March 25, 2021 • 30 mins
With hybrid cloud and AI, businesses today can challenge the limits of how they put their data to work across the organization. In this episode of Smart Talks, Malcolm talks to Rob Thomas, SVP Cloud and Data Platform at IBM about what’s possible, and the steps businesses can take to access more of their data to help them make more informed decisions
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Speaker 1 (00:02):
Hello everyone. This is Smart Talks with IBM, a podcast
from Pushkin Industries, I Heart Media and IBM about what
it means to look at today's most challenging problems in
a new way. I'm Malcolm Gladwell. Today I'm chatting with
(00:24):
Rob Thomas, the senior vice president of IBM Cloud and Data,
where his responsibility is bringing new ideas to life. But
despite being on the cutting edge of these technologies, he
still has an appreciation for age old problems. There's a
(00:47):
rabbit and a beaver and they're staring at the Hoover
dam and the beaver says the rabbit, No, I didn't
build it, but it's based on an idea of mind.
And the point of that story is there's ideas are
a dye that doesn't so great story. Everybody's got a
bunch of ideas. By the way, we're too quick to
(01:08):
dismiss the beaver. He's right, but I have you seen
beaver dams. I mean, he's right, it was his idea,
but he had nothing to do with the giants Cement
Hoover dow. In my interview with Rob will touch on
the importance of the cloud during the pandemic and how
IBM has been playing a part in vaccine distribution Stay tuned. I,
(01:38):
for one, had no idea about what it means to
be the senior vice president of IBM Cloud and Data,
so I asked Rob to break it down for me
in Layman's terms, we build software, and software is the
lingua franca of our time. Anything that will get done
in businesses and even interaction with consumers is going to
(02:02):
be done with software. It's really the language of everything
that's happening in the world. That's what we build. We
are focused on doing that for businesses. So how how
long have you been at IBAN twenty years or one?
I guess to be precise. And I started in consulting,
and then I moved into our semiconductor business, and I
(02:24):
was doing consulting. And the moment that really changed my
whole career was doing work with Nintendo where we were
designing the microprocessor for the Nintendo We and I realized,
we're going to do this one time, but then they're
going to be building software that we get copied billions
(02:45):
of times and used by people all over the world.
Maybe I'm not in the right business. And that really
piqued my curiosity around software, which then led me to
move into the IBM software business, where I've been for
most of my career at this point. So I've been
in software total twelve thirteen years. But you have seen
(03:06):
I'm guessing, so twelve years in software. Am I right
in thinking that Morris happened in those twelve years of
software than happened in the entire history of software before? That?
Is that a fair statement? Close? It's I'd say close.
It's certainly the rate and pace of innovation has increased.
Now has actually something hasn't read an outcome? Maybe that's
(03:27):
a different question. But if you think about you know,
software dates way back to even the first main frame
that we ever built in the fifties. So a lot
of good things have been happening in software for a
long time. But the rate and pace is a level
that we've never seen, and that's certainly been what is
accelerated in the last decade. I mean, I remember my
(03:50):
dad was a mathematician at the University of Waterloo. I
remember coming home as a kid, going into his office
and seeing stacks of computer cards. So in my lifetime
I have I have gone from looking at stacks of
computer cards to something far more so. I mean, I
am aware of just how fast this fast, this uh,
(04:11):
this pace is gone and it will be different a
year from now. Right, that's how fast this is moving.
Let's zero in on that a little bit. Um, What's
what's shifting right now? Imagine I'm a client and I
come to you and I say, you know, I want
to be prepared for next year and the year after next.
(04:31):
What should be at the top of my mind. Let
me give you a quick story, if you don't mind.
There was a time in the US where you could
not easily get from one city to another. And at
that point, back in the nine fifties, there was a
decision that said, let's actually build the infrastructure to connect
(04:53):
every city in America. And the result was fifty plus
years of work fo dollar ours and we now have
forty eight thousand miles of highways that connects all these cities.
But the real impact is more profound than that, because
you're able to eliminate traffic at intersections by building over passes.
(05:15):
There are all these second order businesses that were built. Hotels,
gas stations, the salty snacks that you buy in a
gas station, fast food rest areas, so an entire economy
was built around the idea that the first step was
just to connect all the cities in the US. And
(05:39):
that's what's happening right now with software. It is connecting
businesses and individuals in a way that we've never been
connected before, and we are just at the beginning of
all the second order effects that will come as a
result of them. And the biggest problem in software it's data.
Just like you had all these disparate cities and you
(06:01):
were building highways to connect those cities, every company has
all these different data sets all over the place and
it's a really hard problem. But AI is not going
to be a reality for businesses until the data problem
is solved. That's one thing that I spent a lot
(06:22):
of time on Right now, we're dig digging from it
that into that meaning of that phrase, the data problem.
I think every individual wants any company they interact with,
whether it's their local bank or restaurant, or the local cleaners,
whatever it may be, they want that business to know them.
(06:45):
It's the whole idea of when you had towns where
there was just one general store and the owner knew you,
they knew what you wanted. I think everybody wants that
level of engagement, and that is what software enables. And
the basis of that is data. And the biggest problem
(07:05):
every business faces today is how do I understand my data?
What it tells me about my customers, what it tells
me about my products. So this is fundamentally about how
do we live in a better way. You're talking about that.
I'm I'm a big company, and I have different sets
of data and they're all in different places, and they
(07:26):
don't speak to each other, and I can't combine them
and make sense. Is that what you mean by the
data problem? Correct? And even if I can combine them
and connect them, the data is not in a usable form.
You know, one one data, says m Gladwell, the other
one says Malcolm g Is that the same person? Maybe?
Maybe not. It's really hard because these systems have been
(07:50):
built up over time. We do work with a company
called Wonderman. Thompson story that they shared with me just
this month was doing work with Peloton. So Peloton collects
a lot of data what you call first party data
from a bike or the tread. I think you're a
runner if I recall and w P P. Wonderman Thompson
(08:14):
has all this third party data, which is what do
they know about consumers? So just to connect those two
data sets, build predictive models, and then to turn that
into an advertising campaign. The AI part is actually relatively easy.
It's actually connecting the data, rationalizing the data, cleaning the data.
That's the really hard part that nobody talks about because
(08:36):
all we ever see is you know the outcome, yeah,
which is so I understand this is super interesting. So
let's imagine you Robb or a Peloton user, and so
we have a data stream that comes from the bike
which says that you bike. Let's just say for them,
I'm gonna flatter you an hour and a half a
day at some insane pace and neither of which are true.
(08:59):
But keep going. I did do a half hour today,
but it was a very slow pace. I gotta tell you. So,
I'm and I'm looking at your via whatever it is
I'm collecting. I'm assuming Peloton collects a lot of sort
of physiological and you stat up on the bike, and
from that we can generate a rough sense of who
you are, how what your athletic interests are, how fit
(09:20):
you are, all those kinds of things, and Wonderman's Old
shop wants to know, how can I use that picture
of the kind of athlete you are to help bring
you the kinds of ad messages that you'll respond to.
Is that a fair? Is that the problem? It could
be bringing it to me? But it's more likely because
obviously they d anonymize all this data. It's more of all, right, so,
(09:42):
how do we find somebody else that's like rob? What
are the attributes of that person? And then how do
we relate to them in a way that makes it
feel like we're talking to them as opposed to talking
to a cohort or a group. The number one prediction
that most companies want to is what's going to happen
to my sales next month or the month after or
(10:04):
the month after. And what we found is that tends
to be a product of as many as fifty or
a hundred different inputs. How many people are visiting the website,
how many people are calling the call center, how many
sales calls if I have a face to face salesforce
are they making? How many marketing campaigns am I running?
(10:24):
If you take all of these different data points, which
is awful in fifty or a hundred. You feed those
into a model. Then the first month you see how
close with the model. Then you adjust, second month, you
see how close was the model? And these models get
really good over time. And we think we can help
(10:44):
companies predict their financial performance in a month and a
quarter in a year based on all these different data sources,
all these different inputs. That's pretty valuable to let's say
every company. So IBM, what's IBM's role in that you've
described that problem to me? What is you guys come
in and you say, we'll do what? A couple of
(11:07):
years ago I started. I was trying to think about
what is the right metaphor so that I can educate
our customers on this and built this concept that I
called the AI ladder. To think of it as steps
that you take up a ladder towards AI. The bottom
(11:30):
rung is collect data. So you have to be able
to collect all your data. I'll use a library analogy.
This is just you have to get books. You have
to get books into the library that's collecting. Next is
you have to organize that data and the now the
a lot back to library analogy. That's the card catalog.
So where are all the different data sets. I might
(11:51):
have five copies of the same data. How do I
know that's the same copy. Maybe one's checked out, maybe
one's on microfilm. These are actually all problems that existing businesses.
So you've got to collect data, you've got to organize data.
Then you have to analyze the data. So you're actually
starting to do data science machine learning in the library
metaphor that's where you're displaying your best seller list or
(12:14):
you're displaying, you know, popular magazine titles. And then the
top of the ladder is what I call infuse. So
then how do you take those models and infuse them
into a business process. So it's those four steps the ladder.
You have to collect, organized, analyze, and fuse. We build
(12:36):
software that helps customers with each of the wrongs of
that ladder, helps them do the collection. We actually build
what we call a data catalog to help you organize
your data. So we help them with all wrongs of
that ladder. Because ultimately, then you've probably heard of IBM Watson,
that is our AI platform. Once you've done those things,
(12:57):
you can use AI and get really great outcomes. Imagine
if someone from the White House came to you and
said we're about to do something we've never done in
this haven't done in this country for seventy years, which
is try and vaccinate everybody in the shortest possible time.
(13:20):
We have a multiple sets of three and eventually probably
four or five different kinds of vaccines being administered by
tens of thousands of local municipalities too, people who have
a wide ranging set of risk factors, urgency, pre existing
conditions going on, you know, on and on and on
and on on um. Can you help us do this
(13:43):
as efficiently and cost effectively and socially consciously as possible?
Is that a kind of task that you're talking about
now that is in part as much a logistics problem
as it is a data problem. Let me describe to
you one of the data problems though, that exist around
(14:05):
this because we're doing the work with CVS on the
COVID vaccine rollout. Yes, and so if you're CVS where
you're actually administering, their biggest problem is everybody has a question.
CVS can't hire enough people to answer the ten questions
(14:25):
you have, the ten questions I have, the twenty questions
your cousin has. They came to us and said, can
we use AI too respond to all the inquiries we're
getting and actually help route people to where they can
figure out they can get the vaccine when they're eligible.
So we built an AI agent for them that is
(14:47):
now dealing with the the vaccine rollout every day that
starts with data. They have a place that they store
data about different questions. We've got models that we have
trained on language, meaning we can understand different types of questions,
which really inferred versus implied versus what is stated. That's
(15:09):
a real data problem. That's where we've spent the majority
of our time looking at this, this current situation. So
you would that you when you say it's a data problem,
meaning that you started by trying to anticipate, by looking
at the data and using that to try and anticipate
all the possible questions that someone might ask, Is that
(15:30):
what you're correct? Yes, um, and then training a machine
learning model based on those inputs so that when the
question was asked, we had a high probability of giving
the right answer. How long did it take you to
build that system? Now this is the wonders of modern software.
To your question on acceleration, we did this in forty
(15:53):
five days. Are you serious. Yeah, it's insane. How how
many people worked on it? Thirty somewhere in that room.
It's not a huge group. Wow. Their thing about systems
like this is you hope it's really good on day one,
but you know for sure it's going to be better
(16:15):
on day ten. It's gonna be better again on day twenty.
These are learning systems. They do get better over time.
And the thing is with with the really difficult problems.
And this is this is why I like to talk
about AI is giving human superpowers. Most people want to
say it replaces humans. I actually think given superpowers because
in these cases you start to move the harder problems
(16:37):
to the humans, and so therefore your your customer satisfaction
goes up because people are getting their problems resolved. Would
you ever get to Probably not, because there's always going
to be something that's too difficult for the AI to handle,
But I think you can keep moving it up for sure.
Or maybe given what you've just said, would it be
more fair to say you don't want to get to
(16:57):
a hundred, that you want to rise nerve a certain
category of problem for a human human interaction, because that
might be ultimately more satisfying to the question. We have
that discussion a lot, and certainly in the ones that
I've worked on, that's that's typically the case, because let's
not forget these are businesses, and the goal of most
(17:19):
businesses is to sell something. So sometimes the best way
to sell something is to really help somebody with their
problem and then show them how your other product can
make their life even easier. When you think back in
the cases that you have kind of problems that that
your group has been asked to solve it, IBM of
last couple of years, what was the hardest I don't
(17:42):
know that I could name a single thing that's harder
than others. The ones that are the most time consuming,
things like regulatory compliance. If you're a bank, you've got
a lot of different regulations that you have to to
live up to. And it's easy to help build AI
that can make loan decisions yes or no, good idea,
(18:05):
bad idea, eliminate bias from that decision. That's very doable.
Am I Compliance with the regulations of where that individual
is based, because they're in a zip code, or they're
in a state, they're in a country, those problems get
really difficult because you're kind of connecting, you know, reams
of legality to a day to day business process. Those
(18:28):
get those those get really difficult. Has any customer ever
come to you with a problem that you, guys said,
we can't solve that. We are way too curious to
ever give up that easily. It's more of, you know,
it's the it's the cheap, fast, and good triangle. If
you've heard that, you know you only get two of those.
(18:51):
Do you want it cheap and fast, it's probably not good.
If you want it good and fast, it's probably not cheap.
If you want it cheap and good, it's probably not fast.
I think all of these situations come down to that triangle.
So you have a group of people who work on
these kinds of problems, and I'm curious, what do you
look for when you're bringing someone onto that team. Is
(19:14):
there a set of skills associated with dealing with this
area of the application of AI to these very complicated
data fields. Is there a specific set of skills that
are crucial and rare hard find? The skill that's easy
to test for is do you have the technical ability?
(19:38):
Do you understand Python? Do you understand machine learning? You
can kind of see from somebody's body of work and
what they've studied. Do they have that skill? Part where
it gets harder is the empathy question. Can you actually
understand a situation, understand a user, and empathize with what
(20:01):
they're trying to do such that you're not just building
a model for a robot, You're actually building this for
a human on some end, that one's hard, harder to
test for. And then the third one is I would
just call it curiosity, how widely read as somebody do
they understand business business problems because those kind of softer skills,
(20:24):
those make a huge difference when you're solving these kind
of problems. So it's easy to test for the first.
The other two are a little harder to test for,
and the best data scientists in the world have all
three of those. Let's talk about um the cloud. I
see this word hybrid cloud. I don't know what it means.
(20:48):
So can you start by telling me what it means
and then fit this into the conversation we've been having.
So any company that's been around for more than three years,
maybe five, they've got somewhere that they keep their data
and they keep the different technology that they have, and
(21:11):
in many cases that's in their office or that's in
a data center right right near their office. They've also
started over time to start to build new data sets
or new software in a public cloud, something from you know,
something inside of IBM Cloud or Amazon Web Services or
(21:34):
Microsoft Azure. The minute that you have more than one environment,
you have a hybrid cloud, whether whether you know it
or not. So think of it as I've got multiple
data sets and multiple places to kind of back to
the US Highway example, or I've got software applications and
(21:55):
multiple places. You have to get that to act like
a single technology environment. That is the essence of hybrid cloud,
which is I can manage that as a single environment.
The average company now has five different environments cloud wise,
it acts like one. I can connect the data sets
the average company has. Is that by by design because
(22:20):
they feel it's safe for Is that just because the
hodgepodge nature in which we grow our I team needs
means that we end up being all over the place.
It's because there's a lot of people that work in
every company, and everybody wants their own thing. That's how
it happens. So that this department started in their own
(22:41):
data center, This department started on IBM cloud. This department
wanted a CRM system from Salesforce, this department wanted to
use Azure. It's human nature. People just go do what
they want to do. And you wake up one day
and you realize, hey, we've got a lot of different
cloud elements. And so if you're storing your customer data
(23:03):
with Salesforce and you've got these three other environments, how
do you get the customer data to inform you know
what you're doing in the other parts of your business.
That's a hybrid cloud problem. And how how hard of
a problem is that? I mean, as that total naive
outside or I would have said, oh, surely all these
(23:24):
cloud businesses would have made it really easy to share
stuff in one place with stuff you've got in other place.
Is that not true? Unfortunately the opposite is true, because
for the pure play public cloud providers, the incentive was
actually the opposite. It's Hotel California. For them, you can
bring your stuff in, but you know you can. You
can check in, but you will never let you check out,
(23:45):
and they charge actually enormous fees if you want to
get your data out. So it's a bit of a
strategy tax for them to make it easy. It's also
a hard problem just because you're trying to connect different
data sets. Do you have in card cataloged that connects
all these different sources. It's actually not easy to do.
(24:06):
And what happens when you don't do that then you
end up rebuilding everything and so suddenly you're storing all
the same data five times. That gets very expensive. So
let's imagine what Having this conversation give me your sense
of where will be what would what would we be
talking about five years from now, we'll probably be having
(24:28):
very similar discussions as possible. Technology will be more advanced,
but a lot of them probably talked about. Let's be honest,
these have been around for for quite a while. There's
a story this this guy, Charles Towns, he was the
inventor of the laser, and he tells his story. There's
a rabbit and a beaver and they're staring at the
Hoover Dam and the beaver says the rabbit, no, I
(24:51):
didn't build it, but it's based on an idea of mine.
And the point of that story is there's ideas. Are
a dime? Doesn't it so? Great? Story? Everybody's got a
bunch of ideas. By the way, we're too quick to
dismiss the beaver. He's right, but have you seen beaver
Dam's I mean, I know he is right. It was
(25:13):
his idea, but he had nothing to do with the
giants Cement Hoover do. Yeah. The reason I I'd share
that story is a lot of people have ideas, now
what about what they can do? But what's going to
make a difference five years from now is what do
you go try and do? And I encourage companies that
(25:36):
you've got to be willing to have pretty high failure rate,
knowing that if you go try a bunch of things,
you know, maybe only half of them will work out.
I mean, if I look at AI today, so there's
five major things I see companies doing generally successfully. It's
customer service. We talked about that, It's financial budgeting, It's
(25:57):
regulatory compliance. We talked about that. That one's or harder.
It's employee experience hiring that type of thing. And it's
using AI to run their I T systems. So using
software to run the systems. Those are the five big
things today. I actually think those five things will still
be the topic in but will be a lot more
(26:20):
advanced on each of those because today it's a little
bit we're doing it for the first time, whereas will
be a much more advanced as we get out. I
do think quantum computing will be commercialized at that point.
That's pretty revolutionary. So more to come on that one.
Let's end on some more case studies. Tell me a
(26:41):
couple of examples of people you've worked with where the
outcome is it was really exciting or or unexpected. Or
we've worked with Sprint T Mobile. They have this classic
problem of they've got to do aftermarket service for all
(27:02):
the different telecom equipment that they sell and the data
that they have on those different systems, the warranty when
they were built, how they're running, it's spread across a
thousand different different data sources. We were able to build
an AI system for them that sits across those systems,
(27:24):
that was able to intelligently route how they do all
of their aftermarket service. So do you and I feel
that in our day to day life, Well, we we
feel that if they don't fix things, then it's obvious
because there's an outage or something that doesn't work. But
it was something that they had so much data on this.
They could have never done this by i'd say a
(27:47):
typical approach. So these are the kinds of things that
the average consumer doesn't see every day, but they do
make a difference in our life. And you're talking about
things like what like cell towers or yeah, it could
be cell towers, or it could be you know, the
power cable, not the power cable, the power boxes sitting
(28:08):
next to the cell tower. It could be any of
those things. Oh, I see. So you're so they have
all of these systems that might have been bought at
different times, made by different people, installed by different people,
And so what you want to do is to give
them a system that allows them to look at them
all in real time and figure out where there might
be an issue. Yes, we call it predictive maintenance. Right, Okay,
(28:30):
all the signs are that there's about to be a
problem on this one, they go out there, they check
it out. Yep, well and behold, there is a problem.
I have two cars. Can you build one for me?
So we haven't scaled down to that level quite yet,
but stay tuned. We're open to it. Why not, Why
are you neglecting I'm the ultimate end user. I'm I'm
(28:52):
one guy with two cars. That's a good question. Well
we'll bring it to you soon. We have any um,
have any professional sports teams work with you? Guys, Toronto
Raptors have been a publicity on that a few years ago.
You're You're I'm Canadian. This is that's my team. You're
(29:12):
warming my heart right now of course. Well this has
been really fun. Um, thank you so much. Yeah, I'm welcome,
appreciate it. I'd love to help out the Toronto Raptors
if I had the chance. Thanks again to Rob Thomas
for an intriguing conversation about data and the cloud. Smart
(29:37):
Talks with IBM is produced by Emily Rosteck with Carl Migliari,
edited by Karen shakerge engineering by Martin Gonzalez, mixed and
mastered by Jason Gambrel. Music by Granmascope. Special thanks to
Molly Sosha, Andy Kelly, Mia La Belle, Jacob Weisberg, Head
(29:59):
Fane Aerk Sander, Maggie Taylor and the teams at eight
Bar and IBM. Smart Talks with IBM is a production
of Pushkin Industries and I Heart Media. You can find
more Pushkin podcasts on the I Heart Radio app. Apple
podcasts or wherever you like to listen. I'm Malcolm Gladwell,
(30:21):
See you next time.
Hello everyone. This is Smart Talks with IBM, a podcast
from Pushkin Industries, I Heart Media and IBM about what
it means to look at today's most challenging problems in
a new way. I'm Malcolm Gladwell. Today I'm chatting with
(00:24):
Rob Thomas, the senior vice president of IBM Cloud and Data,
where his responsibility is bringing new ideas to life. But
despite being on the cutting edge of these technologies, he
still has an appreciation for age old problems. There's a
(00:47):
rabbit and a beaver and they're staring at the Hoover
dam and the beaver says the rabbit, No, I didn't
build it, but it's based on an idea of mind.
And the point of that story is there's ideas are
a dye that doesn't so great story. Everybody's got a
bunch of ideas. By the way, we're too quick to
(01:08):
dismiss the beaver. He's right, but I have you seen
beaver dams. I mean, he's right, it was his idea,
but he had nothing to do with the giants Cement
Hoover dow. In my interview with Rob will touch on
the importance of the cloud during the pandemic and how
IBM has been playing a part in vaccine distribution Stay tuned. I,
(01:38):
for one, had no idea about what it means to
be the senior vice president of IBM Cloud and Data,
so I asked Rob to break it down for me
in Layman's terms, we build software, and software is the
lingua franca of our time. Anything that will get done
in businesses and even interaction with consumers is going to
(02:02):
be done with software. It's really the language of everything
that's happening in the world. That's what we build. We
are focused on doing that for businesses. So how how
long have you been at IBAN twenty years or one?
I guess to be precise. And I started in consulting,
and then I moved into our semiconductor business, and I
(02:24):
was doing consulting. And the moment that really changed my
whole career was doing work with Nintendo where we were
designing the microprocessor for the Nintendo We and I realized,
we're going to do this one time, but then they're
going to be building software that we get copied billions
(02:45):
of times and used by people all over the world.
Maybe I'm not in the right business. And that really
piqued my curiosity around software, which then led me to
move into the IBM software business, where I've been for
most of my career at this point. So I've been
in software total twelve thirteen years. But you have seen
(03:06):
I'm guessing, so twelve years in software. Am I right
in thinking that Morris happened in those twelve years of
software than happened in the entire history of software before? That?
Is that a fair statement? Close? It's I'd say close.
It's certainly the rate and pace of innovation has increased.
Now has actually something hasn't read an outcome? Maybe that's
(03:27):
a different question. But if you think about you know,
software dates way back to even the first main frame
that we ever built in the fifties. So a lot
of good things have been happening in software for a
long time. But the rate and pace is a level
that we've never seen, and that's certainly been what is
accelerated in the last decade. I mean, I remember my
(03:50):
dad was a mathematician at the University of Waterloo. I
remember coming home as a kid, going into his office
and seeing stacks of computer cards. So in my lifetime
I have I have gone from looking at stacks of
computer cards to something far more so. I mean, I
am aware of just how fast this fast, this uh,
(04:11):
this pace is gone and it will be different a
year from now. Right, that's how fast this is moving.
Let's zero in on that a little bit. Um, What's
what's shifting right now? Imagine I'm a client and I
come to you and I say, you know, I want
to be prepared for next year and the year after next.
(04:31):
What should be at the top of my mind. Let
me give you a quick story, if you don't mind.
There was a time in the US where you could
not easily get from one city to another. And at
that point, back in the nine fifties, there was a
decision that said, let's actually build the infrastructure to connect
(04:53):
every city in America. And the result was fifty plus
years of work fo dollar ours and we now have
forty eight thousand miles of highways that connects all these cities.
But the real impact is more profound than that, because
you're able to eliminate traffic at intersections by building over passes.
(05:15):
There are all these second order businesses that were built. Hotels,
gas stations, the salty snacks that you buy in a
gas station, fast food rest areas, so an entire economy
was built around the idea that the first step was
just to connect all the cities in the US. And
(05:39):
that's what's happening right now with software. It is connecting
businesses and individuals in a way that we've never been
connected before, and we are just at the beginning of
all the second order effects that will come as a
result of them. And the biggest problem in software it's data.
Just like you had all these disparate cities and you
(06:01):
were building highways to connect those cities, every company has
all these different data sets all over the place and
it's a really hard problem. But AI is not going
to be a reality for businesses until the data problem
is solved. That's one thing that I spent a lot
(06:22):
of time on Right now, we're dig digging from it
that into that meaning of that phrase, the data problem.
I think every individual wants any company they interact with,
whether it's their local bank or restaurant, or the local cleaners,
whatever it may be, they want that business to know them.
(06:45):
It's the whole idea of when you had towns where
there was just one general store and the owner knew you,
they knew what you wanted. I think everybody wants that
level of engagement, and that is what software enables. And
the basis of that is data. And the biggest problem
(07:05):
every business faces today is how do I understand my data?
What it tells me about my customers, what it tells
me about my products. So this is fundamentally about how
do we live in a better way. You're talking about that.
I'm I'm a big company, and I have different sets
of data and they're all in different places, and they
(07:26):
don't speak to each other, and I can't combine them
and make sense. Is that what you mean by the
data problem? Correct? And even if I can combine them
and connect them, the data is not in a usable form.
You know, one one data, says m Gladwell, the other
one says Malcolm g Is that the same person? Maybe?
Maybe not. It's really hard because these systems have been
(07:50):
built up over time. We do work with a company
called Wonderman. Thompson story that they shared with me just
this month was doing work with Peloton. So Peloton collects
a lot of data what you call first party data
from a bike or the tread. I think you're a
runner if I recall and w P P. Wonderman Thompson
(08:14):
has all this third party data, which is what do
they know about consumers? So just to connect those two
data sets, build predictive models, and then to turn that
into an advertising campaign. The AI part is actually relatively easy.
It's actually connecting the data, rationalizing the data, cleaning the data.
That's the really hard part that nobody talks about because
(08:36):
all we ever see is you know the outcome, yeah,
which is so I understand this is super interesting. So
let's imagine you Robb or a Peloton user, and so
we have a data stream that comes from the bike
which says that you bike. Let's just say for them,
I'm gonna flatter you an hour and a half a
day at some insane pace and neither of which are true.
(08:59):
But keep going. I did do a half hour today,
but it was a very slow pace. I gotta tell you. So,
I'm and I'm looking at your via whatever it is
I'm collecting. I'm assuming Peloton collects a lot of sort
of physiological and you stat up on the bike, and
from that we can generate a rough sense of who
you are, how what your athletic interests are, how fit
(09:20):
you are, all those kinds of things, and Wonderman's Old
shop wants to know, how can I use that picture
of the kind of athlete you are to help bring
you the kinds of ad messages that you'll respond to.
Is that a fair? Is that the problem? It could
be bringing it to me? But it's more likely because
obviously they d anonymize all this data. It's more of all, right, so,
(09:42):
how do we find somebody else that's like rob? What
are the attributes of that person? And then how do
we relate to them in a way that makes it
feel like we're talking to them as opposed to talking
to a cohort or a group. The number one prediction
that most companies want to is what's going to happen
to my sales next month or the month after or
(10:04):
the month after. And what we found is that tends
to be a product of as many as fifty or
a hundred different inputs. How many people are visiting the website,
how many people are calling the call center, how many
sales calls if I have a face to face salesforce
are they making? How many marketing campaigns am I running?
(10:24):
If you take all of these different data points, which
is awful in fifty or a hundred. You feed those
into a model. Then the first month you see how
close with the model. Then you adjust, second month, you
see how close was the model? And these models get
really good over time. And we think we can help
(10:44):
companies predict their financial performance in a month and a
quarter in a year based on all these different data sources,
all these different inputs. That's pretty valuable to let's say
every company. So IBM, what's IBM's role in that you've
described that problem to me? What is you guys come
in and you say, we'll do what? A couple of
(11:07):
years ago I started. I was trying to think about
what is the right metaphor so that I can educate
our customers on this and built this concept that I
called the AI ladder. To think of it as steps
that you take up a ladder towards AI. The bottom
(11:30):
rung is collect data. So you have to be able
to collect all your data. I'll use a library analogy.
This is just you have to get books. You have
to get books into the library that's collecting. Next is
you have to organize that data and the now the
a lot back to library analogy. That's the card catalog.
So where are all the different data sets. I might
(11:51):
have five copies of the same data. How do I
know that's the same copy. Maybe one's checked out, maybe
one's on microfilm. These are actually all problems that existing businesses.
So you've got to collect data, you've got to organize data.
Then you have to analyze the data. So you're actually
starting to do data science machine learning in the library
metaphor that's where you're displaying your best seller list or
(12:14):
you're displaying, you know, popular magazine titles. And then the
top of the ladder is what I call infuse. So
then how do you take those models and infuse them
into a business process. So it's those four steps the ladder.
You have to collect, organized, analyze, and fuse. We build
(12:36):
software that helps customers with each of the wrongs of
that ladder, helps them do the collection. We actually build
what we call a data catalog to help you organize
your data. So we help them with all wrongs of
that ladder. Because ultimately, then you've probably heard of IBM Watson,
that is our AI platform. Once you've done those things,
(12:57):
you can use AI and get really great outcomes. Imagine
if someone from the White House came to you and
said we're about to do something we've never done in
this haven't done in this country for seventy years, which
is try and vaccinate everybody in the shortest possible time.
(13:20):
We have a multiple sets of three and eventually probably
four or five different kinds of vaccines being administered by
tens of thousands of local municipalities too, people who have
a wide ranging set of risk factors, urgency, pre existing
conditions going on, you know, on and on and on
and on on um. Can you help us do this
(13:43):
as efficiently and cost effectively and socially consciously as possible?
Is that a kind of task that you're talking about
now that is in part as much a logistics problem
as it is a data problem. Let me describe to
you one of the data problems though, that exist around
(14:05):
this because we're doing the work with CVS on the
COVID vaccine rollout. Yes, and so if you're CVS where
you're actually administering, their biggest problem is everybody has a question.
CVS can't hire enough people to answer the ten questions
(14:25):
you have, the ten questions I have, the twenty questions
your cousin has. They came to us and said, can
we use AI too respond to all the inquiries we're
getting and actually help route people to where they can
figure out they can get the vaccine when they're eligible.
So we built an AI agent for them that is
(14:47):
now dealing with the the vaccine rollout every day that
starts with data. They have a place that they store
data about different questions. We've got models that we have
trained on language, meaning we can understand different types of questions,
which really inferred versus implied versus what is stated. That's
(15:09):
a real data problem. That's where we've spent the majority
of our time looking at this, this current situation. So
you would that you when you say it's a data problem,
meaning that you started by trying to anticipate, by looking
at the data and using that to try and anticipate
all the possible questions that someone might ask, Is that
(15:30):
what you're correct? Yes, um, and then training a machine
learning model based on those inputs so that when the
question was asked, we had a high probability of giving
the right answer. How long did it take you to
build that system? Now this is the wonders of modern software.
To your question on acceleration, we did this in forty
(15:53):
five days. Are you serious. Yeah, it's insane. How how
many people worked on it? Thirty somewhere in that room.
It's not a huge group. Wow. Their thing about systems
like this is you hope it's really good on day one,
but you know for sure it's going to be better
(16:15):
on day ten. It's gonna be better again on day twenty.
These are learning systems. They do get better over time.
And the thing is with with the really difficult problems.
And this is this is why I like to talk
about AI is giving human superpowers. Most people want to
say it replaces humans. I actually think given superpowers because
in these cases you start to move the harder problems
(16:37):
to the humans, and so therefore your your customer satisfaction
goes up because people are getting their problems resolved. Would
you ever get to Probably not, because there's always going
to be something that's too difficult for the AI to handle,
But I think you can keep moving it up for sure.
Or maybe given what you've just said, would it be
more fair to say you don't want to get to
(16:57):
a hundred, that you want to rise nerve a certain
category of problem for a human human interaction, because that
might be ultimately more satisfying to the question. We have
that discussion a lot, and certainly in the ones that
I've worked on, that's that's typically the case, because let's
not forget these are businesses, and the goal of most
(17:19):
businesses is to sell something. So sometimes the best way
to sell something is to really help somebody with their
problem and then show them how your other product can
make their life even easier. When you think back in
the cases that you have kind of problems that that
your group has been asked to solve it, IBM of
last couple of years, what was the hardest I don't
(17:42):
know that I could name a single thing that's harder
than others. The ones that are the most time consuming,
things like regulatory compliance. If you're a bank, you've got
a lot of different regulations that you have to to
live up to. And it's easy to help build AI
that can make loan decisions yes or no, good idea,
(18:05):
bad idea, eliminate bias from that decision. That's very doable.
Am I Compliance with the regulations of where that individual
is based, because they're in a zip code, or they're
in a state, they're in a country, those problems get
really difficult because you're kind of connecting, you know, reams
of legality to a day to day business process. Those
(18:28):
get those those get really difficult. Has any customer ever
come to you with a problem that you, guys said,
we can't solve that. We are way too curious to
ever give up that easily. It's more of, you know,
it's the it's the cheap, fast, and good triangle. If
you've heard that, you know you only get two of those.
(18:51):
Do you want it cheap and fast, it's probably not good.
If you want it good and fast, it's probably not cheap.
If you want it cheap and good, it's probably not fast.
I think all of these situations come down to that triangle.
So you have a group of people who work on
these kinds of problems, and I'm curious, what do you
look for when you're bringing someone onto that team. Is
(19:14):
there a set of skills associated with dealing with this
area of the application of AI to these very complicated
data fields. Is there a specific set of skills that
are crucial and rare hard find? The skill that's easy
to test for is do you have the technical ability?
(19:38):
Do you understand Python? Do you understand machine learning? You
can kind of see from somebody's body of work and
what they've studied. Do they have that skill? Part where
it gets harder is the empathy question. Can you actually
understand a situation, understand a user, and empathize with what
(20:01):
they're trying to do such that you're not just building
a model for a robot, You're actually building this for
a human on some end, that one's hard, harder to
test for. And then the third one is I would
just call it curiosity, how widely read as somebody do
they understand business business problems because those kind of softer skills,
(20:24):
those make a huge difference when you're solving these kind
of problems. So it's easy to test for the first.
The other two are a little harder to test for,
and the best data scientists in the world have all
three of those. Let's talk about um the cloud. I
see this word hybrid cloud. I don't know what it means.
(20:48):
So can you start by telling me what it means
and then fit this into the conversation we've been having.
So any company that's been around for more than three years,
maybe five, they've got somewhere that they keep their data
and they keep the different technology that they have, and
(21:11):
in many cases that's in their office or that's in
a data center right right near their office. They've also
started over time to start to build new data sets
or new software in a public cloud, something from you know,
something inside of IBM Cloud or Amazon Web Services or
(21:34):
Microsoft Azure. The minute that you have more than one environment,
you have a hybrid cloud, whether whether you know it
or not. So think of it as I've got multiple
data sets and multiple places to kind of back to
the US Highway example, or I've got software applications and
(21:55):
multiple places. You have to get that to act like
a single technology environment. That is the essence of hybrid cloud,
which is I can manage that as a single environment.
The average company now has five different environments cloud wise,
it acts like one. I can connect the data sets
the average company has. Is that by by design because
(22:20):
they feel it's safe for Is that just because the
hodgepodge nature in which we grow our I team needs
means that we end up being all over the place.
It's because there's a lot of people that work in
every company, and everybody wants their own thing. That's how
it happens. So that this department started in their own
(22:41):
data center, This department started on IBM cloud. This department
wanted a CRM system from Salesforce, this department wanted to
use Azure. It's human nature. People just go do what
they want to do. And you wake up one day
and you realize, hey, we've got a lot of different
cloud elements. And so if you're storing your customer data
(23:03):
with Salesforce and you've got these three other environments, how
do you get the customer data to inform you know
what you're doing in the other parts of your business.
That's a hybrid cloud problem. And how how hard of
a problem is that? I mean, as that total naive
outside or I would have said, oh, surely all these
(23:24):
cloud businesses would have made it really easy to share
stuff in one place with stuff you've got in other place.
Is that not true? Unfortunately the opposite is true, because
for the pure play public cloud providers, the incentive was
actually the opposite. It's Hotel California. For them, you can
bring your stuff in, but you know you can. You
can check in, but you will never let you check out,
(23:45):
and they charge actually enormous fees if you want to
get your data out. So it's a bit of a
strategy tax for them to make it easy. It's also
a hard problem just because you're trying to connect different
data sets. Do you have in card cataloged that connects
all these different sources. It's actually not easy to do.
(24:06):
And what happens when you don't do that then you
end up rebuilding everything and so suddenly you're storing all
the same data five times. That gets very expensive. So
let's imagine what Having this conversation give me your sense
of where will be what would what would we be
talking about five years from now, we'll probably be having
(24:28):
very similar discussions as possible. Technology will be more advanced,
but a lot of them probably talked about. Let's be honest,
these have been around for for quite a while. There's
a story this this guy, Charles Towns, he was the
inventor of the laser, and he tells his story. There's
a rabbit and a beaver and they're staring at the
Hoover Dam and the beaver says the rabbit, no, I
(24:51):
didn't build it, but it's based on an idea of mine.
And the point of that story is there's ideas. Are
a dime? Doesn't it so? Great? Story? Everybody's got a
bunch of ideas. By the way, we're too quick to
dismiss the beaver. He's right, but have you seen beaver
Dam's I mean, I know he is right. It was
(25:13):
his idea, but he had nothing to do with the
giants Cement Hoover do. Yeah. The reason I I'd share
that story is a lot of people have ideas, now
what about what they can do? But what's going to
make a difference five years from now is what do
you go try and do? And I encourage companies that
(25:36):
you've got to be willing to have pretty high failure rate,
knowing that if you go try a bunch of things,
you know, maybe only half of them will work out.
I mean, if I look at AI today, so there's
five major things I see companies doing generally successfully. It's
customer service. We talked about that, It's financial budgeting, It's
(25:57):
regulatory compliance. We talked about that. That one's or harder.
It's employee experience hiring that type of thing. And it's
using AI to run their I T systems. So using
software to run the systems. Those are the five big
things today. I actually think those five things will still
be the topic in but will be a lot more
(26:20):
advanced on each of those because today it's a little
bit we're doing it for the first time, whereas will
be a much more advanced as we get out. I
do think quantum computing will be commercialized at that point.
That's pretty revolutionary. So more to come on that one.
Let's end on some more case studies. Tell me a
(26:41):
couple of examples of people you've worked with where the
outcome is it was really exciting or or unexpected. Or
we've worked with Sprint T Mobile. They have this classic
problem of they've got to do aftermarket service for all
(27:02):
the different telecom equipment that they sell and the data
that they have on those different systems, the warranty when
they were built, how they're running, it's spread across a
thousand different different data sources. We were able to build
an AI system for them that sits across those systems,
(27:24):
that was able to intelligently route how they do all
of their aftermarket service. So do you and I feel
that in our day to day life, Well, we we
feel that if they don't fix things, then it's obvious
because there's an outage or something that doesn't work. But
it was something that they had so much data on this.
They could have never done this by i'd say a
(27:47):
typical approach. So these are the kinds of things that
the average consumer doesn't see every day, but they do
make a difference in our life. And you're talking about
things like what like cell towers or yeah, it could
be cell towers, or it could be you know, the
power cable, not the power cable, the power boxes sitting
(28:08):
next to the cell tower. It could be any of
those things. Oh, I see. So you're so they have
all of these systems that might have been bought at
different times, made by different people, installed by different people,
And so what you want to do is to give
them a system that allows them to look at them
all in real time and figure out where there might
be an issue. Yes, we call it predictive maintenance. Right, Okay,
(28:30):
all the signs are that there's about to be a
problem on this one, they go out there, they check
it out. Yep, well and behold, there is a problem.
I have two cars. Can you build one for me?
So we haven't scaled down to that level quite yet,
but stay tuned. We're open to it. Why not, Why
are you neglecting I'm the ultimate end user. I'm I'm
(28:52):
one guy with two cars. That's a good question. Well
we'll bring it to you soon. We have any um,
have any professional sports teams work with you? Guys, Toronto
Raptors have been a publicity on that a few years ago.
You're You're I'm Canadian. This is that's my team. You're
(29:12):
warming my heart right now of course. Well this has
been really fun. Um, thank you so much. Yeah, I'm welcome,
appreciate it. I'd love to help out the Toronto Raptors
if I had the chance. Thanks again to Rob Thomas
for an intriguing conversation about data and the cloud. Smart
(29:37):
Talks with IBM is produced by Emily Rosteck with Carl Migliari,
edited by Karen shakerge engineering by Martin Gonzalez, mixed and
mastered by Jason Gambrel. Music by Granmascope. Special thanks to
Molly Sosha, Andy Kelly, Mia La Belle, Jacob Weisberg, Head
(29:59):
Fane Aerk Sander, Maggie Taylor and the teams at eight
Bar and IBM. Smart Talks with IBM is a production
of Pushkin Industries and I Heart Media. You can find
more Pushkin podcasts on the I Heart Radio app. Apple
podcasts or wherever you like to listen. I'm Malcolm Gladwell,
(30:21):
See you next time.
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