Hello, and welcome to Data Science Wednesday.
My name is Tessa Jones, and I'm a data scientist
here at Decisive Data.
And today, we're going to be talking about
prescriptive analytics, which is really the
last set of analytics within the spectra of
analytics that we've been covering, starting
with descriptive analytics, which is really
the baseline of all analytics in general.
And this really tackles the question of, what's
going on in my business?
Then, we move into diagnostic analytics, which
gets us into why things are happening, why
is my revenue going up or down, things like
that.
And it really helps us to understand how to
predict things, which leads us right into
predictive analytics.
So, predictive analytics provides a model
that's going to tell us what's going to happen
in the future.
So, now what?
You know what's going to happen in the future.
The beautiful thing about prescriptive analytics
is it tells you what to do with that information.
It gives you an action that you can run with
to drive your business.
So, let's take an example.
Let's pretend that you are a grocery store
owner, and you probably have a good baseline
of descriptive analytics, some nice dashboards
to help you understand what's going on in
your business.
You probably have information about how different
products are selling over time, things of
that nature.
And then, you probably went into diagnostic
analytics, where you're looking at why things
are happening, what causes revenue to go up
or down.
And that really supports the building of a
predictive model.
So, this is an example of the output of a
predictive model.
We have two products here.
One is blue, and one is red.
And the blue product here, you can see that
it's very seasonal, right.
Like, the blue product sells a lot more during
the summer, and the red product sells a lot
more during the winter.
And this line here indicates what's going
on today.
So, the predictive model says, "Wow, sales
in blue is going to go way up, and sales in
red is going to go way down."
So, now what?
What do we do with that information?
That's where prescriptive analytics comes
in.
We integrate that with the descriptive information
we have, which, down here, this is telling
us what is on hand.
We can see that the blue product, the inventory
in the store, is going down and we're about
to run out, whereas the red products, we have
an abundance of those.
So, if we take this information and integrate
it into the business process, we can say,
"Well, for cereal on week 36 in store 10,
we predict that we're going to sell 56 units
of cereal, but we know we only have 40 on
hand."
We also know that this particular business
doesn't want to ever run out of products because,
to them, that means they're losing revenue.
So, we want a buffer of that, so we decide
we're going to ship 18.
And if this is really a good prescriptive
model, then this is seamlessly integrated
into the business so that these actions are
just happening.
So, it frees you up to make all kinds of other
business decisions that are very important
and more insightful.
So, to recap, prescriptive analytics is the
suite of analytics that gives you actionable
things to do with the data that you have.
That's prescriptive analytics, and thanks
for joining.
