MIKE BRANCH: I think one of the things,
if I were to rewind five years and play things over again--
and it seems very, very simple.
It does, it's very intuitive.
But I think this one thing would go a long way for anybody
starting their dream.
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BRUNO AZIZA: Hi, everyone.
I'm Bruno Aziza, and welcome to another great episode
of "Data Journeys."
This is the place that we come to learn from leaders in data
and analytics-- leaders who have achieved amazing results
and growth, significant outcomes,
and are willing to spend some time with us to share
their lessons, their dos and don'ts, and their stories.
And so today, I have the pleasure of talking with Mike.
Mike is Vice President of Data Analytics at Geotab.
He was a CEO before of a company whose team
was acquired by Geotab.
He's an amazing entrepreneur, and has
achieved very amazing things over the last five years.
So we want to hear from him.
Mike, thank you so much for taking the time
to talk to us today.
Let's get started.
What does Geotab do first?
MIKE BRANCH: Yeah, and a pleasure being here, Bruno.
So Geotab, we're the world's largest commercial telematics
provider.
What that means effectively is, we
work with a lot of commercial fleets.
So we've got a device that plugs into the onboard diagnostic
port of vehicles, over two million commercially connected
vehicles, and voted number one by ABI
Research across the globe.
And we provide all of our customers
with a number of insights, everything
from safe driving to fleet optimization, better routing.
And we have this wide network of partners,
and we've created this platform to allow our partners to help
our customers to build some amazing products on top
of the ecosystem.
And then from all of this data, it's
my team that really looks at this data,
and figures how do we drive further insight
for our customers from this vast amount of data
that's coming into the ecosystem on a daily basis?
BRUNO AZIZA: So 2 million commercial vehicles,
that's 40 billion data points a day.
We're talking a lot of data, a lot of changing data.
So take us through the use cases that are deployed today.
MIKE BRANCH: So, I mean, we've got a number of different use
cases.
So we've got about 40,000 to 50,000 customers that
are kicking around out there.
And the problem with that is you have so many disparate use
cases.
I've got customers who are running fleets of size 1
to over 100,000 in different verticals.
Some are municipal vehicles, some
are vehicles that do last-mile delivery, some construction
vehicles.
So it's just such a wide array.
So what we've done is try to look
at what we can create broadly that will
help our ecosystem deliver more impact.
And one of the areas that we've done a lot of work in
is predictive maintenance.
Picture a vehicle driving around.
You want to be notified if there's something
wrong with that vehicle.
And the instant that we looked at-- we
actually asked one of our largest customers,
what is the single biggest problem for you?
And they said it's the electrical system.
That's the biggest problem for us.
And it's not the cost of a battery,
because batteries are super cheap in cars.
But it's the cost of the downtime
whilst that vehicle's on the side of the road.
So we looked at how can we better predict
when a battery is going to fail in a vehicle
to help this customer out.
And so we did that.
And actually there's, a couple of years of work.
We developed all the pipelines that were required,
looked at all the feature engineering.
We developed some predictive models.
And we actually developed predictive models
to clean the data as well, because as the data is coming
into the ecosystem, you look at something like cranking
voltage, which is one of the features we use to figure out
whether a battery is going to fail or not.
And there's noise in that data as well,
so you have to establish ML algorithms that
look at bad cranking events versus good cranking events.
And so the net at the end of the day,
this ended up saving this one customer millions of dollars
a year.
And as a result, we work closely with the customer,
but have now started to deploy that
across the entire ecosystem.
So we can issue these, OK, we think your battery is
going to fail in this vehicle.
You should really check it out.
And then what the customers can do and the partners can do
is they can take that insight and integrate it
into any other downstream maintenance
applications for reminders and these kinds of things.
BRUNO AZIZA: Predictive maintenance
is going to be coming to a lot of folks listening to us.
And we're talking about lots of data here.
I think the other dimension is, you're
working with a lot of real-time data, right?
I think you've got about 11 petabytes of data
you're working with, real-time streams going into your system.
So beyond predictive maintenance,
what are the other use cases you've
deployed using the Google technology.
MIKE BRANCH: I mean, they're so varied.
And we've got a team--
I've got a strong team.
We are about 65 on the team right now doing
all sorts of things.
But one of the areas where I'm especially proud
of this year in specific is some of the work
that we did with regard to COVID.
And I'll explain how that came to play.
So of course, everything hit in March,
and the world kind of stopped.
We looked at ourselves and we thought,
how can we better help?
What can we do?
And we thought, you know what?
We're in a position right now-- and we use that Johns Hopkins
dashboard that showed how the countries across the world
were faring when it came to COVID cases.
We used that as a bit of a litmus test
for what we could do.
And so we looked at commercial transportation recovery.
And we said, you know what?
With two million commercial vehicles across the globe,
we could be a benchmark for our customers
and for the public sector to better understand, and make
informed policy decisions, as it relates to recovery,
not only within a country, but by sectors within a country.
So how is the construction sector faring versus pre-COVID.
How is the oil and gas sector faring?
How is transportation logistics?
Really important indicators, and so we
use a number of different Google technologies for that.
The data was made available through Geotab Ignition, which
is our open aggregate data portal, which
is fueled by Google BigQuery and these aggregates.
What we do, we made it freely available for everyone
to access.
And we created and established connectors.
And one of the most interesting ones, I think,
was with Deloitte did with that.
So Deloitte connected up to that dataset,
which we updated on a daily basis,
this high-frequency data, and pulled it
into their economic recovery dashboard.
And I think the first week was about 100,000 thousand views
of the dashboard.
And I think what that instilled is the need
to look at alternative data sources at a high frequency
to be able to enable policy and decision-making.
And so we started to see a lot more interest
at not looking at necessarily traditional means for looking
at things, like economic recovery.
So I think a really great example--
and the tools that we use, Google BigQuery,
of course, the service the data, we're using behind the scenes
a whole [INAUDIBLE] of other things.
If you want, I mean, I can talk to some of--
would it make sense to talk to some
of that now, too, Bruno, yeah?
BRUNO AZIZA: Well, I think it would,
because you're using data flow as well in the background.
Before we go there, I mean, you have so many interesting use
cases.
I want to hear about the third one
around the corridor analysis, which
I think a lot of people that are traveling
will be able to relate to.
Tell us more about what you're doing there.
MIKE BRANCH: Yeah, this one is exceptionally interesting, too.
And keep in mind, our core customers
are our fleet customers.
But we believe that through the aggregate use of this data,
there's a lot of assistance that we can be to the public sector
from an infrastructure perspective.
And this one came by the way of what
we did with the Regional Transportation Commission
in Las Vegas.
And they were looking for, again,
this high-frequency way of looking
at corridor analysis and signal timing
to lessen congestion within the arteries
in and around Las Vegas.
And traditionally how it's done is,
there's a lot of physical, manual deployments
of infrastructure.
And we said, we think we can do this with just data.
So what we did is first devised models
to figure out what an intersection actually
looks like.
And that sounds like a trivial thing.
Oh, isn't it just a point on the map, Mike?
Well, no, it's a little beyond that,
because you have to understand the exact geometry
of an intersection as it relates to vehicles traveling
through there to be able to provide these metrics that we
look at-- corridor analysis and signal timing.
So we developed machine learning models--
this is part of the intersection.
This was a guy that's fixing something
at the intersection, a light, or sitting
at a parking lot that is very close to the intersection.
So now if I'm the traffic engineer in Vegas,
I can pull up this application in near real time,
and have a fantastic idea of what signal timing looks like.
You can choose a whole corridor along an avenue,
and you get the same diagrams that a traffic engineer would
be used to seeing, but on a high-frequency basis
in real time without any deployment,
but the vehicles moving around.
And again, all aggregate, all privacy-compliant,
which is another big component of this approach.
BRUNO AZIZA: So this is precise.
It's real-time.
It's accessible by everyone.
These are amazing use cases.
And I do want to drill into the products, as well
as their learning.
So as you're deploying these products--
and here we're talking about BigQuery and Dataflow
and a host of other technologies.
What are the things that you've learned-- maybe the dos.
Let's start with the positive news,
which is what are the things that everybody
needs to go out and do when they get started on their journey?
MIKE BRANCH: I think one of the things,
if I were to rewind five years and play things over again--
and it seems very, very simple.
It's very intuitive.
But I think this one thing would go a long way for anybody
starting their dream now.
Just treat data as you would any other product.
Oftentimes, data is a byproduct of products,
and people look at it in that fashion.
But if you don't treat data as a product,
you end up with issues downstream.
So what do I mean by that?
Well, I need to make sure you have proper version
control of everything.
The lineage, I need to understand
the flows of from one dataset to the next
across my entire ecosystem, the notion of data operations
as well too is critically important.
So somebody has created the dataset.
They leave the company.
What happens to it?
Where does that institutional knowledge go?
So it's all of these things that you would think about
if you were developing a product,
but sometimes go by the wayside if you're thinking about data.
So I think the notion of treating data
as a product is absolutely essential.
I mean, one of the biggest things that we've learned--
and if you're a data scientist and you're not
familiar with BigQuery, you should be,
because it is one of the most instrumental tools
that we leverage in use at Geotab.
And traditionally, you look at data science,
you're doing a lot of scripting in Python or R.
And that's where you're doing a lot of your workloads.
Look at BigQuery for doing it.
We have, I think, an amazing talk
that's done by one of our data scientists, Daniel Lewis.
And it talks about just do it in SQL, right?
And so there's so much power that
can be had by just leveraging BigQuery
for these massive workloads.
And the geospatial analysis that comes with those tools,
absolutely incredible.
So don't feel like you have to do everything in Python.
Let BigQuery do more for you, because it can.
And then the third thing-- because this
is where people will get tripped up if they
go too far along a path.
Have a tight integration with your privacy office,
because every single data product that you develop
needs to be privacy-compliant.
And that isn't easy in the lens of data nowadays.
There are so many different categories
of data, legislation just changing
every day on a state-by-state level.
And to understand the implications,
to have a tight alignment with data and privacy
is absolutely crucial to getting these products out the door.
BRUNO AZIZA: This is amazing, and you
do have an amazing team.
I mean, I look at the work Dan's doing.
I was reading this weekend the work
that Terence is doing, on publishing
some of the learnings on machine learning and so forth.
You really have an amazing team that does great work with data.
Now let's talk about the flip side of that.
What are some of the mistakes that people should avoid,
and what you've learned that you probably
wouldn't do again if you were just getting started?
MIKE BRANCH: R&D is crucially important.
It's vital to creating new innovation, new product.
I think we spend a lot of time in R&D.
But we have to learn how to extract ourselves
from that at some point in time, and know when what
you developed is good enough.
And so often, we think, oh, you know what?
I'm shooting for that 98% accuracy on that model.
Well guess what?
The customer would be thrilled to have something
that has a 90% accuracy, depending on the product
that you're releasing.
And so what we found has helped is
having this notion of an applied data scientist,
so somebody who sits between product and data who
understands the data world, and can talk the product world as
well and says, OK, R&D team.
We think that what you have there
is ready to take to production.
Let's put it into a pipeline and let's get going.
Otherwise, you end up burning cycles in too much R&D.
And then allow them to continue on with further refinement
for sure.
And then of course, I'd say there's an easy tendency--
because BigQuery is so easy to leverage and use,
people will just dump things into BigQuery.
And it creates--
I wrote an article a few years back about
don't create these data swamps, because data lake--
well, data swamp is not something that you want.
And it's very easy, oh, just push it
to BQ we'll figure it out later.
No, have a consolidated approach.
It all comes down, again, to that treating data
as a product.
And then the last thing, before you even touch on any modeling,
whether it be analytic or ML-based,
make sure you truly understand your data
and the nuances in that data.
How was it collected?
How does it change?
I mean, we're dealing with IoT devices and firmware updates.
What happens when firmware is updated to the data
that you're collecting?
If you are in predictive maintenance,
getting a deep understanding for the mechanical system
behind it, or the electrical system,
is fundamental to understand what happens when a vehicle is
turning over and cranking, so that you understand
how you can construct your features to further develop
your model.
So it's-- a lot of times, people just jump to the model,
and think that the data will--
and there are some amazing tools to allow you to extract insight
from data.
And that's being more democratized by Google as well,
too.
But you have to fundamentally understand
what the data is all about before you can
continue to the modeling phase.
BRUNO AZIZA: Well, Mike, this has been amazing.
I learned a ton, and I wish I could continue
talking to you for hours.
I learned about the best practice
on treating data as a product.
This means think about version control, data operations,
lineage.
Don't think about data as a byproduct of the work
that you're doing.
And of course, this concept of applied data science,
that I hope a lot of people here will take and apply
at their own company.
Again, thank you so much for taking the time
to talk to us today, Mike.
And for everybody else, if you want to hear more stories just
like this, make sure that you click on the link down below.
Until next time, I'm Bruno Aziza.
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