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BRUNO AZIZA: Welcome to another episode of "Data Journeys."
This is the place that we come to learn from data leaders,
people that have experienced amazing growth and have great
experience to share with us their dos and their don'ts.
And, today, I have the pleasure of talking with Matteo.
He's the Senior Director of Global Data Products
and Analytics at Delivery Hero.
Matteo, thank you so much for taking the time
to talk to us today.
MATTEO FAVA: So Delivery Hero is a-- it's
a European-based company.
We are the largest global food network outside China.
Actually, we are supporting-- we are running
the business in over 700 cities across the world,
in 45 countries, and we are running more than 5
million orders a day.
It's, more or less, the information.
The business, the company has been founded in 2011.
And kind of part of our strategy is also
to keeping adding and increasing our global footprint.
Also, we have acquisitions and we extended-- expanded it
recently not only on food, but we also-- we
start expanded in other verticals
like groceries or Q commerce.
Q commerce is this hyperlocal delivery
of any kind of goods in maximum 15 minutes
from the place where you are ordering from.
BRUNO AZIZA: So what's amazing about your business is
you've got a lot of data, you've got
speed that's required, a lot of real-time analysis.
I was looking at one of the case studies--
174 data sets, 5,000 tables, 2.7 petabytes of accessible data,
7 million queries a month.
So take us through the use cases that you're
solving for with data analytics today at Delivery Hero.
MATTEO FAVA: So the number that you just
mentioned is part of this project
that we just kicked off recently.
We started rebuilding a completely new data
infrastructure.
Because, obviously, when you start
to having, also, a lot of acquisitions and you're
running multiverticals, you face,
sometimes, one of the biggest problems
is like how to access data, right?
And we tried to solve it just building a new infrastructure.
We tried to build in this concept of data mesh,
where we start adding and onboarding
more and more business units-- we call this internally--
but, in general, are domains that
are really clear boundaries between each other.
And we start just to providing access and granting access
to all the organizations and really leveraging
all this power data.
And we just started recently, and the number that you just
shared is just the first small pieces of the data
that we started to put-- in give access to everybody
in the organization.
BRUNO AZIZA: And you're also streaming over 50 terabytes
of data a day.
So what are the use cases where you use this data?
What are you doing with this data today?
MATTEO FAVA: So, obviously, these data
are used for, for example, to knowing how many people are
visiting our platform in order to understanding
how many riders we need to have on the street
in a specific area, or how to keeping the defining what
is delivery zones based on the demand and based on the offers
that we have.
The same is understanding what is the best
type of recommendations we can have for the customers
or really understanding how to support
a customer into ordering the best food that they
can find in this area.
These are some of the use cases that we tried solving.
And, obviously, this is a combination
of data coming from restaurants, from how the application are
used, but also how riders are moving across on the streets.
BRUNO AZIZA: So how did the journey with Google
get started?
MATTEO FAVA: So we started with Google
just in a kind of like a small project,
where we started to kind of like shape the Product Analytics
function, and we start using Google Analytics as a solution.
Then we start rolling out Google Analytics across the group.
And, obviously, in 2014, we wanted
to use the raw data of Google Analytics,
and we started using Google BigQuery,
also to really make sure that we can standardizing
some level of access in terms of user behavioral data.
So this was one of the first use cases.
And this use case obviously start to expand and growing.
We have, also, the needs of the company and the needs
of getting more and more data in the same place
for different type of use cases.
BRUNO AZIZA: And then, of course, this now
started to expand to other types of scenarios.
You've got time of arrival prediction.
You've got routing services.
You've got dynamic inventory.
What else?
Behavioral predictions.
What are some of the-- kind of the interesting use
cases that you've been kind of building over the years?
You've been there over seven years now.
MATTEO FAVA: The main goal is always, like,
on our central-- to say our point of view
is really solving these really unique kind of challenges.
That can be from fraud detection or understanding what is
the difference between markets.
For example, how the riders or how
the customers or how the addresses are
built in countries like Singapore or Hong Kong
versus Finland.
And, as you can imagine, the customers are different.
Riders need to have a completely different type of journey
as well to delivering the food.
The distances between a country--
like Dubai is different than a country in Hong Kong, where,
for example, we can have walker or the delivery
can be done with a lot of pickup orders,
for example, next to a station.
Or when Dubai, for example, that we have really long distances
that we need to cover.
These some of the examples that the algorithms
are trying to solve.
BRUNO AZIZA: And, of course, in some of these cities that
have high density, you're saying people
spend more time in the elevator than actually
going to locations.
So how do you track for that?
How do you handle that?
MATTEO FAVA: Obviously, as I mentioned,
every single country or every single kind of geographics
need obviously you need to customize some component.
And, obviously, we, in some markets like, as you mentioned,
Hong Kong, or country where there is a lot of verticality,
we need obviously to make sure that we can optimizing, also
taking consideration the timing of inactivity of riders,
or how it's complex, for example,
to deliver food in big condos, for example,
in big condominiums or in big houses, where it's not
so straightforward to delivering the food to the end customers.
We then put in all this data in this big infrastructure
to make sure that we can providing access
to all the different producers and consumers
to make sure they can combine and keep
optimizing the algorithms of the different functions.
BRUNO AZIZA: What is part of the infrastructure?
What are the various components of the Google Cloud
that you're using today?
MATTEO FAVA: So we are using a different part of Google Cloud.
It depends, obviously, on the use case.
And use case, for every single use case,
we try to make sure we have the right technology
and the right pieces of the puzzle that
can help us to delivering everything
needed in the right moment.
And then we make sure that we follow the stakeholders' needs.
But we are using, for example, we're
leveraging a lot of Dataproc or we are using a lot of Pub/Sub,
for example, for queuing to make sure we can subscribe--
that our different consumers and different producers
can communicate each other as well via some
of the Google Cloud solutions.
BRUNO AZIZA: That's excellent.
So, now, we've talked about your scale,
we've talked about the stack.
Let's talk about the dos and don'ts.
Throughout this seven years you've been at the company,
you've learned a lot through how to deploy
real-time services like what you're doing today
and this concept of the data mesh.
What are the positive lessons you learned?
What are the three things that everybody
needs to do in order to be as successful as you are?
MATTEO FAVA: The main three dos that I-- wait.
I'd like to think a lot about it.
And I think the first one, especially for our ecosystem,
is like how to find what is the key, the right key
to drive adoption and really make sure that you can empower
team, especially if you're running
a business with multiverticals.
So it's how to driving empowerment.
The second one is really make sure
that you can measure the kind of activity
that you're doing on data side to make sure
that everybody in the organization
understands those, the initiatives itself,
and how to measuring the impact.
And, obviously, the last one is really
how to make sure we have the right way of communicating
in terms of incident management, data quality, SLA,
and make sure we can really build in global frameworks
that everybody can speak the same languages.
BRUNO AZIZA: What did you do to drive adoption?
MATTEO FAVA: To drive adoption, there
is always-- like, normally, there are some kind of keys.
Right?
So one is obviously make sure that, for example, quality
is a really nice way to make sure
that it's driving adoption.
For example, we tried with this data mesh concept as well.
We tried to really give in to the producer
to make sure the producer is accountable for data quality.
And we tried to make sure that the producer is
the first person, that is also the first consumer of the data
that he's producing.
So we try to make sure that these
also, like-- always have really understanding
and is on top of the data quality of his products or data
that he's sharing it in order to make sure that he can also
be the first one person that can accountable,
but also fixing it, right?
So we try to make sure we can provide
to a lot of the different platforms
that we are running it some global solutions.
And, in order to using these global solutions that
are supposedly to be really like on the state-of-art
of technology, in order to using these solutions,
you need to obviously implementing or having
some level of quality of the data
that you're providing to these Global Services teams.
So this is a little bit some of the keys
that we are using in order to really driving the adoption.
BRUNO AZIZA: But I guess what I'm
trying to understand is the producer, then,
is the source of information?
So it's one.
Is it like someone who's providing
inventory information?
Or who-- well, like, what's a profile of a data producer?
MATTEO FAVA: You know, it's really similar.
With this concept, we try to kind of replicate
on the data world, what happened a few years ago, in terms
of product development, where you start moving away
from this monolith solution and you
build a lot of microservices.
And in this way, if you think about from the data side,
you kind of really decompose the data
that the company is creating across different domains.
And in every single domains, you create boundaries.
For example, all the data that the producer
can be-- the check-out team, for example,
is taking care of all the payment information.
And, in this case, it's accountable for the quality
of the payment information that other teams
across the organization will use and leverage it.
And what we are doing on our side, with these concepts,
we try to make sure we're providing the right tooling
and the umbrella of solutions to really leveraging-- the teams
can really leverage in order to having
less things to bother about.
BRUNO AZIZA: So if I'm, for once,
the provider of check-out information
back to the central infrastructure, what
are the tactics that you're using to make sure I'm
accountable for data quality?
MATTEO FAVA: The key, to make sure
that the platform or the teams, the producers
is also accountable, it is because it
should also using this data directly
from this interface and this new infrastructure, right?
So if the producer is also the first consumer of his own data,
obviously, there is a higher interest in terms of quality.
And what we try to do on our side
is try to make sure that we can have multiple consumers
as well that can keep adding and keep
informed and leveraging it, all this portfolio of solutions.
This is one part-- so the who is producing is also consuming.
And what we have try to do, on our side,
we try to build in the tooling that can help delivering teams
to understand if what they're producing also
is a high quality and it's really reliable
and it's really fitting all the needs and all these framework,
our requirements.
BRUNO AZIZA: Are you using Data Studio or using any BI tool,
or anything like that that you're reporting
on the quality of the data?
MATTEO FAVA: Yeah, for quality, we're
using different solutions from in-house to not in-house.
But, obviously, we do a lot of reporting.
For reporting, we're using, also,
kind of a couple of different solutions.
Depends on the use case.
Mainly for core reporting, we're using Lookers and Tableau.
I don't know if it's--
and but also we have a lot of internal Data Studio reports
that are helping us, really quick, to understanding
and understanding what's happening or--
and allowing, really, the analysts or even
the engineering just to put data together
and understanding who is the consumers, or who
are the users, or what is the quality of the data.
So it really depends, also, on use case.
It's not really trivial.
What are you using for what?
And it's really depending from team to team,
and that's why probably it's a little bit
hard to understanding it.
BRUNO AZIZA: Curious about that.
How are you-- are you letting people decide what BI tool
to use?
If you have Data Studio, Looker, and Tableau, are you--
they decide for themselves, or how is it done?
MATTEO FAVA: So I think this is exactly what I
mentioned, a little bit before, on what are the best dos.
Right?
So one is empowering them, and make sure
that you have the same kind of processes
and the same kind of frameworking.
This is one part.
And, obviously, what is--
one of the don't do, if you think about it,
it's like we should not just providing access to the data.
But I think it's an organization point of view.
We really need to spend time into onboarding
teams, educating teams, understanding
what is the best practices.
What is the best practice in terms of tooling?
What is the best--
what is the common practice in terms
of leveraging BigQuery on the maximum of its potential,
for example?
How to defining the right balance between on-demand
or slots, for example.
So this is what we are providing as a central team.
When I talk about the tooling or these umbrella solutions,
it's also like education.
And obviously make sure that we can really, like
every kind of repeatable processes can be streamlined,
and we can really build in collaboration models
across the organization.
BRUNO AZIZA: So we've talked about the dos.
Now, let's look at the opposite.
Let's talk about the things that people need to avoid.
MATTEO FAVA: I think, also, if you get to a list of three,
I think one is to obviously not start to work in a bubble
and start to creating a project in a bubble.
And make sure-- and but really make sure
that there was a communication really clear,
and we can provide continual visibility.
And another point can be make sure we don't only
provide accesses to the data.
But as an organization, we should really
try to spending time to onboarding every single team,
educating the customers, our data customers,
internal customers, on how to using tools, what are the best
practices, and really how to onboarding
and empowering the full organization.
And obviously the goal, as a central team,
is really how can we ensure that every kind
of repeatable processes or standards
can be streamlined and increasing
the speed of collaborations and building
these new collaboration models.
BRUNO AZIZA: Well, Matteo, thank you so much
for the learnings today.
We talked about how to drive adoption,
how to drive accountability around data quality
with producers and consumers of information,
and, of course, the concept of data mesh.
I hope a lot of people are going to reach out to you
to learn directly from you.
If you want to find out for more episodes like this,
click on the link down below.
Until next time, I'm Bruno Aziza.
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