There are many reasons you might want to do an ad-hoc data exploration project — maybe you need to make the case for expanding your organization’s field program or perhaps you are curious about how groups of people you work with are represented within the Census dataset.
Regardless of what you are hoping to explore, sourcing, storing, and exploring data doesn’t have to be overly complicated or expensive. We’re hoping that this guide can serve as a resource to folks working in movement spaces (whether or not they are data practitioners) that are interested in using data to better understand and solve problems.
As an example, we did a project looking at demographic trends sourced from the Census dataset in the Detroit area. We engineered the source dataset and stored it in Google’s BigQuery data warehouse, and then performed analyses in both Google Data Studio and Tableau. We’ll use this project as a guide for how to accomplish these tasks in the most efficient way possible.
Thank you to our Fellowship Mentors, Charles Douglas and Michael Ingram. Thank you to Oluwakemi Oso, Toria Boldware King, and the Re:Power team for providing us with resources, data learning labs, and an inspiring peer cohort.