Prospect Matching Program Dashboard
>Fall 2022 - Fall 2023
>The New Philanthropists
>Team: Self
>Tools: Tableau, Tableau Prep, Python
The success of board prospects using The New Philanthropists’ Board Matching Program varied greatly between different demographics.
The New Philanthropists (TNP) is an organization that focuses on increasing diversity, equity, and inclusion amongst non-profit board members, as well as providing DEI training for current and prospective board members. Specifically, TNP is hoping to have a greater understanding of the success of their board matching program, which matches diverse board prospects to non-profit boards seeking new members. They want to understand if there are any gaps in their current leadership pipeline and determine potential growth opportunities.
>Fall 2022 - Fall 2023
>The New Philanthropists
>Team: Self
>Tools: Tableau, Tableau Prep, Python
The success of board prospects using The New Philanthropists’ Board Matching Program varied greatly between different demographics.
The New Philanthropists (TNP) is an organization that focuses on increasing diversity, equity, and inclusion amongst non-profit board members, as well as providing DEI training for current and prospective board members. Specifically, TNP is hoping to have a greater understanding of the success of their board matching program, which matches diverse board prospects to non-profit boards seeking new members. They want to understand if there are any gaps in their current leadership pipeline and determine potential growth opportunities.
Full project documentation can be found here.
The New Philanthropists’ team can use the dashboard to find
connections between which board prospects are getting matched and what
demographics may need additional outreach and trainings/workshops/etc. to be
successful in the board matching program.
For the dashboard, TNP wanted to see the total number of unique and non-unique matches for board prospects, as well as their demographics and professional background. As of now, TNP is focusing on racial diversity, with an intent to expand into class, gender, and LGBTQ+ diversity. To support this, the dashboard currently focuses on filtering based on race and ethnic groups. Multiple dashboards are used, one which showing more information regarding the prospect’s different demographics, give/get ranges, income etc., another that displays professional experience, and a final which shows prospect demographics and matches over time. This required a level of interactivity on each dashboard, where prospects can be filtered down by their race/ethnic group and whether they were matched. Since the dashboard will have to house a lot of varied information, there is a level of interactivity that can prevent information overload on behalf of the user.
For the dashboard, TNP wanted to see the total number of unique and non-unique matches for board prospects, as well as their demographics and professional background. As of now, TNP is focusing on racial diversity, with an intent to expand into class, gender, and LGBTQ+ diversity. To support this, the dashboard currently focuses on filtering based on race and ethnic groups. Multiple dashboards are used, one which showing more information regarding the prospect’s different demographics, give/get ranges, income etc., another that displays professional experience, and a final which shows prospect demographics and matches over time. This required a level of interactivity on each dashboard, where prospects can be filtered down by their race/ethnic group and whether they were matched. Since the dashboard will have to house a lot of varied information, there is a level of interactivity that can prevent information overload on behalf of the user.
I chose to use BANs, a graph, and a select tool as filtering
options. TNP mentioned that they needed both an understanding of demographics,
as well as professional experiences for their candidates. They wanted these
metrics to be able to be broken down by race, as well as if the prospect was
successfully matched. To do so, I split the data into two dashboards, one which
focused on demographic information and the other on professional experience. I
utilized their race/ethnicity information as a filter by allowing the viewer to
click on the corresponding bar chart to filter the information. Additionally,
the viewer could filter based off successful matches, so they could see which
prospects within a certain racial demographic were matched to a board. These
filters could then be used in tandem so the viewer could see details for Latinx
prospects who were successfully matched, Asian prospects overall, etc.
Additionally, understanding how the data changed over time was equally important, as it could indicate if certain outreach programs or initiatives were successful in generating more applicants. Therefore, it became important to keep a running total of all board prospects and successful matches, while also including graphs that conveyed if prospects from similar personal and professional backgrounds applied to the program around the same time periods.
Lastly, there was a need to understand the proportion of data that was occupied by a certain racial group or successful matches. Therefore, I split a lot of the data into columns and tried to keep the BANs about matches and total prospects on the top portion of the dashboards. There was a lot of detailed information to go through, so many of the graphs needed to be split into smaller subsections to avoid too much information overload.
Additionally, understanding how the data changed over time was equally important, as it could indicate if certain outreach programs or initiatives were successful in generating more applicants. Therefore, it became important to keep a running total of all board prospects and successful matches, while also including graphs that conveyed if prospects from similar personal and professional backgrounds applied to the program around the same time periods.
Lastly, there was a need to understand the proportion of data that was occupied by a certain racial group or successful matches. Therefore, I split a lot of the data into columns and tried to keep the BANs about matches and total prospects on the top portion of the dashboards. There was a lot of detailed information to go through, so many of the graphs needed to be split into smaller subsections to avoid too much information overload.