welcome back to data science Wednesday my name is Tessa and I'm a data
scientist over here at decisive data and today we're going to be talking about
descriptive analytics you might be asking why are we talking about
descriptive analytics that's not data science I know it's not data science
right when we think of data science we think of predictive prescriptive
diagnostic but not really descriptive having said that descriptive is really
necessary as a launching pad before we can really dive into the advanced
analytics we want to have a really good grasp on what's going on so as we walk
through an example I want you to imagine that you are a grocery store owner and
you want to optimize yourself spacing so if you have products on your shelf that
are not selling very well but they're taking up a lot of space that can have a
really negative impact on your revenue so you want to optimize that so your
questions are what are our top selling products also its what are our least top
selling products so you kind of want to know both so let's drive through an
example so when you're looking at descriptive analytics most of the time
you're going to be looking at a dashboard through tableau or power bi or
something of that nature and you're gonna drive through to try and drill
through to try and answer these questions so let's look at this we look
at the upper left-hand corner and we're looking at revenue by product category
so we're saying pantry meats produce and you see that your pantry products are
doing really well but you want to know what which ones are
doing really well and which ones they're not doing so well so that you can you
know optimize your shelving so a really good dashboard would be interactive so
that when you click in here you can go over here and see how good each product
in that category is doing right so then you're over here and you see that
Frosted Flakes is doing really well Captain Crunch is doing really well and
Joe Oh's is not doing very well so then you can go and you can have a
conversation we your inventory people or your executives
and you can say we need to buy more of this and less of this to optimize your
revenue it can really Drive really good conversations a lot of times people who
are using these dashboards don't really understand everything that it takes to
build and support this kind of a dashboard so let's just take a minute to
kind of talk about that we're gonna break it down into four basic things
cleaning relating summarizing and visualizing so cleaning we've we've all
kind of heard of this right before dirty data nobody really wants to deal with
dirty data because you might have text filled with some have cap somehow
lowercase you don't know how to relate them to each other you have dates that
don't make any sense or knowles that are not handled properly all of these kind
of things can cause inaccurate reporting which you know you don't want to deal
with so then the next thing down is relating so a dashboard like this could
be built on one table it could be built on hundreds of tables but you need to
know how all of that data is related for example if you have Captain Crunch up
here you need to know that Captain Crunch belongs to the pantry category
and the only way that you do that is by relating those different pieces of data
together so that's a really crucial step next you want to summarize your data so
this is really how you get an overarching view of what's going on you
don't want to look at your data line by line you don't want to know what's going
on day to day per se but you want to be able to Center summarize so you maybe
want to know well how many sales happen throughout the whole month or by
category or in two different areas any kind of different things gonna
involve some sort of summarization next this is a pretty crucial one
visualization so as we talked about you know this is the visualization here and
so when you're when you're creating this visualization that's really where the
geeky data side of everything collides with the business decision-making side
of everything and so it's really critical that this
this not only is very usable for the business person but also displays
everything accurately so that's a pretty crucial step so what is descriptive
analytics well we know that it's not data science but how do you know when
you're in descriptive analytics well anytime you're answering questions that
describe your business you're probably in descriptive analytics and what does
it take to support descriptive analytics well it takes a lot of data massaging
cleaning relating getting everything kind of prepped and then creating these
really great visualizations and that's when you know that you're in descriptive
analytics thank you