Hi.
My name's Johann du Toit.
I am the co-founder of Get Me Known,
which is a national microchip database featuring everything
from dogs to horses, and where we
look at patterns on that data to then better understand
how these records are spread out across the country.
Today, I'm going to be talking about how we use BigQuery
to get insights from data that's constantly being streamed in
from across the country on records from pigs to farmers.
So Get Me Known was founded about three years ago.
Coming from a family that's passionate about animals,
we were registering when we had to fill in [? free ?] forms.
So we decided that's not how we want it,
and we built a company where you can go to websites,
fill in all your details.
And it assigns a certain number to your information.
So if a pig gets lost, stolen, is found somewhere,
you've got a record that links it to you,
and they can contact you and get your little guy home.
So users use the service by actually going to the websites,
signing up, filling in the data.
That data is then stored in the back end which
we stream to BigQuery, where we look
at data to get some insights.
So I see this being used in almost every organization.
And this day and age, we've got data
coming in from every side, every angle.
And every piece of your business is
going to be using some metric, which you can look at.
And BigQuery is essentially where
we're aggregating all this and looking for patterns.
So what sales are doing good, what records are not coming in,
where are you actually getting data from, and just looking
for patterns to grow your business.
So we use BigQuery to essentially
look at all our sales data, where we look for patterns,
where they're coming from, and how much.
We tend to lay this out in a nice heat map,
so you can actually, by one glance,
see where your business is taking off
and where you need to put more pressure on to push sales
in a certain region.
So Get Me Known in itself is just a website where anyone can
go to to get data about a microchip on a dog, or a cat,
or a horse that they found and get the contact information
to speak to the breeders.
We also partner with all the local databases that
are also running.
So you can get information if we might not have it.
So the idea is you'd log in.
And here's your account that you have set up with us.
And you'd have a list of microchips.
So these are all registered to your name.
And they pipe their data into a back end,
where we keep a big log of all the changes happening
on all of these records.
So we can actually go back and look
at when the chip was registered, when the name was changed,
when an owner might have changed,
how long the owner has been the owner,
and print the certificate.
So that would be the fountain to what we call the back end
service, which we actually use.
And that pipes into this big record
set where we keep all this information
and look for patterns.
So using BigQuery, we've got a schema of all these records
where we can actually go run popular queries on this thing.
So we might want to look at breed, color, and popularity.
So if we're on that, you'll see this here Rottweiler
and black and tan combined with the most popular.
And then if you go a step back and look at maybe flag records.
Flag records are records that you
give an extra piece of information about [INAUDIBLE],
so who to contact if the pet is found.
So these will essentially be stolen cattle, dogs, cats.
And we can actually go look at where they were stolen,
what city, when.
So using this information in the back end,
we can then look at hotspots.
So we know which areas of say, Johannesburg and Pretoria
are known for getting pets lost or stolen.
And we've got a certain error rate.
But when that hits, we call and notify external services
about these locations and give them the top 10 records that
actually just tell them something's
going on then to have a look.
And we do this nation-wide.
This data I got by looking at our main record set
and pulling out a limited amount from BigQuery,
and then just rendering it using Google Maps.
So this is a smaller data set that I took just
to show you distribution and how we
use BigQuery to pull this out.
And we'd internally any maybe look at only South Africa
and pull out details from South Africa
and look at certain cities.
So say we want to look at anything in Cape Town,
or Johannesburg, or Pretoria.
Using this and small little utilities that we write,
we can map out our data and see where our customer base is,
and where we need to look at to help ease local problems.
Thanks.
And that's how we use BigQuery and the insights
we get from it to get pets home safe.