Two ways to get better spaghetti charts
The spaghetti chart. Probably one of the most common visualizations in a pitchbook. Often used to present time series data for a company in comparison to its peers. It looks something like this:
But, it has flaws. Namely that its similarity to spaghetti makes it almost impossible to differentiate between the individual strands.
Let’s take a step back and consider what information this graph is trying to communicate. Usually, it’s one of two things:
- How is a subject company performing in relation to its peers?
- How have comparable companies been performing and how does this relate to the subject company and the rest of the set?
At Pellucid, we’ve applied a few design adjustments to the standard spaghetti chart to better extract these insights.
1. Performance of a subject company in relation to its peers
Often the set's comparable strands are added to give a cross-sectional perspective to the subject company. It’s not necessary to know which strand is which, but instead, the emphasis is on understanding how the subject company is performing relative to its peers. De-emphasizing the comparable strands and removing the labels reduces clutter on the chart, making this easier to see.
The needed comparative context is still there, but the subject company is the hero.
2. Performance of comparable companies in relation to a subject company and set
Sometimes comparable companies need to be shown more clearly, and small multiples are a powerful way to do this. Rather than comparing each series on a single, cluttered canvas, it can be re-dimensioned so each occupies its own smaller chart.
Small multiples give a detailed evaluation of the individual company’s data while still providing an easy basis for comparative analysis. This can be seen in the charts above where the subject company’s performance is plotted against its peers separately for direct comparisons.
These two approaches can be extended and combined so each mini-chart in the grid plots one subject company against the faded distribution of the others in the set.
Let me know if this is something you're planning to use. Are there other charts you think need design enhancements to be clearer? Email me at firstname.lastname@example.org.