The pitfalls of data visualisation

The way we view and consume data has changed. SMEs expect data analytics to be interactive and instant in a digitally-driven world, which is why more are investing in dashboards. These tools work directly with real-time data, ensuring that trends and correlations are digestible, but there are seven pitfalls to avoid:

1. Opting for design over function

An aesthetically pleasing dashboard doesn’t always do the job. Effective visualisations incorporate design best practices to enhance data communication. Less is more. When used properly, colour helps identify good and bad trends at a glance but, using the wrong colour can lead to confusion or misinterpretation. Also, with many viewers using mobile devices, it’s important to design for, and test your visualisations on a wide range of platforms.

2. Assuming people will ‘get’ what you mean

What you want to convey through visualisation may be obvious to you, but don’t assume it is to others. Think about your audience and their ability to understand the information. What questions need to be answered by the visualisation? Filter and highlight the data to make a point, or make your visualisations interactive to empower viewers to explore. Don’t confuse your viewers. Enable them to quickly analyse data and draw their own insights.

3. Using the wrong type of chart

Stick with using a clean line, bar, pie, and scatter chart. As a guide, highlight and compare values using a bar chart. To show change over time, or how one value affects another, opt for a line graph. To show how a whole is divided up, use a pie chart – although only for up to seven categories, otherwise the big picture can be lost. To show relationships between values, use a scatter chart. If sharing multiple charts, put the most important chart first and label each chart properly.

4. Trying to be too clever

People want data displayed in a logical way, ordered by categories, alphabetically, sequentially or by value. For example, income growth year by year is usually a bar chart with years rising chronologically from left to right, and bars growing accordingly. When working with large values, it can be tempting to crop a visualisation or zoom in on a chart to emphasise small but significant differences. However, this can mislead or distort data so be obvious if you do crop or zoom.

5. Overloading your viewers with information

You don’t need to visualise all data. Visualisation is only part of the story. Be selective with your data and filter the results to highlight differences. Use more than one chart if it makes understanding easier. This is important for complex data sets as multiple charts are needed to interpret each analysis properly. Also, keep visualisations simple. The less data to interpret, the easier it is to understand.

6. Letting incorrect data trip you up

Data needs to be easy to understand but don’t get distracted by making visualisation look attractive. Address any data issues first and, if possible, work with the original data source. Use tools that enable you to identify errors before you get to the visual stage.

7. Using the wrong tools for the job

Using the wrong data visualisation tools can be a costly mistake, so think about your needs now and in the future. Look for applications that are scalable and work with your existing data sources. Find a data analytics and visualisation tool that produces more than static charts too. Use create interactive visuals with drill-down capabilities and dashboards for monitoring key performance indicators (KPIs).

Robert Dagge is Managing Director at Dynistics