Tipping Point Community, a grant-making organization aimed at breaking the cycle of poverty for people in San Francisco, was having trouble showing quantifiable proof of the struggles that low-income families face in the Bay Area. The non-profit was consistently going into high-level meetings with local government officials armed only with anecdotes and news reports that low-income families were especially penalized by parking citations. Tipping Point needed data-driven proof to convince lawmakers to turn some of these outdated parking rules around.
Tipping Point needed to back-up their “hunches” that low-income families’ lives were being turned upside down by unfair parking tickets into provable facts. Tipping Point turned to TIBCO Data Science for help.
TIBCO Data Science to the rescue
The collaborative, web-based nature of the TIBCO Data Science platform meant that the Tipping Point team was able to interact with TIBCO’s team of data scientists who were really excited about getting involved. Through the concept of shared projects, shared data, and shared workflows, which are usually prohibited in more desktop-based applications, TIBCO Data Science and the data science team helped Tipping Point make sense of the miles and miles of data they had about parking citations in San Francisco.
Tipping Point says there were four important attributes about the TIBCO Data Science platform that was really critical for the project:
- Its ability to handle large datasets (millions of rows of citations to be combined with neighborhood attributes, demographics, towing data, and more)
- Its ability to apply complex machine learning and Extract, Transform, Load (ETL) procedures to those datasets that make it easy for the end user
- The collaborative nature of the application
- The platform was as nearly as easy-to-use as Excel, the team’s tool of choice
The findings from TIBCO Data Science confirmed what the non-profit was hearing anecdotally: That people who cannot pay their parking tickets right away due to lack of funds rapidly accumulate fees that can be fully half of the original fine. The result: they often lose their cars and the ability to get to work.
Quantifying these hunches and anecdotes was the first step in helping Tipping Point make the system fairer. Armed with this data, Tipping Point eventually went on to convince lawmakers to change the parking rules in San Francisco, reducing parking fees for low-income families by hundreds of thousands of dollars. You can read more about the Tipping Point story of how TIBCO Data Science turned hunches into hard cold facts.
One of the biggest lessons learned from this example is that nonprofits, or any organization wrestling with very complex problems such as poverty or human trafficking or whatever it might be, there are ways to back up your “hunches” with real and meaningful data. And, that can often make a more persuasive case when it comes to presenting findings to governing bodies. Being able to take anecdotes and then drive them back through data to meaningful conclusions and concrete justifications is a really powerful thing and is available to you now with the help of platforms such as TIBCO Data Science.