WEBVTT - Accelerating Innovation with Hybrid Cloud at the Edge 0:00:04.000 --> 0:00:07.560 Hello, Hello. This is Smart Talks with IBM, a podcast 0:00:07.600 --> 0:00:11.520 from Pushkin Industries, iHeart Media and IBM about what it 0:00:11.560 --> 0:00:14.400 means to look at today's most challenging problems in a 0:00:14.400 --> 0:00:22.479 new way. I'm Malcolm Gladwell. Today I'll be discussing the 0:00:22.480 --> 0:00:26.960 innovations around hybrid cloud with Lumen's David Shakochus and IBM's 0:00:27.000 --> 0:00:31.200 Howard Boville. David is Vice President for Enterprise Technology and 0:00:31.440 --> 0:00:36.240 Field CTO at LUMEN, where he's helped clients across industries 0:00:36.600 --> 0:00:42.239 create new business opportunities through unique digital interactions. David has 0:00:42.240 --> 0:00:45.680 been immersed in cloud computing long before his time with Luhmen, 0:00:45.960 --> 0:00:50.720 working with companies such as Unit, Digital Island and fuse Point. 0:00:51.280 --> 0:00:54.400 You're putting computing capacity in places that didn't used to 0:00:54.400 --> 0:00:56.800 be thought of as data centers before. There's an element 0:00:56.840 --> 0:01:00.800 of novel challenge and so they're inherently there's there's more complexity. 0:01:01.960 --> 0:01:05.440 Howard is the head of IBM Cloud Platform. In this role, 0:01:05.680 --> 0:01:11.200 Howard has focused on driving digital transformation for enterprises, especially 0:01:11.440 --> 0:01:15.760 in highly regulated industries. Before joining IBM, Howard was Chief 0:01:15.800 --> 0:01:18.959 Technology Officer for Bank of America, where he led the 0:01:19.000 --> 0:01:22.679 transformation of the Bank's infrastructure and developed one of the 0:01:22.760 --> 0:01:27.160 largest internal private clouds. The times you have to kind 0:01:27.160 --> 0:01:29.200 of be a technology of angelists in terms of what 0:01:29.240 --> 0:01:31.920 the art of the possible is against the problems. In 0:01:31.959 --> 0:01:35.679 this episode, we'll explore working and living in a world 0:01:35.720 --> 0:01:39.520 of cloud technology. Will show you how new innovations in 0:01:39.560 --> 0:01:43.679 cloud computing have reimagined a world where computing can happen 0:01:43.840 --> 0:01:48.720 anywhere and businesses can use data to accelerate innovation to 0:01:48.840 --> 0:02:07.400 improve service and performance. Let's dive in. Welcome everyone, Howard 0:02:07.400 --> 0:02:11.400 and David. Thank you for joining us today. Let's jump in. David, 0:02:11.440 --> 0:02:15.920 I'm gonna ask you to define some terms and that 0:02:15.960 --> 0:02:18.399 will be easy for you but useful for the rest 0:02:18.440 --> 0:02:22.480 of us. First of all, the Fourth Industrial Revolution? What 0:02:22.680 --> 0:02:27.840 is it? Yeah? Good one, so we can really look back. 0:02:27.880 --> 0:02:32.680 I think on history in these periods of technology advancement, right, 0:02:32.720 --> 0:02:36.320 you know, the period of industry that was defined by 0:02:36.360 --> 0:02:39.560 steam power, the period of history, and the industrial period 0:02:39.600 --> 0:02:43.760 of history that was defined by electrical distribution. We commonly 0:02:43.800 --> 0:02:46.320 think of the third one is really this information age, 0:02:46.360 --> 0:02:52.600 the information revolution of digitization of process creation and online 0:02:52.639 --> 0:02:56.080 connectivity of data. Is this third industrial revolution of the 0:02:56.120 --> 0:03:00.840 digital age? Information technology systems community caating with each other, 0:03:00.960 --> 0:03:04.280 and the advent of all that you can do in 0:03:04.280 --> 0:03:09.080 industry with those technologies, and the Fourth Industrial Revolution is 0:03:09.080 --> 0:03:12.800 really this reflection of the explosion of data that gets 0:03:12.840 --> 0:03:16.120 created by all that connectivity. Taking data and being able 0:03:16.120 --> 0:03:19.440 to acquire it, analyze it, and take action upon it 0:03:19.480 --> 0:03:22.320 is opening up a wide range of new industries and 0:03:22.360 --> 0:03:26.880 new business opportunities and new regulatory challenges. UM and that's 0:03:26.919 --> 0:03:29.360 what we mean when we say the Fourth Industrial Revolution. 0:03:31.720 --> 0:03:35.240 How did Luminum IBM come together, and what's the logic 0:03:35.280 --> 0:03:38.080 behind your collaboration in this field? You can't take the 0:03:38.160 --> 0:03:41.360 heritage of both companies. So limit are a world class 0:03:41.360 --> 0:03:45.520 global networking company. They connect things together at the highest 0:03:45.600 --> 0:03:48.320 level of quality, lowest latency and so on. And it's 0:03:48.360 --> 0:03:51.040 and it's hard through all the actual transformations that IBM 0:03:51.040 --> 0:03:53.400 has been through. Is where a compute company on which 0:03:53.400 --> 0:03:56.240 the software runs and we write that also the software 0:03:56.240 --> 0:03:58.480 in certain contexts as well. So the combination of the 0:03:58.520 --> 0:04:03.520 two capabilities solves for the problem. We've been working together 0:04:03.720 --> 0:04:06.280 for years. I think the the advent of what we've 0:04:06.280 --> 0:04:10.240 been focused on with IBM Cloud Satellite has really been 0:04:10.840 --> 0:04:14.400 initiated by Lumen's investment in making our network a place 0:04:14.440 --> 0:04:17.359 where you can run software workloads more readily and easily. 0:04:17.640 --> 0:04:20.479 And IBM cloud satellite is a great modality that just 0:04:20.520 --> 0:04:23.760 snaps right into that network. Yeah, you work for Lumen. 0:04:24.560 --> 0:04:27.400 Is the simplest way to describe Lumen that Lumen is 0:04:27.440 --> 0:04:31.160 a Fourth Industrial Revolution company. We're we're a Fourth Industrial 0:04:31.160 --> 0:04:34.120 Revolution company because we believe at the core of all 0:04:34.160 --> 0:04:37.240 of it is connectivity. All that data and all those 0:04:37.240 --> 0:04:39.040 sources of data and all the ways that you need 0:04:39.080 --> 0:04:42.480 to interact with that data requires a substantial amount of 0:04:42.560 --> 0:04:46.640 aggregate networking capacity. We're now kind of hitting this tipping 0:04:46.640 --> 0:04:48.919 point in the Fourth Industrial Revolution where the amount of 0:04:48.960 --> 0:04:52.440 data coming inbound from cameras and from sensors and from 0:04:52.520 --> 0:04:55.960 devices and gaming consoles and and a variety of uh 0:04:56.279 --> 0:04:59.279 input sources like that is actually exceeding capacity in the 0:04:59.279 --> 0:05:02.080 other direction. So that's really why for the Fourth Industrial 0:05:02.120 --> 0:05:05.280 Revolution of work, you need massive amounts of network connectivity. 0:05:05.279 --> 0:05:08.480 And that's what what LUMAN does. So this brings up 0:05:08.520 --> 0:05:10.360 the second word I want you to define, and that's 0:05:10.640 --> 0:05:15.640 edge computing, which I'm assuming is edge computing is it 0:05:15.720 --> 0:05:19.520 is a technological response to the phenomenon you've just described 0:05:20.040 --> 0:05:23.240 it is. It's one way to think about edge computing. 0:05:23.360 --> 0:05:24.880 The way we talk about it a lot is it's 0:05:24.880 --> 0:05:30.480 moving workloads software workloads closer to digital interactions, and a 0:05:30.560 --> 0:05:35.120 digital interaction could be between things and people and business models. Yes, 0:05:35.240 --> 0:05:36.840 I mean, just to add to some of David's point 0:05:36.880 --> 0:05:38.920 with some kind of practical use cases that we kind 0:05:38.920 --> 0:05:41.839 of we're involved in. So so, first and foremost, the 0:05:41.920 --> 0:05:45.080 edge computing piece actually is joining the analog world to 0:05:45.120 --> 0:05:47.840 the digital world, Whereas until this point you would look 0:05:47.839 --> 0:05:49.760 at the digital world through the screens that we all 0:05:49.839 --> 0:05:53.120 spend sumers time looking at. Whereas on the edge, it's 0:05:53.160 --> 0:05:57.359 actually looking at physical locations like retail branches, like shipping 0:05:57.360 --> 0:06:01.680 concern as, like welds on on a well, but an automobile. 0:06:01.720 --> 0:06:05.320 And there's there's two practical examples. So there's thermal imaging 0:06:05.360 --> 0:06:07.719 techniques that we now use to look at the quality 0:06:07.720 --> 0:06:10.520 of a weld all the way through a production process 0:06:10.560 --> 0:06:15.080 in an automotive plant that wasn't possible, that connects in 0:06:15.160 --> 0:06:18.279 that local location, gathers that data and determines that the 0:06:18.279 --> 0:06:20.960 welders at the actual right quality or on a shipping 0:06:21.000 --> 0:06:24.480 content basis, it's it's the combination of r F I 0:06:24.600 --> 0:06:29.000 D tags, connecting to networks that contract with that level 0:06:29.040 --> 0:06:32.719 of accuracy and giving you that experience. Come in terms 0:06:32.760 --> 0:06:36.560 of the how has this come about, it's because as 0:06:36.600 --> 0:06:40.039 we've become more familiar with the amount of data that 0:06:40.080 --> 0:06:43.440 we can capture through a digital interaction through a screen, 0:06:43.480 --> 0:06:46.520 whether that's a mobile phone or whether computer, and all 0:06:46.520 --> 0:06:48.560 of the analytics that you can then do on kind 0:06:48.560 --> 0:06:53.120 of humans behavior, the same questions that get posed the 0:06:53.120 --> 0:06:56.560 physical locations or physical assets, the physical interactions or the 0:06:56.600 --> 0:06:59.800 physical assets, and it's the wedding of those two things 0:07:00.080 --> 0:07:04.000 create this I T problem. The companies like Lumen and 0:07:04.080 --> 0:07:06.440 IBM sold for at the edge so that you can 0:07:06.480 --> 0:07:10.240 actually tie together the digital world and the physical world 0:07:10.720 --> 0:07:12.680 in the same way as you were capturing the data 0:07:12.920 --> 0:07:16.080 purely from a digital world. And it's then human's curiosity 0:07:16.760 --> 0:07:18.840 that I say, Okay, well we've got these questions answered 0:07:18.840 --> 0:07:21.200 from the kind of the Third Industrial Revolution, I dad, 0:07:21.240 --> 0:07:23.400 we went through. How do we apply that through the 0:07:23.440 --> 0:07:27.480 Fourth Industrial Revolution into the analog world? Yeah? Yeah, you 0:07:27.520 --> 0:07:30.320 know what this makes me think of if I was 0:07:32.000 --> 0:07:35.040 and and start me if this is too speculative if 0:07:35.080 --> 0:07:38.400 I was a basketball coach, I would love to have 0:07:38.440 --> 0:07:41.320 an edge computing system which picked up data from my 0:07:41.400 --> 0:07:44.200 players on the court in real time and told me 0:07:44.760 --> 0:07:49.840 who was getting tired, told me whose performance was subpar, 0:07:50.800 --> 0:07:54.800 told me how quickly someone was responding on defense. And 0:07:55.400 --> 0:07:57.520 I mean, that's a that's kind of what That's an 0:07:57.520 --> 0:07:59.520 example of what you're talking about. How it isn't it. 0:07:59.520 --> 0:08:02.320 It's like the and wor all that had previously been 0:08:02.480 --> 0:08:06.120 entirely analog perhaps perhaps bang on a trash can when 0:08:06.120 --> 0:08:10.320 they see something. But but but that but that in 0:08:10.400 --> 0:08:12.560 parts to the point you're making is in reality because 0:08:12.600 --> 0:08:15.840 that there are tracking devices now on athletes in practically 0:08:15.880 --> 0:08:20.080 all disciplines at tracking how many kilometers are, how many 0:08:20.120 --> 0:08:24.120 miles they're running average pace, And that's been tracked, and 0:08:24.200 --> 0:08:27.080 that will be analyzed at the halftime break or the 0:08:27.160 --> 0:08:29.200 quarter time break to being upon the actual sport that's 0:08:29.200 --> 0:08:30.680 been followed or the third time I guess of its 0:08:30.720 --> 0:08:33.200 ice hockey. So that has been tracked. What isn't is 0:08:33.200 --> 0:08:36.200 the physiological elements that you talked about. But I guess 0:08:36.280 --> 0:08:38.600 kind of that will be at some point because humans 0:08:38.600 --> 0:08:41.520 curiosity will drive into that element to say Okay, what 0:08:41.679 --> 0:08:43.560 what level of fatigue are we are? Therefore, was the 0:08:43.559 --> 0:08:46.480 optimal moment to actually make a substitution of a different 0:08:46.480 --> 0:08:50.240 player onto the pitch? Yeah? Yeah. Or if I'm if 0:08:50.240 --> 0:08:52.800 I'm a hospital, I want to monitor the performance of 0:08:52.880 --> 0:08:57.120 my surgeons. I mean an hour four of a complex operation. 0:08:57.160 --> 0:08:59.320 I would love to be able to in real time 0:08:59.320 --> 0:09:02.760 crunch a whole series of data that tells me, you know, 0:09:02.800 --> 0:09:06.480 who's working well and who's flagging. Another kind of hallmark 0:09:06.520 --> 0:09:08.880 of edge computing is when you really need to correlate 0:09:08.960 --> 0:09:13.360 things locally. Um. You know a public safety use case 0:09:13.480 --> 0:09:16.440 where you know, a gunshot rings out, um, and an 0:09:16.480 --> 0:09:19.880 audio sensor picks that up well, correlating that with all 0:09:19.920 --> 0:09:21.920 the stop lights in the area, all the lights in 0:09:21.960 --> 0:09:25.200 the area, you know, any other public safety device that 0:09:25.280 --> 0:09:29.960 is within a particular geographic boundary. UM. That intense correlation 0:09:30.240 --> 0:09:33.480 of events to other outcomes may need to happen within 0:09:33.520 --> 0:09:36.720 split seconds, uh, you know, for for a public safety 0:09:36.720 --> 0:09:39.280 outcome to be achieved. So so so it's it's not just 0:09:39.520 --> 0:09:41.840 the fact that you know, we're tracking, we're analyzing data, 0:09:41.880 --> 0:09:44.520 and then we're getting lessons learned at halftime of you know, 0:09:44.760 --> 0:09:47.440 which one of our players run around. The more fine 0:09:47.480 --> 0:09:50.880 grained like milliseconds matter kind of use cases is another 0:09:51.120 --> 0:09:57.600 place where edge computing really shines. In step one, you 0:09:57.679 --> 0:10:00.600 analyze that kind of data, say the past coutball player 0:10:00.640 --> 0:10:04.200 or the surgeon after the fact. So you have the 0:10:04.240 --> 0:10:07.480 meeting the next day and you say, you didn't perform 0:10:07.600 --> 0:10:10.400 very well yesterday, Malcolm on the court. But if I 0:10:10.400 --> 0:10:13.240 can do it in real time, then I can actually 0:10:13.280 --> 0:10:17.480 affect the outcome of the game as it's happening. And 0:10:17.520 --> 0:10:21.400 that that shift from being able to make those judgments 0:10:21.840 --> 0:10:25.800 immediately and make those judgments after the fact is huge. 0:10:26.320 --> 0:10:30.040 It's I could win the game, yeah, didn't otherwise lose 0:10:30.200 --> 0:10:33.720 And I'm echoing, I'm capturing your excitement. You are kind 0:10:33.720 --> 0:10:35.800 of echoing that position where we've kind of gone from 0:10:35.800 --> 0:10:39.680 the digital perspective where people are playing online games, sporting 0:10:39.760 --> 0:10:42.200 games and making judgment spist up on what they can 0:10:42.200 --> 0:10:45.000 see from the analytics they get in that digital realm, 0:10:45.040 --> 0:10:47.720 and then translating that into ideas that could be extended 0:10:47.760 --> 0:10:51.400 into the animal row and therefore that desire. You can 0:10:51.440 --> 0:10:54.800 imagine there are people as we speak now putting together 0:10:54.800 --> 0:10:58.120 innitative solutions that can address that very problem. Yeah. The 0:10:58.679 --> 0:11:01.079 other thing too, is it we're talking about all the 0:11:01.120 --> 0:11:04.080 whiz bang use cases and and there's sort of a 0:11:04.120 --> 0:11:06.880 subtext to everything we just said, which is that there's 0:11:07.000 --> 0:11:12.200 good software designed at scale able to run and achieve 0:11:12.240 --> 0:11:17.320 those outcomes better basketball performance, public safety use cases. There's 0:11:17.360 --> 0:11:19.640 software that needs to go and collect all that data 0:11:19.679 --> 0:11:22.120 and take action against it. And the other sort of 0:11:22.120 --> 0:11:24.400 really the dimension, and certainly with a big dimension of 0:11:24.400 --> 0:11:27.560 the IBM and LUMIN relationship is being able to enable 0:11:27.720 --> 0:11:31.400 great software development anywhere that the network can reach. All 0:11:31.440 --> 0:11:34.959 these use cases don't happen unless there's software that goes 0:11:35.000 --> 0:11:38.200 and runs that business logic or runs that analysis, or 0:11:38.360 --> 0:11:42.520 processes those inputs into actionable outputs and respond to an 0:11:42.520 --> 0:11:51.280 event stream. Yeah. Yeah, talk a little bit about the 0:11:51.760 --> 0:11:56.079 cloud piece of this. Why does hybrid cloud How does 0:11:56.120 --> 0:11:59.360 it fit into this puzzle that you've been describing. So 0:11:59.679 --> 0:12:03.959 the hyhbrary cloud space essentially encapsulates all the points that 0:12:04.040 --> 0:12:06.800 David has gone through. So it's a cloud essentially is 0:12:07.120 --> 0:12:10.960 a building with computers in that run applications. And the 0:12:11.000 --> 0:12:14.480 paradigm until probably about ten fifteen years ago was that 0:12:14.720 --> 0:12:16.800 a large corporate would have a big data center have 0:12:16.880 --> 0:12:20.440 its own computers in them and would have that capability. 0:12:20.679 --> 0:12:23.839 And then what created a huge innovation was the actual 0:12:23.880 --> 0:12:25.880 ability for our developer to come up with an idea 0:12:26.240 --> 0:12:29.040 not need to unbuild a big data center for computers, 0:12:29.080 --> 0:12:32.240 and it could actually rent the space and then turn 0:12:32.320 --> 0:12:34.520 that idea into software and turn that software into a 0:12:35.040 --> 0:12:37.920 Facebook or a Netflix or whatever it may be. So 0:12:37.960 --> 0:12:41.280 it reduced barriers of entry, and that was the first phase. 0:12:41.320 --> 0:12:43.080 The first that PHAs that were now in is this 0:12:43.200 --> 0:12:46.000 kind of synthesis between the digital and the analog at 0:12:46.040 --> 0:12:49.120 the edge, and that's the hybrid cloud computing, where we 0:12:49.120 --> 0:12:53.400 can actually create many data centers specific to particular needs 0:12:54.120 --> 0:12:56.400 all around the world, not just within the assets that 0:12:56.480 --> 0:12:58.560 IBM has or the assets that are the cloud service 0:12:58.600 --> 0:13:02.080 providers have. And it's these partnerships. There's also kind of 0:13:02.160 --> 0:13:05.600 new economic models in the marketplace where companies can operate 0:13:05.600 --> 0:13:08.160 with humility to recognize, Okay, we may be large companies, 0:13:08.160 --> 0:13:10.599 but actually we can see the assets in another company 0:13:10.679 --> 0:13:12.240 and the brilliant people that exist there, and if we 0:13:12.240 --> 0:13:14.840 could partner with them, we could create something valuable for 0:13:14.880 --> 0:13:18.360 the marketplace. What the what's the challenge if I'm a 0:13:18.400 --> 0:13:20.760 company and I want to do something sophisticated with all 0:13:20.800 --> 0:13:23.880 of this data. Where am I gonna What's going to 0:13:24.000 --> 0:13:25.960 keep me up at night? A part of this puzzle 0:13:26.800 --> 0:13:28.720 when you have this explosion of data and it can 0:13:28.720 --> 0:13:31.000 be at the edge, the key thing that we need 0:13:31.080 --> 0:13:34.240 to be very mindful of is cybersecurity risk in that 0:13:34.240 --> 0:13:36.320 that data gets in the hands of their own people 0:13:36.679 --> 0:13:38.480 who then can actually use that to their own to 0:13:38.520 --> 0:13:41.840 their own gain, or to whatever purpose they want to use. 0:13:42.240 --> 0:13:44.480 So every solution that has to be built has to 0:13:44.480 --> 0:13:48.280 be built at a very high gradive cybersecurity, so ensuring 0:13:48.280 --> 0:13:51.520 that we protect our customers data and also we protect 0:13:51.559 --> 0:13:53.720 them from the laws, rules and REGs that they have 0:13:53.800 --> 0:13:57.800 to be obligated to. Broadly speaking, you're you're putting computing 0:13:57.840 --> 0:14:00.199 capacity in places that didn't used to be out of 0:14:00.200 --> 0:14:03.880 the data centers before. Right there, there's an element of newness, 0:14:03.960 --> 0:14:07.840 There's an element of novel challenge that you may be 0:14:07.960 --> 0:14:11.840 running into, and so that inherently there's there's more complexity. 0:14:12.040 --> 0:14:14.400 The other thing that keeps a lot of it leaders 0:14:14.640 --> 0:14:16.280 up at night is whether they are going to be 0:14:16.320 --> 0:14:19.880 able to write software and deliver it at a pace 0:14:19.920 --> 0:14:22.200 of change that is actually going to be able to 0:14:22.200 --> 0:14:25.320 take advantage of or or solve the problem they're trying 0:14:25.360 --> 0:14:27.920 to run. So I want to go back. I want 0:14:27.960 --> 0:14:29.600 to do a for example here, because it seems to 0:14:29.680 --> 0:14:32.360 be a really interesting and important point when I raise 0:14:32.400 --> 0:14:36.240 that example of this surgeon and we want to gather 0:14:36.360 --> 0:14:38.960 data from the surgical suite, we want to make sense 0:14:39.000 --> 0:14:41.920 of it in real time, we want to inform the 0:14:42.040 --> 0:14:46.560 surgery itself. But then you also want to share that 0:14:46.680 --> 0:14:50.080 data with lots of other hospitals and use that to 0:14:50.160 --> 0:14:54.520 build some kind of system that can improve surgery generally. 0:14:54.760 --> 0:14:56.480 So what you're saying is, in order to do that 0:14:56.600 --> 0:14:59.520 last piece, which is arguably the most important of the pieces, 0:15:00.400 --> 0:15:02.880 everyone's going to be reading from the same book, right, 0:15:04.520 --> 0:15:06.600 And the key around that is there's a level of 0:15:06.640 --> 0:15:08.920 complexity as also reading from the same book means that 0:15:08.960 --> 0:15:12.160 the actual the format is the same, the language is 0:15:12.160 --> 0:15:14.200 the same, the type face to carry that analogy on. 0:15:14.680 --> 0:15:17.280 So getting consistency in terms of the data models as 0:15:17.280 --> 0:15:21.840 it's known is super important, as is the provenance so 0:15:21.880 --> 0:15:23.960 that you know that the actual quality of the data 0:15:24.440 --> 0:15:26.160 is at the highest level of insecurity. And the reason 0:15:26.200 --> 0:15:28.320 why that's important is you would take all of that 0:15:28.400 --> 0:15:31.960 inside all of those lessons that are turned into data 0:15:32.360 --> 0:15:36.040 and put them into an artificial intelligence model to treat 0:15:36.120 --> 0:15:38.720 what's not as training that model so that it actually 0:15:38.760 --> 0:15:42.880 can come up with hypotheses that are actually continually initiatively 0:15:42.920 --> 0:15:45.680 improved based upon the amount of data. But if there's 0:15:45.720 --> 0:15:49.800 any issue, any corruption in that data, it will compromise 0:15:49.840 --> 0:15:52.560 the actual outcomes. And because the volumes of data can 0:15:52.560 --> 0:15:55.200 be so large, it is actually difficult to ensure that 0:15:55.200 --> 0:15:58.680 actually the outcomes are trained correctly. So there's a huge 0:15:58.720 --> 0:16:00.760 amount of work has to go into ensure the integrity 0:16:00.760 --> 0:16:02.160 of the day to the providence of the days or 0:16:02.280 --> 0:16:05.160 is correct so the AI doesn't get trend in the 0:16:05.200 --> 0:16:09.360 wrong way. Yeah, that idea of software distribution. The in 0:16:09.760 --> 0:16:13.080 our data analytics practice. One of the industries they work 0:16:13.120 --> 0:16:16.280 with extensively is manufacturing. And one of the things that 0:16:16.320 --> 0:16:19.400 we see organizations challenged by, and as a phrase you 0:16:19.520 --> 0:16:21.760 are one of our data scientists uses all the time, 0:16:22.240 --> 0:16:24.200 is that it's actually kind of easy to go and 0:16:24.240 --> 0:16:26.320 collect a lot of data locally on the shop floor, 0:16:27.000 --> 0:16:29.080 and it's kind of easy to get all of the 0:16:29.160 --> 0:16:32.080 data historically that you've ever had once it's available in 0:16:32.080 --> 0:16:34.600 your data center to go have a data scientist analyze 0:16:34.640 --> 0:16:38.640 it and come up with you know, widely held best 0:16:38.680 --> 0:16:42.280 practices and the source of what should be the most 0:16:42.280 --> 0:16:44.960 efficient way to do things and what should be the 0:16:45.000 --> 0:16:48.360 most efficient data model that can analyze all the sensors 0:16:48.360 --> 0:16:50.640 in the factory. The challenges is getting it from the 0:16:50.680 --> 0:16:53.280 top floor to the shop floor. It's fine to get 0:16:53.360 --> 0:16:56.440 that lesson in your shortcore data center. It's fine. It's 0:16:56.440 --> 0:16:58.800 fine to go collect a lot of data. The challenges 0:16:58.840 --> 0:17:01.200 connecting them together, and that's really where this idea of 0:17:01.560 --> 0:17:04.720 consistent delivery of new software when you learn the lesson 0:17:04.760 --> 0:17:06.760 in the top floor says this is the way it 0:17:06.760 --> 0:17:08.880 ought to be. How do you get that code out 0:17:08.960 --> 0:17:11.600 into your built environment so that that software is actually 0:17:11.640 --> 0:17:14.400 taking effect. It's not just a theory that is a 0:17:14.440 --> 0:17:16.399 model in a data center, but it's a model that 0:17:16.440 --> 0:17:20.439 can make a difference. Tell me about how this collaboration 0:17:20.520 --> 0:17:23.920 between your two companies addresses that problem. Can you give 0:17:23.920 --> 0:17:28.359 me an example, Well, yeah, I think what however, was 0:17:28.600 --> 0:17:31.160 alluding to. One of the customers we're working with right 0:17:31.200 --> 0:17:33.919 now is in the financial services industry. But this is 0:17:33.920 --> 0:17:37.879 a digital interaction between the financial services business model of 0:17:37.920 --> 0:17:40.679 banking and the people that walk up to it. And 0:17:40.720 --> 0:17:44.440 there's a security risk out there in the world whereby 0:17:44.600 --> 0:17:48.800 bad actors will target a t M machines and it's 0:17:48.800 --> 0:17:52.399 called skimming, where they'll go and walk up to an 0:17:52.400 --> 0:17:54.840 a t M, put a device that looks same color, 0:17:55.000 --> 0:17:59.200 same fitting over the credit card slot, and surreptitiously scan 0:17:59.320 --> 0:18:01.520 the credit card as it's being inserted into the machine. 0:18:01.880 --> 0:18:05.320 The user doesn't know that it happened, and the bank 0:18:05.760 --> 0:18:08.399 doesn't necessarily know that it happens. In the the the 0:18:08.640 --> 0:18:13.439 point at which they can take most efficial effective action 0:18:13.680 --> 0:18:16.480 against that bad actor is the point at which they're 0:18:16.480 --> 0:18:18.440 walking up to the machine which has a video camera 0:18:18.440 --> 0:18:21.159 inside of it, and inserting that device. And so there 0:18:21.200 --> 0:18:23.120 are certain patterns you can be looking for. Are they're 0:18:23.119 --> 0:18:24.800 walking up to it with a bag, are they reaching 0:18:24.840 --> 0:18:26.920 into the bag? They are they taking on a certain 0:18:26.960 --> 0:18:31.040 posture against that a t M interface to know maybe 0:18:31.080 --> 0:18:33.960 there's further correlation we need to take against this person. 0:18:34.080 --> 0:18:36.520 But so financial companies would look at that and say, 0:18:36.600 --> 0:18:38.320 you know, that could be a needle in a haystack 0:18:38.720 --> 0:18:41.359 kind of analysis problem. And if you get better and 0:18:41.400 --> 0:18:44.000 better at getting closer to figuring out who is skimming 0:18:44.040 --> 0:18:46.680 off your A t M machines and who isn't. Once 0:18:46.720 --> 0:18:48.880 you get good at building that model and then deploying 0:18:48.880 --> 0:18:51.120 that software to all your A t M s, you're 0:18:51.160 --> 0:18:54.440 in a situation where your overall risk to your customers 0:18:54.480 --> 0:18:57.760 and your brand that the payoff becomes immeasurable. So that's 0:18:57.760 --> 0:18:59.520 one of the things that we're working on with IBM 0:18:59.560 --> 0:19:01.840 and some of the great video analytics software they have 0:19:01.960 --> 0:19:04.560 that we can put out closer to some of these 0:19:04.560 --> 0:19:08.800 financial institutions, acquire analyzed, but then act upon the data 0:19:08.920 --> 0:19:16.240 that's involved. Oh, I see. So to your point, the 0:19:16.280 --> 0:19:20.199 insight number one is this particular a t M has 0:19:20.200 --> 0:19:23.320 been compromised, But the much more useful bit of information 0:19:23.400 --> 0:19:27.199 is it's been compromised by such and such a person, 0:19:27.880 --> 0:19:31.560 and we're observing that person compromising it in real time, 0:19:32.119 --> 0:19:36.160 right right, right, So whether that a t M learns 0:19:36.280 --> 0:19:38.640 what a bad actor looks like walking up to it 0:19:38.920 --> 0:19:42.359 in Minneapolis, well that's good. But the key is then learning, 0:19:42.560 --> 0:19:45.080 updating the model, getting that new software tested, and then 0:19:45.080 --> 0:19:47.639 getting it deployed consistently to all the other places that 0:19:47.640 --> 0:19:50.080 can benefit. I want to go back to this partnership 0:19:50.119 --> 0:19:53.439 between Lumin and IBM. You said, you guys have been 0:19:53.440 --> 0:19:55.840 working together for some time. How long when did you win? 0:19:55.880 --> 0:19:59.320 Did it first start? We've had relationships with IBM and 0:19:59.400 --> 0:20:01.600 some of it's a Elia companies in one way, shape 0:20:01.680 --> 0:20:05.240 or form for a few decades. The other thing to 0:20:05.280 --> 0:20:07.840 remember is Lumen is a service provider, right, so we 0:20:08.040 --> 0:20:10.680 contract with our customers to go deliver services for them. 0:20:10.680 --> 0:20:13.120 In a lot of cases, those services have always involved 0:20:13.600 --> 0:20:18.720 IBM software, IBM data capabilities, working with the IBM cloud, 0:20:19.240 --> 0:20:21.920 and so IBM as a technology entity has been connected 0:20:21.960 --> 0:20:25.040 to the end points of Lumen networks, you know for 0:20:25.080 --> 0:20:29.080 all that time. Yeah, what does from a customer standpoint, 0:20:29.119 --> 0:20:32.200 what does the partnership between Lumen and IBM look like? 0:20:33.000 --> 0:20:35.520 I mean, are you if I'm that financial service companies 0:20:35.600 --> 0:20:37.639 is trying to trying to stop my A t M 0:20:37.680 --> 0:20:40.480 s from being hacked? Is am I dealing with a 0:20:40.600 --> 0:20:45.240 kind of task force made up of lumon and IBM folks, 0:20:45.680 --> 0:20:47.879 So that the solution that we're putting together there is 0:20:47.920 --> 0:20:52.600 precisely that. So the out of technology companies continue to evolve, 0:20:53.160 --> 0:20:54.959 they have these kind of tusk forces that you talk 0:20:55.000 --> 0:20:57.960 about that actually work on problems and then reapply let 0:20:58.000 --> 0:21:01.639 us technology innovations to those problems. We then create new 0:21:01.680 --> 0:21:04.679 go to market offerings. As I mentioned earlier, kind of 0:21:04.920 --> 0:21:06.719 the business models that are kind of really worked now 0:21:06.800 --> 0:21:09.520 is where you actually get and understand with humility the 0:21:09.520 --> 0:21:11.520 assets that you have as a company and combine them 0:21:11.520 --> 0:21:14.200 with assets of other companies. And the thing that really 0:21:14.240 --> 0:21:16.120 makes it come a live is in getting too very 0:21:16.119 --> 0:21:18.600 smart groups of people together to actually face off to 0:21:18.640 --> 0:21:21.480 those business problems. So the the problem that DIV was 0:21:21.520 --> 0:21:24.480 going through, there was a conversation in the meeting room 0:21:24.480 --> 0:21:26.600 which is, we have this problem, how would you think 0:21:26.640 --> 0:21:30.240 about this? And then we combine our engineers various components 0:21:30.280 --> 0:21:32.800 that we have worked up what we call proof of 0:21:32.840 --> 0:21:35.920 concepts to kind of work through is there are there 0:21:35.920 --> 0:21:37.919 there in some of the solutions that we can put together, 0:21:38.280 --> 0:21:40.199 and then increasingly that becomes something that we would call 0:21:40.240 --> 0:21:42.960 a production offering, which actually becomes more generally available in 0:21:43.000 --> 0:21:51.200 the marketplace. What's the what's the hardest problem is always 0:21:51.400 --> 0:21:54.159 I think called latency is always the hardest thing, and 0:21:54.200 --> 0:21:57.399 it's in the both domains were probably primarily in the 0:21:57.400 --> 0:22:00.000 limit demand, and that's where you kind of forever put 0:22:00.040 --> 0:22:04.080 shing physics to actually get as close to the speed 0:22:04.119 --> 0:22:07.200 of light in terms of how quickly you're you're transmitting data. 0:22:07.840 --> 0:22:10.480 And it's a tough two problem to solve for but 0:22:10.560 --> 0:22:13.359 because of the huge volumes of data and because of 0:22:13.560 --> 0:22:17.440