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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 his 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 2 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 has 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 2 million vehicles
can tell us what the temperature is on the side of the road
and whether a road is bumpy 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 sets.
The products involved, 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 fleets, 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.
Product doesn't get to the door.
SLAs are broken.
And I think there were some learnings here as well.
I think our goal, on the onset, was all to predict
a maintenance problem.
Everybody talks about mean time to fail.
We need to tell this customer, within three days,
your battery is going to fail.
And what we learned through the short POC
was that 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 the roadside downtime was reduced.
And there were little side effects that we didn't expect.
We had almost 10% of less labor costs,
because there weren't so many surprise battery replacements
or electrical-systems tests 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 and 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--
BOB BRADLEY: Yeah.
BRUNO AZIZA: --use case, as an outcome.
BOB BRADLEY: Yeah, 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 it out live with-- it actually
took us about a year.
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 focusing 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
there's 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 one thing that taught us an awful lot--
and it had a waterfall effect in lessons--
was engaging with customers early in the process.
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, mean time to 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.
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?
What 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 a 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--
BOB BRADLEY: Yeah.
BRUNO AZIZA: --down the line.
Did you have to hire different types of folks
to make sure that you'd managed 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 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 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 better products means
engaging with customers early.
And 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 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.
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