Bad Data Visualization Examples – Don’t make these 5 mistakes!

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Bad Data Visualization Examples -
Don't make these mistakes!
Data analysis is now possible for professionals
with limited or no technical skill sets. However,
while it has broadened data visualization's
scope, it has also given birth to many
challenges. This article elaborates some of the
critical bad data visualization examples you
should look out for and ensure your reports do
not have them.
Critical Bad Data Visualization Examples
Not following conventional axis We use
infographics for presenting critical data because
of its impact. If your analyst does not follow
the conventional method of starting an axis at 0
or tweak the intervals between the y-coordinates,
it will misconstrue the information. For
instance, if your sales bars Y-axis doesnt
start at 0, you will hardly get an accurate
picture by looking at its height. Not using the
correct visuals Most of the BI tools are loaded
with visuals. Power BI even has a marketplace,
from where you can download more visuals. To
make your reports impressive, your data analyst
may be tempted to experiment with them. While
there's no harm in it, the BI analyst should also
know the suitable usage before implementing any
chart. For instance, one of the most common and
critical Bad Data Visualization examples is using
a pie-chart where the total isn't coming up to
100 or for more than five metrics. Avoiding
titles data labels Titles and data labels make
visuals clear without a chance of ambiguity.
Looking at the report, viewers exactly know what
they're looking at and which visuals they need to
focus on. Without titles, they'll keep hunting
through the reports to find what they need. And
without visuals, theyll fail to understand your
graphs and charts. Overpowering colors and
images Colors and images are required for
storytelling. But, overpowering dashboards to
impress stakeholders with too many colors or
unnecessary images can confuse them. So, use
striking colors only to highlight information
and keep them in sync with your viewers culture.
Keep your dashboards simple, using crisp text,
meaningful pictures, and standardized
colors. Too much information in one visual One
of the common bad data visualization examples is
information clutter. Instead of putting in too
much information in one chart, ask your data
analyst to divide the data into different charts.
When placed correctly with necessary details,
filters and visuals will give a better picture
than one chart trying to share the entire
business case.
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To Conclude
Those were some of the most common bad data
visualization examples, which you can avoid
easily. Along with your objectives and audience,
if you keep these simple tips in mind, your
dashboards will be more compelling and
impactful. For more details please visit our
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