Using data is a powerful communication method. When interpreted and communicated correctly, data can quickly send a compelling message to your audience.
Decision-making audiences – including ministers, delegates and boards – are often time-poor, may not be familiar with the finer details and complexities, and depend on simple, concise information to guide their decisions.
Using data effectively enables you to write concise yet compelling documents, such as policy and ministerial briefs, executive summaries and business cases. These documents can then quickly communicate the details and complexities that decision makers need to understand.
Note on terminology: data is the plural of datum, and is traditionally treated as a plural – ‘the data are..’ not ‘the data is..’.
However, many people now use the term as a singular noun; check your organisation’s style guide to see which you should be using in your work.
Choose data that support your goal and key messages
Using data in your communication provides the evidence to back up your messages. For your audience to trust your statements, the data must:
- serve a purpose – random facts can clutter your text and confuse your audience
- be interpreted correctly – data literacy is critical to ensure that you have drawn the correct conclusions from the data available.
Knowing which data to communicate can be difficult, especially when there is too much or too little available. Choose data that directly supports the goal of your communication.
For example, if you are writing a policy brief to support more electric vehicle charging stations, data on the current and estimated future number of electric vehicles will better support your request than data on the benefits of more electric vehicles – even if these data are available.
Data literacy means understanding how to evaluate, choose, interpret and communicate data without distortion or misrepresentation. Working with data can require specialised knowledge – your own or that of experts.
Data literacy is important to achieve your goals. Reliable, relevant and timely data presented accurately and directly make your communication objective and influential.
Use visuals and text appropriately
For your audience to trust your statements, the data you include must be communicated accurately and clearly – data visualisation must not distort or misrepresent the message, and should be easily understood.
Some data works well as visuals, others work well as text. Visuals can be eye-catching and support a key point but they can also draw attention away from important text.
When deciding whether to communicate data as text or visuals, consider the following:
- The message – is a visual the best way to communicate your message? For example, the short statement, ‘90% of participants strongly agreed’ sends a stronger and more concise message than a graph showing the breakdown of participant responses.
- The type of data – which format best suits the data type? Are the data more suited to a graph, table, infographic or text?
- The format – is your audience viewing the communication digitally or printed, on a phone or a larger screen?
- Accessibility – will the visuals have appropriate contrast and be clearly summarised in alt text?
Data visuals that work
Present the data in a simple format. People without special training in data and statistics should be able to easily interpret the visuals.
If you need to explain the visual in the text, it’s too complicated. Refine the design or communicate that data with text instead.
Tables are best for showing specific data values when the relationships or trends are not important, such as the total number of known COVID-19 cases in each state and territory at a particular time.
Graphs are best for showing relationships and trends in data. There are many types of graphs, which use different methods to visually encode data and present quantitative relationships.
Choosing the correct type of graph for your data is critical, to ensure that these relationships are clear.
More often than not, the simplest graphing option will be the best:
- Line graphs are a good way to show a trend over time.
Specific values can be difficult to interpret from line graphs, so they are best used to support messages about trends – for example, a line graph works well to show trends in the number of COVID-19 cases per month over time. - Bar and column graphs help the audience to quickly compare data.
Like line graphs, specific values can be difficult to interpret from these, so they are best for showing high-level comparisons – for example, a column graph could show the number of COVID-19 cases per capita in each region, to highlight where the largest outbreaks are. - Pie graphs are popular however, due to their shape, the sizes of segments are difficult for readers to judge and compare.
The choice of colour can also influence how well readers can compare the sizes of sections – sometimes a smaller section will appear larger because it is in a strong colour.
Eliminate clutter that can confuse your users. Leave out visual information that isn’t needed to support your message, including:
- other types of data in the same dataset
- background colours, gridlines (unless needed) and decorative features
- visual treatments such as patterns, heavy lines, shadow and 3D effects.
Language of evidence
Make sure the language you use to describe the data is as plain as possible, and describes the data accurately without using emotive or biased language that might prejudice the reader.
Compare the paired sentences below, and see which is accurate and which is trying to influence the reader:
- ‘Injuries from falls in the workplace have halved’ vs ‘Injuries from falls have gone from 2 last year to 1 this year’.
- ‘The benefits of eating vegemite sandwiches to reduce stress levels have not been studied’ vs ‘The effects of eating vegemite sandwiches on stress levels have not been studied’.
- ‘As many as 2 in 7 people say they don’t trust statistics’ vs ‘As few as 2 in 7 people say they don’t trust statistics’.
Also be wary of implying causation when the data only shows correlation. Be clear about what the data show and don’t go beyond that.
Remember..
- Inaccurate or irrelevant data or graphs will damage rather than support your case.
- Visuals provide the most value when used sparingly to support key points.
- Explain the data clearly and simply without emotion or bias – just the facts, please!
Learn more
- Find more guidance on communicating data visually in Biotext’s Quick guide to effective data visualisation and infographics.
- Keep an eye on Biotext training courses – we run short online workshops on data literacy and data visualisation, and writing and editing complex content. Our Fundamentals of data literacy and visualisation online course is coming up on 23–24 May – sign up now!
- Subscribe to the Australian manual of style to access detailed guidance on presenting data, and communicating complex and technical information.
