Conceptualizing better data work in non-profits: First steps toward practice

In the last couple years, I’ve published 3 articles regarding the use of data in non-profit organizations. As I see it now, I’ve accomplished 2 out of 3 goals I had when I started this work many years ago as a dissertation project.

1. To identify, study, and give name to the experiences I and others were having working in non-profit, youth-serving organizations regarding data. Namely, that implementations of data systems felt rushed, poor, distracting, and harmful.
2. To propose alternative frames and frameworks that would give us handles to start to pull ourselves out of this mess, helping us to name the problems, challenges, and possibilities.
3. To actually design, elaborate, and use better approaches to data work, premised on the questions and concepts in #2.

Each of these stages has been a serious challenge for me. The ideologies and fetishism around data work make it difficult to grasp hold of and name. Worse, the foul odors emanating from everything with the word “data” in it felt nearly untraceable to their source. I kept feeling like there was something more if I could just peel back one more layer. Of course, there,’s no singular thing at the core – it’s a constellation of dirty deeds that makes data work bothersome to those of us in the field. Racism, colonialism, and capitalism live deep within the Mordor of data work, which historically has been about tracking and surveilling clients as a form of social control (see, fox example, Gilliom, J. (2001). Overseers of the poor: Surveillance, resistance, and the limits of privacy. University of Chicago Press.).

Those of us in Critical Data Studies, Science and Tech Studies, and the like have long critiqued the ways that data is analyzed too. We’re worried about the ways it is used to tell stories (by whom and about whom and with what sorts of built-in biases?). We’re worried about the ways it hides its biases, shortcomings, and complexities behind the veil of all-knowingness. I (and others) are also suspicious of the very foundations of data analysis. Statistics is a very narrowly specific way of telling stories about people, reliant upon theories of probability that deserve analysis outside the statistical community. These concerns are part of that deep-below-the-earth stench – hard to trace, but definitely present. Reading Stephen Jay Gould’s The Mismeasure of Man is a wonderful way to trace this olifactory malfeasance, but a recent Nautilus article does a spectacular job of tracking the origins of statistical methods (and the methods themselves, not just their supposed progenitors) to eugenics.

How Eugenics Shaped Statistics

In my own work, I never made it this far “down” the mountain. I looked at the ways data systems were impacting youth-serving organizations, young people, and those that work with them in said orgs: youth workers.

In Real Big Data: How We Know Who We Know in Youth Work, my partner Marisol Brito and I examined the ways typical data systems brought an analytic gaze to young people, youth workers, and the orgs serving them. I proposed instead holding true to the field of youth work’s relational orientation, starting first from relationship with young people, and then moving toward programs, outcomes, and data. Focus groups with young people revealed a clear set of ideas to help build better data systems. It suggested that data:

• be created through relationships of trust and oriented toward action that supports young people rather than organizations or funders.
• remain local and personalized (for helping see/support a particular young person, not to help abstract that young person for others’ purposes).
• help frame young people positively, through their strengths and aspirations, not limitations, weaknesses, or challenges.
• focus on what’s important to young people, rather than what’s “important” to the organization. (Fink & Brito, 2020)

We also began to propose a framework of questions to reclaim analytic data systems to serve young people better. These questions were:

• How is analytic big data situated within relationships of trust and care?
• How can existing analytic big data be used to understand structural issues?
• How can analytic big data be re-situated in the lives of young people and their communities?
• How can young people and their communities be involved in the process of developing and designing the foci of big data, and addressing social issues through data? (Fink & Brito, 2020)

My next two papers, written with Ross VeLure Roholt, expanded on these perspectives, which were driven by young people, to include youth workers and organizational leaders. We proposed yet more questions as the start of a framework for how organizations could begin to rethink their data work. Building off the previous ones, these became slightly more specific.

1. How can we co-create data collection systems that begin with the needs of young people and front-line workers and primarily aggregate data that views young people from the perspective of their strengths, aspirations, talents, hobbies, and desires?
2. How can data collection instruments be revised or co-created with the involvement of young people, front-line workers and the specific communities that will be surveyed by them?
3. How can funders, who have greater resources and ability to manage technological change, adapt to the data collection systems of organizations, rather than vice versa (as most often happens now, creating duplicative systems and significant data hoarding)?
4. How can data collection processes center first around developing and maintaining real relationships of trust and caring between young people and youth workers? How can it occur only through consent of all involved?
5. How might data collection focus on data about systems, institutions, and environments that affect young people’s lives, rather than young people themselves?
6. How can data sharing processes ensure that young people do not come to additional harm from systems and institutions that surveil and police them? (Fink & VeLure Roholt, 2022a)

In our most recent paper, we built off these question banks to start to create a broader, but more summative, framework for thinking about data work in youth-serving organizations. We framed them as 3 primary challenges:

• Negotiating power dynamics in demands for outcome measures
• Struggling toward data justice
• Nurturing participatory and collaborative processes

The third challenge / approach was the most familiar to us, as we’d each spent years doing participatory research and evaluation with young people as a format for developing critical consciousness about the various oppressive systems they were encountering in their daily lives and affecting change on them (Fink & VeLure Roholt, 2022b).

However, though these were the best we could come up with at the time of writing these studies, each set of questions / ideas felt like it came up short of actually proposing something different. I’ve had organizations ask “what can we do??”, and the best I could offer was to facilitate participatory processes around data with young people/clients in ways that critically assessed the roles data was serving and the harms it might be causing. Short of that, I suggested resistance and reform.

Resistance and reform are legitimate and important strategies in the short term. There are all sorts of ways human service and youth workers resist the negative impacts of data work, too many to list here. But these strategies do work! And can be used both by individuals and organizations. Clients / young people resist too, often by making themselves only partially visible to data systems, or by manipulating the data that is captured.

Reform is also a viable strategy in the short term. Making data collected more centered around client strengths and aspirations is one example. Removing unnecessary data collection and hording, gathering only the data that is of immediate use, implementing strong consent policies around data, and working alongside clients to invite their agency in managing their data are all strategies for reform.

However, I think it is time to offer something more. I’m committing myself to sorting out some real, practical approaches that people can do to build data systems and data work that serve data justice. I’ve recently been re-immersing myself in popular education literature for another project (The Experiential Politics of Popular Education) and was struck by the ways one approach, Reggio Emilia, integrates practices of documentation into its school-based practices. I hadn’t made the connection before, but their version of documentation, which closely aligns with participatory and democratic values, is just another way of naming an organizational data practice that actually does better by and for everyone. In my next blog post, I’ll highlight the ways Reggio documentation can be used as an organizational data system.

I also think there are at least four other approaches that I will detail as I am able, but for now I’ll list all 5 approaches here:

1. Reggio Emilia progetazzione and documentation
2. Data cooperatives, Warm Data Labs (inter-institutional)
3. Participatory Action Research / Evaluation / Design.
4. Systems Analysis (ala social determinants, or better, Ida B. Wells studies of lynching): Creating data about oppressive systems that help to undermine, challenge, and transform them.
5. Epistemic (In)justice: Data created that counters, complicates, or upends hegemonic epistemological frames. (ex: Indigenous Mapping Collective)
6. Infrastructural inversion: Digging into and deconstructing the ways we’ve established existing data infrastructures.

Before ending, I want to add one caution: while much more practical than in previous papers, there is no formula. Building more just data systems necessarily involves the participation of all stakeholders in serious and significant ways, including decision-making at every step of the process. As Reggio Emilia insists, theses are approaches, not methods. They are better read as a starting place for consideration and planning – prompts for developing an approach that works in a specific context. If done right, that is all there will ever be – specific approaches for specific contexts. There is no model except a model build locally by and with those involved.

References

Fink, A., & Brito, M. (2020). Real Big Data: How We Know Who We Know in Youth Work. Child & Youth Services, 1–29. https://doi.org/10.1080/0145935X.2020.1832888

Fink, A., & VeLure Roholt, R. (2022a). Surfacing Human Service Organizations’ Data Use Practices: Toward a Critical Performance Measurement Framework. The Journal of Community Informatics, 18. https://doi.org/10.15353/joci.v18i1.4712

Fink, A., & VeLure Roholt, R. (2022b). Emerging Tensions in Data Work: Staff and Youth Perspectives in Youth-Serving Organizations. Social Service Review, 96(4), 617–654. https://doi.org/10.1086/722277

Gilliom, J. (2001). Overseers of the poor: Surveillance, resistance, and the limits of privacy. University of Chicago Press.