Giving Compass' Take:

• Louise Lief provides some principles and approaches for how philanthropy can help build better data justice that includes community input to influence social change. 

• How can more accurate and representative data sets better influence policy? 

• Learn how prison data accelerate criminal justice. 

Today, data governs almost every aspect of our lives, shaping the opportunities we have, how we perceive reality and understand problems, and even what we believe to be possible. Philanthropy is particularly data-driven, relying on it to inform decision-making, define problems, and measure impact. But what happens when data design and collection methods are flawed, lack context, or contain critical omissions and misdirected questions? With bad data, data-driven strategies can misdiagnose problems and worsen inequities with interventions that don’t reflect what is needed.

Data justice begins by asking who controls the narrative. Who decides what data is collected and for which purpose? Who interprets what it means for a community? Who governs it? In recent years, affected communities, social justice philanthropists, and academics have all begun looking deeper into the relationship between data and social justice in our increasingly data-driven world. But philanthropy can play a game-changing role in developing practices of data justice to more accurately reflect the lived experience of communities being studied.

Simply incorporating data justice principles into everyday foundation practice—and requiring it of grantees—would be transformative: It would not only revitalize research, strengthen communities, influence policy, and accelerate social change, it would also help address deficiencies in current government data sets.

Data justice not only opens the door to greater impact and helps underserved communities build capacity to act on their own behalf, but philanthropy is uniquely positioned to accelerate its adoption.

Some principles and approaches to get started:

  1. Take advantage of what already exists.
  2. Learn by doing.
  3. Build expectations into grant guidelines.
  4. Experiment with other data collection methods.
  5. Emphasize transparency and two-way communication.
  6. Revisit notions of impact.

Read the full article about data justice by Louise Lief at Stanford Social Innovation Review.