Formula E Racing involves cars, speed, data, and advanced technologies such as AI and machine
learning.
We're live in Brooklyn, New York, at the site of the Formula E Race.
Sylvain Filippi is the general manager and chief technology officer of the Envision Virgin
Racing Formula E team.
Sanjay Srivastava is the chief digital officer of Genpact.
Gentlemen, thank you so much for taking time and for being with us on CXOTalk.
Thanks for having us.
Sylvain, tell us about Formula E. Formula E is the first international racing
championship dedicated purely to electric cars.
The idea is to achieve two main things: develop technologies that will trickle down to make
electrical cars better and also to use the platform—basically, motorsport as a marketing
platform—to talk about these technologies and all the things we do around it.
What is your role?
I am managing director of the race team, so my primary focus, you would say, is to make
sure we win races with all the stuff that comes with it, so there's an engineering aspect
and so on.
Also, of course, responsible for marketing, commercial, and media and making sure that
the world knows about it.
Sanjay, tell us about Genpact and tell us about your role.
Well, Genpact is a global professional services company.
We deliver digital transformation for large, global clients across the world.
I'm a chief digital officer, which is to say that our teams help our clients with artificial
intelligence, automation, analytics, and experience-based approaches to transforming the business and
business processes.
What's the relationship between Genpact and Formula E?
We're partnering very closely on helping Envision Virgin Racing with the data, the analytics,
and artificial intelligence.
It's very similar to the work we do with clients across the globe in helping them win and get
ahead in the race.
To extrapolate on that, you could think that motorsports are just about developing a fast
car, put a fast driver in it, and win races.
Actually, there is so much more to it.
This is only the beginning of the story.
What really makes the difference between winning and losing races is analyzing the data and
there is a lot of it, not only to get the good insights but do it in a really timely
manner.
Formula E is a one-day event, which is very rare in motorsports, which means we have very
little time, literally minutes between sessions, to analyze all the data and make the correct
decisions.
Basically, the Genpact guys are helping us to make sure we go through all that data,
gather the right insights and, hopefully, make the right decisions.
Tell us about that data.
We're helping build a foundation for data across many fronts, right?
There is a set of telemetry data that comes right off the cars, and that's a streaming
set of large data sets that are coming through as the cars are on the road.
There's also data on competitive and driver maneuvers because we can track the rest of
the drivers as well, analyze that, and then, over time, machines learn from them.
Then there is data that comes from weather, satellite, traffic, and road patterns that
allow us to understand traffic and local conditions.
What we do is we interpolate all of that data to try and come to a prediction.
We'll talk about that a little bit.
Then the fourth set of data is actually the audience data.
You have to have a really solid foundation for data because you're bringing in data from
four different sources, it's streaming, and it's large.
Once you have the right foundation in place, then you actually enable yourself to be able
to drive analytics and AI on top of that.
That's really what we're doing here together.
All of this, of course, is real-time.
It has to be real-time.
Yeah.
This is not, "Well, let's go back to the office, we'll look at this for three or four months,
and we'll get back to you."
No, it's not.
It is real-time.
What's even more important is, you've got to get the foundation for data to really be
able to even get started.
Then you have to drive AI and analytics to be able to make real-time inputs to the decision.
Then the third part of the challenge is, how do you actually expose it?
How do you drive the user interface?
How do you actually get the users to experience it in a way that they can actually act on
it instantaneously?
That's been a really interesting part of the discussion, which is getting the experience
right so it's absorbable at the right second instantaneously and can be acted upon.
Yeah.
Sylvain, the user, in this case, is the driver.
Well, it's the drivers and the engineers, mostly, actually.
The driver's main job is to drive the car and then for us to help the driver basically
make the right decisions.
Back to Sanjay's point, I think what's really interesting here is that we can really use
motorsports really as a case study for how to use data because many companies have a
lot of data.
For sure, we have a lot of it because every mileage on the car generates a huge amount
of data.
What's interesting is that we have to learn to really structure the data really well so
we can then use it really quickly, as we touched on.
If you have years to look at data, it's actually not so difficult.
But what companies will need to do more in the future is to use all of their data and
use it really quickly.
Ultimately, up to real-time.
So, this is what we're doing, by definition, because, during a race, we have less than
a second to make decisions between using an attack mode, overtaking, or so on.
We have to do a lot of homework to make sure the data is really structured and then Genpact
is helping us with the correct algorithms and all the things you need in place in the
background to make sure you can make these quick decisions.
Let's drill down more into the types of data, so the data sources, and then the consumers
of that data.
How does all of that ultimately affect the race?
Hmm.
One of the main changes, again, which could be a change for many companies, is the point
is that the data comes from very different sources and very different formats.
It would be easy if it was one big spreadsheet, right?
But you have the telemetry coming live from the car, which is a particle format.
Then we have most of the data we get from the car, which we download when the car comes
back in the garage.
That's hundreds of channels all at different frequencies and so on.
Then you have the radio from the drivers, from our drivers but also the other teams.
Then you have the TV feeds, so video format coming from the broadcast.
The list goes on.
There are many sensors on the car that all send their own signal.
What's really difficult is that all these come in different formats, so there's not
enough time to look at all these things individually and then try to make sense of it.
So, a lot of the work we've been doing is to basically develop an infrastructure that
integrates all of that data, synchronizes against the time of day, really literally,
so the engineers, between sessions or even during sessions, can say, "I want to look
at what happened at that moment," and get the whole picture because of the danger.
I guess it's the same for many companies.
If you only see part of the data, you could be misled into making the wrong decision.
If you look at one part of the data on the car, it could lead you to think that this
is happening but, actually, if we have all of the data, then we're like, "No, actually
not.
This is happening because that, over there, was happening."
Yeah.
It sounds obvious, but it's really difficult to do.
What we've been quite good at doing and getting better every day is to structure all that
data to make sure that we have the whole picture really quickly.
One of the things I think we've done and it might be a good way to think about this, Michael,
is to think about layers of a cake.
You're building a cake and you've got layers.
Clearly, the first layer is actually the layer of data which is the foundation.
We talked a little bit about all of the challenges of getting it together in the right fashion.
The next layer, if you will, is the AI and the analytics layer.
What you're talking about here is, in the end, this is a prediction problem.
We are going into a race and we have to predict how many laps are still remaining so we can
plan the management of our energy better.
It's a prediction problem, if you will.
We're using artificial intelligence and AI to be able to bring that out and be able to
come back as soon as possible in the race with a good prediction on what's remaining
so we can manage the energy better.
The next layer of the cake actually is what I call bilinguals.
It's the ability for our teams to work together in a fashion that we combine the understanding
of the domain, in this case, racing and all of those parameters around it, with the science
of AI and data analytics.
That bilinguality is important because, at the end of the day, you have to still contextualize
the readings.
You still have to go orient the machine learning.
You still have to distill the analytics that comes out in the context of the race itself.
That's a really, really important part.
As I said earlier, the final layer of the cake is actually the experience because now
you've got to get it back to someone who is going to act on it in an instant on that piece
of advice.
How you expose that information becomes very important, so it actually becomes actionable.
We've thought about it as different layers of the cake and I think that's one way to
approach that.
That's perfectly correct.
What is also interesting is that it's not just our data.
It's also the competitors.
At the end of the day, we are here on the track to beat all the other guys.
Of course, we know what we know is our data, but trying to predict what the other guys
are going to be doing is super important and, obviously, difficult.
This is what we are starting to do.
Once we have done all of this, we need to also somehow integrate the behavioral patterns
will be of the other teams.
That should hopefully provide the best outcome.
We might want to spend just a minute on what actually happens in the heat of the moment,
right?
Actually, right at the start of the race, we have to start estimating the number of
laps remaining.
It's a timed race, yeah.
Yeah.
It's a timed race, so it's not a traditional race in the sense that there's a distance
that you have to cover and who gets there fastest.
It's a specific amount of time that you have to race for and plus a lap.
Then you have to go as far as possible or you have to be first in that.
Key and critical to this is actually estimating how many laps are remaining at any point in
the race.
The reality is, if you were driving on a straight road at a given speed for a specific distance,
you can actually figure all of that out.
In a race, that's not the case.
The situation is changing on the ground.
There's weather.
There's traffic.
There are other cars, actually, and then there are moves that other drivers are making in
the relative instance itself.
What we have to do is, we have to take all of that data, and then we have to apply analytics
and artificial intelligence to predict based on weather, based on track, based on the specifics
of the day, based on past behavior, and based on what we expect other drivers to be doing—
Yeah. –on how that race is going to turn out and
then predict.
Yeah.
Yeah, it's a very dynamic position because, to go into a bit of the details, the reason
why it's so important to understand the total distance and how many laps remaining is to
calculate our energy management.
The races are 45 minutes plus one lap.
But, as Sanjay said, depending on the pace of the leader, you will cover either 30 laps,
35 laps, whatever.
It depends.
It could vary for many reasons.
It's really critical for us to understand really what will be the exact total number
of laps until the end because that allows us to calculate really precisely how much
energy we can use per lap.
These are very complicated models, very complicated data sets.
Very briefly, tell us about the user interface.
How do you present this data to the various users so that they can get it and then act
on it so quickly?
We have a variety of dashboards, but what's also interesting, if you look in my garage,
I have about 12 engineers that do that during the few practice sessions and during the race.
Each of them has very specific roles, so we have developed dashboards for all of them
individually.
You'll get the race engineers or the main engineer on the radio to the drivers who is
really mostly interested in what's happening on the track, what's our delta in terms of
timing versus the other cars, what kind of strategy calls we can make, and so on.
Then you have a bunch of performance engineers and these guys are really looking at the car
data, really trying to understand how we can make this car go faster.
It's a whole set of different data.
Then, in a way, some of the most important guys are the simulation and strategy engineers,
also sitting in a garage.
They are the people who actually see all of the data, and they are the one who will go
on the radio to the race engineer and say, "We should the attack mode here or we should
save energy there," and all these things.
These have two or three screens, a lot of information displayed, and that's a work in
progress.
There are always things we can do better, but developing the model, understanding how
we link what the other teams are doing versus where we are is the most difficult part.
But we are really quite good and, well, we still have some work to do, always.
Mm-hmm.
It's a never-ending job.
That's right.
The success of data science, AI, and machine learning efforts always require subject domain
expertise, in this case, the race, together with data science and technology expertise
around AI.
Sometimes, that's a hard collaboration, and so how do you make it work?
In our case, it's actually not hard at all.
I'm a firm believer of, if you want to be the absolute best in whatever field you are,
you have to create these collaborations.
You have to work with the absolute best experts in their field.
That's the only way to be the best.
In our case, it's quite simple.
I have a team who are absolutely the best in the world in setting up a racecar, both
mechanically in terms of software, coaching the drivers, managing the energy, all the
things that we are experts of.
I wouldn't expect Sanjay's team to know how to mechanically set up a car.
It doesn't make any sense.
I have a team of software engineers, but they are nowhere near as focused and experts in
their field as Sanjay's team.
So, we have to work together.
The beauty, I guess we're a bit lucky in our field where software and data, in a way, even
though my team and Sanjay's team do different things, they speak the same language.
At the end of the day, it's software, data, and AI.
They use the same coding platforms and so on.
We're also even lucky that you don't even have to be at the same place.
You can be at other different parts of the world.
You exchange codes and you do video conferencing.
It works fine.
I guess it could be maybe a bit more difficult for an industrial company or something like
that.
In our case, it's fine.
We serve many clients across the globe.
If I had to just sort of look back in the work we're doing together, Sylvain, and talk
about what are some of the learnings that might apply more broadly, clearly getting
the talent and the team composition is a really important part.
This notion of bilinguality, which is, you have a team that understands the data science,
the analytics, and the AI, but also a team that actually understand the specifics of
the race and the conditions around that.
I think that bilinguality and that composition is right.
I think we've got the talent piece right in this case.
I might just add to that.
there are a couple of other learnings that come through, as I think about it.
Clearly, you have to enable the best teams with the right data foundation.
I think the effort we put upfront on actually getting that foundation right and getting
all of the data streams in place in a way that that was usable is really important.
As I look across the broader set of clients we serve, I think that's a learning and an
opportunity that I think we can leverage a little bit more in getting that foundation
right.
I look at the industrial world and the reality is, 90% of the data infrastructure is going
to be re-platformed in the next 4 or 5 years, and so there's a real opportunity to get this
right to set them up for success in the long-run.
The third thing I would add to that, and this is actually the fun part, I mean our guys
love to work with Sylvain's team.
One of the reasons behind that is a small, little thing, but it's really important.
IT's about purpose.
What we are finding here, as I talk to my teams, I think what I'm finding is people
are looking at this and saying, "Yeah, we're doing all the work.
Yes, we want to win the race tomorrow, and that's part of it.
But what we're really working on is actually setting up the foundation for mobility in
the future.
We're going to make autonomous driving more safe.
We're going to make this more practical, that more people will be able to use that.
I think one of the learnings, at least for me, has been, as we work on projects, finding
that purpose and then being able to get that through the teams so this becomes purpose-driven.
In this case, we're talking about the future of sort of humanity and how we use e-transportation.
That's just been a really interesting insight as we work together.
As we finish up, let me ask each of you for advice to businesspeople on that collaboration,
on building the right type of team.
I think the advice is very straightforward, and I would say that it's important to think
about this in three or four steps.
I think getting that data foundation right is super important, particularly in the business
world.
Look, the reality is, there are two kinds of companies out there.
There are companies whose core value proposition is data and the value the data brings in,
and they are corporations that have a value proposition that is more traditional.
I think that's one.
I think the second one is this bilinguality, which is getting the right composition of
the teams, getting that right, and putting that into place.
Then the third one is experience.
We talked about making sure that the insights are actionable and we could do something about
it.
Yeah, you're absolutely right.
The only thing I will add is that, for me, I always go back to that.
I always say, start from the end.
What is it that you're trying to achieve?
In our case, it's easy: winning races and develop better technology.
It's applicable across many industries.
Then most companies would be amazed how much insight they can generate from their data
regardless of the business to then achieve that goal.
You mentioned the last time, 90% of the data in the world is not used, or something crazy
like this.
I think our team is a good case study and a good example of how using every single bit
of data we get is helping us achieve our goal.
It sounds simple, but many people don't do it yet.
Gentlemen, thank you so much.
Thank you.
Pleasure.
Good luck with the race.
Thank you.
We always need a bit of luck.