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Show Notes: http://www.superdatascience.com/183 1
SDS PODCAST
EPISODE 183
WITH
DOMINIC LIGOT
Show Notes: http://www.superdatascience.com/183 2
Kirill Eremenko: This is Episode number 183 with founder and Chief
Technology Officer at Cirrolytix, Dominic Ligot.
Welcome to the Super Data Science Podcast. My name
is Kirill Eremenko, Data Science Coach and lifestyle
entrepreneur. Each week we bring you inspiring people
and ideas to help you build your successful career in
data science. Thanks for being here today, and now
let's make the complex simple.
Welcome back to the Super Data Science Podcast,
ladies and gentlemen. Today we've got a very
interesting episode. We've got Dominic, or for short,
Doc Ligot, joining us on the show, and we are talking
all about creating businesses in the space of analytics
consulting. Dominic is the founder of Cirrolytix, a data
science consulting firm in the Philippines, and they are
servicing clients and helping them introduce data
science. They're conducting trainings in the space of
data science, they're conducting consulting projects,
and so on, so a very exciting space to be in.
In this podcast you will learn how Dominic got started
out. You'll also learn about the space, the
environment, the analytics environment in Philippines,
but don't fret if you are not in the Philippines yourself,
because we actually discuss in the episode how all of
this, everything we talk about, is actually applicable to
any data science environment, whether it's a city, or a
country, and how to see the telltale signs for that.
Interestingly enough about this episode is that
normally on the podcast, we try to cover a variety of
topics. We try to go in the technical side of things, we
try to talk about business, we talk about careers,
Show Notes: http://www.superdatascience.com/183 3
however in this specific podcast, we don't talk about
anything technical, so if you are after technical topics,
then this podcast is probably not for you.
This podcast is for you though, if you are considering
creating a start up in the space of analytics, or if you
might be considering sometime down the track doing
so, or getting into the space of analytics consulting,
because we got so carried away with the topic, it was
such an interesting conversation, we just thought it
would be better not to dive into the technical
components of the work that Dominic does, and rather
specifically focus on the challenges of starting a
analytics consulting business, and where the world is
going in the space of analytics in general, and the
demand for analytics from the industries and
businesses.
So a very interesting chat, I personally learned a lot. I
can't wait for you to hear it all. So without further ado,
I bring to you Dominic Ligot, founder of Cirrolytix.
Welcome to the Super Data Science Podcast, ladies
and gentlemen, and today we've got a very exciting
guest on the show, Dominic Ligot. Dominic, welcome,
how are you going?
Dominic Ligot: Hi Kirill, good apart from the not-so-good weather in
Manila, but we're all doing fine, we're all nice and dry.
Everyone's wet outside, but yeah, excited to be on the
podcast.
Kirill Eremenko: It's so great to have you. We were just chatting before
the podcast about the Philippines, and how the
Philippines is in the peak, or just about to enter the
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peak of the typhoon season right now. How does that
usually go down?
Dominic Ligot: Well yeah, so the usual is floods, trying to avoid water,
trying to get from point A to point B. Actually, it's
interesting, because I remember talking in another
forum about the Philippines having what you call a
typhoon economy. So there's a part of the economy
that's reliant on typhoons hitting, so that all the
reconstruction, and the plumbers, and the carpenters
get something done.
Kirill Eremenko: Oh, wow.
Dominic Ligot: It's kind of a weird thing, because there was one year
where we had an interestingly low number of typhoons
from the average, and that actually hit the GDP a little
bit, so there might be some credence to that theory.
It's bizarre.
Kirill Eremenko: Wow, that is so, so counterintuitive. Wow. Interesting.
Okay, good to know. Yeah, we have one person
working in the Philippines at Super Data Science, and
whenever you guys get into typhoon season, there's
always problems with the internet, and it's always so
hard to get in touch. In fact, I know that sometimes
people have two internet providers, just as like a
backup at home, in case one goes down.
Dominic Ligot: Yup.
Kirill Eremenko: All right.
Dominic Ligot: Absolutely.
Kirill Eremenko: Dominic, so probably first and most important
question, very interestingly, as you mentioned, people
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call you Doc, so I'm probably going to be calling you
Doc throughout the podcast, and to prepare our
listeners for that, could you tell us the story behind
why people call you Doc?
Dominic Ligot: Yeah, no worries. Actually, it's kind of like a normal
kind of preamble, like I say, "Hey, I'm Doc. I'm not a
doctor." It's always good for a few seconds of laughs.
It's actually a school yard thing, so as early as ... I
don't know, maybe six years old, people were calling
me Doc for no apparent reason. The name stuck. For a
brief moment in time, I was actually considering
becoming a medical doctor, and when I realized how
much blood that was going to be involved, and
cadavers, it just wasn't my thing.
Then much later, I think now especially for the data
scientists, you do meet a few doctors in terms of PhDs,
and that's always interesting. So people keep asking,
"So what did you do your PhD in?" And I say, "Well,
I'm not really a doctor." It's always a point for
conversation.
Kirill Eremenko: Yeah, wow, that's definitely a great icebreaker. "Hello,
I'm Doc, but I'm not a doctor." Raises a few questions.
All right, well thank you. Let's dive straight into it. For
the purposes for our listeners to get to know you a bit
better, you're the founder and Chief Technology Officer
of Cirrolytix. Can you tell us a bit about the company
and what Cirrolytix actually does?
Dominic Ligot: Yeah okay, so Cirrolytix, just so you demystify the
name, cirrus clouds, we're all about doing analytics on
the cloud, and of course analytics, so Cirrolytix. We're
Show Notes: http://www.superdatascience.com/183 6
a small company, barely 10 consultants, give or take a
couple of freelancers.
I started the company in 2016, so we've been around
for going on two years. The inspiration for Cirrolytix
actually came about when ... In a past life I actually
worked for an IT company, and you know how it is
with these big IT vendors, you do meet clients,
especially in data and analytics. They need what you're
selling, but some of these solutions, especially when
you get into the hardcore data warehousing and
software can get pretty expensive.
That was actually a heartbreaker for me, especially
working in a country like the Philippines, which is still
an emerging economy. There are many small and
medium enterprises who really need the benefits of
data, but they can't afford it. So I said, "Okay, why
don't I just do it myself, after going on 20 years,
actually in the industry? I might know enough to do
my own thing."
And yeah, so far so good. We've been at it for going on
two years. Our main clientele are usually medium
sized companies, so normally those with less than 100
employees. They span the gamut from retail, e-
commerce, product companies, also other consultants,
and usually their needs don't stray too far from the
norm. They're starting to accumulate data themselves.
Not at the level that enterprise companies and big ones
... So data scientists normally don't stray too far from
the gigabytes, occasionally a terabyte, but now they're
struggling, because it's stuff that doesn't fit on Excel
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sheets, and now they realize that that data can be
useful for running their business.
When they go out and start talking to the IT vendors
and the consultants, they get shocked at just how
expensive it gets. That's normally what gets us in the
door and say, "Hey, you need your data sorted out.
You need to start getting your feet wet with machine
learning on a simple level, like for your e-commerce
company." Those are the companies we go for.
Kirill Eremenko: Very interesting. Like here, I probably want to mention
something that's ... also we chatted before the podcast,
that so Philippines, very interesting geographical
location, very interesting country, especially for people
who haven't been to the Philippines, I think we need to
paint a bit of a picture of how this country is set up in
terms of analytics, why this need is growing. I'll just
mention my side of the story, and maybe then you can
add in yours.
So I've never been to Manila. I'm really looking forward
to going to Manila one day, I heard so many great
things. I have been to an island called Cebu and an
island called Malapascua, and my experience was that
it's very far from civilization, very non-commercial,
non-industrial, like I went there for scuba diving and
for the nature, the jungle, and those things.
It's kind of like that was my impression of Philippines.
But now you're talking about analytics and all this
need, and how the data is growing. Tell us a bit about
Manila. What kind of city is it, and what kind of ... like
these companies that they operate, are the industries
developed? Are the companies themselves growing and
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developed, and is it like a big market in general, a big
economy?
Dominic Ligot: Yeah, yeah. So I guess just for context, you are correct.
In anywhere else in the Philippines, it's a tropical
country, beaches, and jungles, that's pretty much the
scene. Then you have a couple of areas. You
mentioned one, which is Cebu, and then of course
there's the capital, which is Manila, and there's
another one further down south called Davao, these
three ... I would call them, are really full-fledged
metropolises, truly sprawling. Manila in itself at any
given time of the day would have anywhere from 10 to
30 million people, so it's really, really big.
I think the big thing about how the Philippines is
evolved, especially in the last 10 years, is that it's
become a major destination for outsourcing, so call
centers, BPOs, KPOs, have been coming here. A big
part is because, well, number one, the government
situation, the political situation has stabilized
somewhat.
So the Philippines of today is a very stable business
environment. There's a very strong American
influence, everyone speaks English. I think that's been
the fundamentals that's brought a lot of outsourcing to
the country, so you've got everyone from the big
banks, like Bank of America, JP Morgan, to the big IT
firms like Accenture, IBM, and Teradata.
They've all set up initially customer service centers
here, and that's branched out in the past 10 years to
include other things, like technical support, legal and
medical transcription, so it's really given the economy
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a second wind. A lot of it is really dependent now on
outsourcing now than anything.
I think it's reaching a new level of maturity, because
you basically created a new workforce that's
technology savvy, communication expertise is pretty
good, that now even the local industries are starting to
pick up in terms of, "Hey look, we can use the people
that are coming out of these outsourcing centers."
Analytics is one of those thing that it's giving the
workforce additional opportunities in addition to being
an outsourcing hub.
Kirill Eremenko: Gotcha, gotcha. And so you're in a very interesting and
lucrative, I would say, position, as long as you know
how to take advantage of it, which it looks like you do,
that you are in an emerging market, or like ... It is a
big city, but in terms of the need for analytics, it is
only now realizing the demand, or like the value of
analytics, and you as a consultant, you're positioning
yourself that you can provide that service, that value.
You can add it to the businesses.
I think a lot of our listeners on the podcast, like in
different locations, might find themselves in a similar
situation. It might not be like it's a country, like it's a
different country, like in the Asia-Pacific, or it's some
remote location with jungles on one hand, and big
cities on the other. It might be somewhere in Europe,
or it might be somewhere in the U.S., but if you take
those ... if you strip away those ... like the geographical
side of things, and you look at the context, it might be
exactly the same that your city, this is for the
listeners, that your city or maybe even your country as
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a whole is now only getting to the stage where the
industries and the economy in general are seeing value
of analytics, and positioning yourself as a person or a
company that can provide the value is a great step in
growing a business.
So Doc, can you give us a ... you already mentioned
how you came up with the idea, you were 20 years in
the industry, but what did it take to actually get
started? Because ultimately I would see it as quite a
challenging thing to start a business and position
yourself out there saying that, "Hey, I can provide this
service," and getting your first client, and all those
things. If you don't mind sharing a bit of that.
Dominic Ligot: Yeah. A lot of it is really just being fortunate enough to
be in the proverbial right place at the right time, and
when you say right time, alongside the development of
let's say offshoring and outsourcing in the country, the
state of telecommunications has improved, and that's
actually empowered a lot of ... I don't know if you guys
have heard of the term, "digital nomads," so we've got
people moving in and out. They can do most of their
work from home, and the emergence of let's say cloud
services has made doing a lot of work that previously
involved a lot of technology locally, now you can do it
all in the cloud.
It's easier to collaborate now, it's easier to share data,
share files, and just the proliferation of a lot of the ... I
think information, especially in analytics on the
internet, these are kind of the ... all the factors that got
in. If I'm going to point to the single most, I think,
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factor that got me really started, it's that you find a
need, and you see it every day.
Like I started when I was still working in IT,
companies are now struggling with information, with
data, and on the other hand, you've got a little bit of
knowledge that you think you can solve that problem.
I think in the first instance it starts there. You know,
you start businesses not thinking of money, not
thinking of capital, and it's best if you start it with a
need or a problem to solve.
Of course once you find that need, the other half is
can you actually sell it, or can you actually convince
people that it's worth paying for? I think that's where a
lot of the people who are thinking of getting into
businesses, especially analytics businesses, are going
to struggle a bit. Even though data and analytics has
been around for, I don't know, 20 or 30 years, it's
always been a back office thing, so it's always been
kind of like in the background. Now it's becoming more
of a foreground investment for companies, but there's
still a lot of confusion as to, "Okay, what's a good
amount to charge? What's a good amount to pay for
this stuff?"
That's classical evolution. I mean, web development
and the internet started out the same way just in the
'90s, no one knew that the internet would be
important, and you had all these occupations related
to the internet, like web developers, web designers,
even graphic designers. They didn't know how to place
themselves back in the day. Then now you've got a
very rich freelancing industry related to the internet,
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and every company kind of takes it as a given that you
have to be on the web.
So I kind of see data and analytics moving into a
similar evolutionary cycle, but it is early days. I would
say with a few exceptions in the world, most
economies, most countries are still kind of getting into
data and analytics as a more formal field.
Kirill Eremenko: So you would say even despite the challenges of
convincing clients to buy, you would say that it is a
good time to consider starting an analytics business?
Dominic Ligot: Yeah, absolutely. I think the biggest shift, one of many
anyway, is that analytics is suddenly not just an IT
problem, because back in the day, I'm sure many can
relate, when you buy a BI tool, or run a few even
Microsoft Excel for the first part, that used to be stuff
that the IT department was worried about, just
installing it on your PC and getting it out there.
Nowadays, because these tools are very important to
business, it's becoming more of a business investment,
and that's shifted the conversation a lot. Now you have
marketing people, HR people, finance people,
concerned about what kind of tools, what kind of
analysis they need to put into play. It's no longer
possible to do it by hand, it's no longer possible to do
it manually.
A lot of the conversation has shifted from purely IT to
now business, and I think that's where analytics best
thrives, and kind of like the business domain rather
than the pure technology discussion.
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Kirill Eremenko: Yeah no, that is definitely a good example. I was just
thinking of like, there are just some things that you ...
it's better to give to the experts, right? Like yes, you
can do it in the back end, but then you need to make
sure you have a dedicated focus as a business. If
you're going to do analytics as part of your back end
operations, you got to make sure you have a dedicated
focus to analytics and that you're building out the
team, you know what you're doing, and you're
following all these industry trends and standards.
And innovation as well. Some things might not be
standard, some things might be cutting-edge, leading-
edge technology, and at the same time, like ... or you
could go find a company such as yours, and say,
"Okay, how about you guys do it, and I don't have to
worry about it," especially even if a big organization is
considering to implement analytics as a back end
operation, then at the start, it's going to be hard,
right? While you're doing that, you don't want to fall
behind your competition, and you still want to be on
top.
Plus, I'm sure when you guys go into a business, you
coach them, you provide insight. You don't just give
the analytics, but you also provide insights on how it's
done, and what your approach was, what the
methodology was. At the end of the day, my thing
would be if you can come in and provide a service,
that's great, but if you can coach them to do it on their
own, I don't think that's a ... that's actually a good
thing, right?
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For me, I'd feel great if I'm a consultant, I go into a
business, and I guess I'm going to lose them as a
client, because they'll build out their own internal
capability, but I'll feel good that I can actually help a
business grow and do that. What are your thoughts on
that?
Dominic Ligot: Well yeah, you brought up a very good point. Even
back when I was working in an IT company, that's
always kind of the conundrum, right? The moment
you introduce a solution, the moment you teach a
client how to do something, the initial motivation will
always be, "Okay, I don't want to be paying a
consultant forever. Let's do it ourselves," or, "Let's pay
them long enough so that we learn it."
I think that's fair enough. I think it's important to
recognize that even analytics itself or if we use the
more, I think popular term, which is data science,
right? There are levels to look at. I think there's a basic
level where everyone needs to generate reports,
everyone needs to be able to manage and cleanse data,
and it's descriptive analysis, if you want to talk about
it across the spectrum.
But then at the same time, given the developments in
say not just in technology, but kind of in the types of
data, in the types of use cases that have come to fore
in the last 10 years, there's also a need to do a little bit
of what I would call ... I don't know if this is the proper
term, knowledge compression.
So for example, let's take something esoteric like
machine learning. Once upon a time, no one cared
about it, or only like proper computer scientists and
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researchers would even think about doing machine
learning, and this is like classical machine learning
where you're ... not even the deep learning stuff, where
you do neural networks and logistic linear regressions,
kind of that level of machine learning.
Most businesses wouldn't care about that, but now
that you've got such a rich tapestry of data to choose
from, the use cases become even more interesting, and
the cost of technology has fallen down. Suddenly,
PhDs have a job in what would otherwise be a
marketing department. That's only been a recent
phenomenon, and you don't pick those guys up from
the street, you need experts to come in. Even if you did
find these talented individuals, retaining them would
be costly, and there isn't enough supply of that
expertise. I think that's the niche where a lot of
analytic consultants such as myself and some start
ups can hop in, because you don't need this high-level,
PhD level type of machine learning every day, not like
we would need to pick a report, right?
But from time to time, you do need these services to
gain an edge on the competition. To give you an
example, an esoteric machine learning use case object
detection, right? So you want to tell if a picture is a cat
or a dog. That used to be just the stuff of science
experiments, but now with the advents of open source
libraries and machine learning, it's now being
democratized, you can actually without spending a
dime, build your own object detection and image
recognition system in your laptop.
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But if you don't have ... let's say a guy has been doing
computer vision research, or proper big data
technology knowledge, and it's tossed out to any
individual, that could spell disaster for a business. On
the other hand, if you had the right expertise, the right
tools, you can spend that in many, many different
ways. You can use object detection for security, for
instance, for fraud detection, or you can use that to
detect inventories on your shelves without having to
resort to manual counting.
These are some of the emerging use cases that
suddenly people who used to do this stuff just purely
for research is now coming into the commercial
domain. I think that's a space where at least for the
time being, there is a niche for specialized consulting
to come in. But again, we don't just do that, we kind of
do everything end to end, a full spectrum, so it's just
an interesting development that wouldn't have been
possible years ago, given that the ... would be hard to
come by, and the technology was too expensive, and
the data wouldn't be there. But now you've got a lot of
these things happening now.
Kirill Eremenko: Okay, gotcha. When you say full spectrum, can you
tell us a bit more? What does that mean?
Dominic Ligot: Yeah okay, so Cirrolytix, our basic let's say verticals,
would kind of fall into three areas. One area is in the
data engineering side, so this is like the boring stuff
most companies don't think they need, but they do. So
things that range from how to ingest data from your
data sources, or from outside, or just getting data
digitized into a proper form, storing that. So not quite
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full scale data warehousing, like the likes of what IBM
and Oracle offer, but small scale data warehousing,
like the stuff we can do on Amazon, or on Azure, or on
a Snowflake instance.
Then moving on up the value chain to business
intelligence, machine learning, and analytics. Then on
the far end, getting the outputs of these analysis. It is
interesting, because without revealing too much, I
think this is a gap right now in the data science
industry. You've got a lot of people who can do a lot of
fancy analysis, a lot of fancy models, fancy charts, et
cetera, but in terms of making it friendly for business
user, that's kind of still lacking. I think that's where I
would say more traditional software development,
application development comes in.
So yeah, never mind that you've got a very good, say
neural network that can identify potential customers
with 98% accuracy, but if you have to run a whole
slew of code to do it, your average marketer won't do it,
but if you can get them an app that could do it
automatically, then that's kind of bridging the last
mile. That's kind of like one vertical for us, getting
everything from sourcing the data, all the way to trying
to get into an app. That's the data engineering vertical.
The second vertical would be more around consulting,
so determining what use cases are appropriate for
your company. This is less of a technology discussion,
more about transformation, more about what kind of
use cases do you do? What do I need to do to improve
my profit and revenue? I think we're just fortunate in
the company to have people who have worked at
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[inaudible 00:27:21] or banking, such as myself, or
retail, such as a colleague of mine, Patricia. So all
coming in from various business fields, but we're all
coming together in the interception, which is data.
We're all going to use data to improve businesses, so
we dispense that advice.
Then on the third leg, we also do training, so especially
in the Philippines where we have to admit skills are
still in short supply, so there's never a shortage of
people who want to do training, so we do that as well,
whether it's in-house training or public training. We're
not really marketing ourselves as a training company
though, but it is a good source of leads, so that's
another ... want to get into consulting. For those
wanting to start analytics service companies, do
consider training, which can be very complimentary.
You can test ideas and products in the training
classes. Of course apart from booking a little revenue
as a trainer, you can use that as a rich source of leads.
Normally the ones who would sign up for our training
classes incidentally work for companies who do need
analytics services, so it's been a very, very helpful and
successful for us in the past 24 months, finding
customers attending these training classes.
Kirill Eremenko: That's very cool. Thank you so much for sharing and
diving into the description, so I'm just going to recap
on that. Especially I think it will be useful for those
who are considering starting a business, or maybe like
somebody listening to this podcast might not be
considering it now, but maybe one day you'll come
back to it, and you can re-listen to this bit.
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So vertical one is data and engineering, where you do
the whole suite from data sourcing to BI, ML,
analytics, and you make it friendly for the business
user, which as you mentioned, is a critical point. Then
you got vertical two, which is the consulting side of
things, and you more step away from the technology,
but you talk about the use cases for the specific
company that you're working for, and make the
approach tailored for them, so they realize what they
can get, what value they can get out of analytics.
Vertical three is the training component where you
have in-house and public training, and those are
great, rich sources of leads for you and your business,
because people who need training, incidentally, they're
most likely working for companies that might need
analytics services. It's a really good set up. I can see
how you have lots of synergies between the verticals.
Dominic Ligot: Yup, yup, and it's also good to attract talent that way,
so normally if you can't find clients in these training
classes, you will find a future freelancer or a future
collaborator, because they suddenly quote unquote,
"See the light," and say, "Hey, I've been looking for this
all my life, and now you've showed me how I can
become more productive."
In fact, a couple of the guys who are working with us
now started out as students, and they since done a
career shift. That's kind of like a lesser, I think less
taxing way to get into the industry is rather than go
full on and start your own company, maybe find a
start up that you can apprentice with, or do some
freelance gigs with. I think over time, there will be
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more and more companies such as ours, who will be
on the lookout for talent, and training is a great way to
find them.
Kirill Eremenko: Gotcha, gotcha. And speaking of talents, I have a bit of
a more of a business question for you. You mentioned
you're 10 people right now, and what I was wondering
is are you planning to grow the business? I've seen two
types of ways consulting, analytics consulting firms,
can develop. One way is when you keep growing, and
you grow into a larger, more mature analytics business
where people are trained in the different components
of analytics, for instance, in the different parts of the
verticals that you described, and you have specific
people doing specific roles.
On the other hand, there are businesses who choose
to stay smaller, more boutique analytics consulting
firms, but they train up their staff to be like Swiss
Army knives of data science, and they can do almost
anything. They can still be competitive with 10 people,
and because it's such a small firm, they don't have the
large overheads, and yet they can still charge large fees
for their services. So there's kind of like two ways that
I've seen analytics firms develop. What is your plan for
your business, if you don't mind sharing, of course?
Dominic Ligot: Yeah, yeah, so that's a great point, and I totally agree.
I don't know if this is going to be counterintuitive, but
we're going to be more of the latter, for the most part.
One of the things that so far over the past 24 months
worked to our advantage is we're quite fast in
delivering outcomes for clients, and that's why they
stick to us.
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If we're going to grow, and most of our manpower
comes from freelancing, so it's probably not going to
grow that much from the current size, in terms of the
actual hands-on work. You hit it on the nail when you
say the goal is to make Swiss Army knives, so like
jack-of-all-trades. Like if I'm going to talk about
myself, I did start from say the business side of things.
I got into the IT side, learned the engineering, and then
in my past life I was in banking. That's where I picked
up some of the statistical knowledge on the data
science.
So I'm a little bit of a jack-of-all-trades, and that's also
how I found the people I collaborate with. On the other
hand, we are conscious that there are some parts of
our verticals that are growing really quickly relative to
market demand, and that might deserve a second look.
For example, the training that I mentioned earlier,
there's a huge demand on the ground for us to do
training, and now it's actually coming to a point where
the training's getting in the way of actually doing the
job, or doing the rest of the work.
Some of us enjoy doing the work more than teaching
it, so that is a serious consideration to us to say as
early as 2019, 2020, do we spin off, say a proper
analytics training center, a proper school for analysts?
Or do we even go further than that and become more
of an analytics recruitment center, where we come in
in a sausage factory, give you the training, and then
place you in a job, all of which could be lucrative, at
least in the near term?
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Those are serious considerations, but my default
position would be we're doing well at the moment,
being a good vendor, we're doing great work, we're
actually looking to start creating a few products that
our clients can actually start subscribing to, so you get
a little bit of passive revenue without doing extra work.
Then in the medium term think about spinning off
more proper division, let's say for training, which could
be a good play in this kind of market environment.
Kirill Eremenko: Okay, gotcha. Well, thank you very much for sharing. I
hope none of your competitors hear this, because
you're sharing everything on your strategy. I'm sure
everybody appreciates it [inaudible 00:34:46].
Dominic Ligot: Yeah, we talk about competitors, and again, this is
hopefully it doesn't end up shooting business in the
foot, but I'll tell you why it won't, because right now, I
don't know how many of your listeners will be able to
relate to this. This data analytics industry is really still
quite silo, so we mentioned data engineering for
example. Even that isn't really properly defined, so
you've got some IT people who know how to extract
data, and maybe you have a few DBAs who know how
to put it in a database.
Then you have a few analysts who kind of know how to
get it out of the database, put it in your Python
notebook, and come up with some visualizations. Then
you have another application developer who will get
the output of that, turn it into an app. So you need
those four people to really cooperate, and the irony is,
you have IT vendors, you have database vendors, you
have analytics vendors, and you have application
Show Notes: http://www.superdatascience.com/183 23
developers, but they all only know their little piece of
the pie, so there's really, really opportunity in stitching
these things together, being more of a full service or
say generalist type of vendor.
I mean, it won't fit everyone, but there is opportunities
in stitching together several things. I think the
industry will see more of that, because you don't want
to be paying four different people to do what one
vendor or two vendors can do, or do really, really well.
As I said, we don't really play the enterprise space, so
the clientele we attract aren't also the type who would
be hiring like five or six different vendors from mega
companies. They prefer a one-stop-shop.
I think that's an opportunity, especially for people who
are starting out. I think maybe easily nine out of 10
people I meet who are starting out data scientists, kind
of focus more on the analysis. While that's very good,
it's very, very rich field to get into it, there's a lot of
things to do, but don't ignore the other parts of the
value chain. While you're studying your R and your
Python, or maybe your data visualizations, your
Tableau, don't forget the back end, because that's
where the data's going to come from.
Normally when you get into a job, even if you're not
starting a company, you're going to start out as an
employee. You're going to have to do that anyway.
You're going to have to run a few queries, get data
from someplace. The company would appreciate if you
could do both rather than have to rely on the IT
department to do that.
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So that's kind of an open trade secret that for some
reason, people are collectively ignoring, or at least
maybe in my end. Maybe everywhere else it's already
developing, but rather than keep it to myself, I think
we will all benefit if people become more and more
multidisciplinary as a result.
Kirill Eremenko: Yeah, yeah, totally, totally agree with you. You gave me
the story as well that the analytics industry is also not
mature at all, as opposed to the accounting industry,
or like some finance, areas of finance. There's lots of
room for many companies. I think it's very admirable
that what you're doing by sharing this information,
because ultimately, instead of making even
competitors, like instead of competing with companies,
companies can create alliances and work together.
Dominic Ligot: Absolutely, absolutely. Just as an example, another
one, and we can talk about this more later, I said we're
not a proper training company yet, but we're trying an
experiment in September and October. We're going to
run a few niche classes, and the target is really not
data scientists or data engineers, as such. The target
would be business decision-makers, and maybe
business analysts, and run them through what we
would call a Masterclass, where we can take them
through the entire value chain.
"Hey look, this is where you get the data. Hey look,
this is how you store it. Hey look, this is how you
analyze it." But rather than focusing on what
everybody seems to be doing in training, which is
teach code here, teach software there, of course that's
important, but no one's actually out there teaching the
Show Notes: http://www.superdatascience.com/183 25
business decision-makers, so just exactly why do you
need the data, or how would you use these types of
reports?
That's another niche that's waiting to be filled in terms
of now you've got a rich source of data, you've got a lot
of tools at your disposal. Maybe you've built your
analytics team, but then the gap is what are they going
to do? I mean, they don't speak the same language as
the business, or vice versa, the business people don't
speak enough of the data language to translate their
objectives into analytic models and strategies.
Then that's it, that's a lot of sunk investment right
there. As a smaller niche to that, just to put it on the
table, now that we're getting into more automated
decision-making, more algorithms, there's a looming
need for what I would call data ethics professionals, so
if you think about the stuff that recently happened
with Cambridge Analytica and Facebook, on the one
hand, or a couple of months ago, the self-driving car
ran over someone in Florida, and that was purely on
the basis of the failure of some object detection
process.
So now people are getting hurt and they're dying
because of data, and no one actually seems to be
stepping up and saying, "Hey look, there should be
this code of conduct or ethical standards when you
use data, in the same way we have similar things for
medicine or law." When you get into a more mature
field, there is an ethical line that needs to be drawn
and how these things are being used.
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The only thing people seem to talk about now is
privacy, and that's the tip of the iceberg when it comes
to ethics. That's another field that I wish maybe I had
more head space. Setting up a data ethics consultancy
would be probably the thing for 2020 and beyond, so
you know, it's just an early shout out for people who
are coming from let's say the legal profession, or the
ethics profession. Data is out there waiting for you if
you want to do something.
Kirill Eremenko: Wow, fantastic. Thank you for those two use cases of
data and training. You mentioned the executives or the
business decision-makers training, and ethics. I
understand this whole ethics side of things, and think
you describing quite a bit of detail has got a lot of
opportunity. But I'd like to talk a bit more about this
business decision-maker training. How did you come
up with that idea?
It's interesting how we haven't spoken before, but
we're thinking in the same direction, because that's
exactly what we're focusing on right now. We've also
identified this as a niche, and we're thinking, "How can
we help executives and business decision-makers
better understand data and better use it to their
advantage to help grow the businesses?" How did you
come up with that idea?
Dominic Ligot: I think a lot of it is inspired by I guess my own
adventures back in the day when I was working in
banking. I spent 14 years in banking before I went into
IT, and I was a business decision-maker. Through
numerous frustrations, because I couldn't get an
analyst to cough up the report that I wanted, I kind of
Show Notes: http://www.superdatascience.com/183 27
ended up doing it myself or getting my own people to
do it.
Then on the other hand, numerous struggles with IT to
source the right information, because if you're a
decision-maker, you need information, and the
information doesn't come easily, especially if you're in
a company that's not quite mature. That was the
primary inspiration. There's probably tens of
thousands of people exactly going through the same
challenges that I did, and there's nothing out there
that's helping them, so that's in the first instance.
Maybe if we cough up something people would be
interested.
The other thing, I guess from a broader perspective,
that I'm usually pretty conscious of ... let's say, I
would call it changes in let's say generational habits,
so everyone calls ... everyone groups people into like
20 year buckets, right? We have these Baby Boomers
for the first 20 years after the war, then the Gen X,
and then now we've got the Millennials, and now you
have Gen Y and Gen Z. So all of them have very, I
would say as a general group, have different habits.
One thing that has made a big change now,
particularly as we approach 2020, is many companies
are being run by Gen Xers and Millennials, and the big
difference between these guys, including us and our
parents and grandparents, is we grew up in a very
digital environment. We played computer games, we ...
internet, and we kind of want to manage businesses
that way, you know? The biggest inspiration for
analytics is I think computer games. You want a score
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card, you want an indicator of how many customers
you tap. Everyone responds to that quite naturally.
Intuitively, you know what data is supposed to be used
for, but in terms of the availability of proper training
out there, like, "Hey look, if you respond with a score
card, what does a score card look like for your
business?" For example. Or if you like using apps like
Google Maps, or Waze, like Waze is pretty popular
here, and you use that to get around, what's the app
that you need to help you navigate your business
strategies? Do you have an equivalent of a Waze or a
Google Maps for your business?
That takes a lot of not just number crunching, but a
lot of insight. You need people to be guided to think
about data in a certain way, and whether you're in HR,
or marketing, or an operations job, whether you're in
finance or [inaudible 00:44:43] the needs are very, very
similar. You want to make sure your business is
viable. You want to be sure you make money. It's very
rare that you can find opportunities to link data and
that together, so that was kind of the background.
I said, "Okay, why don't we start listing down what are
the typical use cases for say marketing?" So marketers
want to acquire customers, or they want to retain
existing customers, or they want to understand why
customers are complaining or about to leave, so these
are very normal things marketers do. But then guess
what? Now that we're in additional era, all of this have
an equivalent data point, and analysts know about
those data points, the engineers know about these
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data sources, but it's in stitching it together, that's the
crucial mix.
So that was the inspiration for the Masterclass, and
yeah, hopefully it works out. The initial response has
been very positive, but again, you run into the usual
challenges of since it's never been done before, or it's
very rare, no one knows how much to pay for it, or
whether it's worth paying for. That's the proverbial
kind of first mover issue that needs to address. But
yeah, I think it's the way people should be thinking
about data moving forward.
Kirill Eremenko: Yeah, yeah. Another challenge I find with this type of
masterclass is as you say, because it's something so
new, business decision-makers don't really know how
to pitch it to the board of directors, or to their
managers. Not ultimately you're going to get the CEOs,
they just might be like the CTO, or it might be just like
a high-level manager.
They need to include in their budget, right? So they
don't really know how to pitch it to their manager to
say, "Hey look, I need this training because it's going
to benefit the business." Then their default fall back is
thinking that it is an out-of-pocket expense for them,
and because ultimately you cannot run this as a ... the
same way as you run a training class, you cannot get
like 100 people in the room, you can only do it very
specifically-
Dominic Ligot: Very small.
Kirill Eremenko: Yeah, yeah. You want like 10 people in the room max,
or 12, I don't know. Because of that, the price is going
Show Notes: http://www.superdatascience.com/183 30
to be high, and then they got this dilemma, then on
one hand, they know is business value, they don't
know how to pitch it to their boss. On the other hand,
it's very expensive, so they can't really pay out-of-
pocket, and they're like, "You know what? I'm just
going to probably pass on this opportunity," when it's
ultimately, it's the thing that's going to change so
much, because if you change what's happening at the
top, the whole business changes.
Dominic Ligot: Yeah, yeah. Pitch, it's almost like it's not just a
business transformation issue, it's a cultural
transformation issue, if you're not used to thinking
about data training, or analytics training as a business
expense. As I said, this probably will probably end up
by default in the IT department or the CIO's purview.
You do meet on a rare instance sometimes that it is
the IT department that's encouraging the business to
join them, and that's usually a good peg they find, or
you have the newer type of executive, like a Chief
Digital Officer, or a Chief Data Officer who kind of sits
in between IT and the business, and normally it's their
initiative to get into this, but that's kind of a rare
thing.
On the other hand, just as another tip, what I've seen
work really well is if you land or find a company that
are hitting the proverbial brick wall in terms of their
growth. They used to be a small company and they've
hit the medium sized level, and they're still running it
like a mom-and-pop shop, and now they're suffering.
Or the other way around, like you have a medium
sized company, and they're about to enterprise
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territory, but they're still kind of doing everything
manually and now they're suffering. So there's
suddenly a pain point that they can't address, and
that usually gets an audience with the decision-maker,
and you say, "You know what your pain point is, is
that you're still doing it the way a small company does
it." That's where data and analytics can come in and
sort it out.
You kind of see it as, "Okay, I'm not really sure if what
you're telling me is true, because I've never heard of it
before, but it's worth a little experiment. Okay, maybe
I'll send five people or six people," and then you take it
from there.
It's a maturity thing. Over time it will become normal.
If you can imagine maybe 15 years back, people were
thinking about e-commerce and the internet pretty
much the same way, like, "Hey, I need a website," or,
"What kind of digital marketing do I need?" Even back
in the day, people refused to acknowledge that digital
marketing was part of marketing.
So, "Yeah, we're a marketing department, but digital
marketing's that guy, and he's part of the IT
department." It's the same. I mean, now it's a given. If
you're not online, it's marketing suicide. Chief
Marketing Officers need to have a digital strategy. It's
just this first hump that we all need to get through,
but yes, it's fascinating that you're getting through the
same challenges we are, and yeah, maybe we need to
have more discussions like this to understand how we
can do it better.
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Kirill Eremenko: For sure, for sure. Well, that's been very exciting, and
we're slowly coming to ... like it's crazy how time flies.
It's already come to the end of this podcast. I have an
interesting question for you that I would like to get
your opinion on. For what you've seen like 20 years in
the industry, and now you've moved to consulting,
doing your own consulting in the space of analytics,
and growing a team, and helping other businesses,
where do you think the field of data science is going,
and what should our listeners look into to prepare for
the future?
Dominic Ligot: Well, I think from what I'm seeing in the local market,
and I think this kind of mirrors what's happening
across the world in various degrees. Maybe three big
trends I'm seeing. The first one is democratization of
knowledge and skills. Back in the day, when data
science wasn't even a term, it was very hard.
When I say, "back in the days," like as late as the '90s,
very hard to find information about analytics. You had
to find special books, and you had to find special
people, and you're usually stuck in statistics
departments and computer science departments, and
they don't talk to each other.
Now we're seeing every major university coming up
with some sort of a data science course. I think that's
more good than bad, because the one thing everyone
still struggles with is what is the proper definition? I'd
rather not get into that debate anymore. It's more
about, "Hey, you know what? Learn as much as you
can, because the market's waiting for you."
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And of course the internet has been very helpful, the
rise of open source trend, and everyone now can pretty
much learn Python and R, and all these open source
software on their own, watch a few videos, listen to
podcasts like these. So there's never been a time in
history for knowledge has been more democratic, but
adoption has been slow.
That's the second trend that I'm seeing. I think after
2008, so as a banker, that was a pivotal moment for
me is the financial crisis. That financial crises have a
habit of knocking out businesses that aren't robust,
are inefficient, and that's given rise to a more
conscious need to, "Okay, how do I end the
competition? How do I get ahead?" Margins are getting
slimmer every day, and regulations are getting tighter.
The need for that new thing, that new Holy Grail to get
ahead of businesses, data is one of them. Of course
you have other big, big, big stuff, like the usual stuff
like blockchain and all these other trends. So I would
analytics falls smack dab in the middle of that. Before
it used to be niche, like it's a luxury. Now you have
companies, the most expensive companies in the
world, like the Googles and the Facebooks. These are
all data companies. It's foolish for you to ignore it.
In a country like the Philippines, which is pretty
protected, more and more industries are getting
opened up to liberalization and market competition.
We only need to look at what happened to Uber, for
example, and Grab for Asia, and how that's messed up
the taxi industry, and see how getting a bit of data and
analytics into your business model can really, really be
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destructive. So that's the second trend. You're going to
see more of that moving forward.
I think the third one is interesting, because there's a
subset of analytics, and for me at least it's a subset,
which is the whole area of deep learning and artificial
intelligence. It's still for the most part, I mean, if you
listen to the media, it's still in science fiction territory.
Everyone's worried about the rise of the machines, and
the terminators, and all that.
I think that's an area which is interesting to watch,
because the more you think about it, the more
intelligent algorithms start to permeate processes in
the workplace. I don't think it's necessarily machines
rising up against the humans, but it's more about how
do humans work together with machines better? It's
not going to be Kasparov versus IBM, Deep Blue
anymore, it's about how do I get a chess algorithm to
beat a normal chess player?
That's going to be interesting, because when machines
become independent, you shouldn't be worried about
how they're going to make your life horrible, what's
exciting is to see how they're going to make your life
more efficient and better. When cars start driving
themselves, imagine how more efficient that will make
transportation, for example. There's going to be an
analog everywhere else you go, and AI, machine
learning, deep learning, these are not easy things to
do, and that means there's going to be a massive
demand for people who understand not just the
technology, but the maths and the sciences behind it.
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Again, there's never been a better time to get into the
nerdy stuff, like computer science and math. That's
great. I mean, Gen Z and Gen Alpha, there's a very
good chance most of them are going to be a data
worker of some sort, just like once upon a time there
was one computer operator in a workforce of 100, and
then now everyone has a laptop. Now you've got maybe
a couple of people who know data in a workforce don't.
It won't be very long before everyone's a data worker at
some level, so there's a lot of new jobs that can come
out of that.
Kirill Eremenko: Fantastic. Thank you so much for such a detailed
overview, very insightful. I'm just going to recap. Three
big trends that you're seeing, so first is
democratization of skills. It's never been easier to learn
things, especially with online. Then second trend was
the proliferation of data science, data science is
becoming more commonplace. An example such as
even Uber showing how disruptive it can be, is those
things are pushing businesses to not see data science
as just like a ... something like a nice toy to play
around with, but something that is going to become
part of their operations, like an integral part of their
business.
The third trend is machines working with humans,
and that is concerning more AI, machine learning,
deep learning. The complex things or nerdy things, or
the things that used to be considered just nerdy, are
now becoming more and more as well commonplace,
and they're going to be helping us make our lives
better, so it's a good time to jump onto this trends.
Show Notes: http://www.superdatascience.com/183 36
Dominic Ligot: Yeah.
Kirill Eremenko: So thank you so much, Dominic, for sharing all those
insights. I'm sure lots of people learned a ton. I
personally learned a ton today from you. If anybody
would like to follow you or learn more about you,
things you share, follow your career, maybe you can
get in touch, what are some of the best places and
ways to contact you?
Dominic Ligot: Well, I'm really only on LinkedIn on a personal basis,
so just hit me up on LinkedIn. You can look for
Dominic Ligot, or dot Ligot on LinkedIn. My company's
on Facebook though. I don't have a Facebook account,
because I'm such a privacy nut, but the company's
there. I mean, if you know enough about data, it
spooks you out too much, so interestingly.
But yeah, you can search for us on Facebook, just
search for Cirrolytix or Cirrolytix Research Services at
C-I double R, O-L-Y-T-I-X. Then we also have a
website. You can find us at Cirrolytix.com. If you're
based in Asia or in the Philippines, you might be
interested to hear about our Masterclass, so the URL
is upskill.ph, so U-P-S-K-I double L dot ph. Or you
could also look for our business analytics masterclass
on Google, and I'm sure it's one of the things that will
pop up, so yeah. Looking forward to linking up with
you guys.
Kirill Eremenko: All right, and thank you very much, and we'll definitely
share all those links in the show notes. I just have one
last question for you today. What is a book that you
can share with our listeners to help them in their
careers?
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Dominic Ligot: Okay, so there are actually two. One is an older one
and one is a newer one. The older book that I keep
defaulting back to, and this is not a technical book,
because there are too many of those already. There's a
book called Competing on Analytics by Tom
Davenport. That for me has just in the classic bible for
me in terms of what differentiates a company who uses
analytics for not just as a toy, but for competitive
advantage, versus the ones that don't. So yeah. I'm
sure if you read more of Davenport's books, he talks
about very similar things moving forward, so that's
one.
The other one is more on the philosophical side.
There's a book called Life 3.0 by Max Tegmark, and he
talks about all the hypotheses related to AI from the
really crazy ones where the AI enslaves us, to the more
I would say realistic ones, where we kind of merge with
machines, eventually, and that kind of gives us the
next step in the evolution.
Now, I like that book, because not only does it spark
the imagination, but it also gives you some practical
grounding to look forward to, like why are we all
studying this? Why is this a big deal? I think the
secret is, it's a major part of human existence now.
Data is us, and the digital and the real world are
blending together very, very quickly. The future
belongs to those who understand data very well. It's
the new real world, technical.
Kirill Eremenko: Totally agree, totally agree. There's lots of movies to
portray that, that came out recently. Just to recap the
books, Complete Analytics by Tom Davenport. By the
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way, for our listeners, Tom Davenport is the person
who together with D.J. Patil wrote that article that
proclaimed data science is a sexiest job of the 21st
century.
Dominic Ligot: [inaudible 01:00:03].
Kirill Eremenko: Yeah. The second book was Life 3.0 by Max Tegmark.
So thank you so much, Doc, for coming on the show
today. Once again, really appreciate you spending the
time taking out of your busy schedule to share all
these insights.
Dominic Ligot: Thanks for having me.
Kirill Eremenko: So there you have it, that was Dominic Ligot. I hope
you enjoyed today's episode. I personally enjoyed it a
lot, and also learned a ton. Probably my favorite part of
today's show was just the variety of business tips that
Dominic was supplying, and the fact that despite the
temptation, we didn't switch to talking about the
technical aspects.
I know that probably a lot of you are thinking that it
would have been nice to talk about the technical
[inaudible 01:00:54] but we have lots of podcasts to
choose from in that space. Here I think the value and
the advice that Dominic was sharing in the space of
actually building a consulting business in the space of
analytics was extremely valuable.
On that note, if you'd like to get the show notes as
usual, you can get them at
www.superdatascience.com/183. There you will also
find the URL for Dominic's LinkedIn. Make sure to
connect and get in touch, and especially if you're in
Show Notes: http://www.superdatascience.com/183 39
Southeast Asia or in the Philippines, then reach out to
Dominic and maybe attend one of his training
sessions. Maybe he can help you with some consulting
work or maybe you can just exchange some
information about what's going on in the space of
analytics.
On the other hand, if you know somebody who is in
that region and who might benefit from connecting
with Dominic, then be the connector and connect
those two people. I'm sure they'll say thank you to you
at the end of it. On that note, I hope you enjoyed
today's episode as much as I did. Can't wait to hear
you and see you back here next time. Until then,
happy analyzing.