Making Better Decisions with Data

The Client

On The Rise is a homeless shelter and community for women, dedicated to their mission of providing the relationships, safety, and resources needed to get out and stay out of homelessness. Their primary offerings are day shelter services, housing, health services, legal services, financial advice, etc., while focusing on relationships as the foundation for staff advocacy within the program.

The Problem

On The Rise has a vast amount of collected data about its programs and clients, but its staff was overwhelmed identifying what information needed to be the focal point of their funder and partner reports. There were a few ideas for performance metrics they’d like to see, but no clear path for how to accurately identify or report them. On The Rise needed to develop an analytical tool for data aggregation and reporting to better explore some specific aspects of the organization’s performance. These aspects include relationships between program participants’ engagement and other attributes (risk factors, tenure, housing status), how the organization is functioning as a whole, and gathering information about descriptive and diagnostic analytics reporting. Building a tool to easily identify and draw conclusions for these outcomes would help them achieve their main goal of identifying data to better communicate program effectiveness with funders and human service provider partners.

The Approach

Our PBA volunteers got together and started the process of sifting through On The Rise’s data to identify what would be the most helpful for achieving their outcomes. They determined that they would compare participant demographic data, risk factors, and contact data (how the participant interacted with the organization) to identify a quantitative data analysis with potential impact on the overall program design. Since this was one of the initial attempts at identifying choice information for performance reporting, our volunteers built a few analytics models as tools for a more accurate graphic depiction of the data. The models were set up and populated with engagement data measuring consistency (number of visits per month) and involvement (time spent per month) from participants.

The Solution

Identifying this preferred engagement and building the preliminary models laid the groundwork for On The Rise to further investigate phases of analysis for their policymakers and funders. Our volunteers helped them realize the potential for using analytics to help identify program performances and make data driven decisions. They are inspired to continue collecting and identifying key data on program participants to complete a well-rounded, quantitative analysis of their organization and more accurately tell the story of their successes.