Seven tips for how public servants can create better data visualisations
Source: apolitical Published: June 23, 2020
The world we live in today is made up of billions of small data points. We measure everything from air pollution to biodiversity and the average height, weight and happiness of humans. To make sense of all this, data visualisations have emerged as a powerful tool, but also one that is often overlooked in the public sector.
Data visualisation graphics, dashboards, and interactives have become important tools for government and health officials in communicating important information about the spread and impact of the Covid-19 public health crisis. Effective data visualisation tools can quickly summarize important information for many different audiences and has recently been used to deliver accurate reports on the spread of the coronavirus.
It’s important to understand your data — and the story you want to tell with the data you have
But simply having data is not enough. Creating effective data visualisations requires many different types of skills including storytelling, data analysis, and design. This article outlines seven tips to build effective data visuals that will give your constituents clear and accurate information.
Data visualisation is a form of storytelling
Before beginning to design a data visualisation, it’s important to understand your data and the story you want to tell with the data you have. Some of the best data visualisations create clear, simple stories that can quickly engage a user’s interest. Similar to a journalist, a professional designing a data visualisation graphic must ask several basic questions. Who is your audience? How can you tell the story from their perspective? What are the most important points you want your users to walk away with? How can data be used to get your point across? Why would they be interested in the data? How adept are your users in understanding your data and analysis?
Transparency is critical
All data visualisations start with a raw data source, which needs to be cleaned and analyzed to tell the story that you want to convey. There are a lot of steps involved in this process, from first acquiring your data to providing a story that is accurate and insightful for your user. I won’t go in detail with all those steps here other than to say that while you are preparing the data, it’s essential to note the steps and methods you used to filter and interpret the data.