Data visualisation is one of the most important weapons in the arsenal of financial planning and analysis teams. With so many data visualisation tools out there, however, it’s also one of the most badly used weapons. Analysts, with their dodgy Excel skills and default pie charts, are out here bringing knives to gunfights all around.
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Why is data visualisation so important?

We all know people who claim to be visual thinkers, right? Well, according to Wikipedia, 60-65% of the general population describe themselves as such, i.e., those who think through visual processing.

In fact, is it really that hard to believe it, given that before numbers and language, all we had was images? Children start drawing pretty pictures of the same house, tree, and sun landscape well before writing exceptionally bad essays.

Before human civilisation, documented language, and indoor plumbing, we had cave paintings communicating epic hunts and ayahuasca trips. Put simply, it’s easier for our eyes and brains to assess trends, find outliers and understand complex ideas when communicated visually, as opposed to rolling sheets of numbers.
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Not all charts are created equal

Because of tools like Excel and Google Sheets, we can all visualise data very easily. But like the old proverb, a little bit of knowledge is a dangerous thing.

Let’s be real, some charts are appalling. If you disagree, check out this amazing reddit thread for the data professional equivalent of driving past a car crash. I just can’t stop looking, okay. I’m sorry.

The most important nugget of advice I can give regarding honing your data visualisation technique is to choose the right chart. Know what you’re communicating and how best to communicate it. Starting there, it will allow you to fully harness the power of data visualisation.

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Here's a snippet from our infographic that tells you which chart is best for which use case:

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Download the full infographic here

Keep it simple-ish, stupid!

Every visualisation has the power to tell a story. But stories can be short, overly simplistic, and pointless, or convoluted, complicated messes that leave us feeling exhausted. Similarly, the line between a visualisation that is too simple that can mislead, and one that is overly complicated and confusing is a thin one.

So, how do you find the right balance?

• Understand the reader
  Who’s consuming the visualisation and what should they do with that information?

• Cut the fat
  Remove unnecessary clutter.

• Direct the reader
  Use colours, text and annotations to guide the reader’s attention. Remember that not every bit of space needs to be filled with colour, text of chart elements. White space works excellently to emphasise.‍

‍Sidenote: If you’re looking for some more tips, I’ve found Storytelling with Data to be a great resource.

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Data literacy some such

It can be said. But can it be understood? This where the real challenge lies: understanding and effectively communicating it. Here are some key points to consider on this:

FP&A teams often interact with other teams that may have lower data literacy. To bridge this gap, it's important for FP&A teams to communicate their complex ideas in a simple and understandable manner. Consistency in data communication and standardisation helps. Consider the following aspects:

• Colours: Standardise the use of colours to convey consistent meaning across different visualisations and reports.
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• Text standards: Establish guidelines for font sizes, styles, and formatting to ensure consistency and ease of reading.
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• Accuracy: Ensuring accuracy is paramount to foster trust with others.
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When in doubt, just remember these data visualisation best practices:

• Define goals based on your audience and what they will be doing with the data
• Choose the right visualisation
• Simplify without over-simplifying
• Use consistent colours
• Be clear
• Define annotation standards

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