Creating Inviting Dataviz.
Let's start to look at the components of visual form that Dona Wong has laid out for us.
Again, remembering that there are three of them.
I want to now focus on inviting visualization.
There are three different rules that inviting visualization really does conform to.
Those three rules are presented here.
In inviting visualization, we'll highlight a message,
it will eliminate distractions,
it will use visual cues to help lead the audience through the insight,
and it will finally use contrasts,
either size or color to capture
the reader's attention and really
direct them into the important parts of that data visualization.
Let's look at each of them now.
Highlighting a message and eliminating distractions.
Here's an example from Google that I think does that very well.
You're looking at a very clean and simple graph.
This is the google.com query volume in Brazil on a normal Tuesday,
which is demonstrated there on the graph in blue,
and then on June 15th.
June 15th happened to be the day that Brazil was beginning the World Cup.
So, starting from four hours before the kick-off of that match,
through the match, and beyond,
you can see the difference in query volume on that day.
In fact, at kickoff,
query volume on June 15th has fallen off dramatically.
Everyone puts their phones away, they watch the match.
At half-time, the query spike
backup because everyone pulls their electronic devices back out,
and then it goes away again until the match is over and then it resumes normal traffic.
You can see very clear it's highlighted what you should be looking at.
This is a graph that does communicate very effectively.
Here is one that does not.
There's so much going on on this graph from Morgan Stanley.
You have an area chart with a number of different colors.
You have a table inside that chart.
You've got a number with a box and a circle and an arrow, text.
There's so much going on here to overwhelm the reader.
This is clearly a case where the message that is looking to be
communicated has neither been highlighted nor has clutter been eliminated.
In this case, we're looking at some graphic that does this correctly.
They're on the left side of the screen.
A graphic that doesn't do that on the right,
and I think the distinction is pretty clear.
Let's look at the second rule;
using visual cues to help lead your audience through your insight.
Here's a great example of that happening with
Facebook data and looking at status changes.
Where status changes have occurred that are interesting,
you can see direct labeling that points those things out,
points out the idea,
the areas of the graphic that we should be looking at,
and will really deliver that message very cleanly and crisply.
I know, as someone looking at this graph,
where I should look and importantly what I should be
looking to see and what I should take away from those instances.
Here's a case where this is done all wrong.
Here is an illustration of the health care system.
It's enormously cluttered.
There is nothing here that would,
as a viewer of this infographic,
allow me to navigate it,
to know what kind of insight I should be looking for.
There are no visual cues to help me consume or interpret this visual.
So, there again, something on the left that we should all be aspiring for toward,
a graphic on the right that we should not design.
Final rule; using contrasts either size or color to capture our readers' attention.
Here is a beautiful of actually both size and color by Crayola.
Before even explaining this,
with very little text on this page,
you can probably figure out what this is.
This is the typical colors including a Crayola box from the company
launched in 1903 through to today.
You can see just how many more colors are included in that box.
A very visual and beautiful representation there.
Here's a case of that rule not being followed.
First off, using nothing but primary colors in a pie chart is not an advisable approach.
There is too much contrast here,
frankly, and more importantly,
the size of the pie is so consistent and close
that there's not enough size contrast to use this visual technique.
It's a case where the visual technique chosen does not a fit to what the story really is.
Finally, whenever we put together a pie chart,
we should make sure that our slices add up to 100%.
So again, cases where this was done well,
this use of contrast in the case where it was not done well at all.
You can build these rules into your practice,
into your habit by paying attention to them,
thinking and evaluating every graphic that you create along these lines.
Have I highlighted my message?
Have I eliminated distractions?
Am I using visual cues?
Am I guiding my audience through my insight?
Then, am I using contrast, am I doing that well?
Again, a set of correct uses of those rules and a set of incorrect uses.
Try to stay to the top of this graphic in your designs.
Let's see this then come to life in our Bellabeat case study.
So, we have visualized data using a line graph,
and we see patterns.
From here, there are a number of different things that we could do with this data.
We might want to sketch those things out as we talked
about to see which visual form really makes the most sense.
We could very easily introduce some contrast just by using color in this graph,
right, and cleaning it up a little bit with the removal of some clutter.
A visual like this does give a story,
but it is a broad story and probably not the one that we necessarily we want to tell.
That's why this offering from
singularity.com does capture all the meaning that we want to express,
and does it in a much cleaner, crisper way.
This is clearly the clutter has been removed from this graphic,
and moving forward, we would want to take
a visual like this to really tell the story that we want to tell.
Eliminating the parts of the data that don't
play into that story or don't have any bearing on the outcome.
So, in this module, we've done a lot of things.
We've covered a lot of ground.
We have talked about our ability to find patterns in data and to
align the type of visual technique we are using to the pattern we're seeking,
and to be cognizant of the type of chart that we want to create.
We talked about being planful when creating database,
and that is specifically client-ready or everyday database.
As we're moving to a place where we have a story we want to tell,
we want to put a lot of attention to detail there,
and being planful in our approach helps us do that.
We talked about understanding the concept of visual form,
the components of visual form.
What does make good visual form?
We've arrived at the Dona Wong framework to
say there are three different essential elements.
Then we explored that first element of creating inviting
data visualization in this final lesson.
