Improve research communication with data visualisation

ONS Research Capability Conference

Dr Nicola Rennie

December 4, 2025

About me

Data visualisation specialist at the Office for National Statistics.

Background in academia and data science consultancy, specifically in statistics, operational research, and health.

Co-author of Royal Statistical Society’s Best Practices for Data Visualisation guidance.

Why visualise data?

Who is your audience?

What can you assume about your audience?

What if you can’t assume anything?

9 - 11 years old

Average reading age of adults in Scotland.

45%

Percentage of adults who have below primary school level numeric skills.

3,000,000

Number of people in the UK with some form of colour vision deficiency.

10%

Percentage of UK adults with a mobility or motor impairment.

Data visualisation for everyone

“Ensure that data and statistics are easy to use and understandable.”

“Ensure easy access for all when publishing data, statistics and supporting material.”

Why visualise data?

John Snow collected data on cholera deaths and created a visualisation where the number of deaths was represented by the height of a bar at the corresponding address in London.

This visualisation showed that the deaths clustered around Broad Street, which helped communicate the cause of the cholera transmission, the Broad Street water pump.

Snow. 1854.

Your turn!

Think about a project you’ve worked on recently.
Write down who your users are.
What do they know?

03:00

What are you really trying to communicate?

Detailed, accurate numbers?

Or the big picture message?

Line chart showing increase in temperature over time

Warming stripes chart showing increase in temperature over time

Venn diagram of effective charts and memorable charts

What are you trying to communicate?

Data visualisations must serve a purpose.

Ask yourself:

What is the purpose?
Does the visualisation support the purpose?
Is it quick, accurate, and intuitive?

Common relationships

Correlation: The relationship between two variables.
Deviation: The difference between a value and an average or another value.
Distribution: How data values are spread for a variable.
Geography: The pattern of data across different locations or areas.
Magnitude: The size of values.
Parts of a whole: The relative sizes of components within a whole.
Ranking: The position of data within a hierarchy or scale.
Time: How a value changes over time.

Why do 3D charts have a bad reputation?

On the plot on the left, how tall is the bar?

It’s not just about the type of data…

Your turn!

Here’s some data on disposable household income. What message do you want to tell? What type of chart would you use? Draw some ideas using pen and paper.

Gross disposable household income by UK and constituent countries and regions, UK, 2023

Country	Population (million)	GDHI per head (£)	Total GDHI (£ million)	Total GDHI growth from 2022 to 2023 (percentage)	Share of UK total GDHI (percentage)
United Kingdom	68.3	24836	1695436	9.5	100.0
England	57.7	25425	1466758	9.6	86.5
Wales	3.2	20140	63730	7.9	3.8
Scotland	5.5	22908	125768	9.3	7.4
Northern Ireland	1.9	20403	39181	8.5	2.3

10:00

Data visualisation best practices

Sort your categories…

…appropriately!

Colours

Colours should serve a purpose, e.g. discerning groups of data
Colours can highlight or emphasise parts of your data.
Not always the most effective for, e.g. communicating differences between variables.
Match the palette type to the data type

Examples of sequential, diverging, and qualitative palettes

Is this a good choice of colours?

Don’t rely on colour

Are intuitive colours always best?

Example: red and blue used to show hot and cold

Tip: never switch to the opposite meaning!

Are intuitive colours always best?

Example: pink and blue used to show women and men

Tip: think about colour associations.

A picture is worth a thousand words.

But that doesn’t mean you can’t also use words.

Use narrative titles to summarise

Describe the main trend you want the chart to show to the user.
Users are more likely to understand and remember the main trend from a chart where it is also in the chart title.
If the trend described in your chart title is not the most prominent visual trend in the chart, consider what and how you are visualising.
Add annotations to give context.

Number of households in temporary accomodation over time