'Explore Different Ways You Can Use Data Visualization to Help Your Nonprofit' techsoup nonprofit
Reporting on various programs and projects is an integral part of gauging the success of your nonprofit's activities. This exercise is crucial not only to internal stakeholders, but also to funders, donors, and the world at large.
Let's address these questions and dive deeper into how you can craft the right recipe for your target audience, your data, and your nonprofit.Data visualizations break up the monotony of text and make your reports more insights-focused and data-driven. But it's important that you don't see the use of data visualizations as an "add-on" to your research .
For example, at the end of the year, you'll probably need to communicate all that your organization has achieved over the past 12 months. Instead of describing a lengthy list of the programs you offered, the cities in which the programs were offered, and how many people accessed those programs, you can, instead, create a map with this data.
One of the important questions to ask yourself is, "What is the story I want to convey?" Once the key takeaways are identified, selecting the best method to communicate them becomes easier. Maps can be used to highlight the specific number of programs offered in a given zip code, as well as an average number of programs per zip code. The image below shows a collection of programs our sample nonprofit offered in Southern California and clearly highlights the lack of diversity of such programs.
Such visualizations are best suited for numerical data and data collected frequently over a given time period. Below, the line chart allows us to more easily see trends in attendance to our nonprofit's events in 2019. As illustrated below, It is easy to see that there is a greater attendance to the education-centered programs in comparison to the nutrition-focused ones. However, it is challenging to see how much of a difference in attendance there is between the health and parenting programs.-axis. Bubble charts are very similar, but add a third element — the size of the dots. Instead of having numerous data points of the same value, the points are combined and represented by the size of the dot instead.
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