- Unless data charts are thoughtfully,
intentionally designed,
they can be difficult for most people to process
and understand.
When you're designing your charts
to show quantitative UX data,
remember that every single visual element in your chart
is an additional piece of information
that your viewer has to process.
Edward Tufte is a visualization pioneer
who advocates for a minimalist design aesthetic
when it comes to presenting quantitative data.
Tufte refers to any unnecessary or distracting elements
in a chart as what he calls chart junk,
which is just a perfect name for them.
To create easy to understand data visualizations,
we have to clear out that chart junk.
All too often I see UX professionals creating overwhelming,
distracting, and sometimes downright ugly data charts.
When I'm coaching people to improve
their data visualizations and I see them make these mistakes
I'll often ask them about
why they picked those chart styles.
Sometimes people will tell me that they picked those styles
to make the chart look interesting.
Well, your point is interesting enough on its own.
You really don't need gaudy stripes
or 3D effects to dress it up.
And using those will only obscure your charts meaning.
Sometimes these styles can even create an optical illusion,
distorting viewer's perceptions of the data values.
So, while nobody should ever use 3D effects
in their data visualizations,
other design elements can be informational or distracting
depending on the context.
It can be tricky to determine which elements
are truly necessary for your chart.
For example, tick marks might be completely unnecessary
for one chart and totally necessary for another.
Let's look at a common chart element, Axes.
To see when they might count as chart junk.
By default, Excel produces bar charts that have labeled axes
with grid lines.
Depending on the situation, you might be able to remove
those visual elements and simply use data labels.
Let's look at a simple data chart before and after redesign,
and we'll start with the default chart
that Excel gives us automatically.
Using the access and the grid lines alone,
it's a bit tricky to determine exact values.
Looking at that default chart,
is the before value 1,210, 1,215?
We can add data labels to the end of each bar
to help people see the precise value more easily.
But when we add those data labels,
suddenly we don't need the grid lines or the Y access.
They aren't necessary to determine the values anymore.
So by adding one element, the data values,
we've turned two other elements,
the grid lines and the Y axis, into chart junk.
So let's just remove those.
Anytime we can remove redundant visual information,
we're making our charts clearer
and easier for people to understand.
But remember, just because an element is chart junk
in one context does not mean that it's always chart junk.
Chart junk is in the eye of the beholder.
Let's imagine that we wanna show
the seven most visited pages for a hospital website.
We might decide to keep the access and the grid lines
in place if we simply want to give our viewer
a sense of the scale of the differences between those pages.
With grid lines and no data labels,
viewers can still see that the Homepage
and the Plan Your Visit page
are much more heavily visited than the others.
Viewers won't know the exact numbers
but that wasn't our goal in this case anyway.
But remember that you can customize the way
that you show axes in grid lines.
In this case, maybe we wanna reduce the amount
of grid lines to make a more minimalist chart.
Maybe we change the axes unit from 10,000 to 20,000.
Remember that data visualizations
contain a lot of information,
which your viewers will try to process and make sense of.
You wanna remove any unnecessary visual elements
that could distract from your meaning.
- Thanks for watching.
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