Consistency is helpful to those viewing data visualisations
Famous quotes such as: “A foolish consistency is the hobgoblin of little minds” by Ralph Waldo Emerson and Oscar Wilde's "Consistency is the last refuge of the unimaginative" give the concept of consistency a bad name. Like most things though, consistency has its place, and one of them is in data visualisations.
Ideally, the structural elements of a data visualisation should fade into the background to enable viewers to take away the key insights without having to spend a lot of time orienting themselves. Consistency in the use of axes, colour, and chart types can help achieve that. Let’s see how by looking at some examples from a report on the literacy and numeracy skills of New Zealand adults produced by the Ministry of Education.
The report has inconsistencies in the use of axes, colour, and chart types. Resolving those would turn an accurate but clunky data communication into a better, more polished one.
Axis consistency
The main focus of the report is on the literacy and numeracy skills of adults in New Zealand as measured through surveys conducted in 2014 and 2023. The report goes into a lot of detail about the response rate being much lower in 2023 than in 2014, resulting in the need to exercise caution when interpreting and comparing results; however we will focus on how the results are shown rather than how they were derived.
Possible scores on the numeracy and literacy tests that are the focus of the report can vary between 0 and 500 with higher numbers meaning greater literacy or numeracy. Eight different visualisations within the report show results based on those scales, yet none of them show the full 0 to 500 range. The ranges that are actually used vary. Figures 1, 4, 5, 7, 17 and 18 use 180 to 300 whereas Figure 2 uses 150 to 350 and Figure 6 uses 170 to 310. This makes it difficult for users to intuitively grasp the magnitude of differences being described without looking carefully at the axis value labels.
Image reproduced for purposes of education, criticism and commentary.
In a situation like this, it's generally better to show the full range of possible values for a metric. That eliminates the problem of inconsistency across visualisations (within and across outputs) and also results in more accurate intuitive interpretation of the magnitude of differences.
Lesson: When showing the same metric across multiple charts to the same audience (whether in a single output or multiple outputs) the range of the axes should remain constant.
Colour consistency
Another aspect of the design of the visualisations in this report that viewers would need to attend to before focussing on the survey results or their implications is the use of colours. The charts showing the test scores all use two colours – purple and orange; however what is purple and what is orange varies. In Figure 1, literacy is purple and numeracy is orange, but in Figures 2, 5, 7, 17 and 18 the year 2014 is purple and 2023 is orange. In Figures 4 and 6 males are purple and females are orange.
Many organisations have style guides that stipulate the use of particular colours, and that’s likely to have influenced this choice, but using the same colours for different things at best results in viewers needing a little bit more time to digest each chart and at worst can result in confusion or misunderstanding. There are multiple ways to avoid that, even staying within a restricted colour palette.
First, small changes to some of the charts would mean that literacy could always be purple and numeracy could always be orange. In Figure 2 making the marker in the second box (literacy 2023) purple and the marker in the third box (numeracy 2014) orange would maintain the colour convention established in Figure 1: literacy purple, numeracy orange. In Figure 4 consistency could be achieved by changing the structure so the columns show literacy type by gender by year instead of gender by year by literacy type. Alternatively different colours (besides just purple and orange) could be used to designate different things. Even fairly strict style guides typically include more than two colours, and use of different shades and patterns are options for stretching a limited colour palette further.
Image reproduced for purposes of education, criticism and commentary.
Lesson: When using colour to designate different groups, try to make colour assignments consistent.
Chart type consistency
Even though Figures 1, 2 and 4 all show literacy and numeracy scores, they do so using three different chart types. There is no reason for that, and as with the different colours at best it results in viewers needing a little bit more time to comprehend each chart and at worst it can result in confusion or misunderstanding.
In a situation like this when trying to show overall results for a particular metric and then how it varies based on different characteristics it can be helpful to use the same chart type, and gradually build it out or show versions that vary only on those different characteristics that you want to highlight. That makes it easy for viewers to understand what is being communicated and to see where the key differences are.
For example, this report could have started with the data communicated via box and whisker charts as it is shown in Figure 2, but with the colour changes and axis adjustments described previously and then used subsequent charts with the same structure but broken down by characteristics such as gender, age, educational attainment, and time in New Zealand.
Or, depending on the intended target audience, column charts showing averages could be used, such as a modified version of Figure 4, for all of the charts. Either of those options would enable the viewer to orient themselves to the structure of the chart once and from then on focus on changes resulting from showing the same data for different years and groups.
Image reproduced for purposes of education, criticism and commentary.
Since either of those chart types could work for showing the data consistently, the choice between them would come down to the intended target audience. Box and whisker charts would work well if the intended target audience is highly numerate themselves. That’s because the ability to interpret charts is part of the measurement of numeracy. Box and whisker charts contain more information than column charts in that they are a compact way of showing the median (marker in the middle) as well as the 10th (bottom whisker), 25th (bottom of the box), 75th (top of the box), and 90th (top whisker) percentiles. All of that information presented in a compact display would be appreciated by people who are highly numerate, but may confuse those who are less so. Column charts convey less information (in this example only an average), but are easier for a target audience that is less numerate to interpret.
Lesson: When telling a story about if or how a given metric varies based on characteristics such as demographics it's helpful to keep the chart type consistent so that people can focus on just how the focal metric changes depending on the characteristics that are changing.
Once all of the analysis has been completed for a big piece of work, it’s tempting to try to get the results out as soon as possible in a ‘good enough’ format, but spending just a bit of extra time on things like consistency can help ensure that all of that analytical work can be clearly understood and actioned. That work to improve the experience of the audience is not unimaginative. It’s sensible and considerate.
Tables may not be flashy, but they’re often very useful
When faced with data-intensive insights to communicate, common mistakes are to go for a cool new type of chart you’ve recently seen, to include a variety of different visualisations to ‘mix things up’, or to rely too heavily on a single type of visualisation.
It’s helpful to think of the best way to communicate insights derived from data the same way a tradesperson might think about their tools, and select the right one for the job at hand. While they may not be the flashiest tool in the data toolbox, tables are often a good visualisation option, and can be the best one.
This example from Tourism New Zealand helps illustrate why that’s the case. The table shows data related to international visitors' arrivals in New Zealand. It is part of an interactive online dashboard and includes many more countries and territories than can be seen in this screenshot (237 in total). Data for the other countries is visible if you view the data online and scroll or download it.
Screenshot from Tourism New Zealand’s International Visitor Arrivals dashboard accessed February 2026. Image reproduced for purposes of education, criticism and commentary.
This is good data communication because it aligns the visualisation type with audience needs.
Why tables work well when audience interests vary, and there are a lot of possible ways of aggregating and showing the data
If the target audience was only interested in the total number of visitor arrivals, that could easily be shown with a different chart type, such as a line chart showing arrivals over time; however it’s easy to imagine many reasons why people might want to be able to see the disaggregated data for specific countries. Something like a line chart, scatter plot, or bubble chart showing each country or territory individually would be far too cluttered and make it hard to discern precise values for specific countries.
Showing the data in this table format makes it possible for viewers to easily see the data for the countries that are of interest to them. That might vary depending on whether the user is a government policy analyst, someone working for an airline, the owner of a specific tourism-focussed business, etc.
Lesson: Consider what type of data communication will create the best user experience. When there are many possible cuts of the data and different audience members are interested in different aspects, tables often work better than charts.
Smart design choices reduce cognitive load
Good tables aren’t just about showing numbers in rows and columns. Notice the conditional formatting in this example: green shading shows year-over-year increases, with darker shades indicating larger gains. Australia’s substantial 148,116 increase appears in the darkest green. Red text flags decreases, such as the drop in visitor numbers from India and Fiji.
This formatting follows data visualisation conventions that most viewers intuitively understand: red typically signals decreases or concerning values, green indicates increases or positive values, and darker shades represent greater magnitude. These conventions reduce cognitive load - users don’t need to learn a new visual language for each dataset but can instead focus on the insights revealed from the data. Such formatting also lets users quickly spot patterns and outliers without needing to manually compare numbers across rows.
While it’s not obvious from the screenshot, clicking on the titles of any of the columns in the table enables you to sort on that metric and the dots in the upper right corner lead to more options, including filtering. Those features enable users to see the data in the way that’s most helpful for them with minimal effort. For example, a capacity analyst at an airline might want to sort the data as they are by number of arrivals. In contrast, a policy analyst documenting the effectiveness of covid recovery policies might sort on the final column or someone with a tourism business focussing on visitors from just a few countries could filter the data to show only those countries.
Lesson: Good design reduces the work viewers must do. Features like conditional formatting should help users spot patterns instantly, while sorting and filtering capabilities let users organise data for their specific questions rather than forcing them to search through irrelevant information.
When tables work - and when they don’t
For all their strengths in an interactive dashboard context like this one, tables are normally much less effective in a presentation setting. Imagine projecting this table in a conference room. The font would be too small to read if you showed all countries at once. If you enlarged the font and spread the data across multiple slides, audiences couldn’t easily find the countries of greatest interest to them unless the countries were arranged in alphabetical order. But alphabetical ordering would obscure the patterns revealed by metric-based sorting. The difference comes down to how people interact with the data. Dashboard users can sort, filter, scroll, and spend time with individual data points that matter to them. Presentation audiences are passive viewers who see whatever the presenter shows them, in whatever order, for however long the slide stays on screen. Different contexts demand different approaches.
If the data had to be communicated in a presentation, it would be better to focus on the more aggregated data and the overall trends and patterns in that, and then distribute a detailed table via something like a handout or a link. A link would make it possible to offer the types of interactivity described previously, but even in a handout the data could be shown in different orders (e.g., alphabetical, greatest to fewest arrivals, greatest year on year change, etc.) to aid usability.
Lesson: Context determines effectiveness. The same data often requires different visualisations for different viewing situations. Design for how and where your audience will actually view the data.
Choose the right tool for the job
As will be discussed in subsequent posts, there are many things that can be done to polish data visualisations and other communications, but the starting point should be choosing the right type of data visualisation for the audience, the data, and the delivery context. Tables may not be the flashiest choice, but there are many situations where they are the right tool for the job.
Reducing greenhouse gas emissions is hard. So is counting and showing them.
One popular part of the data-related courses I teach through Wellington Uni Professional involves participants critiquing various forms of data communication created by others. The idea is that it's easier to spot things that are confusing, misleading, inaccurate, or just could be better when they were made by other people because we're all too close to our own work and know what we mean even if that's not what comes across to others.
I thought I would share some publicly available examples of data communication and visualisation and comment on what works, what doesn't, and what's okay but could be improved. The idea isn't to disparage the people or organisations that have produced less than ideal data communications – we've all done that – but rather to show how hard it is to craft truly effective data communication, and to share some lessons and tips toward that end.
Let's examine this chart from Danyl McLauchlan's thoughtful column in this week's Listener to see what broader lessons we can draw about effective data communication. I want to start by pointing out that the chart has little to do with the points being made in the column itself.
The chart appears in 'Increasingly common tragedy' by Danyl McLauchlan on page 11 of the Listener, February 7, 2026. Image reproduced for purposes of education, criticism and commentary.
A quick glance at the chart would lead you to believe that the column is about what sources contribute most to New Zealand's greenhouse gas emissions; however that is only touched on briefly in the text. Mainly, the column offers a game-theoretic explanation for the collective failure of the country and the world to reduce emissions sufficiently to avoid the types of weather extremes we are already starting to experience, comments on New Zealand's lack of resilience in the face of those extremes, and discusses the benefits clean energy offers above and beyond its contribution to reducing emissions. Someone just flipping through the magazine might look at the chart and take away the message that climate is really just a problem for farmers, which is not the point of the column. This data visualisation detracts from the important, but nuanced, arguments being made rather than helping to communicate them clearly.
Lesson: Data visualisations are not decorations. They should only be used when they help enhance viewers' understanding of the topic being discussed.
Besides not being particularly relevant to the text of the article, the data shown in the chart is unlikely to be clearly understood by the audience – in this case readers of the Listener. The data comes from The Ministry for the Environment. It is based on measurement standards developed as part of international agreements. Climate professionals would be familiar with those standards, but the average reader of the Listener is unlikely to be.
That's a crucial difference because data communication created for subject matter experts should be different from data communication created for the general public. In this case, a couple important things that climate experts know, but most of the magazine reading public does not are that: 1) Greenhouse gas emissions can be measured on either a production or consumption basis, and 2) The standard way of reporting greenhouse gas emissions excludes international travel and transport.
The data in the chart shows greenhouse gas emissions generated by source in the goods and services New Zealand produces (not consumes). That makes even that first, dramatic, bar hard to interpret because New Zealand exports a lot of animal products. That first bar being the longest doesn't tell us if livestock is farmed more or less efficiently in terms of greenhouse gas emissions in New Zealand than elsewhere. We also can't tell if farming is more or less efficient in terms of greenhouse gas emissions than other industries because we have no information about the outputs associated with the emissions.
With regard to the second point, a reader unfamiliar with the fact that international travel and transport are not included in the data shown might reasonably conclude that their twice yearly international trips are not a problem given domestic aviation is at the bottom of the chart and international aviation does not appear at all. Yet if such a reader were to calculate his or her personal consumption-based emissions and include all sources, they might be surprised to discover that those international flights are likely to be their greatest personal source of greenhouse gas emissions.
Lesson: Data communications should be tailored to the intended target audience.
These two principles of using visualisations purposefully rather than decoratively, and tailoring them to the intended target audience can make the difference between data communication that clarifies and data communication that confuses. In future posts, I'll explore more examples of what works, what doesn't, and what could be better with some thoughtful improvements.