Data Justice


Data justice is building equity into all aspects of our data practices. It's a framework for promoting data practices that uphold truth, learning, consent, and acountability.

Download the Data Justice Brief: 


What is Data Justice? Data justice is the collection, analysis, sharing, and use of data entirely in service of and with accountability to participants and their communities. Data justice is a lens to evaluate the intended or unintended consequences of researching or collecting data on individuals and communities. Data justice values the dignity, privacy, and humanity of all people. Data justice is active resistance to all oppressive systems within the arena of data as a part of building a better world that promotes dignity, consent, truth, accountability and learning. This work will be necessary as long as oppressive systems exist.

Data justice is building equity into all aspects of our data practices. It's a framework for promoting data practices that uphold truth, learning, consent, and accountability. Data Justice weighs the benefits and harms of our practices so that data is entirely in service of the individuals and communities it represents.


Data Justice Standards

For Human Service Organizations

  Do good

Elevate diverse voices and perspectives
Center participants and those with lived experiences in all decisions about data. Take special care to center participants who are negatively impacted by systems of oppression such as white supremacy.

Prioritize the hiring and retention of BIPOC data professionals to ensure diversity of perspectives. 

Prioritize access to services
Reduce data-related barriers to services: fewer questions means quicker access to services.

Consider other available data sources instead of asking participants.

Use data for participants’ benefit
Prioritize evaluation that contributes to program improvement, equity, and access to services.

Regularly analyze the data you collect and take action based on your analysis.

Share the results with all invested people
Share back with participants who provided the data and/or use your services.

Educate your team on what you learn -- your board, upper leadership, development staff, managers, and most importantly, frontline workers.

To avoid reports that seem to negatively reflect upon communities, acknowledge the systems of oppression that manifest in disparity. Consider power imbalances between those who provide data and those who receive results.

Elevate funding practices that support data justice
Apply for and accept funding that adequately compensates human service providers for their data efforts.

  Do no harm

Only collect data with a clear purpose
Audit and edit your data collection practices so that every piece of data you invest in collecting is there for a specific, meaningful reason.

Provide transparency in data sharing
Get informed consent from participants before collecting data. Make sure participants know what is required and what is optional.

Make it clear to participants why you are asking for their information and how it will be used, both by your organization and your funders.

Trauma-informed data collection
Minimize the quantity of questions asked. Collect data infrequently.

Reword or remove questions that are harmful or retraumatizing.

Write strengths-based questions using accessible language.

Protect participant data
Use secure systems, both electronic and physical, to store participants’ data.

Minimize access to data and promote careful stewardship of that data.

Strive towards data quality
Ensure accuracy of data.

Eliminate assumptions. If information is unknown, name it. 

When auditing data, stop at “good enough.” Data perfection is not possible, and has diminishing returns on providing good service.

Develop clear processes for data entry and data collection.

Download the Data Justice Standards:


What is Data Justice?

Data Justice includes...

●Collecting no more than the minimum amount of participant data required to provide services

●Handling participant data securely and maintaining participants’ data privacy

●The analysis and sharing of that data with intentionality, using an asset-based framework, and situated within the appropriate social contexts

●The use of that data to support participants directly, respond to changing needs discovered through evaluation, and proactively address systemic challenges uncovered

●Accountability to the people being served and their communities


Some Background on Data Justice

In the non-profit sector, data is often extracted from the community or program participants for purposes of reporting to interested groups and funders both public and private. The communities sharing data (including both individual program participants as well as non-profits themselves) are rarely involved in decisions about what data is collected, what happens with that data, and what actions are considered from data results. This is an imbalance of power and in a way, transforms the data into a form of currency that can be exchanged for basic needs, housing, or for nonprofit grant dollars. We posit that this relationship has a direct impact on harnessing truth, storytelling, and organizations’ ability to make data-informed decisions to improve the programs and services they offer. This relationship impacts community trust and the quality of the data that people choose to share about themselves and their situation. MACC Data Network envisions a new relationship and partnership with data, communities, non-profits, and funders grounded in data justice.


Who is involved in Data Justice?

Some of the collaborators within Data Justice are:

  • Communities and individuals ​
  • Front-line workers and case managers ​
  • Managers and executive leaders ​
  • Data Administrators​
  • Grant Writers​
  • Evaluators /Story-tellers​
  • Government Contractors/Compliance department​
  • Funders​
  • Boards​
  • Strategic Planning Committees​
  • All of us!
Download the Data Justice Brief:



A Note:
The language on this page and in the working document focuses on data justice within the context of community-based organizations and human service nonprofits. We acknowledge the work already done on this topic prior to our involvement and that parallel conversations are needed to address data injustice in all sectors from social media, healthcare, education, consumer capitalism, and beyond. We are starting our work at home, here in the non-profit sector in our role as the Metropolitan Alliance of Connected Communities. 



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