Accelerating Innovation with Hybrid Cloud at the Edge
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Hello, hello. Malcolm Gladwell here. I want to tell you about a new series we're launching at Pushkin Industries on the 1936 Olympic Games. Adolf Hitler's Games. Fascism, anti-Semitism, racism, high Olympic ideals, craven self-interest, naked ambition, illusion, delusion, all collide in the long, contentious lead-up to the most controversial Olympics in history. The Germans put on a propaganda show, and America went along with all of it. Why?

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Hello, hello. This is Smart Talks with IBM, a podcast from Pushkin Industries, iHeartMedia, and IBM about what it means to look at today's most challenging problems in a new way. I'm Malcolm Gladwell. ♪

Today, I'll be discussing the innovations around hybrid cloud with Lumen's David Chikotius and IBM's Howard Bovill. David is Vice President for Enterprise Technology and Field CTO at Lumen, where he's helped clients across industries create new business opportunities through unique digital interactions.

David has been immersed in cloud computing long before his time with Lumen, working with companies such as UNET, Digital Island, and Fusepoint. You're putting computing capacity in places that didn't used to be thought of as data centers before. There's an element of novel challenge. And so inherently, there's more complexity.

Howard is the head of IBM Cloud Platform. In this role, Howard has focused on driving digital transformation for enterprises, especially in highly regulated industries.

Before joining IBM, Howard was chief technology officer for Bank of America, where he led the transformation of the bank's infrastructure and developed one of the largest internal private clouds. At times, you have to kind of be a technology evangelist in terms of what the art of the possible is against the problems. In this episode, we'll explore working and living in a world of cloud technology.

We'll show you how new innovations in cloud computing have reimagined a world where computing can happen anywhere and businesses can use data to accelerate innovation to improve service and performance. Let's dive in.

Welcome, everyone. Howard and David, thank you for joining us today. Let's jump in. David, I'm going to ask you to define some terms. Sure. And that will be easy for you, but useful for the rest of us. First of all, the fourth industrial revolution. What is it? Yeah, good one.

So we can really look back, I think, on history in these periods of technology advancement, right? You know, the period of industry that was defined by steam power, the period of history and the industrial period of history that was defined by electrical distribution.

We commonly think of the third one as really this information age, the information revolution of digitization, of process, creation, and online connectivity of data is this third industrial revolution of the digital age, information technology, systems communicating with each other, and the advent of all that you can do in industry with those technologies.

And the fourth industrial revolution is really this reflection of the explosion of data that gets created by all that connectivity. Taking data and being able to acquire it, analyze it and take action upon it is opening up a wide range of new industries and new business opportunities and new regulatory challenges. And that's what we mean when we say the fourth industrial revolution.

How did Lumen and IBM come together? And what's the logic behind your collaboration in this field? You kind of take the heritage of both companies. So Lumen are a world-class global networking company. They connect things together at the highest level of quality, lowest latency, and so on. And at its heart, through all of the actual transformations that IBM has been through, is we're a compute company on which the software runs. And we write also the software in certain contexts as well. So the combination of the two capabilities is

solves for the problem. We've been working together for years. I think the advent of what we've been focused on with IBM Cloud Satellite has really been initiated by Lumen's investment in making our network a place where you can run software workloads more readily and easily. And IBM Cloud Satellite is a great modality that just snaps right into that network. Yeah. You work for Lumen.

Is the simplest way to describe Lumen that Lumen is a fourth industrial revolution company? We're a fourth industrial revolution company because we believe at the core of all of it is connectivity. Right.

All that data and all the sources of data and all the ways that you need to interact with that data requires a substantial amount of aggregate networking capacity. We're now kind of hitting this tipping point in the fourth industrial revolution where the amount of data coming inbound from cameras and from sensors and from devices and gaming consoles and a variety of input sources like that is actually exceeding capacity in the other direction.

So that's really why for the fourth industrial revolution to work, you need massive amounts of network connectivity. And that's what Lumen does. So this brings up the second word I want you to define, and that's edge computing, which I'm assuming is...

Edge computing is a technological response to the phenomenon you've just described. It is. It's one way to think about edge computing, the way we talk about it a lot, is it's moving workloads, software workloads, closer to digital interactions. And a digital interaction could be between things and people and business models. Yeah, I mean, just to add to some of David's point with some kind of practical use cases that we kind of were involved in. So first and foremost,

The edge computing piece actually is joining the analog world to the digital world. Whereas until this point, you would look at the digital world through the screens that we all spend too much time looking at.

Whereas on the edge, it's actually looking at physical locations like retail branches, like shipping containers, or like welds on a weld on an automobile. And there's two practical examples. So there's thermal imaging techniques that we now use to look at the quality of a weld all the way through a production process in an automotive plant that wasn't possible that connects in that local location, gathers that data and determines that the weld is at the actual right quality.

or on a shipping container basis, it's the combination of RFID tags connecting to networks that can track with that level of accuracy and giving you that experience in terms of the

How has this come about? It's because as we've become more familiar with the amount of data that we can capture through a digital interaction, through a screen, whether that's a mobile phone or with a computer, and all of the analytics that you can then do on kind of a human's behavior,

The same questions that get posed, the physical locations or physical assets, the physical interactions or the physical assets. And it's the wedding of those two things that create this IT problem that companies like Lumen and IBM solve for at the edge so that you can actually tie together the digital world and the physical world in the same way as you capture the data purely from a digital world. And it's then human's curiosity to

that have said, okay, well, we've got these questions answered from the kind of the third industrial revolution that David went through. How do we apply that through the fourth industrial revolution into the analog world? Yeah, yeah. You know what this makes me think of? If I was, and stop me if this is too speculative, if I was a basketball coach, I would love to have an edge computing system which picked up data from my players on the court in real time and told me who was getting tired,

told me whose performance was subpar, told me how quickly someone was responding on defense. I mean, that's an example of what you're talking about, Howard, isn't it? It's like a world that had previously been entirely analog. Perhaps bang on a trash can when they see something. But that in part, to the point you're making, is in reality because there are tracking devices now

on athletes in practically all disciplines. They're tracking how many kilometers or how many miles they're running, average pace, and that's being tracked. And that will be analyzed at the half-time break or the quarter-time break, depending upon the actual sport that's being followed, or the third time, I guess, if it's ice hockey.

So that has been tracked. What isn't is the physiological elements that you talked about. But I guess that will be at some point because human's curiosity will drive into that element to say, okay, what level of fatigue are we on there for? What's the optimal moment to actually make a substitution of a different player onto the pitch? Yeah, yeah. Or if I'm a hospital and I want to monitor the performance of my surgeons, I mean, at hour four of a complex surgery,

I would love to be able to, in real time, crunch a whole series of data that tells me who's working well and who's flagging. Another kind of hallmark of edge computing is when you really need to correlate things locally.

You know, a public safety use case where, you know, a gunshot rings out and an audio sensor picks that up. Well, correlating that with all the stoplights in the area, all the lights in the area, you know, any other public safety device that is within a particular geographic boundary, that intense correlation of events to other outcomes may need to happen within split seconds.

for a public safety outcome to be achieved. So it's not just the fact that we're tracking, we're analyzing data, and then we're getting lessons learned at halftime of which one of our players has run around. The more fine-grained, like milliseconds matter kind of use cases is another place where edge computing really shines. In step one, you analyze that kind of data, say the basketball player or the surgeon, after the fact, right?

So you have the meeting the next day and you say, you didn't perform very well yesterday, Malcolm, on the court. But if I can do it in real time, then I can actually affect the outcome of the game as it's happening. And that shift from being able to make those judgments immediately and make those judgments after the fact is huge. I could win the game.

that I might otherwise lose. And I'm echoing, I'm capturing your excitement. You are kind of echoing that position where we've kind of gone from the digital perspective where people are playing online games, sporting games, and making judgments based upon what they can see from the analytics they get in that digital realm, and then translating that into ideas that could be extended into the analog realm. And therefore, with that desire, you can imagine there are people, as we speak now, putting together innovative solutions that can address that very problem.

Yeah. The other thing too is we're talking about all the whiz-bang use cases and there's sort of a subtext to everything we just said, which is that there's good software designed at scale, able to run and achieve those outcomes. Better basketball performance, public safety use cases. There's software that needs to go and collect all that data and take action against it and

And the other sort of really the dimension and certainly a big dimension of the IBM and Lumen relationship is being able to enable great software development anywhere that the network can reach. All these use cases don't happen unless there's software that goes and runs that business logic or runs that analysis or processes those inputs into actionable outputs and responds to an event stream. Yeah, yeah, yeah.

Talk a little bit about the cloud piece of this. Why does hybrid cloud, how does it fit into this puzzle that you've been describing?

So the hybrid cloud space essentially encapsulates all the points that David's gone through. So a cloud essentially is a building with computers in it that run applications. And the paradigm until probably about 10, 15 years ago was that a large corporate would have a big data center, have its own computers in them, and would have that capability.

And then what created a huge innovation was the actual ability for our developer to come up with an idea, not need to build a big data center for computers, and they could actually rent the space, and then turn that idea into software and turn that software into a Facebook or Netflix or whatever it may be. So it reduced barriers of entry. And that was the first phase. The first phase that we're now in is this kind of synthesis between the digital and the analog at the edge.

And that's the hybrid cloud computing where we can actually create mini data centers specific to particular needs all around the world, not just within the assets that IBM has or the assets that other cloud service providers have.

And it's these partnerships. There's also kind of new economic models in the marketplace where companies can operate with humility to recognize, okay, we may be large companies, but actually we can see the assets in another company and the brilliant people that exist there. And if we could partner with them, we could create something valuable for the marketplace. What's the challenge? If I'm a company and I want to do something sophisticated with all of this data, where am I going to, what's going to keep me up at night? What part of this puzzle?

When you have this explosion of data and it can be at the edge, the key thing that we need to be very mindful of is cybersecurity risk in that that data gets in the hands of the wrong people who then can actually use that to their own gain or to whatever purpose they want to use. So every solution that has to be built has to be built at a very high grade of cybersecurity.

So ensuring that we protect our customers' data and also we protect them from the laws, rules and regs that they have to be obligated to. Broadly speaking, you're putting computing capacity in places that didn't used to be thought of as data centers before.

Yeah, right there. There's an element of newness. There's an element of novel challenge that you may be running into. And so inherently, there's more complexity. The other thing that keeps a lot of IT leaders up at night is whether they are going to be able to write software and deliver it at a pace of change that is actually going to be able to take advantage of or solve the problem they're trying to run.

So I want to go back. I want to do a for example here because it seems to be a really interesting and important point when I raise that example of the surgeon. And we want to gather data from the surgical suite. We want to make sense of it in real time. We want to inform the surgery itself. But then you also want to share that data with

with lots of other hospitals and use that to build some kind of system that can improve surgery generally. So what you're saying is in order to do that last piece, which is arguably the most important of the pieces, everyone's got to be reading from the same book. Right.

Right. The key around that is there's a level of complexity as well. So reading from the same book means that the actual, the format is the same, the language is the same, the typeface to carry that analogy on. So getting consistency in terms of the data models, as it's known, is super important, as is the provenance so that you know that the actual quality of the data is at the highest level of integrity. And the reason why that's important is you would take all of that insight, all of those lessons,

that are turned into data and put them into an artificial intelligence model to what's known as training that model so that it actually can come up with hypotheses that are actually continually and intuitively improved based upon the amount of data.

But if there's any issue, any corruption in that data, it will compromise the actual outcomes. And because the volumes of data can be so large, it is actually difficult to ensure that actually the outcomes are trained correctly. So there's a huge amount of work has to go into ensuring the integrity of the data, the provenance of the data is correct. So the AI doesn't get trained in the wrong way.

That idea of software distribution, in our data analytics practice, one of the industries they work with extensively is manufacturing. And one of the things that we see organizations challenged by and has a phrase, you know, one of our data scientists uses all the time is that it's actually kind of easy to go and collect a lot of data locally on the shop floor.

And it's kind of easy to get all of the data historically that you've ever had once it's available in your data center to go have a data scientist analyze it and come up with, you know, widely held best practices and the source of what should be the most efficient way to do things and what should be the most efficient data model that can analyze all the sensors in the factory. The challenge is getting it from the top floor to the shop floor.

It's fine to get that lesson in your short core data center. It's fine to go collect a lot of data. The challenge is connecting them together. And that's really where this idea of consistent delivery of new software. When you learn the lesson and the top floor says, this is the way it ought to be. How do you get that code out into your built environment so that that software is actually taking effect? It's not just a theory that is a model in a data center, but it's a model that can make a difference.

Tell me about how this collaboration between your two companies addresses that problem. Can you give me an example? Well, yeah, I think what Howard was alluding to, one of the customers we're working with right now is in the financial services industry, but this is a digital interaction between the financial services business model of banking and the people that walk up to it. And there's a security risk out there in the world whereby bad actors will target ATM machines and

And it's called skimming, where they'll go and walk up to an ATM, put a device that looks same color, same fitting over the credit card slot and surreptitiously scan the credit card as it's being inserted into the machine. The user doesn't know that it happened. And the bank doesn't necessarily know that it happens. And the point at which

they can take most effective action against that bad actor is the point at which they're walking up to the machine, which has a video camera inside of it, and inserting that device. And so there are certain patterns you can be looking for. Are they walking up to it with a bag? Are they reaching into the bag? Are they taking on a certain posture against that ATM interface?

to know maybe there's further correlation we need to take against this person. But so financial companies would look at that and say, you know, that could be a needle in a haystack kind of analysis problem. And if you get better and better at getting closer to figuring out who is skimming off your ATM machines and who isn't, once you get good at building that model and then deploying that software to all your ATMs, you're in a situation where your overall risk to your customers and your brand is

the payoff becomes immeasurable. So that's one of the things that we're working on with IBM and some of the great video analytics software they have that we can put out closer to some of these financial institutions, you know, acquire, analyze, but then act upon the data that's involved. Oh, I see. So to your point, the...

Insight number one is this particular ATM has been compromised. But the much more useful bit of information is it's been compromised by such and such a person, and we're observing that person compromising it in real time.

Right. Yeah. Right. So whether that ATM learns what a bad actor looks like walking up to it in Minneapolis, well, that's good. But the key is then learning, updating the model, getting that new software tested, and then getting it deployed consistently to all the other places that can benefit. I want to go back to this partnership between Lumen and IBM. You said you guys have been working together for some time. When did it first start?

We've had relationships with IBM and some of its affiliate companies in one way, shape, or form for a few decades. The other thing to remember is Lumen is a service provider, right? So we contract with our customers to go deliver services for them. In a lot of cases, those services have always involved

IBM software, IBM data capabilities, working with the IBM cloud. And so IBM as a technology entity has been connected to the endpoints of Lumen networks, you know, for all that time. Yeah. What does, from a customer standpoint, what does the partnership between Lumen and IBM look like? I mean, are you, if I'm that financial service companies is trying to, trying to stop my ATMs from being hacked, is that, am I dealing with a kind of task force made up of

Lumen and IBM folks? So the solution that we're putting together there is precisely that. So

How do technology companies continue to evolve? They have these kind of task forces that you talk about that actually will work on problems and then reapply latest technology innovations to those problems, which then create new go-to-market offerings. As I mentioned earlier, the business models that really work now is where you actually get and understand with humility the assets that you have as a company and combine them with assets of other companies. And the thing that really makes it come alive is getting to very smart

groups of people together to actually face off to those business problems. So the problem that Deva was going through there was a conversation in a meeting room, which is we have this problem. How would you think about this? And then we combined our engineers, the various components that we have, worked up what we call proof of concepts to kind of work through, is there a there there in terms of the solutions that we can put together? And then increasingly that becomes something what we would call a production offering, which actually becomes more generally available in the marketplace.

What's the hardest problem that the two companies have tackled together? I think called latency is always the hardest thing. And it's in the both domains are probably primarily in the Lumen domain. And that's where you kind of forever pushing physics to actually get as close to the speed of light in terms of how quickly you're transmitting data. And it's a tough, tough problem to solve for, but because of the huge volumes of data and because...

And because of increasingly humans' nature for instant gratification in that we want everything now and we want it immediately. And what's hard about that is latency is a particularly hard problem from a technical standpoint because...

In some cases, latency is the amount of time it takes, usually measured in milliseconds, which are less than the blink of an eye, but the amount of time it takes a packet to traverse between two particular endpoints in a network is

But those all add up, right? You can sort of thinking of it in a computer or brain context as processing speed. How fast can I react to things? Well, if it takes a while for the packets to travel through the neurons, to use a brain analogy of a network, the longer it takes for the packets to process through, the longer it takes for an outcome to occur. And if an outcome takes too long to process, then it becomes fairly useless. Yeah, yeah. My first question is, do customers always...

Realize what the potential of all of these different pieces are or is part of your job in helping people In opening people's eyes to what's possible. Yeah, I'm very at the times You have to kind of be a technology evangelist in terms of what the art of the possible is against the problems And it's not because customers Don't have the same ability to see that it's just very often they don't see that the breadth of things that we see when we're working with lots of different industries and we can apply solutions from one place and

to another. The other element in terms of the pace of adoption in organizations is less about the actual people within them, but also the technology decisions that were made in the past. Large investments would have already been made to actually build the technology environments that they have. They're known as legacy environments. And it's getting from a legacy environment to the new environment. And that's a tricky dribble in the sense that you have to look at your balance sheet, you have to look at the amount of work that would be necessary to do that.

You've got to change everything from infrastructure to lines of application codes or data sets and so on. So it's a very complex environment for our customers to be forever thinking about. And therefore, what do they prioritize as their next area of innovation relative to the actual value that they would get for their customers or for their shareholders or whatever their drivers are?

It's really interesting, that word. I always get a kick out of it. Enterprise IT is the only context in which legacy is an epithet.

Right. Like you say legacy to an I.T. person, they roll their eyes and, you know, their their blood pressure goes up. It's like nails on a chalkboard. But to most individuals, like what is your legacy? The word legacy means like it's something to be honored. Right. It's something in an enterprise context. Legacy just means you've made a lot of decisions already.

You've made a lot of decisions. You've made a lot of implementations. You're bringing a lot behind you. That should be a good thing. But in an enterprise IT context and a technology domain, it's really challenging. What I've heard play back to me is kind of, yeah, Howard, God may have created the earth in seven days, but he didn't have to deal with legacy. So it kind of gives you a sense as to the differences in an IT context. Yeah. One last question. I want you guys to jump ahead 10 years from now.

I've gathered the two of you 10 years from now. Tell me what's top of mind in 2031. I think what's really a huge challenge in business and in the ways that business and organizations collaborate is this concept of composability. And I think composability of the ability to go break things down into simple functions and have them be intercombined

We're just still even at the outset of that. You're starting to see that a lot in the cloud, but as we get out closer to edge computing and some of these fourth industrial revolution use cases, the ability to take and compose different capabilities from an IBM, from another software company, from a real estate company that's selling you access to run computing capacity at the end of a physical link.

The ability to compose services together, whether it's through multiple parties or the ways organizations even present themselves to the world, take advantage of us in any way, in any slice that you so choose. Composability is going to open up a massive amount of possibilities. It's maybe a little rooted in the here and now, but it's something that I'm excited about over the next five to 10. Yeah, the thing I'm interested in is kind of the, so we're in the midst of artificial intelligence, right?

that is increasingly starting to tax the inventors of those, which is human beings. So the prefrontal cortex only has so many energy it can burn in a day, and it is being burned out at the end of every day through the actual amount of data that's bombarding it. So the intelligent augmentation, so flipping the two letters from artificial intelligence to intelligence augmentation,

so that we actually can actually work within these environments in a far more accommodative style relative to what we can biologically do is going to be where there's a lot of advancements. And I talked about the partnerships between two technology companies, so Lumen and ourselves, but there'll be increasing partnerships between health and bio companies as well as it relates to technology. Yeah, wonderful. Well, thank you so much. This has been really fun. Thank you very much. It's been a pleasure to spend time with you. Yeah, good. It was fun.

Thanks again to David Chikotius and Howard Bovill for talking with me. It's fascinating to consider how quickly data analysis can change performance in real time and the endless possibilities of hybrid cloud and edge computing. I look forward to witnessing its evolution. Smart Talks with IBM is produced by Emily Rostak with Carly Migliore and Catherine Girideau.

Edited by Karen Shikurji. Engineering by Martin Gonzalez. Mixed and mastered by Jason Gambrell and Ben Tolliday. Music by Gramascope. Special thanks to Molly Socha, Andy Kelly, Mia LaBelle, Jacob Weisberg-Heddafein, Eric Sandler, and Maggie Taylor, and the teams at 8 Bar and IBM.

Smart Talks with IBM is a production of Pushkin Industries and iHeartMedia. You can find more Pushkin podcasts on the iHeartRadio app, Apple Podcasts, or wherever you like to listen. I'm Malcolm Gladwell. See you next time.

Hello, hello. Malcolm Gladwell here. I want to tell you about a new series we're launching at Pushkin Industries on the 1936 Olympic Games. Adolf Hitler's Games. Fascism, anti-Semitism, racism, high Olympic ideals, craven self-interest, naked ambition, illusion, delusion, all collide in the long, contentious lead-up to the most controversial Olympics in history. The Germans put on a propaganda show, and America went along with all of it. Why?

This season on Revisionist History, the story of the games behind the games. Listen to this season of Revisionist History wherever you get your podcasts. And if you want to hear episodes before they're released to the public, subscribe to Pushkin Plus on Apple Podcasts or at pushkin.fm slash plus.