Framing Equitable Outcomes: Data Equity in San José
By: Julia Chen and Christine Keung
To the outside world our place as the Capital of Silicon Valley may not merit a serious conversation around our unique challenges: how our perceived growth has further exacerbated existing inequities, e.g., our 95,000 residents without internet access and 42% increase in our homeless population from 2017–2019. How do we use the resources of the City to determine how we best serve our residents? What is data equity to those who are most affected by how we define it?
Ask any five people what they think is “data equity” and you’ll receive five different answers. At MOTI, we define data equity as using the City’s data ethically and in ways that drive equitable outcomes for our constituents.
What is San José doing differently?
- Unlocked potential: We analyze administrative data rather than survey data. Survey data, with its low response rates, risks introducing bias. City system data, however, is often an untapped, robust, and flexible data source.
- Targeted solutions: We believe data creates choices for improving service delivery. We create operating metrics that enable policymakers to be rigorous in understanding impact and targeted in implementing solutions.
- Equity goals: We can’t address equity gaps without defining equity objectives and outcomes for key city programs. Our data equity framework creates consistency, transparency, and accountability towards equity goals.
Introducing: our Data Equity Framework
Every City department measures equity differently. An equitable outcome for our Parks, Recreation, and Neighborhood Services team cannot be applied broadly to the Department of Transportation. We needed a system that could be adapted to a multitude of different needs across the City, that was scalable and sustainable, but maintained a clear core of common values. Developed by our acting Chief Data Officer and Harvard Business School Leadership Fellow, Christine Keung, and former Harvard data scientist, Matthew Finney, our Data Equity Framework has three distinct stages:
- Equity Objective: What is an equitable outcome for the department and programs?
- Equity Metric: How will we measure this? (derived from the Equity Objective)
- Monitoring & Evaluation: How do we ensure this long term? (supported by City Manager’s office)
To date, we’ve completed three data projects over the course of three months for two separate City departments using this framework. We’re working with data that has gone largely untouched until now and the knowledge that longtime program managers and City staff have shared with us form the backbone of our work. They have the most intimate understanding of how we’re serving San José’s residents and how to do it even better.
Our data-forward vision for the future
Positive change cannot be effected on what we can’t measure and the metrics we define don’t exist in a vacuum. At the end of the day, we’re not doing analysis for analysis’ sake; data isn’t anonymous and San José residents are our north star. An understanding of the local context our work exists in and commitment to centering people in all of our processes is crucial.
We want to not only inspire those in the City with what our Data Equity strategy can achieve, but also create a platform for productive conversations with our communities. In order for our work to grow and adapt for the future we are focusing on:
- Budget: Present a data-driven, equity-informed budget message to City Council.
- Capacity: Build the City’s first data science team and have internal employees complete a data science bootcamp.
- Story: Connect data to impact by sharing our learnings with community organizations and other cities.
- Ecosystem: Engage citizens with open data and work with local schools to increase public data literacy.
In his most recent budget message, Mayor Sam Liccardo states: “All of the talk about equity means little if we’re not measuring outcomes, and driving results with concrete actions toward those outcomes. This requires us to squarely confront data, rather than pointing at anecdotes.”
We’ve failed if our work remains an intellectual exercise. We’ve failed when our output is perceived as analyses, dashboards, and databases. We’ve failed if our work does not drive equitable outcomes for our residents.
Unlike the datasets we work with, there is no endpoint for equity in San José.
Julia Chen is the Data Equity Project Manager and Christine Keung is the Chief Data Officer for the Mayor’s Office of Technology and Innovation.