Gilda's Club
Twin Cities

Gilda's Club Power BI dashboard header image


Gilda's Club Twin Cities is an amazing organization that offers support to individuals battling cancer. They also provide support to the friends and family affected by this drastic life change. With hundreds of programs monthly, a plethora of donation campaigns running, and a full time staff of only 5 individuals, the team is spread thin.

Gilda's Club gathers data via 7+ different sytems tracking their programs attendance, donations, staff volunteer and more. With all of this data across so many channels its difficult to identify what elements of the organization are working well and what elements are not.

The organization approaced Prime with the idea of unifying their data into a single platform that could provide meaningful insights. These insights would then be used to orient business goals and practices. Ensuring the organization is utilizing the donations they receive to the best of their ability.

Gilda's Club

Tools & Methods
Card Sort
Competitive Audit
Deep Dive
Flexible Modeling
Power BI

Current workflow

When we were approched by Ryan Sweeney and the Gilda's Club team, we asked for an overview of the processes in place for tracking data and identifying trends. Ryan outlined the laborious process it took him of downloading data and entering the information into customized excel spreadsheets. He would spend approximately 4 hours weekly working on synthesizing data to provide in a report form to his board of directors and executive team.

While this information was invaluable to the organization, allowing it to make data informed decisions, it created a ton of extra work for Ryan. This work was also not part of his job description but was passed to him as he was the most capable team member. Providing Ryan with an opportunity to synthesize data with less work on his end would allow him to get back to his actual role.


dashboard strategy outlining the necessary steps to build Gilda's Clubs business intelligence system

Between interviews with Gilda's Club team members and an analysis of the data they tracked, our team identified key insights that would aid the business in their decision making. We gathered all 7 sources of data and verified the format in which that data would be received. Once we had our key insights we built mockup dashboards representational of the information we thought would best serve our user.

Ryan Sweeney of Gilda's Club Twin Cities sorts dashboard key insights to identify the best possible format for his organization.

Building buy-in is easier than selling it. We practiced this strategy by incorporating Ryan in a collaborative design session based on card-sorting and flexible modeling methodologies. He was asked to organize key insights by importance to himself, the board, and his executive team members. After we had established an informational hierarchy, we asked Ryan to organize the data into logical groupings. Providing him with this opportunity allowed him to invest himself in the end product and feel as though his voice was truly heard. After our second round of user input we were ready to design our business intelligence system.


Power BI graph showing member retention rate for Gilda's Club Twin Cities
Power BI graph showing the number of attendants per class type

The final dashboard was a compilation of marketing & outreach, budget & financing, and a programs dashboards. These were all summarized in an overview dashboard that provide Ryan and his team with the most important insights at a high level. Portraying quick information related to the quality of programming the organization is offering and the engagement from their community allows Gilda's Club to pivot. Identifying and reimagining less successful programming will help Gilda's Club maximize their money. Creating this narrative will all the small staff to make quicker, more informed decisions on a daily basis.

Looking Forward

Utilizing Power BI our team was able to present invaluable insights into the organizations successes and opportunities for improvement. We provided an additional recommendations report for connecting dynamic databases and additional data relationships that could be made with said databases. The final product will save Ryan and his team hours every week. This time could be spent improving data connections within Power BI to further their data decision capabilities.