SDS PODCAST EPISODE 183 WITH DOMINIC LIGOTKirill Eremenko: This is Episode number 183 with founder and Chief Technology Officer at Cirrolytix, Dominic Ligot. Welcome to the Super Data

Show Notes: http://www.superdatascience.com/183 1

SDS PODCAST

EPISODE 183

WITH

DOMINIC LIGOT

http://www.superdatascience.com/183


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,



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



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



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



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



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



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



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



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,



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,



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.



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?



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



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.



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



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



[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.



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



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.



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?



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



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.



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



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.



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



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



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



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



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



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.



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."



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



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.



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.



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?



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



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



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.


Documents

SDS PODCAST EPISODE 183 WITH DOMINIC LIGOTKirill Eremenko: This is Episode number 183 with founder and Chief Technology Officer at Cirrolytix, Dominic Ligot. Welcome to the Super Data