We live in a time when data is king. Data is everywhere – and it’s tempting to believe that, the more data we have, the more accurate our decisions will be. But regardless of the data, even the most simple graphs can easily mislead you. Even when using the best tools available, your conclusions may not be as sound as they seem if you can’t spot some fundamental issues.
In this series, we’ll be taking bite-sized looks at some of the most common data analysis mistakes made when building and interpreting graphs.
Perplexing pie charts
Pie charts are – to be blunt – just not very good. After all, your analysis depends on you seeing and interpreting the data accurately and preferably quickly. These are both things that pie charts are bad at.
Now we could spend hours rambling on about why, but here are the key points.
In this graph, we’re looking at market share amongst four competitor companies. On the left graph, you can probably see that Soylent Corp has a 25% market share. Yet on the right, could you be confident enough to say the same? We’re programmed to recognise right angles and straight lines very easily but introduce a bit of rotation and all that programming breaks down. Humans just aren’t very good at estimating angles and area.
So, as many people do, we might introduce labels to each slice of the pie chart:
Now we can easily get the actual percentage market share for each company. You may have noticed how often your eyes needed to dart between the pie chart and the legend on the side. That’s time wasted! Of course, you could add the company name to each slice of the pie, but then you end up with what is essentially a colourful, awkwardly laid out table — not something that actually helps you in data-driven decision making.
Instead, try using a simple table or bar chart, that tells the same story but much more clearly:
And don’t even get us started on some of the weird ways pie charts are used!
Avoid using pie charts for anything more than 2 categories.
- a table, when the difference between the values isn’t particularly relevant; or
- a bar / column chart, when there is a trend in the values, or when comparing the values between each other is important.
If you’re using graphs to make data-driven decisions, it’s important that they are accurate and reliable. Graphs can be misleading if the graph type, colours or scales are poorly chosen, so look out for these details before making a decision.
There is no such thing as a “perfect” graph – every graph has its limitations! However, there are many ways in which we can improve our understanding of any given dataset by carefully considering all aspects of the analytical process.
Vaxa Analytics offers free analytics audits to help elevate your business intelligence with insights from experienced professionals who understand what makes a good analysis tick.
Contact us today for more information on how we can work together to create data-driven, actionable insights to help your business succeed.