7 February 2022
The term ‘sneaky data’ almost sounds like industrial espionage or a spy film. And while you’re now wondering if you should take your Martini shaken or stirred, it might be time to re-evaluate how you – or your company – represents data to users, customers, or the public at large.
‘Sneaky data’, a term used by Jane Li (Solutions Architect here at Vizlib), is a visualisation that doesn’t represent the entire story of your data. It’s a way of using graphs and charts to conceal the overall findings that a dataset could otherwise reveal. This isn’t necessarily a malicious action – using the wrong representations for your data can result in them being quite sneaky to the viewer.
In a recent episode of Vizlib Appy Hour, our Industry Solutions team showcased examples of sneaky data, and how they could be fixed.
Here are 3 key takeaways from the episode to help you avoid sneaky data in your Qlik Sense + Vizlib dashboards.
This may seem like an obvious tip, but it’s a mistake that many people make. If your data relies heavily on raw numbers – let’s say sales figures for the past financial year – start the number display from zero, instead of an average metric.
It’s always best to be wary of measures that don’t start at zero as they may not be an accurate representation of the data.
Look at the bar graph below, the zero x-axes add an additional context that the user may not otherwise see.
The implementation of the colour aspect of a graph is important. Using colours that are in the same range, such as light and dark blue, can help to convey grouping, but the opposite is also true. Charts can obscure data by also using colours that are too close together for different stats.
Instead, think about using contrasting colours – like blue, green, and red – to show off your data. These varying colours will draw the user’s eye to the different sections of the graph, instead of lumping everything together.
You could also try using stark colours for the metrics, not the backgrounds. For example, using a white graph on a red background could trick the users into observing the background as the actual graph.
We’ve spoken about data literacy many times before. It’s an important aspect when you’re creating charts and showcasing data in an easy-to-understand way. Charts that are hard to read, such as a 3D pie chart, could be an example of the author using extra bells and whistles to make data look more dramatic than it actually is.
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