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BRUNO AZIZA: Welcome to another episode of "Data Journeys."
I'm Bruno Aziza, and we're here to talk with Joel, who's
the Data Scientist at PDPAOLA.
Today we're going to talk about how he uses data preparation
to work with fast-growing data.
Joel, thank you so much for taking the time
to talk to us today.
Let's get started.
Tell us about the company.
JOEL CHAPARRO: So PDPAOLA is a digital native e-commerce
jewelry brand.
We started in 2014, and we're now in over 100 countries.
We've doubled our sales for almost every year that we've
been in business.
And we're currently on track to generate more than 40
million euros in revenue for the year 2021.
BRUNO AZIZA: All right, Joel.
Well let's just dive right into the company.
Give us some context here.
Fast-growing startup in the jewelry business.
Tell us a little about the company and what it does.
JOEL CHAPARRO: Yes, so PDPAOLA, we're
based in Barcelona, Spain.
It was started by Humbert and Paola
which are brothers, in 2014.
And initially, just like any other startup,
they started very small.
In fact, they literally started from the room and a Shopify
website, one box with all this stuff
that they had at that moment.
And then they began to pretty much advertise online,
and they began to grow really rapidly.
And now we're on track to sell almost--
to have almost 40 million euros in revenue for 2021.
We've been almost doubling our sales over the last few years,
and we're becoming a world-recognized brand.
BRUNO AZIZA: So you're recognized around
over 100 countries, doubling in sizes, so really
a large amount of volume.
Yourself, you came from the business,
and you're now a data scientist.
So tell us a little bit about the use cases
that you're dealing with.
What are you solving for at the company right now?
JOEL CHAPARRO: So as somebody that initially
came from a digital marketing background,
I had a lot of knowledge as to what's necessary for a business
to be able to grow from a marketing perspective
and also from a business perspective.
And one of the main issues that I
had to solve when I first came into the company
is to be able to centralize, clean, and standardize
a lot of data so that we can use it to gather insights and take
actionable steps to be able to grow our business
and become more successful at what we do.
BRUNO AZIZA: And this is a project you called Sauron.
So what's the background behind Sauron?
First, the name is interesting, but also,
what are you trying to do with this data,
and what toolset are you using?
JOEL CHAPARRO: So the history of Sauron is quite funny.
It's a geeky name that my CTO, Jabel, and I came up with.
And Sauron comes from "Lord of the Rings," you know,
the all-seeing eye.
And the main point of this dashboard
is to be able to give visibility to all departments as far
as performance to the product level.
So pretty much we are a Shopify-based e-commerce.
And every time we sell a product,
Shopify already have a lot of metrics and reports present.
But a lot of the information that we
need to be able to determine contribution
margins, gross margins, and other more specific metrics
is not present within Shopify.
So we had to kind of centralize all these different data sets
and data sources into the cloud-- in this case,
of course, Google Cloud--
and be able to perform a lot of transformations
and joins to be able to get specific margins as far
as how much gross margin and contribution margin
we're getting at the product level, use segmentation,
such as countries.
Of course, dates, to be able to--
so that marketing in other departments
can know exactly what's driving sales
and what products are more successful than others,
for example.
BRUNO AZIZA: So this is all about intelligent data
preparation, bringing data together, and making sure
that it's ready for analysis, and it's accurate.
You're running ROAS, return on advertising spend.
What's the process like?
Do you have a lot of people involved here?
What does it look like?
Do you have a huge department to do this
and you're serving a few people, or there's
a few of you serving a lot of people?
JOEL CHAPARRO: There is a few of us serving a lot of people.
Running a startup is not easy, because we've grown so rapidly.
The business needs have grown faster than we can hire,
so it has been an adaptation process for all of us.
And as far as--
all departments are understaffed,
and we're actually hiring more people.
And this year we've been hiring a lot more.
But basically, we have very potent roles
that have been able to use technology and automation to be
able to adapt to the workload that we have.
And how does it usually work?
When it comes to data, I was the first person
to truly focus on data within the company.
Previously, it was programmers that
were kind of filling this hybrid role,
and I was the first person to truly focus
on this field within the company.
And I was able to truly get the job done with the use of tools
such as Google Cloud, BigQuery, Google
Dataprep, and other tools that you guys provide us
with that were able to make my work a lot more efficient.
BRUNO AZIZA: So this is an example of intelligent data
preparation serving the needs of fast-growing data volume
and fast-growing companies using intelligence
to make things a little easier.
Now you've learned a ton through this journey,
and this is the part of the interview where I ask you,
what are the dos and the don'ts?
So let's start with the positives.
What are the things that you learned
that, if you were to talk to yourself five years ago when
you started the journey, you would
say you must absolutely do?
JOEL CHAPARRO: So the first thing
that I would tell myself that I must absolutely do
is to acquire a deeper understanding of the business
and the industry.
Because many times, especially for somebody like me
and other people that work in this field,
we're very technical.
We like technology.
We like problems.
We like to find solutions and choose what technologies
we're going to work with.
But often we can get lost in the final output that we
want to generate, which is, we want to generate insights
so that different departments can
take the correct actions to generate success
within the company.
And I say that the most important thing
is to understand your end user, to understand what pain points
they have, what kind of solutions
you are trying to create for them, and generate the reports,
or whatever data that you're feeding them.
Make it in a way that they can truly understand
and they can make actionable steps to be
able to achieve their goals.
And this is an exercise of getting in their shoes
and truly seeing things through their eyes,
instead of the eyes of a technician.
BRUNO AZIZA: So this is natural for you,
because you came from a business background.
But if I'm a technical leader, what are the best practices?
Do I need to sit down next to them?
Or how do I achieve that?
JOEL CHAPARRO: I say that one of the best ways to do this
is, first of all, let's say, for example, that you're sitting
down within a specific department,
and they're asking you for certain metrics.
Often this department is not even aware of the capabilities
or potential that they could get out
of the data with the skills and the information
that you have available.
So it's important to initially sit down with them,
understand what they want, and just get
some initial example of what you could give them, and just
let them play with it in front of you.
If you're creating a dashboard, just
let them use it as you see.
And this is something that I also learned as a marketer.
We tend to record users' behaviors in websites
to see how they interact and how they--
whether the UX and UI works or not.
And this is something that I also encourage
data specialists to do.
See how your end users are using your dashboards.
See if the filters make sense to them.
See if the buttons and the colors
and the charts that they're looking at they
can really understand it, or if they're
asking you a lot of questions while they're doing it.
Because the point is that it should be mainly intuitive.
It's normal that they will have some questions.
But the information should be presented
in a way in which it just jumps at them
and lets them see, oh, look at this pattern.
You know what I mean?
I'm selling more of this collection in this country.
Maybe we should use more of these stones, colors,
or designs next summer when we launch the next collection
in this specific geographic region, to give you an example.
So it should really take into consideration
their point of view.
See how they interact with your end product,
and make sure that it's well suited to them.
BRUNO AZIZA: So this is a great example of maybe a live use
test, pairing your technical users and your business users,
and then kind of observing how they use the platform.
What about the opposite?
What is the one thing that you would say people
need to absolutely avoid?
Maybe a mistake that they make commonly
that you've found that if you just don't do it,
you'll accelerate the deployment of your big data analytics
solution?
JOEL CHAPARRO: This is very specific
to fast-growing startups, but in fast-growing startups
things can get quite chaotic, because you're growing rapidly.
You're dealing with obstacles and problems
that you may have not accounted for at the beginning.
So sometimes it's hard to give accurate estimates
as to delivery for certain projects.
And it is important to truly take into consideration
the scope of the project.
Work with all the-- talk to all the people
involved, and make sure that you truly understand the timeline
that you're going to need and required to be
able to deliver a project.
So I'll give you an example.
In a fast-growing startup, such as our startup,
I may need information from a specific department-- let's
say, finances-- and I spoke to them,
and they told me that next week they can sit with me
and give me all the information I need.
But then next week comes, and they're in a hiring process,
or something big came up, and they're not available.
So this is something that is more the--
it's not something out of the norm.
Quite the opposite, it's something
that happens quite often.
So it's very important to take into consideration historically
how departments work, if they truly
have the time and the staff that they
need to be able to support you.
And if they don't, take this into consideration
for your timelines.
And make sure you understand how long it's
going to take to perform the data preparation
part of things.
Because very often in companies that are growing and are
starting, the data quality standards are not the best,
and it is very important to make sure that you have all the data
that you will use very clean, very organized.
And this might take a lot longer than you expect,
so I would advise people to never underestimate
how long it will take them to create a project like this,
and make sure that you account for this.
BRUNO AZIZA: Never underestimate the time
it takes you to work with data.
Now, this is a question that we actually did not prepare,
but because you're an expert in the topic, I want to ask you.
What is your favorite feature with Google Dataprep?
JOEL CHAPARRO: That's an excellent question,
but my answer would be I really enjoy two things about Google
Dataprep.
First of all the visualization of the data flows
is something that I find it to be very powerful.
Before using Dataprep, we were using different BigQuery
scripts, and, visually, you just go to the schedule queries type
part of Google Cloud.
You see what queries are executing,
but you don't really see this way it's executing first,
then the data that I generate with this query
gets used in this one, and then it gets used here,
and then I generate my final output here.
So for somebody that didn't create these queries to come in
and to kind of untangle all these queries
and understand the data flow that you're creating
can be very hard to maintain and to understand.
Whereas with Dataprep, you can go in
and you can see all the data sources that you're using.
You can see the direction that you're taking,
these flows and the recipes that you're using in between
to generate the desired output.
And that's where the second part comes, which is,
the actual transformer page of Dataprep is amazing.
You can really-- when you go into the recipes,
you can really see all the different transformations
that you're making in a very organized manner.
And you can see how the data looks
real-time as you are playing with the recipes, which
is something that you don't do with BigQuery.
You have to execute.
You just have to see the tables.
You have to kind of with your eye
catch any anomalies or missing values,
whereas with Dataprep in the transformer page,
it really shows you anomalies.
You can already see the steps that you're taking.
You can go back and forth instead
of trying to remember what query you wrote an hour ago
or what steps you took that finally led you
to what you wanted.
BRUNO AZIZA: This is an amazing answer,
and it's not common of most data preparation applications, where
they don't have this intuitive, visual, real-time view
into what's going on.
And it's exceptional for you as a business user who's
now working with an immense amount of data in a very
visual manner and working on really important pipelines
and workflows.
Joel, we have learned a lot from you
today, from your use of Cloud Dataprep,
to best practices like pairing and then making
sure you have accurate estimates for your project.
I hope people will reach out to you
as they want to learn more about the success you've
gotten at your company.
I want to thank you for taking the time to talk to us today.
Until next time, I'm Bruno Aziza.
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