Three “Data for Good” Lessons from DataKind San Francisco’s Volunteer Day with Lava Mae
Every major urban city has the one social challenge that overshadows all others. For San Francisco, that challenge is chronic homelessness. Juxtaposed against the promise and potential of Silicon Valley abundance, the reality of how pervasive the lack of consistent housing is for residents young and old can feel overwhelming. Amongst the many public and private actors working to alleviate and ideally end homelessness in the Bay Area sits Lava Mae, a nonprofit that provides mobile shower units for underhoused guests at seven San Francisco and five Los Angeles locations five days a week.
Whether scalding hot or courageously chilly, for many of us, a shower is an oft-rushed and mundane aspect of our daily routine. Yet a shower is a luxury that a high number of the estimated 7,500 people in San Francisco without stable housing likely also lack access to.
“Radical hospitality” – the act of rekindling dignity and hope to those experiencing homelessness – lies at the heart of Lava Mae’s hygiene service work.
Earlier this month, DataKind San Francisco members volunteered with a mobile shower unit in the Mission district of San Francisco, where we walked away with a few lessons from Lava Mae’s dignity-based approach:
Remember that people lie at the heart of the Data for Good movement.
The most rewarding aspect of our time volunteering with Lava Mae was hearing the many stories the guests kindly shared with us, from hilarious monologues about Jimi Hendrix and his greatest hits to heartbreaking recollections of brutality and racial profiling at the hands of police. Some shared stories of how they came to be romantic partners and detailed, pride-filled rundowns of their grandchildren, while siblings shared how injuries had put them out of work and exacerbated their inability to settle into housing.
It can be incredibly easy to lose oneself in the throes of an algorithm and forget that a person – another human being with worries and joys similar to our own – lies at the other side of a data point.
Think beyond “sexy” data science – help solve practical problems that have a daily, immediate impact.
When we think of using data science, huge social challenges typically come to mind – ending poverty, solving hunger, or providing education for all. However, even the broadest of social issues can be tackled from a number of practical approaches that provide tangible, immediate benefits for a person’s quality of life. “Where can I take a shower?” is a question most of us are lucky enough to never have to ask, yet the question lies at the top of minds for far too many. While data science is powerful in its boundless ability to solve complex challenges, it can and should also be used to address practical questions like these that have the power to immediately improve someone’s life.
Dignity, and other qualitative metrics of impact, belong in the data for good conversation.
Beyond hopefully serving as a catalyst to help those who are underhoused have an opportunity at gaining employment, stable housing and other supports, the feeling of dignity after a refreshing shower is not something to view as peripheral or unimportant to the work of social change – it is the work. As data scientists and data enthusiasts, we aim to harness the power of data to alleviate the most pressing social challenges of our time – and improve lives in the process. This means we must also consider qualitative measures such as improved dignity and increased confidence in our assessments of impact.
Following this visit, DataKind San Francisco will continue our partnership with Lava Mae on a project helping to analyze data on the guests who utilize their mobile hygiene units. Stay tuned for that project’s findings!
In the meantime, if you’re local to San Francisco, you can support Lava Mae’s work with a donation of funds, time or unused hygiene supplies. If you’re interested in donating your data science skills for good, check out our upcoming events and sign up to get involved. You can also check out and contribute to our GitHub repo for open datasets related to urban poverty.
