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BRUNO AZIZA: Hi, everyone.
I'm Bruno Aziza, and welcome to another episode
of "Data Journeys."
This is where we come to learn from data
leaders, their success, their growth, their best
practices, and their worst practices as well.
So today, we are talking to Jake.
He's the Head of Data Engineering at Koffie Labs.
Jake, thank you so much for taking the time
to talk to us today.
JAKE BAILLIE: Thank you, Bruno.
BRUNO AZIZA: All right, Koffie Labs is an interesting name.
Not many people might not know what the company does.
Tell us.
What does Koffie Labs do?
JAKE BAILLIE: Yeah.
So Koffie is a new insurance company
focused on commercial trucking.
And we're different in a couple of ways from legacy insurance
companies.
The first is, not sure if you know,
but the average time from application submission to quote
is about 10 to 12 weeks in commercial truck insurance.
Why?
Things like fax machines.
Really old till they deliver to databases.
Data from all over the place.
Our service standard at Koffie is
to get that process done same day.
So when you submit an application to us,
we turn around a quote in the same day.
How do we do that?
We ingest hundreds and hundreds of millions of rows every month
of historic crash inspection, fleet operation, marketplace
data.
And we also ingest live telematics data.
And so bringing that data all together
into a single application allows us to generate
quotes much, much, much faster.
The second major difference between Koffie and its legacy
competitors is how we underwrite.
And most legacy competitors will underwrite a fleet.
They'll look at how a fleet has performed
in terms of safety, what kind of operations
they're hauling, maybe where they're located.
They'll look at drivers for violation information.
But they really treat all vehicles the same.
They might look at the age of a vehicle.
They might look at the weight or maybe the replacement value.
But they don't actually look at physical or mechanical
characteristics of the truck.
At Koffie, we look at about 100 different physical, mechanical,
and safety characteristics of a truck
to underwrite the risk at a vehicle level, the thinking
being that as new safety technologies are adopted,
these trucks will become more and more safe.
And newer trucks will start to differentiate themselves
versus older trucks with these safety technologies.
BRUNO AZIZA: So you're delivering quotes faster.
You are understanding these trucks better
because you're taking into account the latest information.
How big is this business?
How many trucks are you dealing with
and how much business are we talking about here?
JAKE BAILLIE: Yeah.
So Koffie is a new program.
We've only been writing premium for about a quarter.
But we've written $1.3 million in business
in our first quarter.
We look at about 10 million trucks on the road
and provide real-time risk scoring for those trucks.
And we ingest, as I mentioned, about 40 years
of historic crash inspection, fleet operations
data, marketplace data, when trucks are bought and sold,
et cetera.
BRUNO AZIZA: Excellent.
So data really is at the center of your business
and your operations and the types
of services you're providing.
Let's talk about the use cases.
How are you using this data?
How is it providing better services
and enabling you to drive a better business?
JAKE BAILLIE: The first big way we've
brought this data together is in our Underwriting Dashboard.
So our Underwriting Dashboard is the core system
that our underwriters and business team
look at to evaluate and price risk.
And so we bring in all of the information
we know about fleets, drivers, vehicle operations,
other fleets that might be related, and put it
into a single interface that our underwriters can move through
quickly to make decisions on these quotes.
Second big area where we use this data
is for lead gen and marketing.
So let's say we wanted an analysis of all of the trucking
companies that haul furniture that have insurance companies
or insurance policies in Illinois that
are expiring next month.
Data is in place and systems are in place
to do that very quickly.
And the third is ongoing coaching.
So it's not just determining whether or not a truck is safe,
but is that truck now engaged in unsafe activity?
And is there any action we can take through coaching
or other actions to make that driver
and make that truck operate more safely?
BRUNO AZIZA: Wow.
So you have the ability to assess risk.
You have the ability to drive your business better.
And you have the ability in real time
to provide coaching to reduce risks.
So really using data in a full circle.
Now, you've been in this space for a long time.
This is not your first data and analytics project.
So let's talk about the best and the worst practices.
If you were talking to yourself at the beginning
of this journey, what would be maybe the one or two things
that you would tell yourself you must absolutely
do these because they are leading to better success?
JAKE BAILLIE: Yeah.
So a couple of things.
One, at Koffie, we've done a really good job
at developing a self-serve culture
around data and analytics.
We want to encourage our teammates
to be curious about the data we have.
So data dictionaries and our internal wiki, SQL
templates that they can just drop into the BigQuery Console,
Data Studio training on how to quickly visualize data
from sources.
I think that's my big first point
is, build that culture of self-service and curiosity
around data.
Second big recommendation I have is, as engineers,
use low-code and no-code tools to bring engineering
and business together.
It's very easy for us to look at a data prep flow
and explain to a business user exactly how
the transformation works and what the output will be.
You can't do that with a Python script.
It's very, very difficult with a business user.
Also, we're in a regulated industry.
Same principle applies when explaining the stuff
to regulators.
Another helpful tip is benchmark data quality.
I think a lot of data engineers and analysts
treat transformation and cleansing
as a one-off activity.
It isn't.
And if you can't measure data quality,
you're not going to be able to manage it.
So what percentage of your attributes are you using?
Can you use more of your attributes?
Are you taking activities or making transformations
or are going to get exogenous data that
will increase the percentage of these attributes
that you're able to use from your data sets?
BRUNO AZIZA: I really like this one because this is something
we don't think about often is, what
is the percentage usability of the data that you're
working with?
It's a big way to assess if your self-service strategy is indeed
working.
All right.
Well, let's talk about the flip side of that.
What are some of the things that might make sense
at first but people really should avoid doing?
JAKE BAILLIE: Big tip I have, don't reach for code first.
So the converse of the low/no-code tip is that code is
not something that's human readable.
It's not something that's easy for nontechnical users
to digest.
And it can be hard to maintain.
Let's face it, right?
As engineers, we've all seen terrible data transformation
and cleansing code.
So don't reach for code first.
Second tip I have, don't automate everything at once.
So as engineers, we tend to want to do everything
100% right off the bat.
Doesn't work that well for startups.
Doesn't work that well for new data sets.
So what we do at Koffie is we take the big red button
approach.
We try and automate 50%, 60%, 70%, where it makes sense,
but we keep those automations running
under the supervision of a human and at the request of a human
until we're sure.
BRUNO AZIZA: That is great advice.
Now, let's talk about maybe one question
I didn't prepare you for.
What is your favorite trifecta data prep feature?
JAKE BAILLIE: Yeah.
BigQuery Predicate Pushdown.
It's a relatively new feature.
It comes from the land of Parquet.
And it makes those transformations
10 to 100 times faster when pulling data out of BigQuery.
So definitely a huge improvement and a very helpful feature.
BRUNO AZIZA: Well, that's something
I didn't prepare you for, but you answered it very quickly.
Jake, this was excellent.
Thank you so much for spending the time with us today.
JAKE BAILLIE: Thank you, Bruno.
BRUNO AZIZA: All right.
And if you want to hear more stories of customers
just like that, be sure to click on the link down below.
Until next time, I'm Bruno Aziza.
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