Speaker 1: Hello everyone. This is Smart Talks with IBM, a podcast
Speaker 1: from Pushkin Industries, iHeartMedia and IBM about what it means
Speaker 1: to look at today's most challenging problems in a new way.
Speaker 1: I'm Malcolm Gladwell. Today I'm chatting with Rob Thomas, the
Speaker 1: senior vice president of IBM Cloud and Data, where his
Speaker 1: responsibility is bringing new ideas to life. But despite being
Speaker 1: on a cutting edge of these technologies, he still has
Speaker 1: an appreciation for age old problems. There's a rabbit and
Speaker 1: a beaver and they're staring at the Hoover Dam and
Speaker 1: the beaver says the rabbit, No, I didn't build it,
Speaker 1: but it's based on an idea of mind. And the
Speaker 1: point of that story is there's ideas, are a dime
Speaker 1: that doesn't Yeah, so a great story. Everybody's got a
Speaker 1: bunch of ideas. Yeah. By the way, we're too quick
Speaker 1: to dismiss the beaver. He's right, But I have you
Speaker 1: seen beaver dams. I mean, I know he is right.
Speaker 1: It was his idea, but he had nothing to do
Speaker 1: with the giants cement Hoover Dame. In my interview with
Speaker 1: Rob will touch on the importance of the cloud during
Speaker 1: the pandemic and how IBM has been playing a part
Speaker 1: in vaccine distribution Stay tuned. I, for one, had no
Speaker 1: idea about what it means to be the senior vice
Speaker 1: president of IBM Cloud and Data, so I asked Rob
Speaker 1: to break it down for me in Layman's terms. We
Speaker 1: build software, and software is the lingua franca of our time.
Speaker 1: Anything that will get done in businesses and even interaction
Speaker 1: with consumers is going to be done with software. It's
Speaker 1: really the language of everything that's happening in the world.
Speaker 1: That's what we build. We are focused on doing that
Speaker 1: for businesses. So how long have you been at IBM
Speaker 1: twenty years or twenty one, I guess to be precise.
Speaker 1: And I started in consulting, and then I moved into
Speaker 1: our semiconductor business, and I was doing consulting. And the
Speaker 1: moment that really changed my whole career was doing work
Speaker 1: with Nintendo where we were designing the microprocessor for the
Speaker 1: Nintendo We and I realized, we're going to do this
Speaker 1: one time, but then they're going to be building software
Speaker 1: that we'll get copied billions of times and used by
Speaker 1: people all over the world. Maybe I'm not in the
Speaker 1: right business and that really piqued my curiosity around software,
Speaker 1: which then led me to move into the IBM software business,
Speaker 1: where I've been for most of my career at this point.
Speaker 1: So I've been in software total twelve thirteen years. But
Speaker 1: you have seen I'm guessing, so twelve years in software.
Speaker 1: Am I right in thinking that Morris happened in those
Speaker 1: twelve years of software than happened in the entire history
Speaker 1: of software before that? Is that a fair statement? Close's
Speaker 1: I'd say close. It's certainly the rate and pace of
Speaker 1: innovation has increased now as actually something hasn't read an outcome.
Speaker 1: Maybe that's a different question, but if you think about
Speaker 1: you know, software dates way back to even the first
Speaker 1: mainframe that we ever built in the fifties, So a
Speaker 1: lot of good things have been happening in software for
Speaker 1: a long time, but the rate and pace is a
Speaker 1: level that we've never seen, and that's certainly been what
Speaker 1: is accelerated in the last decade. Yeah, I mean, I
Speaker 1: remember my dad was a mathematician at the University of Waterloo.
Speaker 1: I remember coming home as a kid, going into his
Speaker 1: office and seeing stacks of computer cards. So in my
Speaker 1: lifetime I have gone from looking at stacks of computer
Speaker 1: cards to something far more so. I mean, I am
Speaker 1: aware of just how fast this fast, this pace is gone,
Speaker 1: and it'll be different a year from now. Right, that's
Speaker 1: how fast this is moving. Let's zero in on that
Speaker 1: a little bits. What's shifting right now? Imagine I'm a client.
Speaker 1: I come to you, and I say, you know, I
Speaker 1: want to be prepared for next year and the year
Speaker 1: after next. What should be at the top of my mind.
Speaker 1: Let me give you a quick story, if you don't mind.
Speaker 1: There was a time in the US where you could
Speaker 1: not easily get from one city to another. And at
Speaker 1: that point, back in the nineteen fifties, there was a
Speaker 1: decision that said, let's actually build the infrastructure to connect
Speaker 1: every city in America. And the result was fifty plus
Speaker 1: years of work, four hundred billion dollar and we now
Speaker 1: have forty eight thousand miles of highways that connects all
Speaker 1: these cities. But the real impact is more profound than that,
Speaker 1: because you're able to eliminate traffic at intersections by building overpasses.
Speaker 1: There are all these second order businesses that were built. Hotels,
Speaker 1: gas stations, the salty snacks that you buy in a
Speaker 1: gas station, fast food, rest areas, so an entire economy
Speaker 1: was built around the idea that the first step was
Speaker 1: just to connect all the cities in the US. And
Speaker 1: that's what's happening right now with software. It is connecting
Speaker 1: businesses and individuals in a way that we've never been
Speaker 1: connected before, and we are just at the beginning of
Speaker 1: all the second order effects that will come as a
Speaker 1: result of them. And the biggest problem in software it's data.
Speaker 1: Just like you had all these disparate cities and you
Speaker 1: were building highways to connect those cities, every company has
Speaker 1: all these different data sets all over the place and
Speaker 1: it's a really hard problem. But AI is not going
Speaker 1: to be a reality for businesses until the data problem
Speaker 1: is solved. That's one thing that I spend a lot
Speaker 1: of time on right now digging from it that into
Speaker 1: that meaning of that phrase, the data problem. I think
Speaker 1: every individual wants any company they interact with, whether it's
Speaker 1: their local bank or a restaurant or the local cleaners,
Speaker 1: whatever it may be, they want that business to know them.
Speaker 1: It's the whole idea of when you had towns where
Speaker 1: there was just one general store and the owner knew you,
Speaker 1: they knew what you wanted. I think everybody wants that
Speaker 1: level of engagement and that is what software enables. And
Speaker 1: the basis of that is data. And the biggest problem
Speaker 1: every business faces today is how do I understand my data?
Speaker 1: What it tells me about my customers, what it tells
Speaker 1: me about my products. So this is fundamentally about how
Speaker 1: do we live in a better way. You're talking about that.
Speaker 1: I'm I'm a big company, and I have different sets
Speaker 1: of data and they're all in different places, and they
Speaker 1: don't speak to each other, and I can't combine them
Speaker 1: and make sense. Is that what you mean by the
Speaker 1: data problem? Correct? And even if I can combine them
Speaker 1: and connect them, the data is not in a usable form.
Speaker 1: You know, one data, says m Gladwell, the other one
Speaker 1: says Malcolm g Is that the same person? Maybe? Maybe not.
Speaker 1: It's really hard because these systems have been built up
Speaker 1: over time. We do work with a company called Wonderman.
Speaker 1: Thompson story that they shared with me just this month
Speaker 1: was doing work with Peloton. So Peloton collects a lot
Speaker 1: of data what you call first party data from a
Speaker 1: bike or the tread. I think you're a runner if
Speaker 1: I recall and WPP Wonderman Thompson has all this third
Speaker 1: party data, which is what do they know about consumers?
Speaker 1: So just to connect those two data sets, build predictive models,
Speaker 1: and then to turn that into an advertising campaign. The
Speaker 1: AI part is actually relatively easy. It's actually connecting the data,
Speaker 1: rationalizing the data, cleaning the data. That's the really hard
Speaker 1: part that nobody talks about because all we ever see
Speaker 1: is you know the outcome, yeah, which just so I understand,
Speaker 1: it's super interesting. So let's imagine you rob or a
Speaker 1: Peloton user, and so we have a data stream that
Speaker 1: comes from the bike which says that you bike. Let's
Speaker 1: just say for them, I'm going to flatter you an
Speaker 1: hour and a half a day at some insane pace
Speaker 1: and neither of which are true. But keep going. I
Speaker 1: did do a half hour today, but it was a
Speaker 1: very slow pace, I gotta tell you so. And I'm
Speaker 1: looking at your via whatever it is I'm collecting. I'm
Speaker 1: assuming Peloton collects a lot of sort of physiological and
Speaker 1: use stata F on the bike, and from that we
Speaker 1: can generate a rough sense of who you are, how
Speaker 1: what your athletic interests are, how fit you are, all
Speaker 1: those kinds of things and Wonderman's Old shop wants to
Speaker 1: know how can I use that picture of the kind
Speaker 1: of athlete you are to help bring you the kinds
Speaker 1: of ad messages that you'll respond to. Is that is
Speaker 1: that the problem? It could be bringing it to me,
Speaker 1: But it's more likely because obviously they d anonymize all
Speaker 1: this data. It's more of all, right, so how do
Speaker 1: we find somebody else that's like rob What are the
Speaker 1: attributes of that person? And then how do we relate
Speaker 1: to them in a way that makes it feel like
Speaker 1: we're talking to them as opposed to talking to a
Speaker 1: cohort or a group. The number one prediction that most
Speaker 1: companies want to make is what's going to happen to
Speaker 1: my sales next month or the month after or the
Speaker 1: month after. And what we found is that tends to
Speaker 1: be a product of as many as fifty or one
Speaker 1: hundred different inputs. How many people are visiting the website,
Speaker 1: how many people are calling the call center, how many
Speaker 1: sales calls if I have a face to face salesforce
Speaker 1: are they making? How many marketing campaigns am I running?
Speaker 1: If you take all of these different data points, which
Speaker 1: is awfull in fifty or one hundred you feed those
Speaker 1: into a model. Then the first month you see how
Speaker 1: close was the model? Then you adjust, second month, you
Speaker 1: see how close was the model? And these models get
Speaker 1: really good over time. And we think we can help
Speaker 1: companies predict their financial performance in a month and a
Speaker 1: quarter in a year based on all these different data sources,
Speaker 1: all these different inputs. That's pretty valuable too, I'd say
Speaker 1: every company. So IBM, what's IBM's role in that you've
Speaker 1: described that problem to me? What is you? Guys come
Speaker 1: in and you say, we'll do what? A couple of
Speaker 1: years ago I started. I was trying to think about
Speaker 1: what is the right metaphor so that I can educate
Speaker 1: our customers on this and built this concept that I
Speaker 1: call the AI ladder. To think of it as steps
Speaker 1: that you take up a ladder towards AI. The bottom
Speaker 1: rung is collect data. So you have to be able
Speaker 1: to collect all your data. I'll use a library analogy.
Speaker 1: This is just you have to get books. You have
Speaker 1: to get books into the library that's collecting. Next is
Speaker 1: you have to organize that data and the a lot
Speaker 1: back to library analogy. That's the card catalog. So where
Speaker 1: are all the different data sets? I might have five
Speaker 1: copies of the same data. How do I know that's
Speaker 1: the same copy. Maybe one's checked out, maybe one's on microfilm.
Speaker 1: These are actually all problems that exist in businesses. So
Speaker 1: you've got to collect data, you've got to organize data.
Speaker 1: Then you have to analyze the data. So you're actually
Speaker 1: starting to do data science machine learning in the library metaphor,
Speaker 1: that's where you're displaying your best seller list or you're
Speaker 1: displaying popular magazine titles. And then the top of the
Speaker 1: ladder is what I call infuse. So then how do
Speaker 1: you take those models and infuse them into a business process.
Speaker 1: So it's those four steps the ladder. You have to collect, organize, analyze,
Speaker 1: and fuse. We build software that helps customers with each
Speaker 1: of the wrongs of that ladder, helps them do the collection.
Speaker 1: We actually build what we call a data catalog to
Speaker 1: help you organize your data. So we help them with
Speaker 1: all wrongs of that ladder. Because ultimately, then you've probably
Speaker 1: heard of IBM Watson, that is our AI platform. Once
Speaker 1: you've done those things, you can use AI and get
Speaker 1: really great outcomes. Imagine if someone from the White House
Speaker 1: came to you and said, we're about to do something
Speaker 1: we've never done in this haven't done in this country
Speaker 1: for seventy years, which is try and vaccinate everybody in
Speaker 1: the shortest possible time. We have a multiple sets of
Speaker 1: three and eventually probably four or five different kinds of
Speaker 1: vaccines being administered by tens of thousands of local municipalities
Speaker 1: to people who have a wide ranging set of risk factors, urgency,
Speaker 1: pre existing conditions, going on and on and on and on.
Speaker 1: Can you help us do this as efficiently and cost
Speaker 1: effectively and socially consciously as possible? Is that a kind
Speaker 1: of task that you're talking about now that is in
Speaker 1: part as much a logistics problem as it is a
Speaker 1: data problem. Let me describe to you one of the
Speaker 1: data problems though, that exist around this because we're doing
Speaker 1: the work with CVS on the COVID vaccine rollout. Yes,
Speaker 1: and so if you're CVS where you're actually administering their
Speaker 1: biggest problem is everybody has a question. CVS can't hire
Speaker 1: enough people to answer the ten questions you have, the
Speaker 1: ten questions I have, the twenty questions your cousin has.
Speaker 1: They came to us and said, can we use AI
Speaker 1: to respond to all the inquiries we're getting and actually
Speaker 1: help route people to where they can figure out they
Speaker 1: can get the vaccine when they're eligible. So we built
Speaker 1: an AI agent for them that is now dealing with
Speaker 1: the vaccine rollout every day. That starts with data. They
Speaker 1: have a place that they store data about different questions.
Speaker 1: We've got models that we have trained on language, meaning
Speaker 1: we can understand different types of questions, what's really inferred
Speaker 1: versus implied versus what is stated. That's a real data problem.
Speaker 1: That's where we've spent majority of our time looking at
Speaker 1: this current situation. Yeah, so you would that when you
Speaker 1: say it's a data problem, meaning that you started by
Speaker 1: trying to anticipate, by looking at the data and using
Speaker 1: that to try and anticipate all the possible questions that
Speaker 1: someone might ask, Is that what you're correct? Yes, and
Speaker 1: then training a machine learning model based on those inputs
Speaker 1: so that when the question was asked, we had a
Speaker 1: high probability of giving the right answer. Yeah. How long
Speaker 1: did it take you to build that system? Now this
Speaker 1: is the wonders of modern software. To your question on acceleration,
Speaker 1: we did this in forty five days. Are you serious? Yeah,
Speaker 1: it's insane. And how many people worked on it twenty
Speaker 1: five thirty somewhere in that room. It's not a huge group. Wow.
Speaker 1: Their thing about systems like this is you hope it's
Speaker 1: really good on day one, but you know for sure
Speaker 1: it's going to be better on day ten. It's gonna
Speaker 1: be better again on day twenty. These are learning systems.
Speaker 1: They do get better over time. And the thing is
Speaker 1: with the really difficult problems, and this is this is
Speaker 1: the way I like to talk about AI as giving
Speaker 1: human superpowers. Most people want to say it replaces humans.
Speaker 1: I actually think given't superpowers because in these cases you
Speaker 1: start to move the harder problems to the humans, and
Speaker 1: so therefore your customer satisfaction goes up because people are
Speaker 1: getting their problems resolved. Would you ever get to one
Speaker 1: hundred percent? Probably not, because there's always going to be
Speaker 1: something that's too difficult for the AI to handle. But
Speaker 1: I think you can keep moving it up for sure. Yeah.
Speaker 1: Or maybe given what you've just said, would it be
Speaker 1: more fair to say you don't want to get to
Speaker 1: one hundred, that you want to reserve a certain category
Speaker 1: of problem for a human to human interaction, because that
Speaker 1: might be ultimately more satisfying to the question. We have
Speaker 1: that discussion a lot, and certainly in the ones that
Speaker 1: I've worked on, that's typically the case because let's not
Speaker 1: forget these are businesses, and the goal of most businesses
Speaker 1: is to sell something. So sometimes the best way to
Speaker 1: sell something is to really help somebody with their problem
Speaker 1: and then show them how your other product can make
Speaker 1: their life even easier. When you think back in the
Speaker 1: cases that you have kind of problems that your group
Speaker 1: has been asked to solve. At IBM of US a
Speaker 1: couple of years, what was the hardest I don't know
Speaker 1: that I could name a single thing that's harder than others.
Speaker 1: The ones that are the most time consuming, things like
Speaker 1: regulatory compliance. If you're a bank, you've got a lot
Speaker 1: of different regulations that you have to live up to,
Speaker 1: and it's easy to help build AI that can make
Speaker 1: loan decisions yes or no, good idea, bad idea, eliminate
Speaker 1: bias from that decision. That's very doable. Am I compliance
Speaker 1: with the regulations of where that individual is based, because
Speaker 1: they're in a zip code, or they're in a state,
Speaker 1: they're in a country, Those problems get really difficult because
Speaker 1: you're kind of connecting, you know, reams of legality to
Speaker 1: a day to day business process. Those get those, those
Speaker 1: get really difficult. Has any customer ever come to you
Speaker 1: with a problem that you guys said, we can't solve that.
Speaker 1: We are way too curious to ever give up that easily. Yeah,
Speaker 1: it's more of you know, it's it's the cheap, fast,
Speaker 1: and good triangle. If you've heard that, you know you
Speaker 1: only get two of those. Do you want it cheap
Speaker 1: and fast, it's probably not good. If you want it
Speaker 1: good and fast, it's probably not cheap. If you want
Speaker 1: it cheap and good, it's probably not fast. I think
Speaker 1: all of these situations come down to that triangle. So
Speaker 1: you have a group of people who work on these
Speaker 1: kinds of problems, and I'm curious, what do you look
Speaker 1: for when you're bringing someone onto that team. Is there
Speaker 1: a set of skills associated with dealing with this area
Speaker 1: of the application of AI to these very complicated data fields?
Speaker 1: Is there a specific set of skills that are crucial
Speaker 1: and rare hard find? The skill that's easy to test
Speaker 1: for is do you have the technical ability? Do you
Speaker 1: understand Python? Do you understand machine learning? You can kind
Speaker 1: of see from somebody's body of work and what they've studied,
Speaker 1: do they have that skill? The part where it gets
Speaker 1: harder is the empathy question. Can you actually understand a situation,
Speaker 1: understand a user, and empathize with what they're trying to
Speaker 1: do such that you're not just building a model for
Speaker 1: a robot, You're actually building this for a human on
Speaker 1: some end, that one's hard, harder to test for. And
Speaker 1: then the third one is I would just call it
Speaker 1: curiosity how widely read as somebody do they understand business
Speaker 1: business problems because those kind of softer skills, those make
Speaker 1: a huge difference when you're solving these kind of problems.
Speaker 1: So it's easy to test for the first. The other
Speaker 1: two are a little harder to test for, and the
Speaker 1: best data scientists in the world have all three of those.
Speaker 1: Let's talk about the cloud. I see this word hybrid cloud.
Speaker 1: I don't know what it means. So can you start
Speaker 1: by telling me what it means and then fit this
Speaker 1: into the conversation we've been having. So any company that's
Speaker 1: been around for more than three years, maybe five, they've
Speaker 1: got somewhere that they keep their data and they keep
Speaker 1: the different technology that they have, and in many cases
Speaker 1: that's in their office or that's in a data center
Speaker 1: right right near their office. They've also started over time
Speaker 1: to start to build new data sets or new software
Speaker 1: in a public cloud, something from you know, something inside
Speaker 1: of IBM Cloud or Amazon Web Services or Microsoft Azure.
Speaker 1: The minute that you have more than one environment, you
Speaker 1: have a hybrid cloud, whether whether you know it or not.
Speaker 1: So think of it as I've got multiple data sets
Speaker 1: in multiple places to kind of back to the US
Speaker 1: Highway example, or I've got software applications in multiple places.
Speaker 1: You have to get that to act like a single
Speaker 1: technology environment. That is the essence of hybrid cloud, which
Speaker 1: is I can manage that as a single environment. The
Speaker 1: average company now has five different environments cloudwise, it acts
Speaker 1: like one. I can connect the data set the average
Speaker 1: company has. Is that by design because they feel it's
Speaker 1: safe for Is that just because the hodgepodge nature in
Speaker 1: which we grow our it needs means that we end
Speaker 1: up being all over the place. It's because there's a
Speaker 1: lot of people that work in every company, and everybody
Speaker 1: wants their own thing. That's how it happens. So this
Speaker 1: department started in their own data center, This department started
Speaker 1: on IBM Cloud. This department wanted a CRM system from Salesforce.
Speaker 1: This department wanted to use Azure. It's human nature. People
Speaker 1: just go do what they want to do. And you
Speaker 1: wake up one day and you realize, hey, we've got
Speaker 1: a lot of different clouds. And so if you're storing
Speaker 1: your customer data with Salesforce and you've got these three
Speaker 1: other environments, how do you get the customer data to
Speaker 1: inform you know what you're doing in the other parts
Speaker 1: of your business. That's a hybrid cloud problem. Yeah, and
Speaker 1: how how hard of a problem is that? I mean,
Speaker 1: as that a total naive outside or I would have said, oh,
Speaker 1: surely all these cloud businesses would have made it really
Speaker 1: easy to share stuff in one place with stuff you've
Speaker 1: got in other place. Is that not true? Unfortunately the
Speaker 1: opposite is true, because for the pure play public cloud providers,
Speaker 1: the incentive was actually the opposite. It's Hotel California. For them.
Speaker 1: You can bring your stuff in, but you know you
Speaker 1: can check in, but you will never let you check out,
Speaker 1: and they charge actually enormous fees if you want to
Speaker 1: get your data out. So it's a bit of a
Speaker 1: strategy tax for them to make it easy. It's also
Speaker 1: a hard problem just because you're trying to connect different
Speaker 1: data sets. Do you have one card cataloged that connects
Speaker 1: all these different sources. It's actually not easy to do.
Speaker 1: And what happens when you don't do that then you
Speaker 1: end up rebuilding everything and so suddenly you're storing all
Speaker 1: the same data five times. That gets very expensive. So
Speaker 1: let's imagine what having this conversation in twenty twenty six,
Speaker 1: give me your sense of where will be what would
Speaker 1: what would we be talking about? Five years from now,
Speaker 1: we'll probably be having very similar discussions as possible. Technology
Speaker 1: will be more advanced, but a lot of the problems
Speaker 1: we've talked about, let's be honest, these have been around
Speaker 1: for quite a while. There's a story this guy, Charles Towns,
Speaker 1: he was the inventor of the laser, and he tells
Speaker 1: this story. There's a rabbit and a beaver and they're
Speaker 1: staring at the Hoover Dam and the beaver says the rabbit, no,
Speaker 1: I didn't build it. But it's based on an idea
Speaker 1: of mind. And the point of that story is there's
Speaker 1: ideas are a dime a doesn't it so? Great? Story?
Speaker 1: Everybody's got a bunch of ideas. Yea, By the way,
Speaker 1: we're too quick to dismiss the beaver. He's right, But
Speaker 1: have you seen Beaver Dams. I mean, I know he
Speaker 1: is right. It was his idea, but he had nothing
Speaker 1: to do with the giants Cement Hoover Dam. The reason
Speaker 1: I share that story is a lot of people have
Speaker 1: ideas now about what they can do, but what's going
Speaker 1: to make a difference five years from now is what
Speaker 1: do you go try and do. And I encourage companies
Speaker 1: that you've got to be willing to have a pretty
Speaker 1: high failure rate, knowing that if you go try a
Speaker 1: bunch of things, you know, maybe only half of them
Speaker 1: will work out. I mean, if I look at AI today,
Speaker 1: so there's five major things I see companies doing generally successfully.
Speaker 1: It's customer service. We talked about that. It's financial budgeting,
Speaker 1: It's regulatory compliance. We talked about that. That one's harder.
Speaker 1: It's employee experience hiring that type of thing. And it's
Speaker 1: using AI to run their IT systems. So using software
Speaker 1: to run the systems. Those are the five big things today.
Speaker 1: I actually think those five things will still be the
Speaker 1: topic in twenty twenty six, but will be a lot
Speaker 1: more advanced on each of those because today it's a
Speaker 1: little bit we're doing it for the first time, whereas
Speaker 1: we'll be a much more advanced as we get out
Speaker 1: to twenty twenty six. I do think quantum computing will
Speaker 1: be commercialized at that point. That's pretty revolutionary. So more
Speaker 1: to come on that one. Let's end on some more
Speaker 1: case studies. Tell me a couple of examples of people
Speaker 1: you've worked with where the outcome was really exciting or unexpected,
Speaker 1: or we've worked with Sprint T Mobile. They have this
Speaker 1: classic problem of they've got to do aftermarket service for
Speaker 1: all the different telecom equipment that they sell and the
Speaker 1: data that they have on those different systems, the warranty
Speaker 1: when they were built, how they're running, it's spread across
Speaker 1: a thousand different different data sources. We were able to
Speaker 1: build an AI system for them that sits across those systems,
Speaker 1: that was able to intelligently route how they do all
Speaker 1: of their aftermarket service. So do you and I feel
Speaker 1: that in our day to day life, Well, we feel it.
Speaker 1: If they don't fix things, then it's obvious because there's
Speaker 1: an outage or something that doesn't work. But it was
Speaker 1: something that they had so much data on this, they
Speaker 1: could have never done this by i'd say a typical approach.
Speaker 1: So these are the kinds of things that the average
Speaker 1: consumer doesn't see every day, but they do make a
Speaker 1: difference in our life. And you're talking about things like
Speaker 1: what like cell towers or yeah, it could be cell towers,
Speaker 1: or it could be you know, the power cable, not
Speaker 1: the power cable, the power box that's sitting next to
Speaker 1: the cell tower. It could be any of those things. Oh,
Speaker 1: I see. So you're so they have all of these
Speaker 1: systems that might have been bought at different times, made
Speaker 1: by different people, installed by different people, and so what
Speaker 1: you want to do is to give them a system
Speaker 1: that allows them to look at them all in real
Speaker 1: time and figure out where there might be an issue. Yes,
Speaker 1: we call it predictive maintenance. Right, Okay, all the signs
Speaker 1: are that there's about to be a problem on this one,
Speaker 1: they go out there, they check it out, yep, lo
Speaker 1: and behold, there is a problem. Yeah, I have two cars,
Speaker 1: can you build one for me? So we haven't scaled
Speaker 1: down to that level quite yet, but stay tuned. We're
Speaker 1: open to it. Why not, Why are you neglecting? I'm
Speaker 1: the ultimate end user. I'm I'm one guy with two cars.
Speaker 1: That's a good question. We'll bring it to you soon.
Speaker 1: We have any have any professional sports teams work with you? Guys?
Speaker 1: Toronto Raptors a publicity on that a few years ago.
Speaker 1: You're I'm Canadian. Is that's my team? You're warming my
Speaker 1: heart right now of course. Well this has been really fun.
Speaker 1: Thank you so much. Yeah, I'm welcome, appreciate it. I'd
Speaker 1: love to help out the Toronto Raptors if I had
Speaker 1: the chance. Thanks again to Rob Thomas for an intriguing
Speaker 1: conversation about data and the cloud. Smart Talks with IBM
Speaker 1: is produced by Emily Rostek with Carl Migliori, edited by
Speaker 1: Karen Shakerje engineering by Martin Gonzalez, mixed and mastered by
Speaker 1: Jason Gambrel. Music by Gramoscope. Special thanks to Molly Sosha, Andy,
Speaker 1: Kelly Miala, Belle, Jacob Weisberg, Heada Fane, Eric Sander, Maggie Taylor,
Speaker 1: and the teams at eight Bar and IBM. Smart Talks
Speaker 1: with IBM is a production of Pushkin Industries and iHeartMedia.
Speaker 1: You can find more Pushkin podcasts on the iHeartRadio app
Speaker 1: Apple Podcasts or wherever you like to listen. I'm Malcolm Gladwell,
Speaker 1: See you next time.