3. Overcomplicating things
Busulwa says that “overstuffing” is a common mistake. As with many design concepts, less is often more.
“The new generation of data analysis and visualisation tools makes it much easier to extract more data and take on more complexity than is really needed to get the message across clearly,” says Busulwa.
While it can be tempting to use every technique available in a Power BI or Tableau visualization, making your audience work too hard to decipher the key point from a cluttered and complicated visualization reduces its effectiveness.
“If it takes you several minutes to explain how to read a visualization, it’s probably time to start over. A good visualization should speak for itself – you can hand it to a random person on the street and have them understand it with minimal context.”
Similarly, a spartan visualization that lacks context can be difficult to understand, even if the axes and units of measurement are not clearly labeled.
“Remember that as a modern accountant, you are a trusted business advisor and not just a number cruncher, so you need visualizations to help you communicate your ideas clearly.”
“If it takes you several minutes to explain how to read a visualization, then it’s probably time to go back to the drawing board,” Busulwa says.
“A good visualization 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: Overload your visualization with too much data.
- What to do: Think carefully about what you want to convey when selecting the data you’re going to present. If you’re presenting a data visualization on screen, think about how large the text should be to make it readable.
4. Selecting the wrong graphics
Not every point can be clearly illustrated with a traditional bar, line or pie chart, but looking beyond that requires understanding the strengths and weaknesses of different options.
The types of charts and tables available also vary between different tools. However, charts generally fall into four basic categories, depending on their purpose:
- Comparison (bar charts, column charts, and line charts)
- distribution (histograms and box plots)
- Composition (pie charts, stacked bar or column charts, waterfall charts, and stacked area charts)
- relationship (scatter plots and bubble charts).
The choice of a graph varies depending on the number of variables and whether the data is static or changing over time.
Stacked line charts are useful for visualizing year-over-year comparisons when there is seasonality in the item of interest, Black says.
Decomposition charts, which do not fit neatly into the four chart types above, can be effective in illustrating revenue and cost structures.
“Radar or spider diagrams are useful for comparing a small number of options against a variety of criteria, which can help with complex decision making,” Black adds.
Busulwa says scatter charts are useful for contrasting trends in two variables, such as sales versus research and development spending.
“Waterfall charts are also effective for showing the series of positive and negative changes that led from an initial result to an end result, such as quarterly changes in net cash leading to an annual result,” Busulwa explains.
How to avoid it:
- Don’t: Make a chart without considering what you want the data to communicate to your audience.
- What to do: Select your charts based on whether you want to communicate comparison, distribution, composition, relationships, or something else, and consider the strengths and weaknesses of each chart type.
5. Prioritize style over content
Relying too much on fancy effects to make a data visualization more eye-catching is the modern equivalent of “death by PowerPoint,” Black says.
“A strong visual metaphor can make a presentation more engaging (for example, visualizing a sales process to see where the company 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, making sure they help enhance understanding rather than simply get in the way.”
“A powerful visual metaphor can make a presentation more engaging (for example, visualizing a sales process to see where the company is losing potential customers), but resist the temptation to add things just to dazzle your audience.”
Often, time spent improving data visualizations would be better spent refining them and ensuring they don’t make any of the typical visualization mistakes.
“If you want to be a trusted business advisor who really delivers value,” Black says, “then you need to work on your communication skills and that includes the ability to communicate effectively through data visualizations.”
How to avoid it:
- Don’t: Rely too much on effects to dazzle the audience.
- What to do: Use visuals sparingly to enhance comprehension.
