November 16, 2022 • 30 mins
Having a good data strategy can streamline the way a company does business. In this episode of Smart Talks with IBM, Malcolm Gladwell takes on this topic with Ronald Young Jr., host of Solvable, and guest Nicholas Renotte, Data Science and AI Technical Specialist at IBM. They discuss how data literacy can help make a business more efficient, the fundamentals of data management, and why data is step one to AI solutions. A study quoted by Nicholas and referenced in this episode can be found here. Some of Nicholas’ guidance on machine learning can be found here.
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Speaker 1 (00:00):
Hey everyone, it's Robert and Joe here. Today we've got
something a little bit different to share with you. It
is a new edition of the Smart Talks podcast series,
which is produced in partnership with IBM. This season of
Smart Talks with IBM is all about new creators, the developers,
data scientists, c t o s, and other visionaries creatively
(00:22):
applying technology and business to drive change. They use their
knowledge and creativity to develop better ways of working, no
matter the industry. Join hosts from your favorite Pushkin Industries
podcast as they use their expertise to deepen these conversations.
Malcolm Gladwell will guide you through this season as your
host to provide his thoughts and analysis along the way.
(00:45):
Look out for new episodes of Smart Talks with IBM
every month on the I Heart Radio app, Apple Podcasts,
or wherever you get your podcasts. And learn more at
IBM dot com slash smart Talks. Hello, Hello, Welcome to
Smart Talks with IBM, a podcast from Pushkin Industries, I
(01:08):
Heart Radio and IBM. I'm Malcolm Gladmo. This season, we're
talking to new creators, the developers, data scientists, ct o s,
and other visionaries who are creatively applying technology in business
to drive change. Channeling their knowledge and expertise, they're developing
more creative and effective solutions, no matter the industry. Our
(01:31):
guest today is Nicholas Renaut, Senior Data science and AI
technical specialist at IBM. Nicholas's job is to help companies
formulate a data strategy that streamlines the way they do
business and prepares them to use sophisticated AI technologies. But
beyond his day to day, Nick is also a content
(01:52):
creator on YouTube, where his channel has over a hundred
thousand subscribers. His videos explain computer science incepts in a
way beginners can understand, and he often demonstrates how to
use machine learning and data science to solve novel problems.
On today's show, How Nicholas learned Data science from the
(02:13):
bottom up, the fundamentals of data management, and how an
innovative data strategy can help businesses create novels solutions, Nick
spoke with Ronald Young Jr. Host of the Pushkin podcast Solvable.
Along with being a frequent contributor to MPR, Ronald also
hosts and produces the podcast Time Well Spent and Leaving
(02:37):
the Theater. Okay, let's get to the interview. So tell
me a little bit about how you got into data
and when you found out like the power that it
really harnesses. Do you have a story or anything that
kind of like when you first piqued your interest in data.
My first interaction with data and with coding was act
(03:00):
really when I was around about eleven years old. So
this was really just getting started with just looking at spreadsheets.
So my dad would come home and after working a
nine or five job, he actually started working with investing
in stocks and doing value based trading that way. I'll
(03:21):
always remember I walked up to his desk one time
and he said, Nick, if there's one thing that you
should learn, I'm seeing all these people work on these
things called macros in spreadsheets, and these people like wizards
inside of my business. I know that you're still you're
still in high school, but I really think you should
(03:42):
learn this stuff. And I started doubling in some Excel
spreadsheets and started just recording macros and tweaking stuff, and
that that's where it all started. But from there, it's
It's always been a recurring vein throughout my career that
I've done some sort of wizardry with data, whether it
a coding or business intelligence or data is it's it's
(04:03):
always had a bit of a strain throughout throughout whatever
I've done, whether they start ups or YouTube or or
what I'm doing now at IBM. Your dad was right.
Let me just say that, because as someone who's trying
to put together a spreadsheet just to manage my personal finances,
trying to look up the formula to actually bring a
value from what what uhet to another is enough of
(04:25):
a struggle for me. So I'm glad to do it. Really,
it's like it's absolutely is uh so like knowing that
you know, this was how you started getting into spreadsheets.
You know you're looking at stocks and all of that. Um,
can you talk to me about how you found out
the importance of data literacy, how you begin to value
understanding what the numbers meant and what power that could have.
(04:50):
I got a cadet ship at one of the big
four accounting firms and started out as an orditor there,
which is pretty much day to focus. So I saw
that these numbers ultimately fed into a significantly bigger picture,
which was a formal annual report, and numbers being wrong
(05:13):
in an annual report can move markets. Right. Those numbers
need to be absolutely bang on. But I think that
is sort of where it started. Where it really culminated
was when I started doing some work at the Reserve
Bank of Australia. And those numbers don't just impact the
metrics for a particular organization, they impact the entire countries metrics.
(05:37):
Getting those numbers wrong on a particular chart or getting
them right on a particular chart can move entire organizations
or can shift an entire country. It's kind of crazy
what the value that doing things correctly with data has.
So when you're presenting a metric, you have to ensure
that you are portraying the appropriate message. It's not just
(06:00):
about the raw number, because correlation does not necessarily imply causation.
So understanding what it is that you're saying is so
so important, and it is so much more powerful now
that we've got so much more data available at our fingertips.
It's really easy to go and grab a bunch of
metrics and go, hey, I'm gonna grab this data from
over here, grab that data from over here for a
(06:20):
measured together. Hey, look, these two lines follow the same trend.
They must be related. Do you find yourself ever looking
at data points and saying those the how do I
don't understand this chart? Why did they Where did they
pull this from? Do you find yourself doing that a
lot of your regular life? Oh? Yeah, that There's there's
some great charts out there as well that you always see,
and they plut like the number of Nicolas Cage movies
(06:42):
against the g d P of Bolivia or something, and
it's like, well, they're going in the same direction. They
must have some relationship. But people can really quickly look
at a picture and go and make an assumption about
what that is saying without actually interpreting. Hey, are these
on the same scales? The what time period is being displayed?
(07:02):
What am I actually looking at here? And I find
myself doing this more and more often when I just
see a child on my hold on, Let's just not
make any assumptions. What is this chart actually trying to say?
What is it actually trying to portray? Because you can
lie with statistics if you know what you're doing. It
is they're so powerful and people can gloss over them
(07:24):
so quickly. We've got attention spends that is so much
shorter of these days that it can be very very
easy to take away the wrong message. So you also
produce content across various platforms, including YouTube and your personal blog.
Uh as a content creator, how did you get started
in that field and what type of content are you creating? Yeah,
(07:47):
that's a crazy story, right. So I always wanted to
get into tech and said, hey, I'd really really like
to work for IBM. I saw what they were doing
with Watson, and I'm like, why people were talking about
this more? And I had no affiliation with with IBM
at the time, and I'm like, well, this is so cool.
There used to be this thing called or this service
(08:09):
available and that the cloud platform called Personality Insights, and
you could plug in a little bit of text and
from that piece of text, it would analyze that particular
person's personality based on the Big five personality traits. And
there actually used to be this demo app where you
could hook it up to a Twitter account, so I
could pass through Oprah's Twitter account or Lebron's Twitter account
(08:34):
and it would actually analyze their profiles. And this is
so cool. It was nuts, and I was like, and
a lot of people don't know how to use this.
So that was quite possibly one of the first two
toils that I made on YouTube, and I actually used
a bunch of videos that I made following after that too.
(08:56):
Finally land a job at IBM. I actually spammed a
bunch of links in my resume and my couple that
I was like, Hey, I'm already working with this stuff
and I could do it. And the person that hired me,
she actually said that that was like such an amazing
way to portray what what you love about what you do.
That that that had such an influencing factor in actually
(09:19):
getting the job. But yeah, I did it because one
the tech was so cool and I thought it was
so interesting and so powerful, and yeah, eventually that helped
me land that job. So you do a lot of
tutorials where you're you're breaking down complex topics to kind
of a wider audience. Why is that important for you
(09:40):
to do? Yeah? I think one of the amazing things
about knowledge is it's one of the things that you
can give away and never lose, right. And I think
one of the trickiest things about the whole data science
and machine learning field is that it can be pretty
tricky to get started, and sometimes we get hung up
(10:05):
with learning from the bottom up right and there's nothing
wrong with learning fundamentals and learning foundations and really getting
stuck in. But in order to stick with something, you
have to find it interesting. So if you can see
the end result and then work your way back up
and work out how that's worked, then it is so
much more attractive because you get that instant gratification and go, hey,
(10:27):
I've just built this machine learning app that is able
to decode sign language. It's so cool. Now I'm going
to go and work out the tech behind it. Admittedly,
not everyone goes and works out the tech behind it,
but what I'm trying to do is make it so
that more people can get involved and get started with it. Lately,
I've been doing these things called code that challenges, and
(10:48):
they're kind of crazy, right, but I love doing them.
So I have to build entire machine learning or data
science applications without looking at any reference code, stack over
a flow, or looking at any documentation within fifteen minutes.
So it is literally just like a trial by fire.
(11:09):
I'll have my phone, I'll set a time, and I'm like,
all right, guys, we're on. Like the edit is literally
just coding NonStop and me explaining on the go. But
it allows people to see and explain my thought process
as I'm developing it. UM, that's obviously super fun, right,
because it's highly engaging and it shows people that, hey,
you can get started in this relatively quickly. Nicholas is
(11:31):
a kind of person whose passion for data science is
so great it spills over from his professional life onto
his YouTube channel. But when he's not making videos, he's
using that same expertise to help his clients make their
businesses work better. At IBM, Nicholas works with businesses to
formulate a data strategy, preparing them to get the most
(11:54):
out of technology like machine learning or deep learning. He
explained to Ronald Wife, thinking critically about the data it
generates can help a company run more efficiently. So there's
a quote that you've used in your presentations say their
firms are trying to become insights driven, but only one
third report succeeding. What is the role of creativity in
(12:17):
the successful one third and how are you at IBM
helping to increase that number. I remember going to a
talk by our previous CEO, and she said that there's
a large number of organizations that are just experimenting with
random acts of digital so they're just testing out some
of these news technologies are saying kind of what's possible.
(12:39):
But the ones that are truly being successful are the
ones that are getting there, that data ready, that data
strategy in play. They're the ones that are starting to
collect their data. They're starting to get it ready and organized.
They're starting to take a look at it and starting
to iterate and prototype and in a st ructured manner,
(13:00):
they're starting to roll this stuff out. The journey to
get something as sophisticated as machine learning into production is
a lot more difficult than I think people realize because
you're now building a box that has its own rules.
You haven't defined those rules yourself, So how do you
(13:22):
explain that when something goes right? But how do you
explain when something goes wrong? And having governance around that
is absolutely critical, which is really whether the data strategy
does come into play. So let's let's get into a
more business focused data strategies. Why is it so important
to have a data strategy in place to fuel AI
(13:43):
modeling and how does data literacy play a role in
getting value from these models. We've got algorithms left, right
and center these days, but I think the thing that
people forget is that you can't use any of these
algorithms unless you've got data. So ensuring that you have
a structure in place too one, collect your data, to
(14:07):
organize it, three, analyze it, and then or infuse to
machine learning or deep learning into it is absolutely critical
because if you don't collect it, you can't do anything
with it. If you don't organize it, you can't discover
what you've actually got, what the quality looks like. You
don't analyze it, you don't know whether or not you
can trust it. Um and then he infused is always
(14:29):
like the icing on the cake, right to the machine learning,
the deep learning, all the cool buzzwords that people throw around.
That is like the last step, and it is always
the coolest step. But you can't ever get to that
last cool step unless you've gone through that the hard
work that that's come before. Let's like expand a little
(14:51):
bit on the pain points for companies when they're developing
or implementing a data strategy. What do those pain points
look like? Honestly, the biggest pain point that I see organizations,
actually the top two that I see them coming back
to over and over again, is collecting and organizing their data.
(15:12):
So let's say, for example, you've got a manufacturing type organization,
and what they want to do is they want to
improve the production quality on a particular manufacturing line. So ideally,
(15:32):
if they see that they've got defective products on the
manufacturing line, they want to get rid of those sooner
rather than later because they don't want to be shipping
him out to the customer going through the whole warranty
and claims process that just costs a ton of money.
So they're like, well, it would be great to use
some computer vision or some deep learning to detect when
we've got defects on the product line, and then we
(15:53):
can grab those and rip them out. Somebody along the
line is like, great, let's go and do it. The
first stumbling block that you're going to trip up at is,
hold on, do you have any images of defective products
from example cameras that are looking at that production line.
So if you haven't gone and collected images of that
or video of that, there is no way in hell
(16:17):
that you can actually go and build that system to
improve your organizational productivity. So knowing well in advance what
data you're likely to need is absolutely critical. It is
the first step in the data science life cycle. So collecting, understanding,
and exploring your data is the absolute first step. The
(16:41):
second one is a little bit more interesting. So let's say,
for example, you sort of want to get in on
the craze that is data science or machine learning, and
you bring on a data science team. The next biggest
stumbling block that I find a lot of organizations trip
up on is discovering their data. They've got a ton
(17:03):
of data, but nobody knows what they've got. So being
able to find, search, discover, rate, review, and rank that
information is paramount because you'll have people come in and
go okay. So a line managers approached me and said
that we want to take a look at our top
performing customers and we want to build a retention strategy
(17:26):
so we're not losing customers anymore. Well, your data scientists
is then going to go, well, do we have data
of customers that have left previously. If you can't easily
search and find out what you've got, that makes it
pretty hard to go and build those models. So collecting, organizing,
and discovering really absolutely critical, but that they can be
(17:46):
a little bit tricky to handle in a large number
of organizations. What kind of supporting technology and new solutions
do we need to meet growing data management issues? It
really comes down to a few things. So ensuring that
you can one collect the types of data that you're
looking at. So I think when people think of data,
(18:07):
they're always thinking of hate it's just going to be
a bunch of spreadsheets. It might just be stuff that
we can throw into a database, But there is so
much more out there. Right, there's video, how do we
store that? How do we hold that? There is images,
there's natural text. Like we're just talking about ensuring that
you've got appropriate processes in place to be able to
(18:29):
store holding catalog that I think is absolutely critical. We
talked a little bit about data cataloging and the need
to be able to search and discover that data. That
is absolutely paramount. Once you've got it collected, how do
you find it? What is IBM's unique approach to facilitating
(18:50):
access to data within companies. So one of the biggest things,
and one of the my favorite things that I get
to work with, is a particular tool set, right, and
this tool set is called cloud Path for Data. So,
without getting too pitchy, that the absolutely amazing thing about
This is that those stages that I was talking about, right,
(19:12):
So collect, organized, analyze, and infused. It actually helps facilitate
each one of those stages. Right. So you can actually collect, store,
and hold your data in a secure and government place.
You've got data catalog in capabilities which allows you to search.
Like one of my favorite things is that you might
have a data set. Right, So I might be a
(19:32):
data scientist, and then we might have another data scientist
on the team. I can have a data set inside
of there, and I can actually rank it and add
comments and go, hey, just be wary of this column
with lot certain features that you need to be mindful of,
and that provides additional metadata understand what is what my
data actually looks like and and things that I should
(19:53):
be mindful for. So I'm I'm Joe employee. How can
data be helpful to me? Great question? So, I mean
data is impacting everyone, right, whether you you like it
or not. Um and more often than not, what you're
going to find is that you can improve whatever it
(20:16):
is that you do by by looking at that data,
whether it's let's take an organization out of it. If
you use sleep trackers, you can begin to see when
you're sleep, or when you're getting good quality sleep versus
when you're getting bad quality sleep. If you start to
collect additional data points like hey, am I drinking enough
(20:37):
water during the day? Am I doing certain things like
looking at my phone just before I go to bed?
Are these things influencing my sleep? And is that causing
a negative impact on my quality of life? So that's
taking a broader view of it. But when you step
into a team or a business view, data can can
(20:59):
make your life for billion times easier. If you know
that there's a particular issue in a system earlier on
in a data pipeline, before something crosses your desk, you
might go and say, hey, look, if we just changed
how we collected these pieces of information, if we just
transformed what we actually did with it, this is going
to streamline my entire workflow and and help me out.
(21:20):
But not only that, Right, So I work a little
bit with the automation team, and they're really big on
robotic process automation. Let's say you're doing something each and
every single day. You're copying a far from here to there.
You're grabbing some information from a website, You're throwing it
into a form and you have to do that twenty
times a day. There are tools that can automate that
(21:43):
entire process for you, and they're smart. They're not just
looking at where you're clicking on the page. They're looking
at what applications you're opening. They're looking at what fields
you're pulling data out of. You can automate those entire workflows.
That means that you don't have to do that repetitive
kind of boring work that you don't really want to.
You can palm that off and do the very bot
and do the stuff that you actually really want to
(22:04):
get involved in. As Nicholas said, the way a company
leverages this data has an impact on every level of
the business. Data informs how we do our jobs day
to day and how we plan for the future. Having
an open mindset about data makes it easier for a
business to come up with creative solutions. In the next
(22:25):
part of their conversation, Ronald asked Nicholas how data science
and creativity come together. So let's talk a little bit
more about creativity. We talked a little bit about your
YouTube channel, UH and how you use that to help
people get started with data science. What does creativity mean
to you? And do you see your work as creative.
I definitely say my work as creative, and I think
(22:50):
creativity is truly thinking outside of the box and looking
at just different ways of doing things. I think the
biggest thing that I try to embody is having an
open mindset and really never being willing to shut something
(23:11):
down or not look at a particular solution or option,
because you really never know where a particular solution might
come from. If you look at where some of the
advancements in that the medical field are coming from, it's
because they're being open to new ideas, new materials, new ingredients,
(23:34):
new recipes, new technologies. Having an open mindset really helps
improve that that that ability to solve complex problems. And
I think for me, creativity is really just having that
that open mindset. Tell me a little bit about how
you approach novel problems. What do you do when you
get stuck? I think the most important thing I really
(23:56):
like when I push myself to do something that I've
personally never done before, and a lot of the time
that yields new solutions to problems that that that might
be really difficult to solve. It doesn't necessarily need to
be using this particular set of techniques. It's what else
(24:17):
can we do to solve this problem? And sometimes like
it'll be staring you in the face and you'll just
have no idea until you go, hey, I'm going to
throw everything out of the box and just give it
a crack and see what is possible. Um. But sometimes
it does require that that little bit of grit to
to push yourself to see just what is possible. And
(24:37):
I think that's when I've come up with some of
my favorite things that I've ever done, so something that
I'm trying to adopt in my in my daily life.
And I'm reading a lot more about stoicism and philosophy,
and I'm seeing that you kind of really just got
to push through sometimes to to see what what's on
the other side. We talked a little bit earlier about
(25:00):
how um folks can take bits of data and kind
of tell their own story with it, especially if they
if they know the story that they're trying to tell.
But let's talk about using that for good. How does
creativity play a role in data storytelling. I think there's
just so much good that you can do with data
(25:22):
that if you have that in your core ethos then
the world's your oyster, right. I always come back to
my favorite project that I've ever done, and that was
using computer vision to try to decode sign language. It
is by no means a state of the art model,
but I forget hold on why is never nobody ever
approached this or at least shared how they've tried to
(25:45):
do it. And I've kind of just had to get
real creative and trying to build that I had. I
literally spent weeks just trying to install stuff, then trying
to get it writting on my computer before I even
got anywhere near building that particular model, And and it's
super hard grow in terms of trying to get it
set up. But there's so many opportunities for good, whether
(26:06):
that's improving accessibility to certain technologies, improving the quality of
life for people that could benefit from us using data
a little bit better. There's a large body of work
with a bunch of different data scientists where they're actually
building language translation models for languages which aren't hyper popular
(26:30):
or aren't as widely spread as we might see in
our day to day lives. If you look at India,
there are a turn of dialects. If you look at
even where my parents from Mauritius. There's there's a whole,
completely separate dialect where if you've never heard it before,
you were like, it's just slang French, but no, it's it.
(26:54):
It's like um, it's its whole separate language. That obviously
allows or improves the ability for people to to to
tap into data and do a little bit of good.
But there's so much I mean, people are using medical
image data to improve medical segmentation and improve diagnoses that
(27:14):
there's just so much amazing work that that's happening in
that space. There is obviously the temptation or used data
for bad, but I'd like to think that the large
majority of the community are really trying to use it
for good. You started talking about a little bit just now,
but what are some future trends and challenges and future
(27:34):
topics or projects you're excited about, anything in particular looking
real further forward. What I'm super excited about and I
still don't know how it's necessarily going to impact me,
whether or not that's going to change my experience as
a developer or not. That we've got quantum computers coming right,
there's a ton of work that's happening in that space.
(27:55):
It's going to radically shift how large a machine learning
model we're able to create, how fast we're able to
train them. I'm just excited to see what happens in
that space. I'm not a quantum physicist by any means,
but I'm still excited to see what I'll be able
to do with him in the future. I love that,
(28:17):
as you'll continued belt this technology, you're excited to play
with it after it's built, which I'm I'm totally bored
that I don't want to have to build it, Nicholas
or not. Thank you so much for a talk with
me today. It's been an absolute pleasure. Thank you so
much for your insightful questions. It's it's been awesome. Ronald
(28:39):
Nick made a point that I think is important to
remember when it comes to technologies ability to improve our businesses,
or make our jobs easier, or even do social good,
a thoughtful data strategy is always the first stepping stone.
Without good data, using machine learning or artificial intelligence to
create in a sative solutions becomes much much harder. Our
(29:05):
technology gets more sophisticated every day, but that doesn't mean
we should lose sight of the fundamentals. If we want
to get the most out of smarter technologies, better business decisions,
more optimized technology, fresh and unexpected insights, we're going to
need smarter data strategy. On the next episode of Smart
(29:28):
Talks with IBM, the Power of Salesforce to transform the
customer experience, we talked with Phil Weinmeister had a product
for Salesforce America's at IBM consulting about transforming digital experiences
with the Power of Salesforce and IBM. Smart Talks with
IBM is produced by Matt Romano, David jaw Roist and
(29:50):
Deserve and Edith Rousselo with Jacob Goldstein were edited by
Sophie Crane. Our engineers are Jason Gambrel, Sarah brug Air,
and Ben Holliday. Theme song by Granmoscope. Special thanks to
Carli Migliori, Andy Kelly, Kathy Callaghan and the eight Bar
and IBM teams, as well as the Pushkin marketing team.
(30:13):
Smart Talks with IBM is a production of Pushkin Industries
and I Heart Media. To find more Pushkin podcasts, listen
on the I Heart Radio app, Apple Podcasts, or wherever
you listen to podcasts. I'm Malcolm Gladwell. This is a
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Hey everyone, it's Robert and Joe here. Today we've got
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Heart Radio and IBM. I'm Malcolm Gladmo. This season, we're
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(01:31):
guest today is Nicholas Renaut, Senior Data science and AI
technical specialist at IBM. Nicholas's job is to help companies
formulate a data strategy that streamlines the way they do
business and prepares them to use sophisticated AI technologies. But
beyond his day to day, Nick is also a content
(01:52):
creator on YouTube, where his channel has over a hundred
thousand subscribers. His videos explain computer science incepts in a
way beginners can understand, and he often demonstrates how to
use machine learning and data science to solve novel problems.
On today's show, How Nicholas learned Data science from the
(02:13):
bottom up, the fundamentals of data management, and how an
innovative data strategy can help businesses create novels solutions, Nick
spoke with Ronald Young Jr. Host of the Pushkin podcast Solvable.
Along with being a frequent contributor to MPR, Ronald also
hosts and produces the podcast Time Well Spent and Leaving
(02:37):
the Theater. Okay, let's get to the interview. So tell
me a little bit about how you got into data
and when you found out like the power that it
really harnesses. Do you have a story or anything that
kind of like when you first piqued your interest in data.
My first interaction with data and with coding was act
(03:00):
really when I was around about eleven years old. So
this was really just getting started with just looking at spreadsheets.
So my dad would come home and after working a
nine or five job, he actually started working with investing
in stocks and doing value based trading that way. I'll
(03:21):
always remember I walked up to his desk one time
and he said, Nick, if there's one thing that you
should learn, I'm seeing all these people work on these
things called macros in spreadsheets, and these people like wizards
inside of my business. I know that you're still you're
still in high school, but I really think you should
(03:42):
learn this stuff. And I started doubling in some Excel
spreadsheets and started just recording macros and tweaking stuff, and
that that's where it all started. But from there, it's
It's always been a recurring vein throughout my career that
I've done some sort of wizardry with data, whether it
a coding or business intelligence or data is it's it's
(04:03):
always had a bit of a strain throughout throughout whatever
I've done, whether they start ups or YouTube or or
what I'm doing now at IBM. Your dad was right.
Let me just say that, because as someone who's trying
to put together a spreadsheet just to manage my personal finances,
trying to look up the formula to actually bring a
value from what what uhet to another is enough of
(04:25):
a struggle for me. So I'm glad to do it. Really,
it's like it's absolutely is uh so like knowing that
you know, this was how you started getting into spreadsheets.
You know you're looking at stocks and all of that. Um,
can you talk to me about how you found out
the importance of data literacy, how you begin to value
understanding what the numbers meant and what power that could have.
(04:50):
I got a cadet ship at one of the big
four accounting firms and started out as an orditor there,
which is pretty much day to focus. So I saw
that these numbers ultimately fed into a significantly bigger picture,
which was a formal annual report, and numbers being wrong
(05:13):
in an annual report can move markets. Right. Those numbers
need to be absolutely bang on. But I think that
is sort of where it started. Where it really culminated
was when I started doing some work at the Reserve
Bank of Australia. And those numbers don't just impact the
metrics for a particular organization, they impact the entire countries metrics.
(05:37):
Getting those numbers wrong on a particular chart or getting
them right on a particular chart can move entire organizations
or can shift an entire country. It's kind of crazy
what the value that doing things correctly with data has.
So when you're presenting a metric, you have to ensure
that you are portraying the appropriate message. It's not just
(06:00):
about the raw number, because correlation does not necessarily imply causation.
So understanding what it is that you're saying is so
so important, and it is so much more powerful now
that we've got so much more data available at our fingertips.
It's really easy to go and grab a bunch of
metrics and go, hey, I'm gonna grab this data from
over here, grab that data from over here for a
(06:20):
measured together. Hey, look, these two lines follow the same trend.
They must be related. Do you find yourself ever looking
at data points and saying those the how do I
don't understand this chart? Why did they Where did they
pull this from? Do you find yourself doing that a
lot of your regular life? Oh? Yeah, that There's there's
some great charts out there as well that you always see,
and they plut like the number of Nicolas Cage movies
(06:42):
against the g d P of Bolivia or something, and
it's like, well, they're going in the same direction. They
must have some relationship. But people can really quickly look
at a picture and go and make an assumption about
what that is saying without actually interpreting. Hey, are these
on the same scales? The what time period is being displayed?
(07:02):
What am I actually looking at here? And I find
myself doing this more and more often when I just
see a child on my hold on, Let's just not
make any assumptions. What is this chart actually trying to say?
What is it actually trying to portray? Because you can
lie with statistics if you know what you're doing. It
is they're so powerful and people can gloss over them
(07:24):
so quickly. We've got attention spends that is so much
shorter of these days that it can be very very
easy to take away the wrong message. So you also
produce content across various platforms, including YouTube and your personal blog.
Uh as a content creator, how did you get started
in that field and what type of content are you creating? Yeah,
(07:47):
that's a crazy story, right. So I always wanted to
get into tech and said, hey, I'd really really like
to work for IBM. I saw what they were doing
with Watson, and I'm like, why people were talking about
this more? And I had no affiliation with with IBM
at the time, and I'm like, well, this is so cool.
There used to be this thing called or this service
(08:09):
available and that the cloud platform called Personality Insights, and
you could plug in a little bit of text and
from that piece of text, it would analyze that particular
person's personality based on the Big five personality traits. And
there actually used to be this demo app where you
could hook it up to a Twitter account, so I
could pass through Oprah's Twitter account or Lebron's Twitter account
(08:34):
and it would actually analyze their profiles. And this is
so cool. It was nuts, and I was like, and
a lot of people don't know how to use this.
So that was quite possibly one of the first two
toils that I made on YouTube, and I actually used
a bunch of videos that I made following after that too.
(08:56):
Finally land a job at IBM. I actually spammed a
bunch of links in my resume and my couple that
I was like, Hey, I'm already working with this stuff
and I could do it. And the person that hired me,
she actually said that that was like such an amazing
way to portray what what you love about what you do.
That that that had such an influencing factor in actually
(09:19):
getting the job. But yeah, I did it because one
the tech was so cool and I thought it was
so interesting and so powerful, and yeah, eventually that helped
me land that job. So you do a lot of
tutorials where you're you're breaking down complex topics to kind
of a wider audience. Why is that important for you
(09:40):
to do? Yeah? I think one of the amazing things
about knowledge is it's one of the things that you
can give away and never lose, right. And I think
one of the trickiest things about the whole data science
and machine learning field is that it can be pretty
tricky to get started, and sometimes we get hung up
(10:05):
with learning from the bottom up right and there's nothing
wrong with learning fundamentals and learning foundations and really getting
stuck in. But in order to stick with something, you
have to find it interesting. So if you can see
the end result and then work your way back up
and work out how that's worked, then it is so
much more attractive because you get that instant gratification and go, hey,
(10:27):
I've just built this machine learning app that is able
to decode sign language. It's so cool. Now I'm going
to go and work out the tech behind it. Admittedly,
not everyone goes and works out the tech behind it,
but what I'm trying to do is make it so
that more people can get involved and get started with it. Lately,
I've been doing these things called code that challenges, and
(10:48):
they're kind of crazy, right, but I love doing them.
So I have to build entire machine learning or data
science applications without looking at any reference code, stack over
a flow, or looking at any documentation within fifteen minutes.
So it is literally just like a trial by fire.
(11:09):
I'll have my phone, I'll set a time, and I'm like,
all right, guys, we're on. Like the edit is literally
just coding NonStop and me explaining on the go. But
it allows people to see and explain my thought process
as I'm developing it. UM, that's obviously super fun, right,
because it's highly engaging and it shows people that, hey,
you can get started in this relatively quickly. Nicholas is
(11:31):
a kind of person whose passion for data science is
so great it spills over from his professional life onto
his YouTube channel. But when he's not making videos, he's
using that same expertise to help his clients make their
businesses work better. At IBM, Nicholas works with businesses to
formulate a data strategy, preparing them to get the most
(11:54):
out of technology like machine learning or deep learning. He
explained to Ronald Wife, thinking critically about the data it
generates can help a company run more efficiently. So there's
a quote that you've used in your presentations say their
firms are trying to become insights driven, but only one
third report succeeding. What is the role of creativity in
(12:17):
the successful one third and how are you at IBM
helping to increase that number. I remember going to a
talk by our previous CEO, and she said that there's
a large number of organizations that are just experimenting with
random acts of digital so they're just testing out some
of these news technologies are saying kind of what's possible.
(12:39):
But the ones that are truly being successful are the
ones that are getting there, that data ready, that data
strategy in play. They're the ones that are starting to
collect their data. They're starting to get it ready and organized.
They're starting to take a look at it and starting
to iterate and prototype and in a st ructured manner,
(13:00):
they're starting to roll this stuff out. The journey to
get something as sophisticated as machine learning into production is
a lot more difficult than I think people realize because
you're now building a box that has its own rules.
You haven't defined those rules yourself, So how do you
(13:22):
explain that when something goes right? But how do you
explain when something goes wrong? And having governance around that
is absolutely critical, which is really whether the data strategy
does come into play. So let's let's get into a
more business focused data strategies. Why is it so important
to have a data strategy in place to fuel AI
(13:43):
modeling and how does data literacy play a role in
getting value from these models. We've got algorithms left, right
and center these days, but I think the thing that
people forget is that you can't use any of these
algorithms unless you've got data. So ensuring that you have
a structure in place too one, collect your data, to
(14:07):
organize it, three, analyze it, and then or infuse to
machine learning or deep learning into it is absolutely critical
because if you don't collect it, you can't do anything
with it. If you don't organize it, you can't discover
what you've actually got, what the quality looks like. You
don't analyze it, you don't know whether or not you
can trust it. Um and then he infused is always
(14:29):
like the icing on the cake, right to the machine learning,
the deep learning, all the cool buzzwords that people throw around.
That is like the last step, and it is always
the coolest step. But you can't ever get to that
last cool step unless you've gone through that the hard
work that that's come before. Let's like expand a little
(14:51):
bit on the pain points for companies when they're developing
or implementing a data strategy. What do those pain points
look like? Honestly, the biggest pain point that I see organizations,
actually the top two that I see them coming back
to over and over again, is collecting and organizing their data.
(15:12):
So let's say, for example, you've got a manufacturing type organization,
and what they want to do is they want to
improve the production quality on a particular manufacturing line. So ideally,
(15:32):
if they see that they've got defective products on the
manufacturing line, they want to get rid of those sooner
rather than later because they don't want to be shipping
him out to the customer going through the whole warranty
and claims process that just costs a ton of money.
So they're like, well, it would be great to use
some computer vision or some deep learning to detect when
we've got defects on the product line, and then we
(15:53):
can grab those and rip them out. Somebody along the
line is like, great, let's go and do it. The
first stumbling block that you're going to trip up at is,
hold on, do you have any images of defective products
from example cameras that are looking at that production line.
So if you haven't gone and collected images of that
or video of that, there is no way in hell
(16:17):
that you can actually go and build that system to
improve your organizational productivity. So knowing well in advance what
data you're likely to need is absolutely critical. It is
the first step in the data science life cycle. So collecting, understanding,
and exploring your data is the absolute first step. The
(16:41):
second one is a little bit more interesting. So let's say,
for example, you sort of want to get in on
the craze that is data science or machine learning, and
you bring on a data science team. The next biggest
stumbling block that I find a lot of organizations trip
up on is discovering their data. They've got a ton
(17:03):
of data, but nobody knows what they've got. So being
able to find, search, discover, rate, review, and rank that
information is paramount because you'll have people come in and
go okay. So a line managers approached me and said
that we want to take a look at our top
performing customers and we want to build a retention strategy
(17:26):
so we're not losing customers anymore. Well, your data scientists
is then going to go, well, do we have data
of customers that have left previously. If you can't easily
search and find out what you've got, that makes it
pretty hard to go and build those models. So collecting, organizing,
and discovering really absolutely critical, but that they can be
(17:46):
a little bit tricky to handle in a large number
of organizations. What kind of supporting technology and new solutions
do we need to meet growing data management issues? It
really comes down to a few things. So ensuring that
you can one collect the types of data that you're
looking at. So I think when people think of data,
(18:07):
they're always thinking of hate it's just going to be
a bunch of spreadsheets. It might just be stuff that
we can throw into a database, But there is so
much more out there. Right, there's video, how do we
store that? How do we hold that? There is images,
there's natural text. Like we're just talking about ensuring that
you've got appropriate processes in place to be able to
(18:29):
store holding catalog that I think is absolutely critical. We
talked a little bit about data cataloging and the need
to be able to search and discover that data. That
is absolutely paramount. Once you've got it collected, how do
you find it? What is IBM's unique approach to facilitating
(18:50):
access to data within companies. So one of the biggest things,
and one of the my favorite things that I get
to work with, is a particular tool set, right, and
this tool set is called cloud Path for Data. So,
without getting too pitchy, that the absolutely amazing thing about
This is that those stages that I was talking about, right,
(19:12):
So collect, organized, analyze, and infused. It actually helps facilitate
each one of those stages. Right. So you can actually collect, store,
and hold your data in a secure and government place.
You've got data catalog in capabilities which allows you to search.
Like one of my favorite things is that you might
have a data set. Right, So I might be a
(19:32):
data scientist, and then we might have another data scientist
on the team. I can have a data set inside
of there, and I can actually rank it and add
comments and go, hey, just be wary of this column
with lot certain features that you need to be mindful of,
and that provides additional metadata understand what is what my
data actually looks like and and things that I should
(19:53):
be mindful for. So I'm I'm Joe employee. How can
data be helpful to me? Great question? So, I mean
data is impacting everyone, right, whether you you like it
or not. Um and more often than not, what you're
going to find is that you can improve whatever it
(20:16):
is that you do by by looking at that data,
whether it's let's take an organization out of it. If
you use sleep trackers, you can begin to see when
you're sleep, or when you're getting good quality sleep versus
when you're getting bad quality sleep. If you start to
collect additional data points like hey, am I drinking enough
(20:37):
water during the day? Am I doing certain things like
looking at my phone just before I go to bed?
Are these things influencing my sleep? And is that causing
a negative impact on my quality of life? So that's
taking a broader view of it. But when you step
into a team or a business view, data can can
(20:59):
make your life for billion times easier. If you know
that there's a particular issue in a system earlier on
in a data pipeline, before something crosses your desk, you
might go and say, hey, look, if we just changed
how we collected these pieces of information, if we just
transformed what we actually did with it, this is going
to streamline my entire workflow and and help me out.
(21:20):
But not only that, Right, So I work a little
bit with the automation team, and they're really big on
robotic process automation. Let's say you're doing something each and
every single day. You're copying a far from here to there.
You're grabbing some information from a website, You're throwing it
into a form and you have to do that twenty
times a day. There are tools that can automate that
(21:43):
entire process for you, and they're smart. They're not just
looking at where you're clicking on the page. They're looking
at what applications you're opening. They're looking at what fields
you're pulling data out of. You can automate those entire workflows.
That means that you don't have to do that repetitive
kind of boring work that you don't really want to.
You can palm that off and do the very bot
and do the stuff that you actually really want to
(22:04):
get involved in. As Nicholas said, the way a company
leverages this data has an impact on every level of
the business. Data informs how we do our jobs day
to day and how we plan for the future. Having
an open mindset about data makes it easier for a
business to come up with creative solutions. In the next
(22:25):
part of their conversation, Ronald asked Nicholas how data science
and creativity come together. So let's talk a little bit
more about creativity. We talked a little bit about your
YouTube channel, UH and how you use that to help
people get started with data science. What does creativity mean
to you? And do you see your work as creative.
I definitely say my work as creative, and I think
(22:50):
creativity is truly thinking outside of the box and looking
at just different ways of doing things. I think the
biggest thing that I try to embody is having an
open mindset and really never being willing to shut something
(23:11):
down or not look at a particular solution or option,
because you really never know where a particular solution might
come from. If you look at where some of the
advancements in that the medical field are coming from, it's
because they're being open to new ideas, new materials, new ingredients,
(23:34):
new recipes, new technologies. Having an open mindset really helps
improve that that that ability to solve complex problems. And
I think for me, creativity is really just having that
that open mindset. Tell me a little bit about how
you approach novel problems. What do you do when you
get stuck? I think the most important thing I really
(23:56):
like when I push myself to do something that I've
personally never done before, and a lot of the time
that yields new solutions to problems that that that might
be really difficult to solve. It doesn't necessarily need to
be using this particular set of techniques. It's what else
(24:17):
can we do to solve this problem? And sometimes like
it'll be staring you in the face and you'll just
have no idea until you go, hey, I'm going to
throw everything out of the box and just give it
a crack and see what is possible. Um. But sometimes
it does require that that little bit of grit to
to push yourself to see just what is possible. And
(24:37):
I think that's when I've come up with some of
my favorite things that I've ever done, so something that
I'm trying to adopt in my in my daily life.
And I'm reading a lot more about stoicism and philosophy,
and I'm seeing that you kind of really just got
to push through sometimes to to see what what's on
the other side. We talked a little bit earlier about
(25:00):
how um folks can take bits of data and kind
of tell their own story with it, especially if they
if they know the story that they're trying to tell.
But let's talk about using that for good. How does
creativity play a role in data storytelling. I think there's
just so much good that you can do with data
(25:22):
that if you have that in your core ethos then
the world's your oyster, right. I always come back to
my favorite project that I've ever done, and that was
using computer vision to try to decode sign language. It
is by no means a state of the art model,
but I forget hold on why is never nobody ever
approached this or at least shared how they've tried to
(25:45):
do it. And I've kind of just had to get
real creative and trying to build that I had. I
literally spent weeks just trying to install stuff, then trying
to get it writting on my computer before I even
got anywhere near building that particular model, And and it's
super hard grow in terms of trying to get it
set up. But there's so many opportunities for good, whether
(26:06):
that's improving accessibility to certain technologies, improving the quality of
life for people that could benefit from us using data
a little bit better. There's a large body of work
with a bunch of different data scientists where they're actually
building language translation models for languages which aren't hyper popular
(26:30):
or aren't as widely spread as we might see in
our day to day lives. If you look at India,
there are a turn of dialects. If you look at
even where my parents from Mauritius. There's there's a whole,
completely separate dialect where if you've never heard it before,
you were like, it's just slang French, but no, it's it.
(26:54):
It's like um, it's its whole separate language. That obviously
allows or improves the ability for people to to to
tap into data and do a little bit of good.
But there's so much I mean, people are using medical
image data to improve medical segmentation and improve diagnoses that
(27:14):
there's just so much amazing work that that's happening in
that space. There is obviously the temptation or used data
for bad, but I'd like to think that the large
majority of the community are really trying to use it
for good. You started talking about a little bit just now,
but what are some future trends and challenges and future
(27:34):
topics or projects you're excited about, anything in particular looking
real further forward. What I'm super excited about and I
still don't know how it's necessarily going to impact me,
whether or not that's going to change my experience as
a developer or not. That we've got quantum computers coming right,
there's a ton of work that's happening in that space.
(27:55):
It's going to radically shift how large a machine learning
model we're able to create, how fast we're able to
train them. I'm just excited to see what happens in
that space. I'm not a quantum physicist by any means,
but I'm still excited to see what I'll be able
to do with him in the future. I love that,
(28:17):
as you'll continued belt this technology, you're excited to play
with it after it's built, which I'm I'm totally bored
that I don't want to have to build it, Nicholas
or not. Thank you so much for a talk with
me today. It's been an absolute pleasure. Thank you so
much for your insightful questions. It's it's been awesome. Ronald
(28:39):
Nick made a point that I think is important to
remember when it comes to technologies ability to improve our businesses,
or make our jobs easier, or even do social good,
a thoughtful data strategy is always the first stepping stone.
Without good data, using machine learning or artificial intelligence to
create in a sative solutions becomes much much harder. Our
(29:05):
technology gets more sophisticated every day, but that doesn't mean
we should lose sight of the fundamentals. If we want
to get the most out of smarter technologies, better business decisions,
more optimized technology, fresh and unexpected insights, we're going to
need smarter data strategy. On the next episode of Smart
(29:28):
Talks with IBM, the Power of Salesforce to transform the
customer experience, we talked with Phil Weinmeister had a product
for Salesforce America's at IBM consulting about transforming digital experiences
with the Power of Salesforce and IBM. Smart Talks with
IBM is produced by Matt Romano, David jaw Roist and
(29:50):
Deserve and Edith Rousselo with Jacob Goldstein were edited by
Sophie Crane. Our engineers are Jason Gambrel, Sarah brug Air,
and Ben Holliday. Theme song by Granmoscope. Special thanks to
Carli Migliori, Andy Kelly, Kathy Callaghan and the eight Bar
and IBM teams, as well as the Pushkin marketing team.
(30:13):
Smart Talks with IBM is a production of Pushkin Industries
and I Heart Media. To find more Pushkin podcasts, listen
on the I Heart Radio app, Apple Podcasts, or wherever
you listen to podcasts. I'm Malcolm Gladwell. This is a
paid advertisement from IBM
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