So let's say you work
for a global shipping company and
your manager asks you to figure out
how to improve operations.
You create a bunch of charts
and tables to crunch
the numbers but they're not
fully explaining where the problem is.
And that's the key. Where?
As a shipping company, what you're
dealing with is location-based data.
There are a lot of factors that can
impact your bottom line when an
entire country or region
or even the whole world is your arena.
Those things aren't always clear when you're
looking at data points in the abstract.
What you need is context,
a way to see how close or far apart stuff is,
how it clusters
together, and how it
moves from one place to another.
That's why SAP Analytics Cloud enables you to
visualize your location-based data in geo maps.
Let's have a look at your shipping data
to illustrate how it works.
You start by making sure your data includes
location dimensions that can
be displayed in maps.
Here in the model, a location
dimension has already been
created for the depots
but the stores still need to be done.
You could just geo enrich
you stores using the names or
ISO codes for the countries
or regions they belong to.
You have a column with
all the country names in it here.
But your data also includes
precise latitude and longitude coordinates,
so you create a location dimension that shows
each store as a unique data point instead.
Easy, right? Next you
add a geo map to your story to
show your store locations.
You could display the stores
as a bubble layer or
a heat map, but you want
to be able show stores by country,
so you choose a choropleth layer, which lets
you drill up and down in the geo hierarchy.
You also change the color of
the country shapes to
reflect the average shipping time.
Well, that tells you where
some of the problems are.
For example, Tanzania seems
to wait a while for its shipments.
But if you want to really
impact the bottom line, you
need to figure out where you
should be focusing your efforts.
So you change the style of
the layer to show the countries as bubbles,
playing a bit with the colors to
highlight where stores are
waiting longer than 15
days to get their deliveries.
And then adjust the bubble size
to reflect gross margin.
Wow, that's a big outlier!
You should definitely be trying to
keep your German stores happy.
Because of the outlier, parts
the map are hard to see.
So you make a quick adjustment to
how the bubble looks when it's that big,
and then drill down into the outlier
to take a closer look.
That's kinda busy,
so you make a few more changes to get
a better look at the individual stores.
So what next? Well,
the sales managers for
the store locations may have some insights,
so you add their names to
the tooltip for the bubbles.
A few quick calls and you have
some suggestions to follow up on.
One idea is that
maybe some of the stores with
longer wait times are farther
away from ports where
the deliveries can come in,
so you add another layer to
show to port locations.
It's hard to eyeball the distances,
so you add
a distance filter to identify stores
that are more than 500
kilometers away from a port.
That's probably not it,
there are stores that are
getting their deliveries in
good time that aren't close to a port.
So you zoom in a bit closer using
a polygon filter and
check out some of the reasons
for the delay in shipment.
The segments of the pie chart are
described here in the legend, but
you're still not seeing
an obvious difference between
the store locations with
speedy delivery times
and those that take longer.
What now? Well, maybe
you're still not looking at
the data the right way.
This time you trace
the path of the deliveries
all the way from depot
to store using a flow layer.
Ok, now you're getting somewhere.
That does not look right.
Why are deliveries being sent
from the US depot to Europe? You do
a quick check of the order volume coming out
of the depots to see
if you're missing something,
but it seems like the US depot
could be downgraded to serving
only the Americas while the depot in
Germany takes over the
deliveries everywhere else.
A quick click on the individual
depots lets you see
more details in other widgets on
the page using linked analysis.
And there you have it, a
solution all mapped out for you.
That was just a selection
of the features available when
you use a geo map in SAP Analytics Cloud.
Give it a try for yourself
and see where it can take you!