 It's theCUBE, covering HPE Big Data Conference 2016. Now, here are your hosts, Dave Vellante and Paul Gillan. Welcome back to Boston, everybody. This is day two of HPE's Big Data Conference. HPE, excuse me. I'm going to be saying that for a while. Hashtag sees the, even HPE says HPE. Hashtag sees the data. This is theCUBE, the worldwide leader in live tech coverage. Mark Lockwood is here. He's with Project Management for Lodgy Analytics. Sorry, Lodgy Analytics. Great to see you. Thank you for having us. And so tell us what's going on here for you guys at the event. Yeah, so Lodgy Analytics is a platform for application owners and developers to build custom operational analytic applications. We've partnered with HPE for a while now. We're actually, I believe, the only vendor who has a native connection to both Vertica and Idle. So we're able to basically blend structured data from Vertica and unstructured data from Idle together and to rather kind of unique and impactful dashboards. In fact, if you were downstairs on the solution showcase floor, you probably saw a social media hub with a couple screens on Twitter, sediment and things like that. That's actually all a Lodgy application. So it's your app backended by Autonomy Haven? Yeah, exactly. So basically we, HPE has great backend systems, best in class, and we provide kind of the front end for them in a custom operational analytic environment. So the Haven APIs must be great for you? Oh, absolutely. Yeah, it's very well suited to the way we do business. We focus on operational analytic applications. We focus on app owners. And so this is a market for us that is very impactful. Now, can you define operational analytics? Yeah, so it's a little bit different. What you'll see today is there's kind of two types of approaches to the analytic market. There's a lot of the data discovery vendors out there are looking at an out-of-the-box experience for a rather contained use case. So if you have five to 20 people in a marketing organization with KPIs that are pretty much out of the box, then there's a lot of great data discovery vendors out there to solve that problem. We focus on really scalable information distribution and operational use cases where, I'll give you an example, one of our customers is Service King, which is a collision repair center. And what they basically do, they have 300 locations across the US in about 20 different states. And what they were doing before is they run different collision repair centers. They have different bays where they do paint and they do repair your dent. They want to track basically how quickly they can do those repairs. They were previously doing that manually. We provide a custom analytic application that's suited to their specific needs, tailored to their end-users' roles and skills and actually embedded within the application they're already using that allows them to track in real-time how they're performing not only within their location, but actually nationwide across other locations. So they're able to see, if I'm doing a paint job in 45 minutes and there's a guy in Newark who's doing it in 20 minutes, I can actually reach out and be like, hey, is there a way I should be doing it that allows me to cut my time in half? It's really that operational visibility and being embedded within the workflow that we focus in. Okay, so tell us a little bit more about the company. How long have you guys been around? How many people are you? Yes, we're based out of Washington, D.C. We're about 200 people. We've been around for a fairly long period of time. We basically have been focusing more and more on creating applications that will allow us to basically tailor to end-users' roles and skills. So we're built around a fundamental problem which is end-user adoption. We do a study every year that measures end-user adoption which is basically the difference between the number of people who want to use analytic applications and the people who actually feel like they have the right analytic applications. We perform this study but Gartner, who's obviously one of the leading industry analysts also performs a study and comes out with similar results which is 91% of people actually want access to analytics. That's not really surprising. Everybody wants to make more data-driven decisions. Who are those 9%? No, 91%. Yeah, who are those 9%? I don't want to. Yeah, probably not a good percent to be in, by the way. But what's the surprising metric is that out of that 91%, only 22% of people actually say that they have the tools, they need to be that data-driven organization which is shocking if you look at how much money's been invested in this market and all the tools that are out there. And so what you've kind of seen is there's a lot of tools out there. Data discovery tools are focused on the analysts. You have traditional BI tools focused on more of the kind of consumers for passively looking at information. We really see that that gap between supply and demand is a problem in taking a one-size-fits-all approach and not tailoring applications to end user experience. And the solution to that problem is embedding analytics in part anyway, visualization and simplicity, talking more about. Yeah, so it's kind of, it's three things. One is being able to tailor analytic capabilities to end user's roles and skills. So not assuming that everybody is a data scientist or that everybody is just passively consuming information. We actually have something we call the continuum which is three kind of personas within an organization. There's kind of the consumer, which is somebody who's on a factory floor who might be looking up at an LCD screen reading information. This is actually probably the majority of most organizations. And at this point, it's really about reading information, understanding KPIs. It's not about interacting with the underlying data. As you kind of become a more advanced information worker, you move into what we call the creators. And these are more of the people who are still operating in fairly managed environment where the data's been vetted and prepared for them. But they want to supplement those dashboards and reports that they're given with their own metrics and measures. And that's also a fairly large portion of most enterprise deployments. And then you have the analysts, which are kind of the guys who want that blank canvas where they can pull in, you know, Marketo data and Google analytics and mash it all together into new insights. And really what we do is we look at the organization as a whole and say, okay, what percent of your users are kind of from each of one of these personas? And then build analytic capabilities tailored to their needs. We also focus, we're a big leader, we're actually number one in Gartner in terms of embedability. And the reason we focus on that is because we want to make sure that that information is delivered in the context of where people are actually working. So if I'm in Salesforce, 40% of my day, I don't want to have to break screens and go to a separate analytic application. I want to see my metrics and measures in Salesforce. And then the last thing that we focus on is really integrating with workflow. So we have a direct connect model to underlying databases, which allows us to do things like real-time reporting and write back that are fundamental for an operational use case. So if my machine goes down the factory floor, if you're in the sales and marketing use case, maybe you can find that out tomorrow. But if I'm in the operational use case, I have to know immediately if my machine is down and that's where our direct connect model is a little bit different. Okay, so a couple of questions from what you just said. So the roles that you sell to service the personas of consumer, creator and analysts, right? Yeah. You said the roles you sell to the application owners. Yeah. Give us some color on that. Who are the application owners? Specifically, who are those people? Yeah, so we serve two markets. The first market is kind of the OEM market. So it's actually product owners within software companies who are looking to develop actual software products and they'll embed us within their actual application and sell it to an end user. And then we also have an enterprise business where we'll sell directly to internal application owners or application development teams and they'll build custom analytic applications for their operations teams or they're basically an internal business unit for creating custom analytic applications or purpose-built analytic applications. Okay, and in the Salesforce example that you gave, you would be, what, a tool in their marketplace or you, is that right? So we actually embed right into the interface. So if you were to open up your home screen for Salesforce, which like most sales people do every day, we would actually be one of those visualizations within your home screen. We allow that really deep embeddability where you can't even really tell for better or for worse actually whether it's Salesforce or Lodgy. And that's a lot of that has to do with our history of being a leader in the OEM space. You know, if we're selling to other software companies, they don't want, I mean, so JP Morgan Chase is a big client of ours and they use this for an external portal and they don't want that portal to look like, you know, a data discovery vendor. They want it to look like JP Morgan Chase and that's their product. And Salesforce, it looks like Salesforce. So when you go in to a dashboard, that very well may be. Right, so we offer complete white labeling, complete customization and there's a rather large market for that. Yeah, in that case, are you working with Salesforce directly or are you using their APIs to embed? So we can just iframe right in. We don't actually need to, I mean we have some integration with certain tools but largely we don't require deep. So you can iframe into anything that supports iframe? Yeah, absolutely, absolutely. And that's largely mostly use cases. Yeah. The, what is, what are some complexities of embedability? I mean, not every, I'm sure not every case is as simple as an iframe. Yes, yeah. When you have to embed into maybe a legacy application of what's involved then. Yeah, and we actually, we have as a leader in embedability, we really pride ourselves in understanding the degrees of the maturity at which embedability happens. We actually have a maturity model around embedability. And what we like to say is like the first stage of embedability is really it's around kind of the user experience so the iframe experience. The second stage is around security. So another thing that's very important to us is we plug into your existing security model. So you don't have to create a different security layer for your analytic application. If you've already built that, we just plug right in and that's important for embedding. And then the second is actually integration with workflow. So unlike some of the other tools out there what we're able to do is let's say you see an insight and sales force and a logic visualization embedded in sales force that is beyond a certain threshold. You can actually trigger an action off that like an email can be sent off that data. So that's kind of the what we call infused embedability which is something really unique to kind of our approach. And really the way we kind of talk about it is it enables you to take action on insights rather than just passively understanding it and then going to your email and trying to take action on it separately. So talk a little bit more about your relationship with HPE, what are you guys doing together and where do you see it going? Yeah, so we've been working together for a fair amount of time. A lot of it is our ability as a company to connect directly to the underlying database. We don't try and pull data into our own internal repository. So HPE has best in class databases with Vertica and Idle. So basically what we provide is a really kind of custom presentation tier or our front end to what they do really well on the backend which allows us to basically translate the value of their backend systems into solutions and business use cases. So you'll see like for instance one of the assets you'll see is the voice of customer asset. And what this is is it allows you to take structured data on customers like how many support tickets they log and these other things and blend it with unstructured data like sentiment from chats or emails for instance. And so if you combine that structured and unstructured data you're able to kind of create a unique profile for that customer and understand like are they ready to churn? Do I need to pay more attention to them? I think that's kind of where the, a lot of HPE customers are going is combine that structured and unstructured data and we're trying to be kind of at the forefront of that. You're saying, go ahead. So where does Vertica fit into that or Haven fit into that model? So we just connect directly to Haven. We pull up the data into our basically platform and then we're able to blend the two together into visualizations and dashboards that are custom built for clients. And then present it like you were saying in the Solutions Expo you've got the big, the billboard with all the cool charts and graphs that we see all the time at the show. So check that out for sure. All right Mark, thanks very much for coming to theCUBE. It was great to see you and good luck. Thank you for having me. All right, appreciate it. Pleasure. Hey everybody, Paul and I will be back with our next guest right after this short break.