-: All right, so it's time to start talking
about design and visualization specifically.
So we talked about chart selection.
That's a big part of data visualization.
But how do you design that stuff
in a way that's gonna best influence,
impact your audience?
And I have to start off by saying something very important.
I would imagine that most of you
were never taught anything about design, right?
Many of you have degrees, or you're studying for science,
you're studying for various other things,
and were never exposed
to an actual formalized design training of any kind.
Fair enough.
I'm not gonna teach you that much about design.
This is not, like, a design class.
But I'm gonna teach you one or two really critical ideas
and show a bunch of examples
that should help you become a better designer today.
'Kay?
So we're gonna cover kind of a lot of ground,
but in a way it's not that much ground. (chuckles)
A lot of specific little things here and there.
But, like, literally, if you pay attention
and you apply all the things that I mention,
you're gonna be a better designer.
It's truly that simple.
And by the way, remember when we looked at...
When was it?
I think during the human visual perception module
when I showed ya those infographics
and what you see in your peripheral vision
and pre-attentive processing and all the rest of that.
And we saw that one ugly infographic alignment.
Remember that one?
That's a big secret to design, like I mentioned.
So just line stuff up. Make sure the edges align.
Like, make a plan just to have alignment be not crazy,
and you're a better designer already, okay?
All right, so let's talk specifically here.
Design is not about making things pretty.
It's also about making things pretty.
Yes, you should make pretty things.
Aesthetics matter.
We saw that research about that already.
But design is more than that.
Design is problem solving.
Design is information hierarchy.
This is what it's really all about.
When I design something, my entire goal
is to help you understand the hierarchy of information.
"This over here is more important than this over here."
And I do that in design by where I place things.
Stuff at the top is more important
than stuff in the bottom.
Stuff in the middle is more important
than stuff out on the edges.
Size.
Giant things are clearly more important than tiny things.
Okay?
Color.
If everything is faded back, subtle,
and boom, this one bright color, then you know what?
I'm telling you that's more important,
'cause I'm asking you to look there.
Like, remember the Reuters iceberg?
You know, it's gonna crash into that island,
that data story?
It was all subtle whites and grays,
and then there was the bright blue
that really drew your attention.
And then we also do it by typography.
And typography is a little bit intimidating
for non-designers,
'cause typography feels like it's about font selection.
I mean, it is also.
But for our purposes, what I mean by typography
is just, "Make decisions about type size."
That simple.
As an example, when you create a PowerPoint slide
and you put 16 bullet points into it...
Which, don't do that.
But if you did, what does PowerPoint do?
It's a very smart software, right?
Every time you add a bullet,
the font size shrinks and shrinks and shrinks
so those bullets fit.
Thank you, software, right?
That's smart.
But odds are out of those 16 bullets, the two at the top
are probably the most important ones.
Information hierarchy.
And the other 14 are just there for context.
So you should manually size up the two that matter
and manually size down the rest of them.
Or even better, put 'em in the appendix, 'kay?
But seriously, make thoughtful decisions about type size.
That was another thing that was wrong
with that horrible infographic.
Remember, I think I pointed it out.
There was this thing that said "nutrition facts,"
which was, like, a header.
And then it had the nutrition facts,
calories, fat, carbs, whatever.
But they were the same font color,
same font weight, same font size.
Like, there's no differentiation between those two.
Tiny subtle changes like that make a huge difference.
And again, it's about the hierarchy of information.
"This is the parent. This is the child.
"This is more important. This is less important."
Okay, I've been lingering on this for quite a while.
Let's move on. (chuckling)
So here's the thing.
It's all about information hierarchy. Yes.
It's also, of course, all about pre-attentive processing,
like I said in the previous module.
So it's pre-attentive, your response to a visual.
The moment you come to my visual, you're judging it.
And I have a goal to communicate information hierarchy.
"This is more important, this is less important."
So I'm gonna orient my design
to trigger that pre-attentive response in you
to see the more important stuff
and to not be overwhelmed or distracted
by the less important stuff.
It's still there for you.
But the design is oriented that way.
I think you get it, right?
This is clear.
I've said it enough times.
I'll say one other thing.
That information hierarchy is driven by what again?
The KWIS.
Okay?
What's the data saying?
What does my audience need from me?
What do I wanna say?
Those three things are the things
that tell me what the hierarchy is.
Okay? (chuckling)
So this stuff all ties together.
You get it, right?
All right. So here's the thing.
If I needed to summarize my design advice in two words,
and I don't need to, but I do, 'cause it's easy,
this is what I would say.
"Do less."
Strip away all the noise and the distractions
so that your audience's pre-attentive response
will be aligned with your information hierarchy
driven by your KWIS.
That simple.
So minimal design will create
more effective, more focused communications.
Now, I have to say something important here.
Minimal design is not the only answer.
Minimal design is not always appropriate.
There is such a thing as complexity in design.
You can have very rich, very visually striking design,
and it can be very impactful.
There is a place for complexity in infographics
and data visualization and all the rest of it.
But data storytelling requires focus.
And the more minimal your design is,
the more likely your audience is able to focus.
So there are exceptions, yes.
But I like to teach the minimalist approach,
because if you default to the minimal,
then you know when to break the bounds
and to do something less minimal.
That's okay.
But the minimal is what really allows you
to focus your audience on what matters most.
Okay.
But again, it's not the only answer.
Now, do less, minimal, et cetera, yeah.
But my favorite quote.
"I would've written a shorter letter,
"but I didn't have the time."
It takes time to do that.
I acknowledge it. 'Kay?
It's kind of a bummer, but it's a reality.
Whether you're editing words or you're editing visuals,
it takes time.
But like I just said a moment ago,
if you can change your default behavior to doing less,
it actually takes less time and less time and less time.
You'll get better and faster at it.
So what I'm gonna do next is this.
This chart is a real chart from a real client of mine.
And I redesigned it.
This is the original.
I'm gonna show you step by step by step,
every step that I took
to make this into a more effective visual for their needs.
And I'm gonna explain what this was all about in a second.
But before I do that, I want you to think
about your pre-attentive response to this visual.
So literally feel free to look away,
physically look away from the visual.
And then look back at it.
And ask yourself,
what is your strongest pre-attentive trigger?
What is the strongest visual element that jumps out at you
when you look at this graphic?
And I can tell you, when I do this session live with people,
I ask this question.
And I usually get
at least four to five different responses.
Some people say the bars really jump out at them,
what happened in January.
Some people say the data lines
with the little circles on it.
Some people say the horizontal dash lines,
'cause they're very thick.
Some people say all the numbers,
like just sort of the general clutter.
Some people say the yellow or the green line
versus the other one.
So the fact that there are four or five different answers
is not a good sign, right?
That's not effective design.
The information hierarchy
is clearly not being communicated here.
It's just a bunch of data, good luck. Right?
So lemme explain what this chart is,
and then we'll walk through the steps.
So what we're looking at here is a chart
that's used by this company for a weekly meeting to track,
or maybe it was a monthly meeting,
to track the progress,
squashing bugs for a new software application.
Actually I shouldn't say "new."
For a software application.
The bars show the user volume,
the number of people using the software every month.
The lines with the circles on them are the error rate,
the percentage of users getting an error
when using that software.
One color is the old technology,
the other color is the new technology.
The horizontal dash lines
are the benchmarks for those two error rates.
"We wanna be below there."
That's what the chart is.
And like I said, it's a monthly meeting
reviewing how we're doing against these goals,
you know, reducin' that error rate.
Now, if it were up to me, what I would do
is I would give you all a new key for your keyboard.
You know, a function key, right?
That you could just magically press
one after another after another,
and it would just magically fix
all the problems with this chart.
I can't do that.
But what I can do is arm you with a way of thinking about it
and hopefully get into your head a little bit,
because ultimately, you'll be able to do something
almost like, "Click this button
"to fix all the problems with this chart."
So as I look at this chart,
and I think about the first thing I might ask about it.
'Cause usually when I'm fixing and editing,
it's often, like, in the form of questions.
The first question I have for this chart is this.
"Do I think it's a good idea that every single data point
"on this chart is labeled, every single number?"
And you know what I say to that?
I say, "F-NO."
And that is your new F key, okay?
It's an F-NO key.
Because I say, "Absolutely not."
Not only should they not all be labeled,
I would argue none of them should be labeled.
None.
No data points. No data labels. Zero.
Now, this is a starting point.
This is a default position.
Could I be convinced to label some of them?
Maybe, right?
Maybe you tell me I should label
the last data point, the most recent.
Maybe the audience needs to see that.
Sure. Maybe.
Or the the highest number, or the lowest number,
or the number from June, because that's an important month.
Sure. Talk me into it.
It's your job to talk me into adding it back in,
not the other way around.
By default, the answer is no. Okay?
So the lesson here is, "Remove unnecessary elements."
All right.
Now we look at the chart, and it's already a lot cleaner.
I would say it's already, you know, immeasurably better.
But we look at it now, and I say,
"Do I think it's a good idea that on the y-axis
"we have half a percentage point increments?
"Do I really need that level of detail,
"3.5, 4, 4.5, 5%, for the error rates?"
By the way, I have grid lines for every one of those,
which is a huge visual distraction.
And while we're at it,
why do I have to turn my neck 90 degrees
to read my axis labels?
So what do I say to those?
I say F-NO to all those too.
Grid lines are gone.
Bottom number and top number only for my y-axis.
And by the way, for the x-axis.
Leftmost and rightmost, nothing else in between.
As a starting point, could you talk me into,
again, maybe 2.5% as an important number,
I need to label that?
Okay, tell me why.
I will add it if it's reasonable.
But by default, the answer is no.
By the way, look at the label.
"Percent of users receiving errors," parenthesis, "line."
Seriously?
Can't it just say "error percentage"?
"Line," maybe.
'Cause in this case we do have dual axes,
which is, like, a whole thing
I'm not even bother ranting about,
but that's a bad idea generally.
Error percentage, right?
Especially for an internal chart for a monthly meeting,
I think everybody knows what it means.
We don't need to be so prescriptive about it.
Okay.
You may also notice, in addition to getting rid
of all the grid lines in the background.
Now that I don't have all the tick marks,
I don't even need 'em.
On top of that, though,
I did have to show the bounds of the chart.
Whether I have a bottom line and a top line
or just the leftmost and then the bottom axis lines,
one way or another, you need to see where the scale,
you know, where the chart starts and ends.
I chose to do it with a light background, gray color.
Fine. That's one choice.
Anyways, mostly removing unnecessary elements
once again here.
Now we look at the chart.
And now that I have that gray background rectangle,
look what happened.
I had to make my bars a little bit darker.
And now they're a little bit more contrasty.
They're popping off the page
a little bit more than they were.
They're a pretty strong pre-attentive trigger now.
So prompted me to go to the client and say,
"Hey client, you know,
"it looks like user volume is going up,
"but error rate's going down.
"Is the story here
"the fact that they're inversely correlated,
"as one goes up, the other one goes down?"
And they said, "Oh yeah, no, no.
"It doesn't really have anything to do with user volume.
"We don't care about that.
Okay, we don't care about it?
So why are we talkin' about it?
Goodbye, right?
Removing more unnecessary elements.
I've removed now one of my three data sets.
1/3 of my data is now gone.
Thank goodness.
Useless. Not part of the conversation.
If they wanted to talk about user volume,
they could talk about that in another slide.
But they literally told me, I asked, they don't care.
All right, so now I look at this chart.
And to me personally, now especially,
the strongest pre-attentive trigger to me
is the horizontal dash lines.
They are the thickest, heaviest thing on the screen.
And they're in color. Everything else is gray.
Now, what is the point of every chart?
What is the star of every chart?
Is it the benchmark, or is it the data?
I would say it's the data.
In other words, the actual data line
should be the emphasis visually, not the stupid benchmark.
I say F-NO to those.
Now, I can't get rid of 'em.
But I'm gonna design 'em way back.
Thin, thin, thin, 'kay?
Now the visually strongest element are the data lines, 'kay?
So I'm focusing the eye on what matters.
Now I look at this and I say, "Hmm."
And I think you can predict my answer. (chuckles)
"Do I think it's a good idea that I have markers,
"those stupid little,"
especially the way I'm phrasing it,
"those stupid, ridiculous, useless little circles
"on every single data point?
"Do they serve a purpose?"
You know what I say?
"Also-freakin-lutely not."
Do not put markers on your lines.
Now, occasionally, frequently even,
I will put a marker on the last data point.
Stops here, right?
Or on the maximum, or the minimum,
sort of like the data labels question.
If there's a reason to call attention to one of 'em,
I'll do that, right?
But every single one? Absolutely not.
Now, slight tangent here.
You may remember, I mentioned it earlier,
the iceberg story from Reuters,
where we saw the path the iceberg traveled.
And one of the views, they actually had little markers,
little dots all along that path.
In that case, I don't have a problem with it.
'Cause the markers helped me see,
"Oh look, it barely moved for these weeks,
"and then there was a big gap.
"It moved a lot in that week.
"Wow, that thing's flying at that point."
If that's an important part of the story, sure, I buy it.
But for most data visualizations,
charts like this, uh-uh, no.
You also may notice, I made my lines thicker, right?
They're popping off the page even more.
They were pretty skinny before.
And the markers are what made them
a little bit visually stronger.
Too skinny.
No, they would've fought with...
Not "fought with."
They sort of blended too much into the benchmark lines,
so I thickened them up a little bit.
A little design thing, 'kay?
All right, so simplifying and reducing distractions
is the lesson there.
Now, I look at this chart.
And I said to the client,
"You know, what's the goal?
"What is the reason that this chart exists?
"Is it to compare the old and the new technology?"
If so, because of parallelism,
remember that gestalt principle,
this does a decent job, right?
I can easily see the lines are sort of further apart,
and then they come together,
and now they're tracking in parallel.
Like, "We're doin' a good job squishin' bugs.
"The error rates really in line with each other,
"one slightly higher than the other.
"Is that the point of this chart?"
And the client said, "Oh yeah, no.
"It's not at all about comparing
"the old and the new technology.
"All we care about
"is how is each one doing compared to its benchmark."
So how is the yellow line doing
compared to the yellow benchmark?
How's the green compared to the green?
And you tell me. Is it easy to see that?
I argue that it's not that easy.
It's only four lines,
and yet somewhat they're sort of obstructing my view
to see the overall size and shape
of, you know, how long ago
and by how much we passed the benchmark.
They're on top of each other.
So I say, "No. Why don't we do this?"
Much easier to see that the gold line
passed a long time ago and by a lot more.
The green line, more recently and not as much, right?
Much easier to see.
Focusing the eye on what matters.
Okay. Now I look at the chart.
And something interesting occurred to me.
This chart is a weird chart.
I said a moment ago that the star of every chart
should be the data, the line.
Usually, yeah.
But in this chart, it's not.
What is the star, what is the point of this chart?
Well, it's when and by how much we cross that line.
So isn't it actually about this?
Isn't that where I wanna draw your pre-attentive attention?
'Cause again, that's my goal,
to draw your pre-attentive eye to the thing
that you should be looking at, comparing, whatever.
In this case, it's the size
of that differential, that variance.
That's why we're using what's called a deviating area chart.
It's that deviation that's interesting.
Not the line itself, not the benchmark.
Neither one.
Now, my line is still a little bit thicker, darker,
'cause that is still sorta the star.
But the point is really that area.
So that's what's emphasized visually.
So focusing on what matters once again.
Now, a moment ago I asked the client,
"Are we comparing the old and the new technology?"
And they said, "No,
"it's more about looking at what we just talked about."
I went back to the client again right here.
And I said, "You know, client,
"like, I know it's not all about comparing them,
"and we're doin' a better job seeing the shapes.
"But I'm still kinda comparing 'em
"'cause they're side by side.
"Is it at all about comparing them?"
And you know what they said?
"Actually no, not at all.
"Because we've already disbanded that team.
"They're not even fixing the bugs anymore.
"We don't even talk about the old technology anymore."
Are you kidding me?
Why is it still in this stupid chart?
Removing unnecessary elements.
Two of my three data sets are now gone, okay?
They were literally unnecessary.
Now we look at the chart,
and we may finally realize visually,
it's all squished. (chuckling)
What's goin' on there?
Why is my scale so weird?
So I'm gonna fix that, right?
I'm gonna stretch it back out.
You know, there's a few rules.
There's one rule called...
I shouldn't even call it a rule.
One best practice that was popular years ago,
and it's still referenced sometimes,
saying you should bank your lines to 45 degrees.
Came from data viz book, best practices,
from a few decades back.
Long story short, the basic idea
is that, you know, sort of the average of your lines
should sort of tilt to 45 degrees
in whatever scale size you have.
And, like, it just sort of feels right when you do that.
Another way of thinking about it
is you take the bottom number and the top number,
and you just add some padding to either one.
Make 'em nice round numbers.
That's another good way to do it.
You have to consider
whether you should start at zero or not.
And there's a lot of variance
about when and why you might do that,
so I'm not gonna teach you any rules about that now.
Long story short, this chart, we can start at zero.
Absolutely. Why not?
'Cause the lowest number is close to zero.
The top number should be 4.5%,
'cause the top value is a little bit below that.
You can even say, you can go to 0 to 5%,
a nice round number.
Round numbers are good.
Your audience will remember round numbers.
Always pick a round number when you can.
Okay.
So thoughtful scales, that's the lesson there.
Now we look at the chart,
and we think about what we've been doing.
Mostly we've been doing less.
Mostly we've been removing stuff.
We removed data. We removed labels.
We removed grid lines. All kinds of removal.
But the last couple changes,
we weren't really removing stuff anymore, right?
We changed to a different chart type.
We adjusted the scale.
In other words, some of our F-NOs
are turning into F-YEAHs, right?
And the final set of F-YEAHs
are just smart final decisions,
such as using inline labeling, 'kay?
Inline labeling.
Light gray color next to the light gray line.
Labeling it.
Darker gray, darker gray.
If I was gonna label the green area
and say something about it,
I might label it over here in green.
I'm also using an annotation.
Something interesting happened here.
And I'm gonna mention what it is.
'Cause it's, you know, part of the data story.
So I'm expressing something here.
I'm not just letting you figure out the data on your own.
And because this thing happened here and I annotated it,
I finally talked myself
into adding one more label to my X-axis.
So, you know, this really feels like it belongs here, right?
Some people would say, "This is too minimalistic.
"Why don't you label every month?"
I say the opposite.
Now, am I right, and they're wrong?
You know, it's debatable.
But the less you have cluttering up your chart,
the more likely your audience is
to see it, understand it, remember it, et cetera.
So strategic labeling and annotations.
By the way, one of the little detail.
I'm gonna go all the way back
to the beginning here real quick.
Look at the legend, the key down below.
So in the original,
we had to define five different things, right?
What the dotted lines are,
what the data lines are, what the bars are.
Now, some of these disappeared,
so that made it easy to remove them.
And then by the end, when I do the inline labeling,
I am left with no legend or key whatsoever.
This is fantastic.
Research has shown if you do inline labeling,
your audience has an easier time
remembering what your colors, your patterns actually mean.
Because think about it.
When you're bobbing for apples
to remember where the colors are,
or, like, watching a tennis match,
even when there's only two colors,
like, I'm literally dizzy doing that. (chuckles)
It is disorienting.
Even when you have two colors, it can be very disorienting.
So keep that in mind.
When you can do inline labeling,
it is a very effective way of doing it.
So we said no a lot.
If you change your default position to no,
if you do less,
then the editing time, like I said,
takes less and less and less time.
Now, like I said at the beginning here,
minimalism is not the only way to design things.
There are arguments for maximalism, complexity, nuance.
Fine.
On top of that, even within minimalist design.
This is important. Listen carefully.
Even within a minimalist design approach,
you should never be afraid to show more data.
So let me share an example.
I did a survey.
I asked a thousand people to rate a product
on a scale from one to five.
The median score was 3.8.
Okay. Mildly interesting.
If literally this was my slide, "median 3.8,"
yeah, you know, like, maybe I'll nap for a little
while you're talkin' about it. (chuckles)
But what if this was what the results actually looked like?
A bunch of people love that product.
Everybody else, same median score.
Or maybe it looks like this.
A bunch of people hated that product. Same median score.
These two stories are very, very different.
I am never gonna be shy about showing you this data.
I'm gonna design it minimally.
I'm gonna just still just have, like, that number,
that band, on the screen,
and nothing else labeled, et cetera.
But I'm gonna let you see all the data points.
I'll never be afraid to show a chart
that has complexity of data points.
But I'm gonna design it by doing less.
So in your capstone projects,
as you're putting your data stories together,
don't have a data story
that's like, you know, literally 10 slides
of a bunch of simple bar charts,
very aggregated, summarized data.
At least one or two of your slides,
please, should be kinda beefy.
Doesn't mean they have to be super complex
from a design standpoint.
In fact, they shouldn't be.
But they can be pretty beefy.
You might have a scatter plot with a thousand points on it.
Fantastic.
Design it simply so I can still make sense of it.