[MUSIC PLAYING]
BRUNO AZIZA: Welcome to another episode of "Data Journeys."
I'm Bruno Aziza, and I'm excited to be here today
with Bob from Geotab.
And so we're going to learn from him
about his journey and the amazing growth and success
he's had.
And he's also going to share with us is Do's and his Don'ts.
Bob, thank you so much for taking the time
to talk to us today.
Before we get started, tell us a little bit about Geotab.
BOB BRADLEY: Hey, Bruno.
No Problem.
Yeah, so Geotab is a world Leader in the telematics space.
And so really, what that means is we
have millions of connected vehicles.
In fact, we have over two million
connected vehicles driving around with our little device
inside of them around the world--
and a really, really dense population of vehicles
here in North America, and a large population
in Europe, Latin America.
And Geotab's been expanding pretty rapidly
over the past few years.
And so it gives us a really, really unique look at the world
we live in from a data perspective
because this data is immensely useful for fleet managers.
So they're using it to manage where their vehicles go
and company operations.
But on aggregate, these two million vehicles
can tell us what the temperature is on the side of the road,
and whether a road is bumpy and in the middle of Europe
or in the middle of the United States.
And we can learn an awful lot about cities around us
and whether intersections take longer
at certain times of the day, and what
traffic patterns look like.
And so over the past five years that I've been at Geotab,
we've gone from about 400,000 of these connected devices
to somewhere in the 2.2 million neighborhood today.
And so that kind of growth has been both amazing and a
challenge for us.
BRUNO AZIZA: And now, the data that you collect, of course,
is 24/7.
It's real-time.
Tell us a little about the volume there.
BOB BRADLEY: Yeah, the data we collect-- you're right--
is real-time and 24/7.
Customers of Geotab range from small mom and pop trucking
companies to emergency services.
So the data needs to be correct and timely.
And so we're streaming this data in every single day.
From a data and analytics perspective,
that means about 40 billion points of data
that we get to play with every single day.
And I think we're somewhere in the neighborhood of 11
petabytes of data sitting in Google BigQuery, which is
our data warehouse of choice.
BRUNO AZIZA: So we're definitely talking about large data sets,
fast-moving data set.
The products involved here-- we're
talking about BigQuery, data flow, real-time data, Cloud
Composer.
Tell us a little bit about the use cases
specifically that you're using these products for.
BOB BRADLEY: Sure.
So our customers are monitoring their own fleets
looking at their own operations.
But as a whole, if we can take these 2.1 million vehicles,
and we can take this data that's coming in at that pace,
and we can actually run analytics on that,
there's a lot we can learn and teach
our customers about their data.
So one of the use cases that comes to mind
is we had customers that were telling us that battery
breakdowns were a big problem.
And so we have fleets that are one vehicle,
but we also have fleets that might be 100,000 vehicles.
And so if you scale up roadside breakdowns,
the batteries themselves aren't very expensive.
But when you're seeing 80% of our on-road breakdowns
happen because of electrical systems, it's not the battery.
It's not the part.
It's the sending of a tow truck.
It's the tow.
It's the sending of a mechanic and the replacing
of these parts that become very, very expensive.
And so we started a POC.
One of the things that I really believe in
is getting customers involved first,
so we can really, truly understand the customer
problem.
And so we thought, OK, this particular customer
has a large fleet.
But in the 2.1 million vehicles, there's tens of thousands
of other vehicles that drive and operate
in similar conditions with similar engine types.
What can we learn from the group that we can then
help this customer identify batteries
that potentially could fail?
And so we did a four-month POC with a very, very large fleet.
And the goal was to reduce these roadside breakdowns.
They're expensive.
The product doesn't get to the door.
SLAs are broken.
And I think there were some learnings here as well.
I mean, I think our goal on the onset
was, oh, it's a predictive maintenance problem.
Everybody talks about mean time to fail.
We need to tell this customer within three days,
your battery's going to fail.
And what we learned through the short POC was that really
wasn't what we needed to do.
What we needed to do was help this customer
with so many vehicles understand which of their vehicles
are exhibiting electrical systems issues
and which of their vehicles appear to have bad batteries
or don't look normal as it compares
to the rest of the 2.1 million vehicles that
operate like they do.
And so it was a pretty successful little POC
that has now spun into a core product
that we can offer to customers in that we were able to reduce
their downtime by almost--
25% of their roadside downtime was reduced.
And there was little side effects that we didn't expect.
We had almost 10% of less labor cost
because there weren't so many surprise battery
replacements or electrical systems
tested on the side of the road.
So it was a great little POC.
Of course, then, the problem becomes,
how do you scale from one customer to many customers?
And I think because we use Google BigQuery in some
of those tools as our core data warehouse,
and because that's where we run a lot of our experiments,
the scaling issue becomes nonexistent for us
as we scale up, and we just lean in on some
of that capacity that is offered to us by some
of those products.
BRUNO AZIZA: And you have been able to scale it.
As much innovation as you put out here,
actually, I think you have about eight patents
on this particular use case as an outcome.
BOB BRADLEY: Yeah, there there's about eight patents
on that particular use case right now as an outcome.
And we're able to scale this up and generalize the solution.
And we'll be rolling out live with it.
It actually took us about a year.
You know, what makes us unique as well as a company is,
since we manufacture the hardware,
we're able to push out updates.
So it became a real team effort at Geotab in that we actually
discovered new novel ways of recording some
of this information and detecting changes to make
the product even better.
So it was a bit of an iterative process
with the whole engineering side of the company
to get this out there.
But yeah, scale has never been the problem.
Ingesting the data hasn't been a problem.
And running queries over the data and models over the data
hasn't been a problem, which really frees us
up, then, to work hand-in-hand with customers and really focus
in on what the actual mechanical problem is.
BRUNO AZIZA: So that's quite an amazing journey,
starting with one customer and then
expanding that to the rest of your fleet
and understanding this a million data points at a time
and with the outcome with an amazing recognition
for your innovation.
What have you learned through the process here?
What are some of the Do's and Don'ts here?
Maybe let's start with the positive first.
What are some of the things that are
the Do's that everybody here listening to us
should go out and do?
BOB BRADLEY: I think the one thing that
taught us an awful lot-- and it had kind of a waterfall effect
in lessons-- was engaging with customers early in the process.
The first few years--
I've been with Geotab for about five years,
and the data team was quite small at the time,
and we've grown rapidly.
And those first few years were really learning, and exploring,
and playing with the data and with customers
to understand the value that we could then derive out
of it in the years to come.
And so working with customers closely and early
allowed us to start conducting research
with a clear path to product.
So we weren't conducting research
for the sake of research-- but with a clear path,
like an applied research approach,
where the outcome should solve a business problem.
And really, the object was to fail fast.
The four-month POC was one of many four-month POCs.
And those other ones, if we fail fast and pivot,
we can get to a solution a little bit quicker.
I think, one of the biggest learnings for any of us
engineers, we wanted to make these 99%.
Like I said, meantime the fail, 99% accurate models,
when, at the end of the day, we're saving real money
and saving real downtime for customers
without needing to be 99% accurate.
So I think it was a lesson learned there
that 99% accurate on a problem that nobody wants you to solve
is not a good approach.
It's better to be reasonably accurate
on a large-scale problem that can really help folks and then
iterate on that problem and bring up that accuracy.
BRUNO AZIZA: Yeah, it's the idea around being directionally
right and then improving on that results,
rather than just trying to be right and imperfect
rather than be done.
What about the opposite of that?
Where are some of the mistakes that you
think people listening to us should look out for and try
to avoid, even though they are the result of things
that might have been common sense in the first place?
BOB BRADLEY: I've said this before.
I think I've said it at some of your offices
doing a small talk.
One of the best things about, say, a product like Google
BigQuery is that it's very, very easy
to get your data into Google BigQuery and move it around.
And one of the worst things about Google BigQuery
is that it's very, very easy to get
your data into Google BigQuery.
And one of the things that Geotab did early on
was we started pushing as much data
as we could into Google BigQuery.
So it's not just the vehicle data.
It's operational data.
It's all of our server metrics, application logs.
That really, really helps us triage our problem
from beginning to end.
So definitely would not change how we did that.
But in doing so, you can't lag data governance and DataOps
far behind because you end up with multiple sources of ground
truth or disparate data sets that are trying
to answer the same problem.
And so I think, with that ease of use and ease
of getting data into BigQuery came
some hiccups along the way, where we need to now clean up
some of those resources.
And we need to figure out which of A and B
is ground truth to solve this problem.
So those data silos can happen pretty quickly.
BRUNO AZIZA: Yeah, and they can cost you a lot down the line.
Did you have to hire different types of folks
to make sure that you'd manage for that?
How did you build it into the process?
BOB BRADLEY: Yeah, over time, I think we started out heavy
on the science and analyst side of things because we have
so many managed services that we leverage from you guys.
But over time, we started to pick up many, many more data
engineers and folks that are focused
on pipelines and best practices, data governance, and data
operations.
So yeah, I think there was a fundamental shift there.
What was amazing about it, as I said,
those first few years that I spent and years at Geotab
were a lot of experimentation and a lot of playing.
And we didn't have to worry about the overhead of pipelines
and things like that.
But now, as things become mature end products,
we need to make sure that those pipelines run,
that there's monitoring, and SLAs, and all of those fun
things.
And I think that we played catch-up a little bit there.
BRUNO AZIZA: That's outstanding, Bob.
Thank you so much for sharing these lessons with us today.
I learned a lot about building data products
means engaging with customers early.
In your case, you learned from one customer
and scaled it to the rest of them.
So it's really quite amazing--
eight patents, an incredible story there.
And then the don't is don't wait to implement DataOps and data
governance early, maybe through data engineers
that you might want to hire or a change made in your process,
so you can avoid data silos because they could cost you
a lot throughout the journey.
Again, like I said, thanks for giving us the time today.
I hope many people are going to reach out to you
and connect with you.
They've got a lot to learn from Geotab.
We appreciate your time, and we appreciate you
being a great partner of ours.
We'll talk to you soon.
BOB BRADLEY: No problem.
Thanks, Bruno.
[MUSIC PLAYING]