Data visualisation

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Finance teams and accounting practices are increasingly expected to be true business partners by providing metrics, KPIs, forecasts and other critical information to support decision-making and to provide value.

Fortunately, they now have numerous sources of data they can mine and combine and a plethora of innovative tools with which to do it.

Which is all good and well, but you need to be able to communicate the data, often to people with varying levels of financial understanding and not in the often all too technical language of finance.

This is where visualisation comes into play.

Data visualisation helps finance professionals communicate analytic insights to a wider audience. According to studies, 65% of people are visual learners, so why not communicate with them in the most effective way? By providing stakeholders – organisational decision makers or clients – with visual illustrations of data grows understanding that may lead to better decisions, which is what it’s all about.

But first things first: know your audience

‘The most important thing is to understand your audience,’ said Alastair Barlow FCCA, founder of accounting practice flinder and a big fan of all things data and digital. ‘As finance professionals, we sometimes forget what is second-nature to us can be alien to others. Where we can see trends or relationships in numbers, more entrepreneurial people will struggle and need to feel the data in another way.

‘They say a picture is worth a thousand words and when it comes to getting a point across to non-finance people, that really is the case. However, some non-finance people understand data just as well or sometimes better than finance professionals, so it’s about pitching it correctly.’

What does the audience need?

Find out what the various users’ and stakeholders’ need. Consider how interactive the visualisation should be, whether it needs to be mobile device compatible, the level of detail required and then design and interface requirements.The more aesthetically pleasing the visualisation, the easier it can be to follow, as will keeping it relatively uncomplicated and lean. Just because you have access to vast amounts of data doesn’t mean they all need to be relayed to the audience; focus on the most interesting points to help tell the story.

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What is the desired outcome?

This is probably the most important element and can be forgotten amid all the data, technology and pretty visuals. Don’t lose sight of the purpose of doing a visualisation in the first place. Is it to enhance decision making, promote better discussions or educate the end user? Visualisations can be very advanced and interactive, but don’t be drawn into overcomplicating… focus on what the audience requires and answering the question: ‘So what?’

How to do it

First, create a basic design that you then modify until you land on a final version. During this process you will likely work closely with stakeholders and end users as you test and refine the visualisation. Set a foundation for defining the visualisation by breaking the audience up into categories or personas. Different groups of users may want different levels of interaction, analysis and data sets in the visualisation.

Finally, while the visualisation can be handed over to the users once ready, with training if complex, this is also when many stakeholders will want finance to ‘tell the story’ of the visualisation and highlight any key findings that may have surfaced during the creation process.

Technology – a quick look

Some tools produce great looking visualisations with little training needed, such as Tableau and Qlik. Cloud accounting platforms, such as Xero, have an ecosystem of visualisation apps that can plug in. There are then broader analytics, business intelligence and reporting platforms that incorporate visualisation capabilities and can attack more complex data and requirements. These are from companies such as IBM, Oracle, MicroStrategy, Microsoft and SAP, among others.

Alastair Barlow’s top data visualisation tips

  • Understand your audience
  • Make it simple: don’t clutter the data points – make your data and specific points stand out easily
  • Make it aesthetically pleasing: make it easy to follow, otherwise users will be talking about how it looks rather than what it means
  • Tell a story: use the data to tell a story and pull out salient points of interest – it’s just another stage in the process, it’s not the end of the journey
  • Include a ‘so what’: arguably the most important stage – what is the action that needs to come out of this? Do we do more of the same or do we make an intervention? What is that intervention?

Neil Johnson, journalist

This article was first published in Student Accountant in August 2020

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