At a glance
1. Not considering the audience and objective
From simple bar graphs to complex multi-axes radar charts, there are a myriad of data visualisation techniques and tools available to help reveal insights and communicate them to decision-makers and other stakeholders.
However, the effort put into gathering, analysing and visualising data can be wasted if the result fails to meet a business need, says Dr Stuart Black, enterprise fellow in data, analytics, disruption and innovation in the Department of Accounting at the University of Melbourne.
“Accurately visualising and presenting data is not enough – you also have to put yourself in the shoes of your audience,” Black says.
“Ask yourself, what is the point of the visualisation and what outcome is it supporting? What are the needs of the audience and the wider content?”
“Given all that, consider whether you have designed the visualisation in a way that clearly supports this, such as framing a choice for a decision maker.
“If the visualisation doesn’t put the audience in a better position to take their next action, it has failed to do its job.”
How to avoid it:
- Don’t: Forget the audience and objective when building your visualisation.
- Do: Ask yourself what the point of the visualisation is or what question it needs to answer, as well as the outcome it needs to support. Review the visualisation before presenting and decide whether it is fit for purpose.
2. Failing to be fair and impartial
Accountants and finance professionals need to be careful not to mislead their audience.
A confusing scale or cropped axis can be unintentionally deceptive, but the choice of data is also key. Selecting or framing to support a biased or incomplete point of view is highly problematic and must be avoided, says Dr Richard Busulwa, senior lecturer in accounting, economics and finance at Swinburne University of Technology.
“Data visualisation is a form of storytelling but, whether your job is to inform or persuade, you need to ensure you present the facts fairly without exaggerating or downplaying any of the relevant aspects,” he says.
“Likewise, be sure not to misrepresent correlation and causation, which can leave your audience with the wrong understanding of how events are related when they are making key decisions.”
How to avoid it:
- Don’t: Mislead or misrepresent the data to convey a biased or incomplete view.
- Do: Make an effort to be fair, clear and impartial by considering whether your data choice has been affected by cognitive biases such as confirmation bias, overconfidence bias, sampling bias and other types of bias that typically affect data.
3. Overcomplicating things
“Overstuffing” is a common mistake, Busulwa says. As with many design concepts, most of the time less is more.
“The new generation of data analysis and visualisation tools make it very easy to pull in more data and embrace more complexity than you really require to clearly convey your message,” Busulwa says.
While it can be tempting to use every technique available in a Power BI or Tableau visualisation, making the audience work too hard to decipher the key point of an overly crowded and complicated visualisation reduces its effectiveness.
"If it takes you several minutes to explain how to read a visualisation then it is probably time to go back to the drawing board. A good visualisation should speak for itself – you should be able to hand it to a random person on the street and have them understand it with minimal context."
Likewise, a spartan visualisation that lacks context can be difficult to comprehend – even by failing to label axes and units of measurement clearly.
“Remember, as a modern accountant, you are a trusted business adviser rather than simply a number cruncher, so you need visualisations that help to communicate insights clearly.”
“If it takes you several minutes to explain how to read a visualisation then it is probably time to go back to the drawing board,” Busulwa says.
“A good visualisation should speak for itself – you should be able to hand it to a random person on the street and have them understand it with minimal context.”
How to avoid it:
- Don’t: Overstuff your visualisation with too much data.
- Do: Consider carefully what you intend to convey when selecting the data to present. If presenting a data visualisation on-screen, think about how large the text needs to be legible.
4. Selecting the wrong charts
Not every point can be clearly illustrated with a traditional basic bar, line or pie chart, but looking further afield requires understanding the strengths and weaknesses of different options.
The types of charts and graphs available also varies across different tools. However, charts are often divided into four basic categories, in terms of whether they aim to show:
- comparison (bar graphs, column graphs and line charts)
- distribution (histograms and box plots)
- composition (pie charts, stacked bar or column graphs, waterfall charts and stacked area graphs)
- relationship (scatter plots and bubble charts).
The choice of a chart varies depending on the number of variables and whether the data is static or changing over time.
Stacked line charts are useful for visualising year-on-year comparisons where there is seasonality in the item of interest, Black says.
Decomposition charts, which do not fit neatly into the four types of charts above, can be effective for illustrating revenue and cost structures.
“Radar or spider diagrams are good for comparing a small number of options against a range of criteria, which can help assist with complex decision-making,” Black adds.
Busulwa says scatter plots are useful for contrasting trends in two variables, such as sales versus research and development spend.
“Waterfall charts are also effective for showing the series of positive and negative changes that led from a starting result to an end result, such as the quarterly changes in net cash leading to the annual result,” Busulwa explains.
How to avoid it:
- Don’t: Make a chart without considering what you want the data to communicate to the audience.
- Do: Select your charts based on whether you want to communicate comparison, distribution, composition, relationships or something else, and consider the strengths and shortcomings of each type of chart.
5. Prioritising style over substance
Relying too heavily on fancy effects to make a data visualisation more striking is the modern equivalent of “death by PowerPoint”, Black says.
“A strong visual metaphor can certainly make a presentation more engaging – such as visualising a sales pipeline to see where the business is losing potential customers – but resist the temptation to add things just to dazzle your audience,” he says.
“For example, use 3D effects, animations and colour scales sparingly, ensuring that they help improve comprehension rather than just get in the way.”
"A strong visual metaphor can certainly make a presentation more engaging – such as visualising a sales pipeline to see where the business is losing potential customers – but resist the temptation to add things just to dazzle your audience."
Time spent sprucing up data visualisations would often be better spent fine-tuning them to ensure they do not make any of the typical visualisation blunders.
“If you want to be a trusted business adviser who actually delivers value,” Black says, “then you need to work on your communication skills and that includes the ability to communicate effectively via data visualisations.”
How to avoid it:
- Don’t: Rely too heavily on effects to dazzle the audience.
- Do: Use visual effects sparingly to improve comprehension.
