(uplifting music) >> Hi, I'm Peter Burris and welcome again to another CUBE Conversation from our wonderful studios here in Palo Alto, California. Another great topic to talk about, we've got Robert Schmid, who is the Chief IoT Technologist at Deloitte. Welcome to The Cube, Robert. >> Thanks for having me. >> You also have your own video cast, so why don't we get that out of the way. What is it? >> Yeah, every Friday at 9 AM Pacific I do a show called Coffee Chat with Mr. IoT and Miss Connected. I just actually added a co-host, I thought I needed someone to help me. And we talk about IoT. It's on YouTube, you can find it on the channel, and it's really odd for me, that you're going to ask me the questions and I'm going to have to answer. (laughing) So I'm going to try to eat my own, my own advice here and be short. >> Well you know maybe someday you can have one of the Wikibon folks in your podcast, or video cast, we'd love to do that. >> Yeah that'd be great. >> Alright let's start here though. Deloitte's a great name, been around for a long time, associated with customer value in very profound ways, complex applications. That certainly characterizes IoT. What's going on with IoT at Deloitte? >> For us, we started a whole practice around IoT, and I'm leading that practice, but the thing for us was, there were a lot of science experiments going on around IoT, technology based, but we really wanted to bring it to what's the value behind IoT? So we really focused on use cases, and today we see that most focuses are on industrial IoT, though we spend a lot of time around connected products as well. I personally actually today worked on a project in a factory in Chicago, on a shop floor, connecting machines and measuring data and providing value. I work with an airline at an airport, around their travel so really helping guide you throughout the day. Interesting fact, you know we swipe away a lot of notifications without actually doing anything with it but when airline tells you, "Please come in 10 minutes early, the TSA wait time is long." I know you and I got to be there. >> You pay attention. >> Yeah, we got to be there early. We actually react to those notifications so I work on that and I work with high tech companies around their platforms, how to make their platforms better. >> You've raised a lot of really, really important issues but let's start with this notion of use cases >> Sure. >> A factory floor with a lot of PLCs, spitting out information, mediated by individuals or users and the data, where's it end up? That's real different from an airport where a lot of the data's being generated by a human being as they move places or is intended to be consumed by a human being. What kind of common patterns are you seeing in these use cases that brings them all under this notion of IoT? >> I always think of IoT as taking sensor data and making decisions based on those and what's interesting to me is that it creates this real interesting dilemma that we thought we knew what goes on with users, how they work and what they do. We do surveys just to find out what they're saying, the survey's actually probably not what they do but now with sensors we know what they do all the way to machines where we have decades of people having experience about, "This sounds a little odd, the machine doesn't sound right" but then they don't know what to do with it and now we can measure that because really at the end of the day, vibration isn't anything else but sound, right? So for me this is all about, and what's common about this, is that we really take that, we think we know to we actually know because we can now measure with sensors what goes on in that area. >> So it's almost like taking a lot of that time motion analysis, operations research that we used to do periodically, episodically with human beings doing their best to record stuff and bringing a lot of that discipline continuously and in real time so that it can better inform overall decisions, right? >> Yeah, I mean almost near real time, many of these cases and that's a really interesting scenario for me, right? Because now can actually see what happens in the factory when I tune the mix or the blend of my raw materials, what happens to the product that gets made at the end of that. >> As we think about the challenges or the changes that we foresee going on, is there a difference in thinking about humans as users or humans as consumers of a lot of this data and machines? I know there is, but how is this, because kind of the machine side has always been associated with SCADA, OT and the disciplines and approaches for that side seem a little bit different than what's coming out of the mobile world which is still very, very closely associated with how we utilize or how we deploy these systems to inform decisions in either case, is that right? >> I don't really know if we do so much about decisions for machines. I think at the end of the day many of the decisions are still made by humans. I mean I think about this like, we have a heating element running over, at the end of the day it still is a human that goes and sort of like says, "Yeah, let's turn that off." >> But there's still automation that takes place? >> Absolutely there's automation but automation takes place today. >> Sure. >> None of this is particularly new. I mean OT has done automation forever, right? >> Right. >> I think the interesting part is now taking the learning and connecting the different data points together. I talked about the factory floor, I just showed, actually, at the show we created a virtual factory line, life size. You can download it, it's the virtual factory by Deloitte. If I get my phone going I can show you, but it's not. Right here. (laughing) I call it "the internet of rubber ducks". >> "The internet of rubber ducks"? >> The internet of rubber ducks. Yeah, it's kind of cute. You have these little yellow ducks and if you load the app you can see them being made. But it's actually really what goes on at the factory and it really shows how when you change the blend at the beginning of a production line, how it effects at the end of the factory line, the outcome, how much scrap you have. What's the scrap? What's the overall equipment efficiency? OEE and so forth. What happens is now we can connect data from the very beginning of the factory line with he very end of the factory line and then combine that with contextual data such for example as temperature or the vibration on the machine or the current which we haven't done before. This whole time series of data that we now correlate becomes really critical and I don't think that's something we've done really as much before. That has not driven automation in this zone. >> If we think about it, we're talking about sensors which as you said, SCADA's been around for a long time and it tends to automate very, very proximate to where that sensor tower might be but a lot of the information that went into decisions was actually then generated by a person, perhaps a shift supervisor or somebody else or a machine operator said, "I heard a rattle" but there's no time so it's difficult to correlate and now we're talking about up leveling a lot of that information so it becomes part of the natural flow out of the machine but still for human consumption to make decisions? >> Yeah, very much like that. As I said, I talked about the blend of the materials that go in and then now we can correlate that particular part of the sheet. We can look on video and see how it looked and check the quality and then see at the end how many pieces of product did we produce. Actually in that particular case, it's really fascinating, it wasn't so much about reducing cost, it was actually increasing output. For them each line costs about 10 million and with the findings we have and what we're doing with them, we can actually give them the ability not to build another line but actually produce more lines because they can sell more which is a great position to be in. >> Sure, absolutely. >> You actually impact the top line rather than just the bottom line. >> Well productivity fundamentally is a function of what work you can perform for what costs are required to perform that work and if you can improve the effectiveness of something, keep the cost the same but get more work out of it, that's a big, big plus on the bottom line. >> And they have the market to sell it in to, right? >> Absolutely. >> If you just make more and you can't sell it- >> Well there's that, too. >> Yeah, which is really the good thing about that particular example. >> But talk about how, for example, you noted that they can look at a video of how the plastic or the sheets coming off the machine or set of rollers perhaps but how does AI start to be incorporated in to this IoT discussion? And what kind of use cases are you seeing becoming appropriate or more appropriate or made more productive by some of these new technologies we bring, some of the analytics and some of the IoT elements together? >> We find that we do a variety of theories. We go in and we say, "Hm, how about this? How about that?" And then we have our data scientists go and look at models for that and see what goes on and then put machine learning in and then we take those machine learning models and feed it back into, we talked before a little bit about this, but age processing is really something where we now process some of those models on the edge. The algorithm development and all the analysis we send that to the Cloud, we do number crunching there and we really take advantage of the unlimited capacity. >> A lot of the training happens up at the Cloud? >> A lot of the training happens in the Cloud and then whatever models come down, we load those on the edge and we actually do make decisions right there on the edge or we give the operator the choices to make the decisions right there on the edge. >> Training up in the Cloud but the inferencing actually is proximate to the actual action so there's locality for the action based on what's in the model and there's a lot of training that can happen, quite frankly, where you don't have to underwrite the cost of the infrastructure to do it? >> Exactly. >> That suggests that there is going to be a fair amount of change in the industry over the next few years in this notion of moving from OT to IT or SCADA to IoT. This is not just a set of technology issues, there's some fundamental other questions that are going to be important. A lot of people just kind of assume, "Oh, well throw a bunch of general purpose stuff at these IoT related things and it's going to be the IT industry all over again." Or is really the expertise associated with the use case going to be more important? How is that use case going to be ultimately realized? Is it going to be a bunch of piece parts or is it going to be more of a holistic approach to really understanding the nature of the solution and making sure that the outcome is the first and focal point? >> I'm going to come back to your question in a second. I just always, I have to smile because, so I have a Masters in petroleum engineering. So when I studied, I built really fancy models, like differential models, indicial models and you know, I simulated fracturing and- >> Process control's built with that stuff. >> I lived a good part of my life in OT and then after I came out of university I really moved more and more into IT so I've spent most of my career in information technology, including being a CIO. I always thought that the most fancy math we'd ever do is percentage calculations and that was pretty fancy. (laughs) Now, I find myself in this awesome place where I can bring together some of that OT, some of that real deep data science work that I did early on in my life, now with some of the process and system implementation expertise and practice that have come out of IT. They really come together, I don't think one takes over the other. I think there's real sort of meeting each other and going like, "Wow, okay. I guess we really got to work together." So that's really fun. About your question around what solutions do we see today? I see a lot of very vertical, very one use case oriented solutions, that go all the way from the sensor to edge to Cloud to, hopefully, integration to the back office systems because without that you can't really take good action. But they're very narrow and so, like in the good old Cloud days when Cloud became really big, there were really good point solutions and the good Cloud providers sold to the business user right there and then and ran around IT. And I see the same in IoT happening right now. You get a very good solution for temperature control on a truck, for example, right? Which is a very narrow solution but the moment you want to start doing something with your warehouse where you have other sensors and you need a horizontal platform, those vertical solutions fall short. That's what I think is sort of like the interesting dilemma right now. You have these vertical pillars and you have the horizontal platforms that the big providers have and so it'll be interesting to see when we're going to see some consolidation in this space when some of the vertical solutions are going to get bought out by the horizontals to provide better use cases. It's a little bit like the ERPs who did every industry and then eventually they realized, "We need industry focused solutions." We'll see the same in the IT space. >> The IT industry has always supposed that we can transfer knowledge we gain in one domain into other customers, into other use cases. It almost sounds like what you're saying is we're going to have that vertical organization of expertise, which is absolutely essential to solve that complex, core business problem. High risk, high value, high uncertainty, often bespoke, never done before but over time we will see a degree of experience sharing and diffusion so that over time we might see better, more applicable platforms that are capable of providing that foundation for a broader set of use cases but that' going to be a natural process of accretion. Is that how you kind of see it? >> Yeah, I mean we're all going to need streaming capabilities. We're all going to need capabilities for machine learning, for cognitive, for video analytics. We'll all need that but I think it'll be specific to the individual use case in a sense of, I'll give you an example, I just had a data scientist show me how he started looking at 20 year old scientific research on gear boxes. What frequencies happen in gear boxes, specifically to certain scenarios. That's not replicable from a gearbox to a pump, you know? >> Right. >> You have different, so there is specific things and yes it might be the same gearbox in one factory that produces, I don't know, rubber ducks to another factory who makes metal sheets but it's still gearbox specific, right? I think this is the specificity we're going to see around models, around learning and around sensors to a certain extent. >> Excellent, Robert Schmid, Chief IoT Technologist at Deloitte, thanks very much for being on theCUBE. >> Thanks for having me, Peter. It was a pleasure, thank you. (uplifting music)