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[MUSIC]
Welcome back.
Colin Ware, who wrote the book Information Visualization Perception for
Design, talked about a hawk flying admits a flock of pigeon.
The hawk stands out since it's only against a flock of pigeons.
But if there were more than just pigeons and other types of
birds within that flock, then it becomes harder and harder to spot only the hawk.
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This is the same idea with data.
We need to make it clear what we're showing.
We need to make it clear to the reader.
If we want the audience to see something in our visual,
we need to make sure that they can actually see it.
And so in this lesson I'm going to show you contrasting visualizations that have
all the information, but is too cluttered for the audience to see.
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The survey results are in and
there's a weighted index of all the questions based on five categories.
These five categories are job placement, campus life,
diversity, advising and course availability.
The weighted performance index is the only data that
the survey administrator gave you.
In other words, it's a pre-calculated metric that summarizes all of the data.
The lower the number, the worse the performance is.
The higher number, the better the performance is.
It's straightforward to graph the results, as I've done.
Take a moment and
see if you can explain how our college is doing versus the others.
You can hit pause, take your time, then hit play when you're ready to continue.
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But before I going to what I think is wrong with this,
I actually want to ask you.
How do you feel about what I've done here with this graph?
I've already kind of said, is it effective?
But think about why.
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The next question I have is, does it meet the threshold of the Gestalt Principles
that we discussed in the last lesson?
Now it's okay to say no actually, because I agree with you 100%.
All of the information is there, but it's just very difficult to figure out.
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Plus, some of the values, and I think that's where the issue with the hours,
the blue diamond is.
Some of the value are very similar to others,
so I can't tell those that almost have the same scores as others.
Now I did that on purpose.
Remember I said that this is a fake data set.
And the reason is that I wanted to give you an example of a situation where we
have all the information.
We do have a fairly interesting looking graph visualization,
it has all the information there.
There's some information that you can glean.
But, there's some major flaws in it.
And what I'm going to do here is show you an example of how it can be improved.
There's more than one way it can be improved, but
I wanted to show you one way that we can improve this to be able to
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take this information and make it useful for the reader.
There's a better way.
And the better way hardly takes any more time.
And this is it.
But what it does do is take into account the importance of how we can use
the contrast to be able to identify exactly what we need to identify for
our needs.
And more importantly, for the reader's needs.
And it takes into account all of the research around the Gestalt Principles.
All the research around cognitive load, all the discussions of trying
to eliminate clutter, and to minimize that cognitive load.
So we're going to be very systematic about this.
And remember that we should not expect the users of this visualization
to know the data as well as you do.
It's your job to know the data.
The user experience must be to get at the data quickly so that they can do their job
and make decisions based on the information you provide.
Remember that I said earlier that the information was not a raw data, but
was indexed to allow comparisons across colleges.
We often have to deal with data like this.
We'd love to have the raw data.
But if we're comparing competitors,
we will often only be given a small amount of information, because they don't want to
reveal raw data from other institutions.
But because it's index information, we can carefully re-index this to meet our needs.
Now I should pause here to say that this is one possible way to be strategic in
the use of contrast.
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So the index was based on a scale from negative 1.5 to positive 1.5.
So all I did was rescale it so
that the lowest you can go is zero and the highest you can go is three.
I do this by simply adding 1.5 to every one of
the values above so that everything is positive.
That keeps every measure at the same relative level.
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And the reason I'm using that is that is the best graph at really being able
to quickly see differences between categories of information.
Horizontal bar graphs are widely used and almost everyone knows how to interpret it.
So here it is.
But there's really no contrast here.
It's all just a bunch of different colors.
We want to be strategic in the use of contrast.
What we want to know is how we, in our college,
are doing versus the other colleges which are A, B, C, D, and E.
And so what I did here was put the other colleges A,
B, C, D, E in gray and our college is a green.
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It's not ordered based on the largest or the smallest or alphabetical order.
It's our college.
And our college followed by E, D, C, B, A.
It doesn't change, no matter the category.
And so we can very quickly look at how we're doing.
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But it's still not quite enough.
So what I did here was just to add some text to help the reader.
And say, okay, especially for things such as job placement.
You could see that our college was number one out of six.
But if you didn't have that text in there, it's a little bit harder to see.
You kind of have to really spend a lot of time figuring out that
we're at number one on that.
And so it's just helping the reader being able to see what's going on.
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But it gives you a very quick snap shot.
And this allows the consumer of the information to say, okay we have this
information and we can address it instead of saying, what is this data saying?
What is it saying?
Let me try to figure this out.
Instead it's like, okay, we have this information, let's move to next step.
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And in contrast, we can look at course availability and say, my goodness,
we're in last place.
What does that mean?
Why is that?
How can we improve, if you want to improve to get our score up?
What can we do to do that?
Those are questions that are actionable, that someone who is
the consumer of the information can look at and just very quickly do.
Instead of them having to figure out from the graph and
trying to discern, you know, what is.
Are we number one or number two?
Just instead, just tell him.
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And so it really moves us, again, into this actionable type of information.
And it's so essential.
And really, the fundamentals.
And it's the fundamental aspect of data visualization.
So what this graph is exactly saying is that
we're taking color and we're just adding it to the information that we need.
And we're doing a very clear carefully.
And we're using that bold color to differentiate from the other items that we
want to show, but we don't need to highlight.
So we show the other information.
We're not hiding that information, by no means.
We're actually showing that information, but it's in the background, as it were.
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And so this is what we discussed about how we should avoid
those brightened bold colors or too much overusing colors.
And we should use it strategically instead of just
using vivid colors for every single college.
Instead, we use it just for ours.
And the other ones don't have any colors at all, which is totally fine.
A pre-attentive attribute of visualization.
So how do we tackle it?
And we're going to have a big decluttering exercise that we're going to go through.
So stay tuned.
Thank you.
Govind AcharyaPrincipal Analyst
Hunter WhitneySr. Consultant, Author, Instructor
