Misunderstood? Try Data Storytelling
Data visualizations help explain complex data, although individuals can and do come to different conclusions nevertheless.
It's a significant problem for data scientists and data analysts, especially when they're trying to explain something important to business people.
Visualizations alone may be confusing.
Part of the problem is one's ability to communicate. Another problem is expecting too much from data visualizations -- specifically, the clear communication of an analytical result.
Data storytelling can help, because it goes beyond data visualizations. It also helps individuals think a bit harder about what the data is saying and why.
Are data visualizations dead?
Clearly not. They remain an extremely important part of turning data into insights, but they do have their limitations. The first limitation is that data visualizations don't always explain the details of what the data is saying and why. Another limitation, as I mentioned earlier, is the possibility of diverse interpretations and therefore diverse conclusions, which, in a business context, can lead to some rather heated and unpleasant debates.
A simple form of data storytelling is adding text to data visualizations to promote a common understanding. Like PowerPoint, however, it's entirely possible to add so much text or so many bullets to a data visualization that the outcome is even more confusing than it was without the "improvement."
The same observation goes for infographics. Bright colors, geometric shapes, and "bleeds" (the absence of a border) do little to aid communication when used ineffectively. It's important to avoid clutter if you want others to understand an important point quickly.
One complaint I hear about using data visualizations alone is that they lack context. Data storytelling helps provide that context.
How to tell a good data story
Humans tend to be storytellers naturally, whether they're explaining how a car accident happened or why they weren't home at 7:00, again.