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What we've learned about data visualisation best practice

Reports with hundreds of rows and columns can contain really useful data, however, being able to understand them can be extremely difficult for the brain to comprehend. Data can become useless if people can't understand and work with it in a useful way.

Using data visualisation removes these barriers and turns numbers into visuals that allow people to grasp and use data so much easier.

As performance analysts, we dig deep into data to find trends and patterns in user behaviour, and we have a range of audiences who are interested in the insights we gain. Those we work with have differing skills and expertise so presenting data in a clear and organised way is crucial to supporting other disciplines.

Tableau, a leading data visualisation tool, defines data visualisation as:

The graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualisation provides an accessible way to see and understand trends, outliers, and patterns in data.

User friendly ways to present data

The performance analyst team discussed how we each approach data visualisation and came up with four themes that we use to present data in user friendly ways.

1 - Target a specific audience

Things to think about are:

  • Who will be looking at the data?
  • What actions do I want people to take with the insights?
  • What challenges do they face? For example, is the information accessible? Is it in the right format? Is the contrast between colours strong enough? Is the visualisation too small to read?

It should be compatible with the audience’s expertise and allow viewers to view and process data easily and quickly. Try to resist temptation to create something for everyone who may look at it.

2. Choose the right visual

What type of data visualisation works best for the data and audience? Different graphs have pros and cons depending on the situation and choosing the right one requires a bit of thought. For example, line graphs are excellent for comparing values over time:

An example of a line graph showing the number of users versus new users
An example of a line graph showing the number of users versus new users

and a scatter diagram is great for exploring the relationships between two variables for a set of data:

An example of a scatter diagram showing how people come to Parliament's website
An example of a scatter diagram showing the number of users who come to Parliament's website and the number of pages they visit

3. Provide context

An example of comparing metrics An example of comparing metrics showing sessions and users compared to the previous year
An example of comparing metrics showing sessions and users compared to the previous year

We want to empower people to act so they need to understand how the performance compares to something such as a goal or benchmark from a previous period. Presenting metrics for easy comparison in your data visualisation will help the audience easily interpret the data they are seeing.

4. Keep things simple and digestible

The overall aim is to help the audience:

  • quickly understand high-level information
  • use data visualisations to answer questions and make better decisions

A ‘good’ visualisation

We’ve created numerous data visualisation dashboards which display how Parliament’s websites are used.

There are multiple ways to show how users interact with our digital interfaces but, using the above principles can help identify good visualisations. A simple visualisation often proves to be the most effective as it’s easier to understand and is therefore more valuable to Parliament.

Using funnel reports to show a user journey

When Parliament starts to investigate problems in a step-by-step process, a funnel report simplifies this into a simple, linear procedure. It explicitly describes which stage in the procedure loses the greatest proportion of users or “drop off between bars”.

An example of a funnel report showing the stages of online petitions
An example of a funnel report showing the user journey of online petitions

This funnel diagram shows the steps to submitting a petition online. It shows the total users to online petitions in the first graph, the majority filling in their details then a steep loss in users successfully submitting their information, and the final number of completions.

Chart showing when users visit Parliament website
Chart showing the most popular times when people visit Parliament's website (between 8am and 6pm)

This chart shows when the most popular day/time to visit the homepage and the visualisation helps the users understand what's being shown. It also provides the whole course of a day from midnight to midnight and is simple, so users can quickly interpret the information.

‘Bad’ visualisation

A bad visualisation of a pie chart as the colours in the key change
A bad visualisation of a pie chart as the key colours change

Bad visualisations can be confusing and misleading. This pie chart example is trying to compare traffic acquisition proportions, but with the colour key changing between the diagrams, it’s confusing to understand what’s being compared.

A bad visualisation of a line graph showing how the parliamentary calendar can be difficult to compare year on year
A bad visualisation of a line graph showing how the parliamentary calendar can be difficult to compare year on year

In the line graph, comparing one year to another without context or understanding of how the calendar shifts in Parliament, can be misleading.

Final thoughts

Data visualisation plays a very important role for us as performance analysts. It helps us to understand the information quickly, spot patterns and relationships, and communicate the story to others.

Following best practice helps us work with colleagues of various disciplines to help them make sense of the data, create fascinating stories, and use the insights to help with decision making and creating strategies.

If you work in Parliament and want some advice on data visualisation, get in touch with the performance analysts. 

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