with their API gateway or the Aggre gateway, all you need to do is then take that generated Swagger definition, and this literally is a right-mouse button, "open" API, and it generates the file for you, from there just drop that into those platforms and now they can be actually managed in those services directly. >> I want to unpack API lifecycle management, cos just for a 101 for people that aren't familiar. We think of API's and we know applications or making calls, and it's, "I'm sending data from this app to that app, "and this is pulling information from that app to this app." That's all pretty straightforward, but what are some of the nuances in lifecycle management of API's that your typical person really hasn't fought through that are A, super important and only increasing in relevance as more and more of these systems are all tied together. >> The use of those API's, some of the things around them that those platforms provide is some advanced authentication. They may be using, wanting to use OWA two-factor authentication, those kind of things. They may want to do some protocol translation. Many customers may know how to consume a SOAP service... generally Legacy, these days-- >> So funny that SOAP is now Legacy (laughs) >> It just cracks me up. I remember, the hottest thing since sliced bread >> Oh yeah! Oh yeah! I still have the Microsoft Internet Explorer four T-shirt-- >> When it was 95 Box too, I'm sure. But that's another conversation for another day. (laughs) >> The management of those API's adding that functionality to do advanced authentication, to do throttling... If you have an API, you don't want all of your back end systems to suddenly be overwhelmed. >> Jeff: Right. Right. >> One of those things that those full lifecycle platforms can do is throttle so that you can say this user may have only 10 requests a minute or something like that, so that stops the back end system being overwhelmed in the event of a spike in usage. That helps with denial of service attacks and those kind of things where you're protecting the core systems. Other things that they can do is the monetization. If you want to atrially expose an API for partners to consume but you want to charge them on that basis, you want to have a way of actually tracking those things to then be able to monetize that and to provide the analytics and the billing on top of it. There's a number of those different aspects that the full lifecycle provides on top of what we provide which is the core API that we're actually creating. >> Right. Is it even feasible to plug an API into a Cloud-based service if your service isn't also Cloud-based cos as you're speaking and talking about spikes, clearly that's one of the huge benefits of Cloud, is that you have the ability to spike whether it's planned or unplanned to massive scale depending on what you're trying to do and to turn that back down. I would imagine (laughs) if your API is going through that platform and you're connecting to another application, and it's Pepsi running a promotion on Superbowl Sunday, hopefully your application is running in a very similar type of infrastructure. >> Absolutely. You do have to plan for that elastic scalability. And that's one of those things with the SnapLogic platform, is it has been built to be able to scale in that way. >> Right. Now there's a lot of conversation too around iPass and integration platforms as a service. How do you see that mapping back to more of a straightforward API integration. >> What we're talking about in terms of API integration here, and the things that we've just recently added, this is the consumption of our API's. The iPass platform that we actually provide consumes API's, all sorts of different API's, whether they're SOAP or REST and different native API's of different applications. That we do out of the box. That is what we are doing, is API integration. >> Right. >> The new functionality that we've introduced is this added capability to then manage those API's from external systems. That's particularly where those external systems go beyond the boundaries of a company's own domain. It's when they need to expose those API's to their partners, to other third parties that are going to want to consume those API's. That's where you need those additional layers of protection. Most customers actually use those API's internally within their organization, and they don't need that extra level of management. >> Right. Right. But I would imagine it's an increasingly important and increasingly common and increasingly prolific that the API integration and the API leverage is less and less inside the building and much much more outside the building. >> It is certainly going a lot more outside the building because customers are recognizing their data is an asset. >> Right. Right. Then having it be a Cloud broker, if you will, just adds a nice integration point that's standardized, has scale, has reliability, versus having all these point-to-point solutions. >> Yeah, absolutely. >> I was going to say, As you look forward, I can't believe we're May 16 of 2018 already (laughs), the years halfway over, but what are you looking forward to next? What's kind of on the roadmap as this API economy continues to evolve, which is then going to increase the demands on those API's integration, those API's in management, as you said the lifecycle of the way all this stuff works together, what's kind of on the roadmap if we talk a year from now, what are we going to be talking about? >> There's a lot of... settling down of what we've delivered that's going to take place, and on top of that, then the capabilities that we can add to add some additional capabilities that the customers want to use, even internally. Because even internally where they're not using a Cloud service, they have requirements to identify who in an organization is utilizing those things. So additional capabilities without having to go beyond the boundaries of the customers own domain. That's going to be some things like authentication, it's going to be some additional... Metrics of what's actually being used in those API's, the metrics on the API's themselves in terms of how are they performing, how frequently are they being called, and in addition to that, what's the response time on those things? So there's additional intelligence that we're going to be providing over and above the creation of the API's that we're looking to do for those customers, particularly inside the organization. >> It's very similar requirements but just different, right, because organizations, take a company like Boeing, or something, is actually not just one company, there's many, many organizations, you have all kinds of now with GDPR coming out, cut of data, privacy and management restrictions, so even if it's inside your four walls, all those measures, all those controls are still very very relevant. >> Very much so. Providing some additional capabilities around that is pretty important for us. >> Alright. Well Craig, you're sitting right on top of the API economy, so I think you'll keep busy for a little while. >> (laughs) That's for sure. >> Thanks for taking a few minutes to stop by. >> Thank you. >> He's Craig Stewart, I'm Jeff Frick, you're watching theCUBE from SnapLogic in San Mateo, California. Thanks for watching. (techno music)

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

>> 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)