Data is integral to tracking and reporting a scientifically backed sustainability strategy. However, far too often badly designed and inappropriate charts make it hard to understand what the data is saying. This leads to misinterpretations of progress and missed opportunities for improvement. Below we’ve listed some key principles for creating effective data visualisations.
A good data visualisation tells a story or answers a question. Before you start creating a visualisation you have to understand the concept or question the data is trying to answer.
This can be simple, such as: how have my greenhouse gas emissions changed over the past three years? Or complex, for example: what incentives are most effective at attracting talented employees?
Throughout the process of creating a visualisation ask yourself, can I see the question my visualisation is trying to answer?
Now you have your concept or question you must pick the chart that will answer it best. The right chart will allow the story to be quickly understood by your audience. It’s worth testing some different charts before picking the final one.
If you’re looking to compare different categories use a bar graph. Comparing trends? Consider a line graph. If you want to show corelations in large data sets, try a scatter plot.
If you’re thinking about using a pie chart, try a bar chart instead. It’s much easier to differentiate the heights of bars than the areas of segments.
Don’t overcomplicate your visualisation with un-necessary labels, drop shadows and colours that don’t have a function. This distracts the eye and makes it harder to see what the data is saying. A visualisation should only include what is needed to answer your question.
Using bright colours to highlight information attracts the eye and ensures it’s seen first. However, it’s important to also consider what’s seen second, third, fourth etc. – this is where grey comes in.
Our eyes let grey sit in the background while we are drawn to other, more captivating, colours. Information in grey should be the detail that backs up the key points been made in a chart. For example, in the combined scatter and line graph below, the data points that created the trend line are grey.
Don’t force the data to fit your vision, let the data speak for itself. While aesthetics are important they aren’t above the question the visualisation is trying to answer. If your chart is confusing, cluttered, and illegible don’t blame the data, take a step back, critique your design choices and develop the visualisation.
Whether it’s for external reporting or internal tools to help you track your sustainability progress we can help you communicate data in an engaging and accessible manner. We have experience in the Retail, Finance, Brewing, and Transportation sector.