 Here's your host, Jeff Frick. Hi, Jeff Frick here with theCUBE. We are on the ground at San Ramon, California at the GE Software Center. Coming out, getting an update. Last time we were here, I think it was 500 people. Now there's 1,200 people. Bill Ruz got him coming in the door and excited to work on big hairy problems, making big impact in the world at a big financial consequence as well. We're joined this next segment by Dave Bartlett, the CTO of GE Aviation. Probably the most cited big data examples of every big data show we go to is to Dave, welcome. Well thank you, and you know, I really like working on big hairy problems. Yeah, absolutely. You can tell just from looking at them, so. Maybe you should slow down on those a little bit. So let's talk about aviation. Literally, we joked a little bit off air. It is the example that people love to cite. They love to talk about how much data comes off a transcontinental flight and it is a tremendous amount of data. But as we were talking, the value of that data is not necessarily consistent across the board. So how should people look at this data when we think, God, now I have the ability to grab and capture and hold. Should I grab a capture and hold? What should I grab, capture and hold? And what does this enable me to do now that I couldn't do before? Exactly, those are the questions you need to be asking. I like to say the art of big data is not in how much data you can get just because you can. But what's the minimum amount of data that you need to get to do the type of analysis or analytical insight to accomplish your objective, whether it's optimization of fuel or optimization of flight path or something related to customer value on the plane. It's really about what's the least amount of data. If you don't have that architectural principle up front, it's not going to scale. Well, it's interesting you say that because a lot of guys will say, people, that now that we can, we should. We don't necessarily need to use sampling methods. We don't need to use some of the statistical methods that we had to put in place because we don't have the capability to grab store and analyze this data. But you're saying all that stuff is still very, very valuable. If not more valuable when you've got this other concept. It really is. I mean, if you look at moving forward, machines are so much better at populating data than people are. They never sleep, they can do it tirelessly. Our new GENX engine, for example, can put out 5,000 data points a second. So that's where you get one transatlantic flight up to half a terabyte of data. But would all that data really be meaningful? Do you need to collect it all? We can. We're setting up a platform that clearly can do that. But if you multiply that times the almost 40,000 GEN engines that are flying every day and then extend that to all the other engines from other manufacturers in the world and then everything else that's connecting to them, you can see, even though you can, first of all, do you want to pay the expense of collecting everything whether you need it or not? Secondly, do you want the liability of holding on to every data about everything in the world or do you really just want to hold what you really need for the time you need it? So I think it's a more intelligent approach. And just as an example, as you fly a plane, the most interesting time is takeoff and landing. Crews, it's a pretty consistent operation. We can model what the data looks like and really just sample for anomalies. And that way we really dramatically limit. Now if we need to collect all the flight, we can. But again, we should really limit it to when it's needed for a purpose. Right, and I think we were talking to So Young earlier today too. And I think what she brought up as a pretty interesting concept is not just the data on the system, right, or the individual component. It's really kind of taking the systems approach and an integrated approach across multiple systems, which is when you really start to get the value. It's not necessarily that jet engine data at crews at 35,000 feet halfway across the country, but what impact does that potentially have on flight operations, on people getting and catching their next flight or versus even the FAA and scheduling things around a big storm or whatever, and really taking a more intelligent systems approach versus just a machine approach if you will. No, that's exactly right. I mean, we started the journey obviously with taking data off of the assets and monitoring them and optimizing that. But the bigger value comes from, as you give it context, context in terms of operations for that flight, operations in terms of maintenance, or extending it even to the ecosystem of aviation. Because at the end of the day, we're not just flying an engine to fly an engine, we're flying it to transport goods or people from point A to point B. And getting that goods from point A to point B involves a lot more than the engine. Engine being, of course, one of the key essential factors there. So it's interesting, from the outside looking in at the aviation world, you look at Boeing 777s or Airbus A380, and there's unbelievably sophisticated machines, the industry's been at it for a while. And they seem like they're pretty well done, so not to be a complete lie. But where are you going next in aviation? Where are some of the really technology opportunities to continue to change the game? Well, so for sure the technology itself, you're right. I mean, the technology is really advanced, it's very impressive, but we continue to make advances, particularly in the area of, you know, few utilization. The newer lines of engines and aircraft are operating 10%, 15% at better efficiencies. That's huge when you multiply that times the number of planes and engines just by themselves. They're operating quieter, so that's really big for passenger comfort. They're operating in a way that's more reliable. So those are all very important. But the big opportunity is also as it extends to the bigger ecosystem that surrounds it. So what does it take to get a passenger, you know, from door to door? You know, I always say when Howard Hughes broke the transcontinental record in 1937 from Burbank, California to Newark, he did that in seven hours and 25 minutes and 28 seconds, or is it 28 minutes and 25 seconds? Anyways. What was he flying? He was flying a plane that was actually turbocharged by GE. It was a monoplane, one that he designed. Howard Hughes, you know, in addition to being a movie mogul, started... Yeah, we were thinking of him in the slow plane, though. The Spruce Goose. Yeah, no, it wasn't the Spruce Goose. It was the Spruce Goose. It was slow and low. But the reason I tell that story is today we can do that flight, you know, clearly in under six hours. If the tailwinds are with you, you know, you can even get in close to five hours. But the door to door time has almost doubled. Number one, there's far more people flying today. In fact, we've surpassed three billion people flying every year now on planes. Huge, right? And then if you think about just getting into an airport, how much time from the time you leave your home to the time you get on a flight do you need to reserve? You know, you're worried about traffic, you're worried about security lines, you're worried about queuing, you're worried about getting that seat. There's so many things in between it. So that flight from Burbank to an office in Newark could literally take up 12 hours of your day. So the actual time on a plane could be half of that time and the rest of the time is spent in that whole, let's say ecosystem. Now, if you're checking- Is that a concern for you? Are you paying attention to that ecosystem in the door to door? Or you just pay attention because it's kind of in your sphere of influence because you're working with that customer. So I'm paying attention to it because it's part of the aviation industry, it's part of the ecosystem. It's things we can affect with big data. We can take data from any part of transportation, baggage handling, food service, security lines and what's the current queue. We have an opportunity to affect all of this. I mean, the good news is it's a huge opportunity. We can make a big impact. Right. Yeah, interesting. And so the other thing that always comes up and it's a great conversation is, at some point in time, I've got the engine, it sits on my plane, I lease my plane. But at the end of the day, I want to sell propulsion. Right? I don't know how you measure it. Propulsion, is it air miles? Is it air seat miles? A different measure is not buying a machine, maintaining that machine, end of life in that machine and then having to get rid of it. But it's really, can I make margin on my services that I wrap around the amenities of really transporting, I guess it would be a seat mile, ultimately. Do you see a day where GE is in a better position or not GE specifically, but the industry where it kind of shifts from the things to really the services that the things provide and you as a global operator of all those engines can probably operate them significantly better than airline A, airline B, or airline C. Yeah, and I really think that's the opportunity that we've got at our doorstep now. And it can shift the whole business model. It can turn it on its head, as you say. And not just selling a seat on a particular airline, but just I want to buy a seat as a passenger. I don't care what airline. I just want a seat. And this is what I want to go along with a seat, certain level of comfort, a certain amount of baggage. So give me that next available capacity that leaves closest to the time I want to leave. And so you're matching it to that. So shifting away from how we traditionally deliver that to a way where you could provision capacity right at the moment when someone's asking for it, regardless of what airline or what service. I mean, think about when just from the beginning of your experience, you go into an airport, the first thing you do is go up to the desk if you're going to check baggage or you need to get your ticket. And your flight is one of the next ones to leave, so the queue line's really long, but then the desk of the other airline next to you doesn't have any flights and it's empty. And the agents are sitting there. Why does it have to be that way? Right? I mean, you got- It's like the old Maubel problem, right? It's the Maubel Mother's Day problem, but they got all the sexist capacity that's generally not being used. Exactly. So there's so much room for efficiency and cost sharing, cost saving. I mean, this is an industry with tight margins. This is a way to really capitalize on some of those efficiencies by leveraging the greater ecosystem. And we do that by better connectedness, better use of that data, better use of that insight. And I think we can still deliver what differentiates airlines in the process. Right. So that's great that you're really thinking of that whole transportation experience, not just the engine on the plane. That's fascinating to me. So kind of wrap up, what's getting you up in the morning right now? What's kind of your short-term mountains that you're trying to take down? Kind of mid-term. I can't tell us anything that Wall Street will run here, but that's okay. So what's kind of exciting for you? What's getting you up this morning? Well, I'll tell you, you know, it's exciting to me and I say this seriously. This is not a job for me anymore. It's a mission. There's so much opportunity to improve our efficiency. So if you think of it in terms of how we utilize resources, how much use we get out of the assets that we use in this industry, how much does it cost to fly? How much, you know, what's our carbon footprint? There's so much opportunity with industrial internet to improve on that. And that's such something that's very near and dear to my heart. When I say, you know, to me, what was a job at one point now is a mission. It's because I see that opportunity to greatly impact not only how we use the precious resources, the finite resources we have, but how can we improve that passenger value, that passenger experience? So they don't feel like they're subject to the industry, but rather they think the industry is giving them an experience that they look forward to. Right, right. That's very powerful. It's a mission. So Dave, thanks for stopping by, taking a few minutes. We'll get back to the mission. Start working on those engines. Yeah, get back to those big Harry problems. Those big Harry problems. They're big Harry problems with big giant solutions that really impact a lot of people, the environment, energy. And I think it's exciting. It's a little different, little different challenges maybe optimizing a game. So you push the thing a little faster, potentially get a little bit better. Add the pops up on your screen as you walk by Starbucks. So that's why you guys are hiring people. So Dave, thanks a lot. Thank you. Absolutely. I'm Jeff Frick. The GE Software Center in Santa Mon, California. We'll catch you next time.