 You're welcome back everybody. Jeff Frick here with theCUBE. We're at our Palo Alto studios having a CUBE conversation about digital transformation, industrial internet of things, AI, ML, all things great, and we're really excited to have representative of GE, one of our favorite companies to work with because they're at the cutting edge of old industrial stuff and new digital transformation and building a big software organization on a standard mode. So we're happy to have our first time, Jeff Earhart. He is the VP Intelligence Systems from GE Digital. Jeff, great to see you. Pleasure to be here. Thanks for having me. So how did you get into GE? You've actually a creature of the valley. You've been here for a little while. How did you end up at GE? I have, I'm a new guy. So I've been here about a year and a half. I came in via the acquisition of a company called Wise.io where I was the CEO. So I've spent the last 10 years of my life building two different analytics startups. One was based around a very popular and powerful open source language called R and spent a lot of time working with much of the Fortune 500. I think the really data-driven companies now that you would think of the Facebooks, the Goldman Sachs, the Mercs, the Pfizer's helping them go through this data-driven journey. Anyway, that company was acquired by Microsoft and is embedded into their products now. But the biggest thing I learned out about that was that even if you have really good data science teams it's incredibly hard to go from whiteboard into production. How do you take concepts and make them work reliably, repeatedly, scalably over time? And so Wise.io was a machine learning company that was a spin-up from Berkeley and we spent time building what I now refer to as intelligent systems for the purposes of customer support automation within things like the Salesforce and Zendesk ecosystem. And it was really that capability that drew us to GE or drew GE to approach us to think about how do we build that gap not just from algorithms but into building true intelligent applications. Right, so GE is such a great company. They've been around for a hundred years, original DAO component. Jeff ML is not there now, but he was CEO, I think for 16 years, a long period of time. Beth Comstock, fantastic leader, Bill Rood building this great organization. But it's all built around these industrial assets. But they've started, you know, they did the industrial internet launch. We helped cover it in 2013. They had the Pritx Cloud, their own kind of industrial internet cloud, had a big developer conference. But I'm curious, it's coming from kind of a small Silicon Valley startup situation. When you went into GE, what's kind of the state of their adoption, you know, kind of how had Bill's group penetrated the rest of GE and were they making progress? Are people kind of getting it or are we still doing some evangelical work out in the field? Absolutely, both, meaning people understand it or implementing. Yet I think there was maybe misunderstandings about how to think about software data in particular analytics and AI and machine learning. And so a big part of my first year at the company was to spend the time coming in really from the top down from sort of the CEO and CDO levels across the different business, understanding what was the state of data and data-driven processes within their businesses. And what I learned really quickly was that the core of this business and this all, you know, public information and well-publicized is in things like GE Aviation. It's not necessarily the sale of the engine that is incredibly profitable, but rather it's maintaining and servicing that over time. And what organizations like them, like our oil and gas division with things like their inspection capabilities, like our power division, had really done is they had created as a service businesses where they were taking data across the customer base, running it through a data-driven process and then driving outcomes for our customers. And all of a sudden the aha moment was, wow, wait a minute, this is the business model that every startup in the Valley is getting funded to take down the traditional software players for. It's just not yet modern, scalable, repeatable with AI and machine learning built in. But that's the purpose and the value of building these common platforms with these applications on top that you can then make intelligent. So once we figure that out, it was very easy to know where to focus and start building from that. So it's just, it's kind of weird, I'm sure for people from the outside looking in to say data-driven company, we all want to drive data-driven companies. But then you say, well, wait a minute, now GE builds jet engines, there's no greater example that's used at conferences as to the number of terabytes of data an engine throws off on a transcontinental flight. Or you think of a power plant or locomotion and you think of the control room with all this information. So it probably seems counterintuitive to most that didn't they have data? Weren't they a data-driven organization? How has the onset of machine learning and some of the modern architectures actually turned them into a data-driven company where before, I think they were, but really not to the level that we're specifying here. Yeah. Your objective where you're trying to take them. Absolutely. So machine learning, AI, whatever buzzwords you want to use is a fascinating topic. It's certainly come into vogue. It's like many things that are hyped, gets confused, gets misused and gets overplayed. But it has the potential to be both an incredibly simple technology as well as an incredibly powerful technology. So one of the things I've most often seen cause people to go awry in this space is to try to think about what is the new things that I can do with machine learning? What is the green field opportunity? And whatever I'm talking to somebody at whatever level, but particularly at the higher levels of the company is I like to take a step back and I like to say, what are the value producing data driven workflows within your business? And I say define for me the data that you have, how decisions are made upon it and what outcome that you are driving for. And if you can do that, then what we can do is we can overlay machine learning as a technology to intelligently automate or augment those processes. And in turn, what that's going to do is it's going to force you to standardize your infrastructure, standardize those workflows, quantify what you're trying to optimize for your customers. And if you do that and standardize it in an incremental way, you can look backwards having accomplished some very big things. Right, and those are such big foundational pieces that most people, I think discount again, just the simple question of where is your data? That's right. What form is it in? So another interesting concept that we cover all the time and all the shows we go to is democratization, right? So it seems to me pretty simple actually, how do you drive innovation, democratize the data, democratize the tool to manipulate the data and democratize the ability to actually do something about it. That said, it's not that easy. And this kind of concept that we see evolving from citizen developer to citizen integrator to citizen data scientist is kind of where we all want to go to. But as you've experienced firsthand, it's not quite as easy as maybe it appears. Yeah, I think that's a very fair statement. And one of the things, again, spent a lot of time talking about it. I like to think about getting the right people in the right roles using the right tools. And the term data scientist has evolved over the past five plus years going from to give Drew Conway some credit of his Venn diagram of a programmer or a math guy in a domain expert into meaning anybody that's looking at data. And there's nothing wrong with that, but the concept of taking anybody that has the ability to look at data within something like a BI or a Tableau tool, that is something that should absolutely be democratized and you can think about creating citizens for those people. On the flip side though, how do you structure a true intelligence system that is running reliably, robustly and in particular in our field in mission critical, high risk, high stakes applications? There are bigger challenges than simply are the tools easy enough to use. It's very much more a software engineering problem than it is a data access or algorithmic problem. And so we need to build those bridges and think about where do we apply the citizens to for that understanding and then how do we build robust, reliable software over time? Right, so many places we can go and we're going to go with a lot of them. But one of the things you touched on which also is now coming in vogue is kind of ML that you can, somebody else's ML, right? As you would buy an application at an app store, now there's all kinds of algorithmic equations out there that you can purchase and participate in. It really begs an interesting question of kind of the classic buy versus build or as you said before we turn on the cameras, buy versus consume because with API economy with all these connected applications, it really opens up an opportunity that you can use a lot more than was produced inside your own four walls for those applications. And you see in that, how's that kind of playing out? So we can parse that in a couple of different ways. So the first thing that I would say is there's a Google paper from a few years back that we love and it's required reading for every new employee that we bring on board and the title of it was machine learning is the high interest credit card of technical debt. And the point and one of the key points within that paper is that the algorithm piece is something like 5% of an overall production machine learning implementation. And so it gets back to the citizen piece about it's not just making algorithms easier to use but it's also about where do you consume things from an API economy. So that's the first thing I would think about. The second thing I would think about is there's different ways to use algorithms or APIs or pieces of information within an overall intelligent system. So you might think of speech to text or translation as capabilities. That's something where it probably absolutely makes sense to call an API from an Amazon or a Microsoft or a Google to do that but then knowing how to integrate that reliably, robustly into the particular application or business problem that you have is an important next step. The third thing that I would think about is it very much matters what your space is. And there's a difference between doing things like image classification on things like ImageNet which is publicly available images that are well documented. Is it a dog versus a cat? Is it hot dog versus not? Versus some of the things that we face within an industrial context which aren't really publicly available. So we deal with things like within our oil and gas business we have a very large pipeline inspection integrity business where the purpose of that is to send the equivalent of an MRI machine through the pipes and collect spectral images that collect across 14 different sensors. The ability to think that you're going to take a pre-trained algorithm based on deep learning and publicly available images to something that is noisy, dirty, has 14 different types of sensors on it and good and good answer is ridiculous. And there's not that many, right? That's the other thing I think people underestimate that the advantage that Google has is we're all taking pictures of dogs and blueberries so that it's got so much more data to work with as opposed to these industrial applications which are much smaller. That's right. So we'll shift gears again in terms of the digital transformation. One of the other often said examples is when will the day come that GE doesn't sell just engines but actually sells propulsion miles to really convert to a service. And that's ultimately where it needs to go but it's kind of the next step beyond maintenance. How are you seeing that digital transformation play out? Do people kind of get it to the old line guys that run the jet engine, see that this is really a better opportunity because you guys have, and this is the broader theme, very unique data and very unique expertise that you've aggregated across in the jet engine space, all of your customers and all of the flying conditions and all of the types of airplanes where one individual mechanic or one individual airline just doesn't have an expertise. Huge opportunity. That's exactly right. And you can say the same thing in a power space, a power generation space. You can say the same thing and the one we were just talking about. You know, things like air inspection technology spaces. That's what makes the opportunity so powerful at GE and it's exactly the reason why I'm there because we can't get that any place else. It's both that history, it's that knowledge tied to the data. And very importantly, it's what you hinted at but as Bear is repeating is the customer relationships and the customer base upon which you can work together to aggregate all that data together. And if you look at what things are being done, they're already doing it. They are selling effectively efficiency within a power plant. They are selling safety within certain systems. And again, coming back to why create a platform, why create standardized applications, why put these on top, is if you standardize that, it gives you the ability to create derivative and adjacent products very easily, very efficiently in ways that nobody else can reach. And I love the whole, for people who aren't familiar with the digital twin concept and really leveraging this concept of a digital twin not to mimic kind of the macro level but to mimic the micro level of a particular part unit engine in a particular ecosystem where you can now run simulations, you can run tests, you can do all kinds of stuff without actually having that second big piece of capital gear out there. That's right. And it's really hard to mimic those if you didn't start from the first phase of how did you design, build and put it into the field. Right, right. So I want to shift gears a little bit just on some philosophical things that you've talked about and doing some research. One of them is that tech is the means to an end. And I know people talk about that all the time but we're in the tech business. We're here in Silicon Valley. People get so enamored with the technology that they forget that it is a means to an end. It is not the end and to stay focused. How are you seeing that kind of play out in G-Digital? Obviously Bill built this humongous organization. I'm super impressed that he's able to hire that many people over the last like four years in San Ramon. Originally I think just to build the internal software working within the G business units but now really to go much further in terms of industrial internet connectivity, et cetera. So how do you see that really kind of playing out? Yeah, I think one of my favorite quotes that I forget who it came from but I'll borrow is, customers don't want to buy a one inch drill bit, they want to buy a one inch hole. And I think there's both an art and a science and a degree of understanding that needs to go into what is the real customer problem that they are trying to solve for and how do you peel the onion to understanding that versus just giving what they asked for? And I think there's an organizational design to how do you get that right? So we had a visitor from Europe, the chairman of one of our large customers who's going through this data-driven journey and they were at the stage of simply just collecting data off of their equipment. In this case it was elevators and escalators. And then understanding how is it being used? What does it mean for field maintenance, et cetera? And but his guys wanted to move right to the end stage and they wanted to come in and say, hey, we want to build AI and machine learning systems. And we spent some time talking through them about how this is a journey, how you step through it. And you could see the light bulb go off that yes, I shouldn't try to jump right to that edge state. There is a process of going through it, number one. And then the second thing we spent some time talking about was how we can think about structuring this company to create that bridge between the new technology people who are building and doing things in a certain way and the people who have the legacy knowledge of how things are built, run and operated. And it's many times those organizational aspects that are as challenging or as big of barriers to getting it right as a specific technology. For sure, I mean, people process in tech, it's always the people at a hard part. It's funny, you bring up the elevator escalator. So we did a show at Splunk Mini Moons ago and we had a person on from an elevator company and the amazing insight, they connected Splunk to it. They could actually tell the health of a building by the elevator traffic. Not the health of its industrial systems and its HVAC, but whether some of the tenants were in trouble by watching the patterns that were coming off the elevator. I mean, just a whole different kind of data driven value proposition than they had before. So again, if you could share some best practices really from your experience with R and then now kind of what you're doing at GE about how people should start those first couple of steps in being data driven beyond kind of the simple in terms of just getting your house in order, getting your data in order, you know, where is it? I think if you connect to it, is it clean? How should they kind of think about prioritizing? How do they look for those easy wins? Because at the end of the day, it's always about kind of the easiest wins to get to support to move to the next level. So I've sort of got a very simple, high little playbook and the first step is you have to know your business and you have to really understand and prioritize. Again, sometimes I think about the, not the build by decision per se, but maybe the build consume decision. And again, where does it take the effort to go through hiring the people, understanding, building those solutions versus where is it just best to say, I'm best to consume this product or service for somebody else. So that's number one and you have to understand your business to do that really well. The second one is, and we touched on this before, which is getting the right people on the right seats of the bus. Understanding who those citizen data scientists are versus who your developers are, who your analytics people are, who your machine learning people are and making sure you've got the right people doing the right things. And then the last thing is to make sure to understand that it is a journey. And we like to think about the journey that we go through in sort of three phases or sort of three swim lanes that can happen both in parallel, but also as a journey. And we think about those as sort of basic BI and exploratory analytics. How do I learn, is there any there, there? And fundamentally you're saying, I want to ask and answer a question one time. Think about traditional business reporting. But once you've done that, your goal is always to put something into production. You say, I've asked and answered once, now I want to ask and answer hundreds, millions, billions of times in a row. And the goal is to codify that knowledge into a statistic and analytic of business rule. And then how do you start running those within a consistent system? And it's going to do and force exactly what you just said. Do I have my data in one place? Is it scalable? Is it robust? Is it queryable? Where is it being consumed? How do I capture what's good or bad? And once I start to then define those, I can then start to standardize that within an application workflow and then move into again, these complex adaptive intelligent systems powered by AI and machine learning. And so that's the way we think about it. Know your business, get the people right, understand that it's a systematic journey. And really bake it into the application. That's the thing that we don't want to make the same mistake that we did with big data. Put it into the application. It's not to stand alone, kind of funny thing. Exactly. Jeff, I'll give you the last word before we wrap for the day. So you've been with G now for about a year and a half, about halfway through 2018. What are your priorities for the next 12 months? If we sit down here, June one next year, what are you working on? What's kind of top of mind for you going forward? Yeah, so top of mind for me. So as I mentioned, sort of our first year here was really serving the landscape, understanding how this company does business, where the opportunities are, again, where those data-driven workflows are. And we have an idea of that with the core industrials. And so what we've been doing is getting that infrastructure right, getting those people right, getting the V1s of some very powerful systems set up. And so what I'm going to be doing over the next year or so is really working with them to scale those out within those core parts of the business, understand how we can create derivative and adjacent products over those, and then how we can take them to market more broadly based upon that exactly as you said earlier, that large-scale data that we have available, that customer insight and that knowledge of how we've been building this stuff. So, well, I look forward to it. I look forward to being back in a year. All right. Jeff Aratt, thanks for watching. G, I'm Jeff Frick. You're watching theCUBE from our Palo Alto Studios. See you next time. Excellent.