 From the MGM Grand Convention Center in Las Vegas, Nevada, it's the queue at splunk.conf 2014. Brought to you by headline sponsor Splunk. Here are your hosts, John Furrier and Jeff Kelly. Okay, welcome back everyone. We are live here at the splunkconference.conf 2014. SplunkConf is the hashtag. I'm John Furrier, the co-founder of SiliconANGLE Medium, joined Jeff Kelly, the number one big data analyst in the world with Wikibon. Jeff, great to see you again. And we're here with Russ Turner with Domino's. Talking about ITOps, engineering manager. Welcome to theCUBE. Hi, thank you. Appreciate you coming on. And we love talking to practitioners and folks in the trenches, really who have to use the tooling. So I got to first ask you, ITOps with data is really a big deal. It is literally on the floor, it's everywhere. How are you guys doing that from a strategy standpoint? Because Splunk is a tool and a platform. And it's always kind of that confusing thing. How do you view that whole data with the Splunk integration? Is it a tool, is it a platform? How are you guys using it? We brought it in originally as a tool and it was for what you do is just traditional system monitoring for servers and things like that, making sure that things are running the way we expect it to. My team is responsible for deployments and things like that, but also the uptime of the site, which is critical of us. About 45% of our business goes through our online channels right now. And so keeping that up 100% of the time obviously is the key. So we brought it in mainly for system monitoring and but what we're starting to see is that on top of the system monitoring, there's other data insights that we can pull. It's the same things that we're feeding our data warehouse, but whereas the data warehouse could take 12 or 24 hours to roll some of that important data up, we see it in real time. So that lag is critical. I mean, that's still not a bad kind of performance from the old school standards. It used to be you throw it out there and the answer comes back maybe weeks later, but let's talk about this modern era. What do you learn in your environment? Dealing with how to have a real-time database, how to have a real-time system that's not traditionally looked at in the old days like that. So this whole new way that this data model is fast data, it's big data, it's small data. What's the architecture look like? How do you guys lay it out and what's your vision? Well, right now we're still trying to find our way. Like I said, what we're discovering is there's more, like we've got this giant, it's a goldmine of data that we can quickly tap into. So we can do, we can create reports quite quickly. And that's what we need. So for an example, all of our, like all of our coupons or all of our promotions are tied to a code. And so now we could quickly find out if that, or we could potentially find out if that was performing as we are. We're not agile enough right now to make a change on the site if it's not going the way we think. And really, we haven't really approached marking with a full-blown use case or here's some of the cool things we could do with it. But another example is we just recently, actually I think it was released today, the press release for is Dom is our voice ordering for our mobile platform. And what that led us to do for the last couple of months is we've had it available, but we didn't announce it. So it was interesting to see how many people were using it and then also allows us to see, since there was such a small amount of people using it, we could see voice ordering versus the traditional mobile ordering. If voice ordering people were abandoning more often or things like that, it wouldn't, so many people, or it was so little used at first that it wouldn't have showed a bump in errors, but now we can break that into pieces. So just look at voice order compared to mobile compared to the overall order. So I wonder, so let's just take a step back. So that's a really interesting use case. In fact, I caught that commercial on TV today for the new application and I noticed Dom knows he's doing some things with them in the mobile channel certainly to better reach customers. And we heard in the keynote this morning from Godfrey Sullivan around, he's hearing from there from Splunk customers, they're going mobile first. And I think Dom knows would fall into that trend. Talk a little bit about the mobile, the value of mobile, not just in terms of reaching customers but what it provides in terms of data coming back in terms of what your customers are doing, how they're behaving and how that's being leveraged hopefully to improve your business. With the, so the mobile customer self which was finding out that of that 45% of a significant, I can't say the number up top of my head, but a significant that is mobile and we're making an investment in mobile too. Like you've seen the, we just recently released the iPad and that's retina and things like that. So what that allows you to do is is see a really rich pizza or something like that that drives up sale and things like that. So then what we can do is now we give, it gives us a chance to compare platform to platform or platform versus the website or we've also got, you can use your browser on the platform to hit, we just went responsive so we could do responsive versus mobile and things like that and that will allow us to do the data insights team can pull that from the data warehouse but if they need to see it real time we can give them well X amount of orders in the last hour of been mobile versus responsive and so that part of it's neat. But like I said, we're still growing and kind of feeling our way out and we still need to build a solid case to show them some of the other cool things that we could do. Well I think what it does do is it demonstrates that Domino's is clearly looking to leverage data as a competitive asset. Absolutely, I think one of the coolest things and it puts a lot of pressure on my team and I but it's exciting is our CEO, Patrick Doyle's come out on multiple shows lately and said don't think of Domino's as a pizza company more, think of us as an e-commerce company that sells pizza so he sees technology as a tool to leverage to get a competitive advantage so to me that's very exciting and it's a cool space to be in and being able to see this data is going to allow us as we roll out more new features and more new partnerships with other companies that will be able to see the fruits of that in real time and that part of it. And just to elaborate a little bit on the importance of that executive buy-in, in terms of establishing a culture where we are going to base our decision making on data-driven insights. How important is that coming from the top versus kind of taking it from the bottoms up approach where you're bringing in technology but not necessarily getting the buy-in or the adoption because you don't necessarily have a mandate from the top. It makes it to me tremendously easier because we're not building a case scenario, like we're not fighting an upstream battle, like he is excited, leadership's excited, they like the technology, they like what our development team's doing, marketing has got amazing ideas that we can't go fast enough for them so for marketing and those guys to be able to see us as an advantage, it makes it easier on us. Like I've been at other jobs where you were building a case scenario and you're fighting the whole entire way to try to implement something where now, marketing's like, we want to do all the stuff you guys got to get cracking, so it's definitely a lot easier having everyone buy-in. I don't burn a lot of cycles arguing for or trying to prove something where everyone is fully aligned and wants to go forward with some exciting stuff. So let's talk about Splunk a little bit more specifically. So you mentioned you're using Splunk to kind of get a real-time view into your operational systems. Put it into context, where does that fit into your larger technology infrastructure related to data? I'm guessing you're using more traditional data warehousing or using some other systems to do whatever it is. So we have a traditional data warehouse right now and then so our store side of it, so all the stores are being monitored. We use certain for Splunk, for application monitoring, for logging and things like that, that's a separate team. They do some for security monitoring also and then my side, the e-commerce side, that's where we originally brought it in for. The first time we brought it in for is because we wanted to jam it in place before the 2011 Super Bowl, I think, because we wanted to see, we were cared about system performance during that time. We wanted to see what it did. The Super Bowl is interesting where we have had busier days, but it happens as the time zone goes along. Super Bowl is fascinating where it happens, all kickoff is at one time regardless of time zone. So we wanted just that, the machine, how our stuff's performing during this one time a year occurrence. But then once we got it in and we start seeing other things that we could correlate and there are other opportunities there for all this data that we had. It was interesting too that we could bring in other people to look at it and they think of what they, a different set of eye or different perspective. So there's stuff that we didn't even think of that people have said, can you do this? And we're looking at, oh yeah, we can. So it's been tremendously beneficial and it keeps paying, like I bet we're just scratching the surface of what we can do with it. And it's interesting because what you're describing is essentially bringing in something like Splunk where you're able to get some of the insights that you're looking for, but also it opens up all new possibilities that you can think of ahead of time. And that's one of the real benefits we're seeing from these more modern approaches to data. So now meanwhile you've got the data flowing, you're kind of tapping that stream that's flowing in your enterprise data warehouse. So I'm curious, how does your organization see the enterprise data warehouse? Is that a technology that's going away? Is it playing a less important role? How do you see the relationship between some of these new approaches and kind of the more traditional EDW model? So right now the data that we're getting is very, I don't want to say unscrubbed, but when it gets to the data warehouse, there's times like if you change your order, if you cancel your order, things like that, we don't see that, like we're not counting that. I only keep three months with the data, so any type of real, if you wanted to go back and do real long trends, things like that, that's not a business that I'm in. So to me I think data warehouse in us hand in hand is more important than, I can't ever imagine a time where data warehouse wouldn't be available. Our data warehouse team is phenomenal and to me, marrying us and them is what would be, that's how, and then giving all that data, giving me marketing opportunities to see real time or all that, to me that's important, that's going to be the key. Ross, thanks for coming on theCUBE. We have one question though, we have some journalists out there and I'm getting some pings on our crowd chat engagement app. It says for you, in DevOps and reliability, have you been able to leverage the real time monitoring to aid in addressing root causes of issues or failures? And how have you used monitoring and historical data to understand system performance, any examples? Well, absolutely, we use it that because of, like I said, the real time portion of it is so critical, we use it for, an example would be, on the site we're constantly running AB tests between two different things. We had an AB test that was, it was a very small percentage of the user base and their code was getting returned incorrectly, payment type was wrong, things like that. So what that allows us to do is quickly identify it before, because it was, order still went through, but it was still, You can see some patterns. Yes, it gave us, and we were able to see it quickly and then stop the test quite quickly. But it's the same, we can start to see it, like I can see the, because of where we're at in network, I can also see the order flow from the customer all the way down to the store and back. So what that means is if we had a VPN problem where a bunch of stores dropped off the network, well, typically my team will see that first and we'll mark those stores down and they'll see it as maybe a drop in online ordering. Well, really it's that whole VPN. Yeah, you get telemetry on real customer data and the consequences of not having real time is you'd miss it and this is the issue that I love about big data is you can use the pattern recognition to see real issues and the consequences are impact to sales. So what right now we don't, we're not pulling in VMware data, we're not pulling in some of that other type of data too, but if we can start pulling that data, then we can see patterns go the whole entire way. Like if there's a VMware problem, we can track that through and see eventually if it shows up in order count, there's an order count, hit it all. So it's fascinating. I mean, like I said, the amount of things, everyone there's open-minded, so we're allowed to, we can bring in that data, it's just finding the time to do it. All right, Russ, thanks for coming on theCUBE, we really appreciate it. We are live in Las Vegas, Russ Turner with Domino's, really using big data to operationalize, get some efficiency going, but also identify opportunities to add value to the business. Great success to her, great to have folks in the trenches on theCUBE. We love talking to customers, it's our favorite thing to do, but we're going to talk to some of the senior executives of Splunk as well. We'll find out what's going on with the product, we'll be right back after this short break.