In the previous section on pre-process data, data visualization was used to explore data. This section focuses on visualizing data to interpret results based on analysis questions and to show insights to key internal stakeholders. As policymakers, you are not expected to visualize data yourself. Instead, this section provides you with guidance on how to best work with data analysts to produce compelling and insightful visuals.
If you are new to data visualization, we invite you to take a few moments to get inspired by what other governments and organizations have done. You may want to reflect on what you like about these visualizations and what you would do differently.
By visualizing data, you can gain a deeper understanding of the data. However, there is also a risk of over-investing in visualization without benefiting from significant additional insights. Before you start asking your team to visualize data, it will often be helpful to discuss with them:
As with many projects, you may want to consider to “start small” and work iteratively. Meaning, first create 3-5 graphs and then discuss if/what additional visualizations may be helpful.
In case you do not yet have an expert on your team, who could support with the data visualization, you may wonder: what profile and skills should I look for?
Usually, the following profiles come with a certain level of data visualization skills: data scientist, data analyst, business intelligence analyst and data visualization engineers/specialists. In terms of skills, check out the section on data capabilities.
To be able to conduct the analysis, the technical expert requires access to a visualization software as well as to sufficient computing power to process the data. In many cases, common software such as Microsoft Excel already fulfils the needs of the technical expert. Other software that is commonly used and more specifically tailored to the purpose of visualizing data is: Microsoft Power BI, Tableau and Google Analytics.
Tip: If you are looking for short-term support with data visualization, an alternative path to hiring an expert is to work with volunteers. One such volunteer group is Viz for Social Good. In addition, local universities may offer student projects for data visualization work.
Once the data expert is on board and there is clarification on the analysis question and thus the purpose of the visualization, the data analyst will need to identify the appropriate visualization type. While this is largely the responsibility of the expert, it may be helpful to understand the different, most common options.
To further effectively guide the data expert creating the visualizations, consider the following good practices:
Tip: Consider doing a very simple user-testing by asking a colleague outside of the team if they understand the visualization.
Example of a less-ideal dashboard
x Unclear flow of information (i.e., the reader doesn’t know where to start)
x A lot of information of limited relevance to most readers
x Many different colors, some without clear purpose
x Many different visualization types and images
x No legends
Example of a stronger dashboard
v Clear layout and structure with good use of white spaces
v Easy-to-understand visualizations and icons without adding too much information
v Consistent and simple color scheme and font style
v Easily readable titles, descriptions and legends for each visualization
Now that you have a good understanding of your data, let’s turn that data into policy.