- [Instructor] In "Alice's Adventures in Wonderland,"
by Lewis Carroll,
Alice, here on the left,
gets some life advice from the Duchess, here on the right.
This is what the Duchess tells Alice.
"Never imagine yourself not to be otherwise
"than what it might appear to others
"that what you were or might have been
"was not otherwise than what you had been
"would have appeared to them to be otherwise."
Now, remember, Lewis Carroll
was an accomplished mathematician and logician.
This is a logically consistent and meaningful statement,
but it's nearly impossible to follow.
Fortunately, the Duchess provided a shorter version as well,
where she simply says, "Be what you would seem to be."
Or even shorter, be authentic.
Now, any time you work with data,
you're attempting to simplify the data
so that it makes sense and you can get some insight.
It is always a matter of simplification.
That's the purpose of data analysis
and big data projects,
and that's one of the major things you can do
with data visualization.
Now, in terms of how you frame things,
it's important to remember
who is making and implementing the final decision.
Is it a machine learning algorithm?
So, decisions by machines,
you don't need to explain them,
you don't necessarily need to visualize them.
It's going to know based on its calculations
whether to recommend a particular product
to somebody or not.
But for decisions that are made by humans,
that's where they're generally looking
for the principles or the abstract concepts
that come out of the analysis
that they can use to guide their own decisions.
And in that case,
visualization is going to be critical
to get the value out of your big data project.
Remember, humans are visual animals,
and you get much higher bandwidth with graphics.
A picture is, after all, worth 1000 words.
And so, let me make a few recommendations
for your graphics in a big data project or anywhere.
Number one, make the graphic as simple as possible.
Now, what I mean to say by that,
as simple as fits the data.
Make it no simpler.
You want to make it as clear as possible
to focus on the data and not get distracted
by other things going on in the graphics.
Second, it's a really good idea
to provide a method to give detail as needed.
Let them click on things, drill down,
get some of the source data, get additional context.
That gives richness to a graphic,
especially one that is initially rather simple.
And then, to state the obvious,
provide labels for your axes.
What's on the X, what's on the Y?
What are the different groups?
And give the sources for your data
so the meaning of it is complete.
And then there are a few things to be cautious about.
First, be cautious of 3D graphics.
Now, there's one kind which is the false third dimension,
like you see right here
where you have bars that have depth.
There's basically no reason to ever do that.
The other one is a 3D graphic
like a 3D scatterplot.
It's really cool as long as it's moving,
but as soon as it stops, it's very difficult to read,
and I personally never use them,
and I recommend that people avoid them
unless there's some really compelling reason to include it.
Another one that's a little contentious
is the issue of interactivity,
not just with VR goggles,
but really putting radio buttons
and sliders on your graphics.
The risk is this.
People will get distracted by the interactivity,
and they'll just start playing with it,
spinning around your graph
and clicking on things to see what happens.
At that point,
the interface has taken attention away from the data itself,
and that's a delicate balance,
something you need to think about carefully.
Closely related to that
is the use of novel graphs like this one.
They're cool, they're pretty, they attract attention,
they are often very hard to read
and hard to read well enough to know what to do about it.
Now, doesn't mean don't use 'em.
It means be very careful and thoughtful
when you think about using a novel kind of graph.
Truthfully, in terms of the best ROI
or return on investment,
or the best value, the most information from a graphic,
it's going to be really simple ones.
Bar charts, histograms, line charts, and scatterplots
will probably get you 90%, 95% of the value you need
from data visualization,
even on a large big data project.
It doesn't matter that the data set is massive
or that the data set gets involved.
By the time you're getting it down,
the simplification to the level that is needed
by the decision-makers to use it in a productive way,
these kinds of graphs will probably give you
the kind of insight you need
without distracting people from the meaning of the data,
and that is the main purpose of visualization
in any big data project.