>> Hey, welcome back, everybody. Jeff Frick, here with The Cube. We're in the Palo Alto studio, talking about customer journeys. We're really excited to have our next guest on, from Vivint. We have Miki Seltzer, she's a data scientist. Welcome, Miki. >> Thank you. >> And with her, also, is Raul Olvera, a senior data engineer at Vivint. First off, welcome. >> Thank you. >> So, for people that aren't familiar with Vivint, what is Vivint? >> So, we are a home security and home automation company. >> Okay. >> We've been around for 20 years. We like to make people's homes safer and smarter, and we're trying to do that in a way that customers can just use their home as they normally would, and we learn from what they do, and make their home smarter. >> Okay, so, I won't call you Nest of security, but probably a lot of people say Nest of security, because we always think of Nest, right, as that first smart home appliance that learns about what's going on. So what does that mean when you say that we learn about what you do and how you move about your house, probably your patterns? What does that really mean, when you talk about learning about a person in their house? >> Well, we have a lot of different devices in the user's house, and we can tell when they come home, how they like their thermostat set, and so all of those things, you know, sometimes you have to do that manually. You know, sometimes people have to come home, and they set their thermostat to 72, and when they go to bed, sometimes they have to set it cooler, because they want to save money when they sleep. >> Jeff: Right. >> But with Vivint, you can set all those controls to happen automatically, and Vivint can detect patterns and know you tend to like your home cooler at night. >> Jeff: Okay. >> And you want to save money during the day, because a lot of times, people aren't home during the day, and so, they don't want to run their air conditioning and cool down a house that's not occupied. >> Right, right. >> So we like to use all those patterns, and just make your home smarter, so that it knows how to save you money, and how to make you safer. >> So, that's a lot of data ingest. So, what are the types of sensors, appliances, inputs that you leverage to feed the front end of that process? >> We have motion detectors, there's locks, there's the main panel that you use to interact with the system, the thermostat, the cameras. >> Miki: We've got smoke alarms, carbon monoxide detectors. >> Oh, a whole host of things. >> We've got a whole host of things, yeah. >> Yeah, and then when people put Vivint in, do they usually want to put it in because of that whole array of stuff, or do they usually start with the doorbell camera, or a thermostat, or a carbon monoxide detector? How does that engagement work, and does it grow over time? >> Well, I think the thing that's really important about Vivint is that we're kind of a one-stop shop solution, so a lot of these products are coming out where you can get a thermostat on its own, and you can get a doorbell camera on its own, and you can get a security system on its own, but the good thing about Vivint is that everything is integrated, and an installer will come to your house, and do everything for you. >> Okay. >> And, so, there's not configuration that has to be done. It's kind of, we come in, we set everything up. >> Okay. >> And you're good to go. >> Okay. >> And a lot of times, people will sign up just for security, and then find out that we have all these great products, and all these smarts that go behind it, and it just makes the product that much more valuable to customers. >> Right, because I would imagine the more of the pieces that you integrate, the more value you get out of the whole system. >> Absolutely. >> One and one makes three type of scenario. And then what's the business model? Do they buy the gear, kind of the classic security, you buy the gear and then you have some type of monthly subscription for the service, or how does the business model work? >> So right now, we are moving more towards a you buy everything up front, and then you just pay a monitoring fee, going forward. >> Jeff: Okay, okay. >> So, you will own all of your equipment. >> Okay, great. So, that's on the data collection side. Now you guys are pulling this back in. You both are data scientists, data engineers, so then what are some of the challenges you have, pulling all this for data? I guess the good news is it's all coming from your own systems, right? Or are you pulling data from other systems, as well? >> It's a lot of the sensor data that we have, and I think a lot of the challenge in that is understanding the data, how it behaves, and creating the metrics out of billions and billions of rows of data. >> Jeff: Right. >> For all the customers that we have, so that's one of our challenges, and we do have other sources from CRM, data sources, to NPS, and other systems that we use, that we combine with all of our data from the sensors, just to get a better view of the customer and understand them better. >> Okay, what's NPS? You said NPS. >> Miki: Net Promoter Score. >> Net Promoter Score. >> Net Promoter Score. Okay, good, and then do you use other external stuff like the weather? I would imagine there's other external factors, public dataset, set impact, whether you turn the furnace up or down. >> Yeah, absolutely. We have a whole host of data sources that we use, in order to power the smarts behind. >> Jeff: Okay. >> Behind our products, and weather is absolutely right. That's one of them. We also need information on peoples' homes in order to figure out how long it's gonna take to heat or cool their house. >> Jeff: Okay. >> Because somebody who lives in maybe a condo, it's gonna take a shorter amount of time to heat up their house than somebody that lives in a 3000 square foot house. >> Right, right. Okay, so then you guys get the data, you can analyze the data, you're both smart people. You both are data scientists. How do you package that up in a way for the consumer? Because I would imagine the consumer interface clearly doesn't have billions of rows of data, and doesn't incorporate that, so how have you guys, I don't wanna say dumbed it down, but dumbed it down to the consumer, so they've got a much easier engagement with the system? >> I think we basically work with each business or person, and from their request, we start working with them, understand what they wanna measure, and usually, as with big data happens, you kind of create a story with metrics for them, so we start with that. It's mostly on a request basis. >> Jeff: Okay. >> And we have some automations, just to keep track of some metrics that we like to keep historical measurements. >> Jeff: Right. >> But it's mostly we talk with the business people to see what they want to track, and kind of create our own story with the data that we have. >> Okay, and then I would imagine over time, the objective would be for the system to take over a lot more the control, without engagement with the consumer in their home, right? Ultimately, you wanna learn what they do and start adapting your patterns to how they act, so that their direct engagement with the system decreases over time. >> Yeah, so that's the ultimate goal, is that we can infer all of these data points without having to confirm with the customer that, yes, I'm not home, or yes, I do want my home to be cooler. >> Jeff: Right. >> So that is something that we're working towards. >> Okay. So, you've been at it for a while. 20 years, the company's been around. That's pretty amazing. How have the challenges changed over that course of time? Are you looking at things differently? Are you pulling in more data sources? Or has it changed very much in the last 20 years, or have you just added more to the portfolio, which adds more data input, which is probably a good thing? >> Well, the journey that we've been on really started in about 2014. >> Jeff: Okay. >> When we launched our own platform for security and home automation, because at that point, that's when we started getting the whole fire hose of data. >> Jeff: Okay. >> And so at that point, that was the beginning of our data journey, and when that happened, we kind of had to harness all of that data and figure out what do people want to know? Like, what does our business need to know about how people are using the system? >> Jeff: Right. >> And so at the very beginning, it was simpler questions, but now that we've kind of evolved more, we can answer the more complex questions that don't necessarily have straightforward answers. So, it's kind of evolved from 2014, when we were able to get all of that rich data. >> Jeff: Right. >> From the platform, and it's evolved to now, where we can use all of that data to inform the smarts for our products. >> And I love the way you said that there's not necessarily an answer. >> Mmhmm. >> Right, it's very nuance, right? >> Right. >> Everything's got some type of a score variable or some type of a trade-off, so have you created your own scoring and trade-off tools internally, to help make those value decisions? >> Yeah, so it's really all driven by context. >> Okay. >> So a lot of our data, without any context, it doesn't matter, it doesn't provide any use. We're in a unique situation, where we define our own success metrics. So a lot of times we'll monitor things like what percentage of the time is a camera connected to the internet? Because if it's not connected to the internet, then you can't view it from your phone or from your computer. >> Jeff: Right. >> So... >> So, a tight relationship with Comcast, hopefully? (laughter) We're all together. >> Yeah. >> Okay, so there's that, and then, again, how much of that stuff do you display back to the customer? How much control do they have? How much control do they want? You know, those are all, kind of, squishy decisions, as well. All right, so you're here on behalf of Datameer. So, you chose them. So what was it that attracted you to the Datameer solution? >> I think it's the fact that just interacting with your big data is way simpler than going to, even if it's on a scale environment like HIVE, it takes a longer process to get your data out, and it's more visual, so you're seeing the transformations that you're doing in there, and I think it allows people with a more analytical skill set to get in to the data, and go through the whole journey from knowing the data from almost raw, to getting their own metrics, which I think it adds value for the end product and metrics reports. >> So more value for the people who have the knowledge and the data science jobs. >> Yeah. >> And how many hardcore data scientists do you have in your team? >> On our team, I think we have about five or six hardcore data scientists. >> Five or six? Okay. >> We're kind of split into two different teams. One teams does real time streaming analytics, and our team does more batch analytics. >> Jeff: Okay. >> So we're all using a whole host of different machine learning and data science techniques. >> Jeff: Okay. >> But on the batch side, we use Datameer a lot to be able to transform and pull insights out of that raw data that would be really difficult otherwise. >> Right, and then what about for the people that aren't in your core team? You know, that aren't the more hardcore data scientists. What's been the impact of Datameer and this type of a tool to enable them to see the data, play with the data, create reports, ask for more specific data? What's been the impact for them to be able to actually engage with this data without being a data scientist, per se? >> They can go into Datameer and get answers quicker than, like I mentioned, just writing something that will take longer time, and we also feed data to them because we have more access to historical data, and aggregations, like probabilities, and those type of metrics, we can create for them, and they can utilize that in their more real time environments, and use probably these metrics for creating or, I forget that one... >> Miki: Predicting. >> Predicting, yes. >> Right, right. >> Predicting actions the customer are going to take. >> Right, right, and I wonder if you could speak a little bit about how the two groups work together between the batch and the real time, because a lot of talk about real time, it's the hot, sexy topic right now, but the two go hand in hand, right? They're not either or. So how do you see the relationship between the two groups working? How do you leverage each other? What's the business benefit that you deliver versus the real time people? How does that work out? >> So when you're doing real time and streaming analytics, you really need to have your analytics based in something that's already happened. So we inform our real time analytics by looking at past behaviors, and that helps us develop methodologies that'll be able to go real quick (snaps fingers) in real time. So using past insights to inform our real time analytics is really important to us. >> Which is a big part of the MLPs, right? The machine learning. You build a model based on the past, you take the data that's streaming in now, make the adjustment to continue to modify it. I'm just curious to get your take on the evolution of machine learning and artificial intelligence, and how your guys are leveraging that to get more value out of the data, out of your platform, deliver more value to your customer. Here's an interesting little example. I always joke with people, they think these big things, I'm like, well how about when Google reads your email and puts your flight information on your calendar? I think that's pretty cool. That's a pretty cool application. I mean, are there some cool little ones that you can highlight that may not seem that big to the outside world, but in fact they're really high value things? >> Well, I think one of the biggest challenges for Vivint is something simple like knowing whether there's somebody home. So occupancy has been a big challenge for us because we have all these sensors, and we can easily tell when somebody's home, because they'll have a motion detector, and we'll be able to see that there's somebody moving around the house. However, knowing that somebody is not home is the bigger challenge because the lack of motion in the house doesn't mean that somebody not home. They could be taking a nap, they could be in a room that doesn't have a motion sensor, and so using machine learning algorithms and data science to figure those problems out, it's been really interesting, and it seems like it's a relatively simple problem, but when you break it down, it gets a little more complicated. >> Check their Instagram feed probably, you get a starting point. >> Right. >> Or if the dog is running around, setting off the motion sensors, I'd imagine is another interesting challenge. >> Yeah, that's also a big challenge. >> All right, so as you look forward to 2018, I can't believe this year's already over, what are some of your priorities? What are some of the things that you're working on? If we were to sit down a year from now, what would we be talking about? >> I think create something that is more approachable, as in people can get their own value from it, rather than doing one of timed requests, is when we're moving from on our data journey. >> Right, so basically democratizing the data, democratizing the tools, letting more people engage with it to get their own solutions. >> Yeah, because like Miki said, the data that we're getting, it wasn't available to us until like 2014. So people are just realizing that we have this amount of data, and first the questions come, and they're kind of specific, and eventually you start getting similar requests to the point that, to speed development on other reports, we want to be able to provide some of the more important metrics that we have received in the past years to a more automated way, so that we can keep track of them historically and for people that need to know those metrics. >> Jeff: Miki? >> Yeah, as Raul said, we're trying to move more toward self service. In the past, since our data is constantly evolving, there are not many people who know the context and the nuance of all of our data, so it's been really important for us to work with our business stake holders, so that we know that they're getting the right data with the right context, and so moving towards having them be able to pull their own data is a really big opportunity for us. >> With that context overlay. >> Absolutely. >> So they know what they're actually looking at. It feels so under reported the importance of context to anything, right? Without the context, is it big, is it small, what are we comparing it to? >> Exactly. >> Well, Miki and Raul, thanks for taking a few minutes of your time and sharing your story. Fascinating little look into more about Vivint, and I guess you just have to get more motion sensors around the house, under the bed, keep an eye on that Instagram account, are they taking pictures? >> Let's not be creepy. (laughs) >> Well that's a great line, right? Data science done great is magic, and data science not done well is creepy. So there's a fine line. So thanks again for sharing your story, really appreciate it. >> Thanks for having us. >> And I'm Jeff Frick, and you're watching The Cube. Thanks for tuning in, and we'll catch you next time. Thanks for watching.