Data for a Just U.S. - Using Data Science to Empower Marginalized Communities
I had no idea growing up in a crime-ridden neighborhood would shape my life experience. Nor did I think it would be a driver in the work I wanted to do in my life as well. Yet, living in a community plagued by gun violence has always made me eager to change the criminal justice and legal system. Whether it was working in the classroom to understand the school-to-prison pipeline, or using crime data to help a local neighborhood stay safe, my interest in helping marginalized communities has always been a part of who I am.
Initially, this journey started with me just wanting to use data to impact lives in local communities. When I joined Teach for America, I learned that I could use data to help positively transform these communities as well. As a teacher, I tracked student growth in reading, writing, and comprehension using pre- and post-assessments. I also tracked mastery of concepts using spreadsheets. This data often helped us determine what students did not know and what we needed to re-teach. By the end of my two years, my students grew two grades in reading and comprehension.
However, the field of data science helped me grow in my data-learning journey. Understanding that you can use data beyond just descriptive statistics is when my data journey truly became enlightening and transformative. Through this journey, my perspective on data science has evolved from seeing it as a purely technical field to recognizing it as a powerful instrument for social justice.
The Transition from Analysis to Advocacy
Initially, my career in the criminal justice field focused on research, crime analysis and working with law enforcement agencies. During this time, I honed my skills in data collection and analysis, developing crime reports and dashboards that provided valuable insights for decision-makers. However, while these roles were impactful, they often revolved around enhancing existing structures rather than challenging or reshaping them.
Pivoting into a data consultant marked a turning point for me, however. For one, I get to pick the clients I work with and generally get to lead the work I want to do. Thus, my goal of leveraging data not just for analysis but to empower marginalized communities is becoming more true each day. Furthermore, having the ability to create strategies and tools that allow these communities to tell their own stories through data is rewarding.
Data Science as a Tool for Empowerment
Working with marginalized communities has been an eye-opening experience. It requires a different approach to data—one that is deeply rooted in understanding the unique challenges these communities face.
One of the most rewarding aspects of this work is witnessing how data science can change narratives. By focusing on the strengths and potential within communities, rather than just the challenges, we can help shift perspectives and foster positive change.
The work I do with the national nonprofit Kids and Car Safety (KACS) is evidence of this. KACS is one of the only organizations collecting data on non-traffic child fatality incidents. By working at KACS, I get to use KACS data daily to advocate for children’s rights and safety. From creating social media posts that juxtapose safety tips with incident data, to sending press releases to national and local media, I am using KACS data to start national and international conversations about children’s safety in vehicles.
KACS has also allowed me to create a data visualization deliverable that I used for a class at Berkeley. As part of my Data Visualization class at Berkeley, I and a team of two other students helped create a dashboard to display car thefts across the U.S. involving children. The goal is for parents, media, and other stakeholders to see how leaving children unattended in vehicles is a growing problem in the U.S. We are hoping to get this dashboard on the KACS website later this fall.
Not only do clients like this get to empower parents to use data to change lives, but they inspire future generations of children. And then those generations do the same.
Overcoming Challenges
This work is not without its challenges. Many of the communities we work with have historically been excluded from data-driven decision-making processes, or not taught how using data science and using data-driven decision-making can help improve their communities. Overcoming this challenge requires building trust and demonstrating how data can be a powerful tool for change. This also includes using data science in a culturally competent and relevant manner.
I have been challenged to do this in my work with Roadmap to Peace (RTP). RTP was born out of an urgent community call to action and change after six Latinx youths were killed over a five-week period in 2012 in the Mission District of San Francisco. RTP is now using data to drive its decision-making. However, the 8-organization collaborative and nonprofit is just now getting on the data train. Couple that with a mixed monolingual and bilingual population that has historically been kept out of decision-making for its own community and the challenges add up.
Although I am slowly developing their internal database, I realized from the start they had a lot of data capacity-building gaps in their staff that were wide. Moreover, almost none of their staff were trained on how to use data. Therefore, learning and then teaching and then training non-technical staff and community leaders to use their data effectively is my number one goal and has been since I started working with them.
Understanding the cultural implications of using data for this community is crucial, too. During our bi-weekly data workshops called Data Cafecitos, I teach RTP’s leaders about the benefits of using data and how to use data to serve the population we care about. I try to also impart wisdom about tools used in the data science field and how RTP leaders can use them in their work as well.
A New Perspective on Data Science
Data has the potential to illuminate inequities and inform strategies that address systemic issues, leading to tangible improvements in people's lives. But it’s all about how you use data and data science to drive impact. Although I started out in a career involving analysis, I soon learned that my actual lane was using data for advocacy.
Participating in the D-Lab’s Data Science for Social Justice Workshop further solidified my wanting to use data for advocacy. The eight-week intensive program allowed me to understand how to use natural language processing as a tool for social justice as well. I intend to take these skill sets learned in Python and NLP and apply them to my clients who need guidance and help as well.
As I continue to explore new frontiers in data science, I am optimistic about its potential to contribute to a more equitable world. Cultural competency when using and implementing data science and the ability to think beyond profit-driven techniques are key.