>> Narrator: Live, from San Jose. It's theCUBE. Presenting Big Data, Silicon Valley. Brought to you by SiliconAngle Media, and its ecosystem partners. >> Welcome come back to theCUBE, we are live in San Jose, at our event, Big Data SV. I'm Lisa Martin. And we are down the street from the Strata Data Conference. We've had a great day so far, talking with a lot of folks from different companies that are all involved in the big data unraveling process. I'm excited to welcome back to theCUBE one of our extinguished alumni, Maribel Lopez; the founder and principal analyst at Lopez research. Welcome back to theCUBE. >> Thank you. I'm excited to be here. >> Yeah, so you've been, a startup conference started a couple days ago. What are some the trends and things that you're hearing that are really kind of top of mind for not just the customers that are attending, the companies that are creating or are trying to create solutions around this big data challenge and opportunity? >> Yeah absolutely, I mean I think we talked a lot about data in the years past. How do you gather the data? How do you store the data? How you might want to process the data? This year seems to be all about how do I make something interesting happen with the data? How do I make an intelligent inside? How do I cure prostate cancer? How do I make sure I can classify images? It's a really different show, and we've also changed some of the terminology a lot more in machine learning now, and artificial intelligence, and frankly a lot of discussion around ethics. So it's been very interesting. >> Data ethics you mean? >> Data ethics; how do we do privacy? How do we maintain the right level of data so that we don't have bias in our data? How do we get Diversity Inclusion going? Lots really interesting powerful human topics, not just about the data. >> I love that the human topics especially where you know AI and ML come into play. You talked, data diversity. Or bias that we were just at that women and data science conference a couple of days ago talking to a lot of female leaders in in data science, computer science, both in academia as well as in industry. And one of the interesting topics about the gender disparity, is the fact that that is limiting the analyses on data in terms of, there may be a few perspectives looking on it. So there's an inherent bias there. So that's one issue, and I'd like to get your thoughts on that. Another is with that thought, lack of thought diversity, I guess I would say going into analyzing the data, companies might be potentially limiting themselves on the types of products that they can create, how to monetize the data and actually drive new revenue streams. On the kind of thought diversity will start there. What are some of the things that you're hearing, and what are some of your recommendations for your clients on how to get some of that bias out of data analysis? >> Yes it's interesting. One is trying to find multiple sources of data. So there's data that you have and that you own. But there is a wide range of openly available data now. There's some challenges around making sure that that data is clean before you integrated with your data. But basically, diversifying your data sources with third party data is one big thing that we're talking about. In previous analytical generations, I think we talked a lot about how to have a hypothesis, and you were trying to prove a hypothesis. And now I think we're trying to be a little more open and looser, and not really lead the data where per se, but try to find the right patterns and correlations in the data. And then just awareness in general. Like we don't believe we're biased. But if we have data that's biased who gets put into the system. So we have to really be thoughtful about what we put into the system. So I think that those three things combined have really changed the way people are looking at it. And there's a lot of awareness now around that. Because we assume at some point, the machines might be making certain decisions for us. And we want to make sure that they have the best information to do that. And that they don't limit our opportunities as a society. >> Where are companies in terms of the clients that you see, culturally in terms of embracing the openness? 'Cause you're right! From a scientific scientific method perspective. People go into, I'm going to hypothesize this because I think I'm going to find this. And maybe wanting the data to say this. Where are companies, we'll say enterprises, in becoming culturally more open to not leading the data somewhere and bringing up bias? >> Well, there are two interesting things here, right? I think there are some people that have gone down the data route for a while now, sort of the industry leading companies. They're in this mindset now trying to make sure they don't leave the data, they don't create biases in the data. They have ways to explain how the data and the analysis of the learning came about, not just for regulation, but so that they can make sure they ethically done the right thing. But then I think there's the other 95 percent of companies that they're not even there yet. They don't know that this is a problem yet. So they're still dealing with the "I've got a pool in the data." "I've got to do something with it." They don't even know what they want to do with it let alone if it's biased or not. So we're not quite at the leading the witness point there with a lot of organizations. >> But that's something that you expect to see maybe down the road. >> I'm hoping we'll get ahead of it. I'm really hoping that we'll get ahead of it. >> It's a good positive outlook on it, yeah? >> I think that, I think because the real analysis of the data problem in a big machine learning, deep learning way is so new, and the people are actually out seeking guidance, that there is an opportunity to get ahead of it. The second thing that's happening is, people don't have data scientists, right? So they don't necessarily have the people that can code this. So what they're doing now, is they're depending on the vendor landscape to provide them with an entry level set of tools. So if you're Microsoft, if you're Google, if you're Amazon, you're trying very hard to make sure that you're giving tools that have the right ethics in them, and that can help kickstart people's Machine Learning efforts. So I think that's going to be a real win for us. And we talked a lot today at the Strata conference about how, oh you don't have enough images, you can't do that. Or you don't have enough data, you can't do that. Or you don't have enough data scientists. And some of what came back is that, some of the best and the brightest have coded some things that you can start to use to kickstart that will get you to a better place than you ever could have started with yourself. So that was pretty exciting, you know. Transfer learning as an example of taking you know, image node from Google and some algorithms, and using those to take your images and try to figure out if somebody has Alzheimer's or not. Encode things Alzheimer's or not characteristic. So, very cool stuff, very exciting and nice to see that we've got some minds working on this for us. >> Yeah, definitely. Where you're meeting with clients that don't have a data scientist, or chief analytics officer? Sounds like a lot of the technologies need to or some have built in sort of enablement for a difference data citizen within a company. If you talking to clients that don't have a data scientist or data science team, who are your constituents there? Where are companies that don't maybe have that skill gap? Who do they go to in their organization to start evaluating the data that they have to get to know what and start to understand what their potential is? >> Yeah, there's a couple of places people go. They go to their business decision analytics people. So the people that were working with their BI dashboards, for example. The second place they go is to the cloud computing guys, cuz we're hearing a lot about cloud computing and maybe I can buy some of the stuff from the cloud. I'm just going to roll up and get all my machine learning in the cloud, right? So we're not there yet. So the biggest thing that I talk to people about right now is, what are the realities around Machine Learning and AI? We've made tremendous progress but you know you read the newspaper, and something is going to get rid of your job, and AI's going to take over the world, and we're kind of far from that reality. First of all it's very dystopian and negative. But even if it weren't that, you know what you can do today, is not that. So there's a lot of stages in between. So the first thing is just trying to get people comfortable with. No you can't just buy one product, and throw in some data, and you've got everything you need. >> Right. >> We're not there yet. But we're getting closer. You can add some components, you can get some new information, you could do some new correlations. So just getting a reality and grounding of where we are, and that we have a lot of opportunity, and that it's moving very fast. that's the other thing. >> Right. >> IT leaders are used to all evaluated once a year, evaluated once every couple of years. These things are moving in monthly increments. Like really huge changes in product categories. So you kind of have to keep on top of it to make sure you know what's available to you. >> Right. And if they don't they miss out on not only the ability to monetize data streams, but essentially going out of business. Because somebody will come in may be more nimble and agile, and be able to do it faster. >> Yeah. And we already saw those with the digital native companies that started born in the cloud companies, we used to call them. Well, now, everybody can be using the cloud. So the question then is like what's the next wave of that? The next wave of that is around understanding how to use your data, understanding how to get third-party data, and being able to rapidly make decisions and change models based on that. >> One of the things that's interesting about big data is you know it was a big buzzword, and it seems to be becoming less of a buzzword now. Gartner even was saying I think the number was 85 percent of big data projects and I think that's more in tested environments fail. And I often say, "Failure in a lot of cases is not a bad effort." Because it spawns genesis of new products, new ideas, et cetera. But when you're talking with clients who go, alright, we've embraced Hadoop, we've got this big data lake, now it's turning really swampy. We don't know-- >> We've got lakes, we've got oceans, we've got ponds. Yeah. >> Right. What's the conversation there where you're helping a customer clean that swamp up, get broader visibility across their datasets and enable different lines of business. Not just you know, the BI folks or the cloud folks or IT. But marketing, logistics, sales. What's that conversation like to clean up the swamp and do more enablement for visibility? >> I think one of the things that we got really hung up on was, you know, creating a data ocean, right? We're going to bring everything all in one place, it's going to be this one massive data source. >> It sounded great. >> It's going to be awesome. And this is not the reality of the world, right? So I think the first thing in the cleaning up that we have to do, is being able to figure out what's the source of truth for any given dataset that somebody needs. So you see 15 salespeople walk in and they all have different versions of the data that shouldn't happen. >> Right. >> So we need to get to the point where they know where the source of truth is for that data. The second is sort of governance around the data. We spent a lot of time dumping the data but not a lot of time in terms of getting governance around who can access it, what they can do with it, for how long they could have access to it. Is it just internal? Is it internal and external? So I think that's the second thing around like harassing and haranguing the swamps, and the lakes and the ponds, right? And then assuming that you do that, I think the other thing is, You know, if you have a hammer everything looks like a nail. Well, in reality you know when you construct things you have nails, you have screws, you have bolts, right? And picking the right tool for the job is something that the IT leadership has to work with. And the only way that they get that right is to work very closely with the different lines of business so they can understand the problem. Because the business leader knows the problem, they don't know the solution. If you put them together which we've talked about forever, frankly. But now I think we're seeing more imperatives for those two to work closely together. And sometimes it's even driven by security, just to make sure that the data isn't leaking into other places or that it's secure and that they've met regulatory compliance. So we're in a much better space than we were two, three, five years ago cuz we're thinking about the real problems now. Not just how do you collect it, and how do you store it. But how do we actually make it an actionable manageable set of solutions. >> Exactly, and make it work for the business. Well Maribel, I wish we had more time, but thank you so much for stopping by theCUBE, sharing the insights that you've seen. Not just at a conference, but also with your clients. >> Thank you. >> We want to thank you for watching theCUBE. Again, I'm Lisa Martin, live from Big Data SV, in Downtown San Jose. Get involved in the conversation #BigDataSV. Come see us at the Forager Eatery & Tasting Room, and I'll be right back with our next guest. (upbeat music)

>> Narrator: Live from San Jose, it's the Cube. Presenting Big Data Silicon Valley. Brought to you by SiliconANGLE Media and its ecosystem partners. >> Welcome back to the Cube's continuing coverage of Big Data SV. >> This is out tenth Big Data event, our fifth year in San Jose. We are down the street from the Strata Data Conference. We invite you to come down and join us, come on down! We are at Forager Tasting Room & Eatery, super cool place. We've got a cocktail event tonight, and a endless briefing tomorrow morning. We are excited to welcome back to the Cube, Scott Gnau, the CTO of Hortonworks. Hey, Scott, welcome back. >> Thanks for having me, and I really love what you've done with the place. I think there's as much energy here as I've seen in the entire show. So, thanks for having me over. >> Yeah! >> We have done a pretty good thing to this place that we're renting for the day. So, thanks for stopping by and talking with George and I. So, February, Hortonworks announced some news about Hortonworks DataFlow. What was in that announcement? What does that do to help customers simplify data in motion? What industries is it going to be most impactful for? I'm thinking, you know, GDPR is a couple months away, kind of what's new there? >> Well, yeah, and there are a couple of topics in there, right? So, obviously, we're very committed to, which I think is one of our unique value propositions, is we're committed to really creating an easy to use data management platform, as it were, for the entire lifecycle of data, from one data created at the edge and as data are streaming from one place to another place, and, at rest, analytics get run, analytics get pushed back out to the edge. So, that entire lifecycle is really the footprint that we're looking at, and when you dig a level into that, obviously, the data in motion piece is usually important, and So I think one a the things that we've looked at is we don't want to be just a streaming engine or just a tool for creating pipes and data flows and so on. We really want to create that entire experience around what needs to happen for data that's moving, whether it be acquisition at the edge in a protected way with provenance and encryption, whether it be applying streaming analytics as the data are flowing and everywhere kind of in between, and so that's what HDF represents, and what we released in our latest release, which, to your point, was just a few weeks ago, is a way for our customers to go build their data in motion applications using a very simple drag and drop GUI interface. So, they don't have to understand all of the different animals in the zoo, and the different technologies that are in play. It's like, "I want to do this." Okay, here's a GUI tool, you can have all of the different operators that are represented by the different underlying technologies that we provide as Hortonworks DataFlow, and you can stream them together, and then, you can make those applications and test those applications. One of the biggest enhancements that we did, is we made it very easy then for once those things are built in a laptop environment or in a dev environment, to be published out to production or to be published out to other developers who might want to enhance them and so on. So, the idea is to make it consumable inside of an enterprise, and when you think about data in motion and IOT and all those use cases, it's not going to be one department, one organization, or one person that's doing it. It's going to be a team of people that are distributed just like the data and the sensors, and, so, being able to have that sharing capability is what we've enhanced in the experience. >> So, you were just saying, before we went live, that you're here having speed dates with customers. What are some of the things... >> It's a little bit more sincere than that, but yeah. >> (laughs) Isn't speed dating sincere? It's 2018, I'm not sure. (Scott laughs) What are some of the things that you're hearing from customers, and how is that helping to drive what's coming out from Hortonworks? >> So, the two things that I'm hearing right, number one, certainly, is that they really appreciate our approach to the entire lifecycle of data, because customers are really experiencing huge data volume increases and data just from everywhere, and it's no longer just from the ERP system inside the firewall. It's from third party, it's from Sensors, it's from mobile devices, and, so, they really do appreciate kind of the territory that we cover with the tools and technologies we bring to market, and, so, that's been very rewarding. Clearly, customers who are now well into this path, they're starting to think about, in this new world, data governance, and data governance, I just took all of the energy out of the room, governance, it sounds like, you know, hard. What I mean by data governance, really, is customers need to understand, with all of this diverse, connected data everywhere, in the cloud, on PRIM, then Sensors, third party, partners, is, frankly, they need a trail of breadcrumbs that say what is it, where'd it come from, who had access to it, and then, what did they do with it? If you start to piece that together, that's what they really need to understand, the data estate that belongs to them, so they can turn that into refined product, and, so, when you then segway in one of your earlier questions, that GDPR is, certainly, a triggering point where if it's like, okay, the penalties are huge, oh my God, it's a whole new set of regulations that I have to comply with, and when you think about that trail of breadcrumbs that I just described, that actually becomes a roadmap for compliance under regulations like GDPR, where if a European customer calls up and says, "Forget my data.", the only way that you can guarantee that you forgot that person's data, is to actually understand where it all is, and that requires proper governance, tools, and techniques, and, so, when I say governance, it's, really, not like, you know, the governor and the government, and all that. That's an aspect, but the real, important part is how do I keep all of that connectivity so that I can understand the landscape of data that I've got access to, and I'm hearing a lot of energy around that, and when you think about an IOT kind of world, distributed processing, multiple hybrid cloud footprints, data is just everywhere, and, so, the perimeter is no longer fixed, it's kind of variable, and being able to keep track of that is a very important thing for our customers. >> So, continuing on that theme, Scott. Data lakes seem to be the first major new repository we added after we had data warehouses and data marts, and it looked like the governance solutions were sort of around that perimeter of the data lake. Tell us, you were alluding to, sort of, how many more repositories, whether at rest or in motion, there are for data. Do we have to solve the governance problem end-to-end before we can build meaningful applications? >> So, I would argue personally, that governance is one of the most strategic things for us as an industry, collectively, to go solve in a universal way, and what I mean by that, is throughout my career, which is probably longer than I'd like to admit, in an EDW centric world, where things are somewhat easier in terms of the perimeter and where the data came from, data sources were much more controlled, typically ERP systems, owned wholly by a company. Even in that era, true data governance, meta data management, and that provenance was never really solved adequately. There were 300 different solutions, none of which really won. They were all different, non-compatible, and the problem was easier. In this new world, with connected data, the problem is infinitely more difficult to go solve, and, so, that same kind of approach of 300 different proprietary solutions I don't think is going to work. >> So, tell us, how does that approach have to change and who can make that change? >> So, one of the things, obviously, that we're driving is we're leveraging our position in the open community to try to use the community to create that common infrastructure, common set of APIs for meta data management, and, of course, we call that Apache Atlas, and we work with a lot of partners, some of whom are customers, some of whom are other vendors, even some of whom could be considered competitors, to try to drive an Apache open source kind of project to become that standard layer that's common into which vendors can bring their applications. So, now, if I have a common API for tracking meta data in that trail of breadcrumbs that's commonly understood, I can bring in an application that helps customers go develop the taxonomy of the rules that they want to implement, and, then, that helps visualize all of the other functionality, which is also extremely important, and that's where I think specialization comes into play, but having that common infrastructure, I think, is a really important thing, because that's going to enable data, data lakes, IOT to be trusted, and if it's not trusted, it's not going to be successful. >> Okay, there's a chicken and an egg there it sounds like, potentially. >> Am I the chicken or the egg? >> Well, you're the CTO. (Lisa laughs) >> Okay. >> The thing I was thinking of was, the broader the scope of trust that you're trying to achieve at first, the more difficult the problem, do you see customers wanting to pick off one high value application, not necessarily that's about managing what's in Atlas, in the meta data, so much as they want to do an IOT app and they'll implement some amount of governance to solve that app. In other words, which comes first? Do they have to do the end-to-end meta data management and governance, or do they pick a problem off first? >> In this case, I think it's chicken or egg. I mean, you could start from either point. I see customers who are implementing applications in the IOT space, and they're saying, "Hey, this requires a new way to think of governance, "so, I'm going to go and build that out, but I'm going to "think about it being pluggable into the next app." I also see a lot of customers, especially in highly regulated industries, and especially in highly regulated jurisdictions, who are stepping back and saying, "Forget the applications, this is a data opportunity, "and, so, I want to go solve my data fabric, "and I want to have some consistency across "that data fabric into which I can publish data "for specific applications and guarantee "that, wholistically, I am compliant "and that I'm sitting inside of our corporate mission "and all of those things." >> George: Okay. >> So, one of the things you mention, and we talk about this a lot, is the proliferation of data. It's so many, so many different sources, and companies have an opportunity, you had mentioned the phrase data opportunity, there is massive opportunity there, but you said, you know, from even a GDR perspective alone, I can't remove the data if I don't know where it is to the breadcrumbs. As a marketer, we use terms like get a 360 degree view of your customer. Is that actually really something that customers can achieve leveraging a data. Can they actually really get, say a retailer, a 360, a complete view of their customer? >> Alright, 358. >> That's pretty good! >> And we're getting there. (Lisa laughs) Yeah, I mean, obviously, the idea is to get a much broader view, and 360 is a marketing term. I'm not a marketing person, >> Yes. But it, certainly, creates a much broader view of highly personalized information that help you interact with your customer better, and, yes, we're seeing customers do that today and have great success with it and actually change and build new business models based on that capability, for sure. The folks who've done that have realized that in this new world, the way that that works is you have to have a lot of people have access to a lot of data, and that's scary, because that's not the way it used to be, right? >> Right. >> It used to be you go to the DBA and you ask for access, and then, your boss has to sign off and say it's what you asked for. In this world, you need to have access to all of it. So, when you think about this new governance capability where as part of the governance integrated with security, personalized information can be encrypted, it can be blurred out, but you still have access to the data to look at the relationships to be found in the data to build out those sophisticated models. So, that's where not only is it a new opportunity for governance just because the sources, the variety at the different landscape, but it's, ultimately, very much required, because if you're the CSO, you're not going to give access to the marketing team all of its customer data unless you understand that, right, but it has to be, "I'm just giving it to you, "and I know that it's automatically protected." versus, "I'm going to let you ask for it." to be successful. >> Right. >> I guess, following up on that, it sounds like what we were talking about, chicken or egg. Are you seeing an accelerating shift from where data is sort of collected, centrally, from applications, or, what we hear on Amazon, is the amount coming off the edge is accelerating. >> It is, and I think that that is a big drive to, frankly, faster clouded option, you know, the analytic space, particularly, has been a laggard in clouded option for many reasons, and we've talked about it previously, but one of the biggest reasons, obviously, is that data has gravity, data movement is expensive, and, so, now, when you think about where data is being created, where it lives, being further out on the edge, and may live its entire lifecycle in the cloud, you're seeing a reversal of gravity more towards cloud, and that, again, creates more opportunities in terms of driving a more varied perimeter and just keeping track of where all the assets are. Finally, I think it also leads to this notion of managing entire lifecycle of data. One of the implications of that is if data is not going to be centralized, it's going to live in different places, applications have to be portable to move to where the data exists. So, when I think about that landscape of creating ubiquitous data management within Hortonworks' portfolio, that's one of the big values that we can create for our customers. Not only can we be an on-ramp to their hybrid architecture, but as we become that on-ramp, we can also guarantee the portability of the applications that they've built out to those cloud footprints and, ultimately, even out to the edge. >> So, a quick question, then, to clarify on that, or drill down, would that mean you could see scenarios where Hortonworks is managing the distribution of models that do the inferencing on the edge, and you're collecting, bringing back the relevant data, however that's defined, to do the retraining of any models or recreation of new models. >> Absolutely, absolutely. That's one of the key things about the NiFi project in general and Hortonworks DataFlow, specifically, is the ability to selectively move data, and the selectivity can be based on analytic models as well. So, the easiest case to think about is self-driving cars. We all understand how that works, right? A self-driving car has cameras, and it's looking at things going on. It's making decisions, locally, based on models that have been delivered, and they have to be done locally, because of latency, right, but, selectively, hey, here's something that I saw as an image I didn't recognize. I need to send that up, so that it can be added to my lexicon of what images are and what action should be taken. So, of course, that's all very futuristic, but we understand how that works, but that has application in things that are very relevant today. Think about jet engines that have diagnostics running. Do I need to send that terabyte of data an hour over an expensive thing? No, but I have a model that runs locally that says, "Wow, this thing looks interesting. "Let me send a gigabyte now for immediate action." So, that decision making capability is extremely important. >> Well, Scott, thanks so much for taking some time to come chat with us once again on the Cube. We appreciate your insights. >> Appreciate it, time flies. This is great. >> Doesn't it? When you're having fun! >> Yeah. >> Alright, we want to thank you for watching the Cube. I'm Lisa Martin with George Gilbert. We are live at Forager Tasting Room in downtown San Jose at our own event, Big Data SV. We'd love for you to come on down and join us tonight, today, tonight, and tomorrow. Stick around, we'll be right back with our next guest after a short break. (techno music) >> Narrator: Since the dawn of the cloud, the Cube

(upbeat music) >> Announcer: From the Fairmont Hotel, in the heart of Silicon Valley, it's theCUBE. Covering, When IoT met AI: The Intelligence of Things. Brought to you by Western Digital. >> Welcome back everybody. Jeff Frick here with theCUBE. We're at Downtown San Jose at the Fairmont Hotel at a small little conference, very intimate affair, talking about IoT and AI, The Intelligence of Things. When IoT met AI. Now, they've got a cool little start up, kind of expo hall. We're excited to have our next guest here from that. It's Mike Wilson, he's the CEO of BriteThings. Mike, welcome. >> Good to be here, Jeff, how you doin'? >> Absolutely. So, BriteThings. What are BriteThings? >> BriteThings are intelligent plugs, power strips, wall sockets, anything that fits into the plug load space. It learns users behavior and then provides them an intelligent on-off schedule. The goal here is to turn stuff off when it's on and not being needed. >> Right. >> So wasted energy. Nights and weekends in the workspace, for example. >> It sounds like such a simple thing. >> Totally. >> But we were talking before we turned the cameras on, this actually has giant economic impact >> It does. >> in building maintenance, which is a huge category >> Yup. >> as you said, I'll let you kind of break down the numbers as to where >> Sure. >> that energy's being spent and the impact that you guys are having. >> Well our customers are building owners and operators, and they pay an electrical bill to run that building. It's a cost of running the building. About 27% of it goes to lighting, about 38% goes to heating and cooling, and all the rest goes to plug loads. And where we come to the market it, of course there's huge lighting companies, famous names, same with HVAC, but no one's doing anything about plug loads, and the reason is is because plug loads are distributed, they're hard to control. And so what we bring to the market is a product that is small, inexpensive, and can suddenly give owners and operators all the control that they enjoy with lighting and HVAC over their plug loads. >> So it's kind of like Dest, in that it takes a relatively simple function, now because of the cloud, because of the internet, you can add a lot more intelligence into a relatively, I don't want to say dumb device, but the device itself doesn't have to have that much power 'cause you can put the application somewhere else. >> Exactly, so if you just imagine, you're sitting here with me right now. Probably at your workplace and at home there's a bunch of stuff turned on, you're not using it, >> Right >> but you're spending money to keep it powered up, and that's causing CO2 to be generated at power plant down the road. So that's bad for your pocket, it's bad for the environment. So if we can automatically turn that stuff off, then people don't have to worry about it. We can measure it, so here's where the money is. >> Right. >> Not only energy savings, but data. So I can tell you when you turned your stuff on and off, so that means human presence. When you're at work, there's a value to that. If you're going to put a floor of an office building out there and heat it or light it, we can tell you if people are there or not. So you can look at that and make, and save even more money. >> Jeff: Right. >> We've got one customer that uses our product for inventory management. If it plugs in, you can see it on our screen, and you can see if it's on or off, if it's connected and how it's running. So that kind of data ends up being valuable, not only for energy savings, because we turn stuff on and off, but human presence, inventory control, the list goes on and on. Our customers actually every year are coming up with new ways to use our device. >> Right. And just for the baseline savings, you just basically plug it in and turn it on, and you're reporting some huge savings just by just the basic operation of your strips versus a regular strip. >> Exactly. So just imagine, this device is learning your behavior, so that's part of our, you know, that's kind of our core competency here, is these devices measure the amount of energy you're using. When you're not using something, it goes into standby mode, or sleep mode. Then we turn that off to save you the money. But the way we're able to do that is using artificial intelligence to learn patterns, and take those patterns and you can basically guess the best optimized schedule for your devices to be turned and off. >> Right. >> On and off. So if you imagine you've got 100,000 employees, 100,000 different schedules, this thing has to be smart and it can't affect worker productivity. >> Right. >> So we have to be smart enough to know when to turn it on before you come into work, when to turn it off to save you the max amount of money, and be able to measure all of that so you can roll that up and see how much money you're saving. How much CO2 are you reducing? >> Right. >> You know, so sustainability officers love our product too. >> So do you integrate with other types of intelligent systems in that space? The lightings, and the HVAC? >> Yeah. Exactly. So one of the most important things is, I've got a portfolio, my office building is a portfolio of devices and systems, so just one of them is our plug load management, right? So I want to be able to see my plug load in my current control panel. So we've got APIs where our cloud technology is able to take that reporting and stick it into, for example, a Lucid control panel. We're working with Trane right now to integrate their BACnet solution for their building control management. >> Right, right. >> So that their customers are able to see lighting, HVAC, and plug load, >> Just what I was going to say. >> right off the same old screen and operating tools that they've always used. >> Right, right. What's kind of the typical ROI that you pitch people just for the straight-up money savings that they're going to get? >> We got our foot in the door by saying we can reduce your plug load cost a minimum of 30%, and what we're seeing on average is about 40 to 45%. >> Wow. >> It's a huge huge reduction. >> Now where do you go next? >> Well, conquer the world. (Jeff laughs) You know, so imagine this, anywhere in the commercial office space where there's a plug, so let your mind go, how many power strips are out there? >> Right, right. >> How many of those-- >> We're using about 20 of them right here. >> Yeah, so, just, you know, every person at every desk is a potential customer. Every time there's a coffeemaker or a break room, a fax machine, you know, any piece of equipment that's plugged in, we can save you money. Vending machines. We have a customer with these, you know, raise and lower desks. Crazy, they want to just see, they don't want to save energy, they want to know who's using that and how often. >> Jeff: Right, right. >> Our device can do that, too. >> Right. >> And that's that data I was telling you about. Once you start collecting data of how people use plugged-in devices, I'm collecting information about you, how you use your laptop, how you use your charger, how often. >> Because the signature on the draw is different depending on the activity of the device. >> You got it. Exactly. >> I love this. You know, it's so funny because the second-order impact of all these types of things is so much more significant than people give it credit, I think. >> It's about the data. >> Jeff: Yeah. >> And our customer's just love that, because the data gives them control, and when you have control, cost savings. >> And is it just commercial, or you sell them for regular retail customers as well? Or do you-- >> I imagine some day in the future that's a potential, but you know, our focus right now, 'cause the big problem out there is that buildings use 40% of all the energy generated in the United States, and commercial space is the big opportunity, because nights and weekends. >> Right. >> Stuff should be turned off, and we can do that right now. >> Right, right. >> We're the market doing it. >> Buildings with big, big POs. >> Yup. (Jeff laughs) >> Alright, Michael, sounds like exciting stuff, can't wait til I can get one at Best Buy or Office Depot, or something. >> Coming to a store near you, or www.britethings.com. >> Alright, thanks a lot, he's Mike Wilson. Save some energy, get one of these things when they're available, or at least tell the boss to get one at the office. (Michael laughs) >> Definitely. >> Alright, I'm Jeff Frick, you're watching theCUBE. When IoT meets AI in San Jose, California. Thanks for watching. (upbeat music)