Equitable Data Collection
This is the first part of our series about equitable data collection best practices. To start, we will dig into the underlying principles for equitable data collection. Later in the series we will cover how to design for your audiences, best practices for demographic questions, and how to steward your data in Salesforce or other CRMs.
We all know data is important. Collecting data about your donors, employees, institutional funders, volunteers, and clients is critical for informing and improving how you run your organization. Understanding these audiences better can fundamentally change the way you do everything from program management to fundraising and even how you recruit, hire, and retain staff. You might track the demographics of program participants to ensure you are reaching target communities, or you may poll volunteers about their social media usage to better engage potential supporters. Data is a powerful key to increasing nonprofit impact.
How data is collected reflects your organization's values. When done well, your processes can be imbued with the principles of equity, inclusion, and respect for diversity in our communities. When done carelessly, efforts to learn more about our community can lead to bias, exclusion, and even harm to those we wish to serve. This guide introduces the foundational concepts of equitable data collection for nonprofits and shares best practices for gathering, storing, and analyzing data.
Part 1 | Basic Principles of Equitable Data Collection
Purposeful
Asking for demographic or personal information without a clear purpose – especially from individuals from marginalized communities – can make respondents feel distrustful or skeptical of your organization. Make sure you have a reason for collecting everything you’re asking for. If you don’t need it, don’t ask for it.
Responsible
Implement systems and processes to ensure that all data is collected and stored safely and securely. Only collect personally identifiable information (PII), such as names, Social Security numbers, and birthdates when necessary. Without responsible safeguards, people can be identified from your data, and their finances, privacy, and safety can be compromised. The risk is even higher when PII is linked to data on sensitive topics like income or voting, which could expose respondents and even whole communities to possible negative consequences.
Grounded and Respectful
Center the community in your collection and use of data. Your methods of data collection should be accessible and inclusive, and they should leave respondents feeling respected and comfortable. Work to build relationships over time with your audiences, and ask them for feedback on improvements to your collection methods. In addition, identify and seek out ways your data or processes can directly benefit participants, whether that's by sharing what you’ve learned or making changes to your programs and services.
Transparent
Communicate all of the above to those you are asking to provide data. The way you communicate and do outreach and follow-up around your data-gathering efforts is just as important as the collection itself. Clearly communicate exactly what data you need and how you will use it. Sharing why you are collecting respondents’ data and the details of how you will store, manage, and use the data can help put concerned individuals at ease.
Takeaways:
Only collect data that has business use. Don’t collect more data than exactly what you plan to use.
Only collect data that is given by individuals with consent.
Make sure you have a plan and systems in place to keep personal information you collect private and secure.
Be transparent about why you’re collecting this data and how you will store and manage it over time.
Take time to proactively ask for feedback and suggestions from your audience about your survey and ways your overall process can directly support and benefit respondents.
Case Study: First Nations Technology Council
Our client, the First Nations Technology Council, is an Indigenous-led NGO that provides free digital skills training for Indigenous people living in Canada. In 2022, the Technology Council published the Indigenous Leadership in Technology study, looking at the current state of Indigenous leadership in technology from both an Indigenous and industry perspective. This study is a great example of grounding deeply in the context of a community and using the research and data collection process itself as an opportunity to benefit those who participated in the research.
By staying grounded in the First Nations’ context, the Technology Council team defined the scope of their project and definitions within their research in ways that made sense for their community. From their report:
An important component of Indigenous self-determination in British Columbia is that "access and opportunities" in technology should not be narrowly defined in terms of employment. Instead, we should seek to understand the ways in which Indigenous people and communities shape the economy through Nation-owned businesses, First Nations’ land-based rights, entrepreneurship, and priorities that lay outside of roles typically captured by labour market studies.
Lauren Kelly, former Director of Sector Transformation at the First Nations Technology Council, shared with us: “There’s an organization based in Canada called the First Nations Information Governance Center, and they’ve done a lot of work around principles of data collection. They created something called OCAP (ownership, control, access, and possession), which we use to guide our processes as much as possible.” Using the framework and principles of OCAP, the Technology Council kept all data collected within a First Nations-led organization and offered participants continued updates and report backs throughout the process.
The Technology Council worked with a variety of stakeholders to make sure that their research methods reflected their values and designed a data collection process that was participatory, culturally safe, and grounded in an Indigenous worldview. Their work fills a gap in labor market research and provides an excellent example of how to intentionally and systematically build equity into data collection.