My middle school basketball coach always used to say: “If the person fails to catch the pass, it’s not their fault.” What he meant by that is that it’s the responsibility of the person throwing the pass to make sure that the receiver can catch the ball in any given situation. Even if the player receiving the pass is not skilled at catching, it’s still the passing player’s duty to understand the ability of their teammates, and to make sure that everyone can catch their passes. This particular coach was a rare human specimen because often I would hear the opposite comments when a player failed to catch a pass – something along the lines of “Why can’t you catch, butterfingers?”
Another generational divide
In creating and reading information through data, I’ve observed the same destructive attitudes. While data literacy has been a buzzword for some time now, it has intimidated some people and allowed others to gloat over their boomer colleagues with the new data skillz they just learned in an online course. Unfortunately, the increasing use of data in fields previously devoid of anything beyond a little arithmetic has been a polarising ordeal.
Many people still skip pages of newspapers and magazines when they see the sight of a bar, line or pie chart. Rightly so – in their experience, nothing good has ever come from charts and tables. Most of our early experiences and introductions to data visualisations were to read or present boring data in a predictable and standardised way only to be then graded by a teacher, who themselves were checking off a to-do list.
This dispassionate affair between the creators and audience of data visualisation set off the whole industry on the wrong foot. Just like a basketball player throwing a pass, it’s us, the creators’ responsibility to fix this relationship and give the audience something worth their attention.
There is no such thing as data literacy or data illiteracy
There is only good or bad data visualisation. This good is separated from the bad by one simple thing: stories. If we, as data visualisers can carry the audience on the story, then we’ve engaged our audience and done well. For this, we need to do a couple of things:
1. Define the story behind the data, making sure that there is some meaning behind it that can invoke an emotional response from the reader
2. Slowly and clearly introduce unfamiliar concepts, considering the level of prior knowledge of your audience.
3. Give the reader a clear line of narrative, but allow exploration within defined boundaries.
Here are some good examples of this process in action:
Data storytelling at 23
Telling stories through data has been regarded as somewhat of a fad by traditional marketeers and journalists – but I think that’s because it’s been done so badly so often. At 23, we differentiate ourselves from data analytics and marketing agencies by focusing hard on the story behind the data and make sure that the audience, whether internal or external, is engaged by it.
If you need help with peering through and creating a story around your data, get in touch with us!