Case Study: Database Development for a Human Services Agency
The client was stuck with a tool that had outlasted its usefulness. Bayer Center expertise gave them a database that could grow with them.
The organization, a human-services provider with a staff of around a dozen full-time equivalents, provides a variety of services to people with mental illness including a “warmline,” a telephone peer support service. People with past or current mental health issues listen, offer support and direct callers to helpful resources. The warmline operates a call center of a few cubicles with up to a half dozen operators answering at any given time.
In their startup phase, they realized that they would need to record information about the calls electronically in order to report monthly to their funder, and they started with an Excel spreadsheet. They knew how to use it, and it was flexible to emerging needs of the program – they could easily add columns as they identified new information they needed to track either internally or for reporting purposes.
Before too long, however, the organization realized that they had maxed out the tool. Although it was initially easy to show operators a row of data to be entered, they discovered that the spreadsheet made entering the narrative summaries of the calls difficult. In addition, the reporting process in general was laborious. Pulling the report was averaging 10 person-hours per month, more than a day’s work to issue a standard report to the funder monthly.
The organization recognized its limited knowledge and contracted with the Bayer Center for Nonprofit Management to develop a tool that could speed up reporting and grow as they grew.
After discussion, the consultant and the program staff determined that the solution was to expand the tool from a spreadsheet to a tidy relational database. The operation was driven by some key building blocks that relate to one another: calls, operators, referral sources, referral targets, and some demographic and geographic attributes. The presence of these relationships made scalability and ease of reporting difficult with a spreadsheet tracking tool. Transforming to a relational database was relatively easy given that the organization had a standard call record format and a standard output – the big report – to produce every month.
The new database had features that paid off immediately, including a built-in quality control function (reducing the amount of time needed to clean up errors before generating reports) and scalability (adding a new operator became a simple, quick step).
The project enabled the agency to grow in the number of people they served. In the course of the year from the previous April to the April after the intervention, the warmline’s call volume grew by almost 1000 calls per month. They also saved at least 9 hours of staff time every month, which translates into cost savings. Despite the added investment needed to move from the spreadsheet to the database, this project saved the agency money in its first year.
What’s more, making this leap did not take excessive amounts of time. They did not have to wait a year to get to a better place. The entire intervention took four months from initial meeting to independent operation with quarterly support.