Data and the community sector

The community sector produces a lot of data for their own use to evaluate programs, learn more about participants, but also for funders. In recent years new technologies and tools have increased the availability of quantitative data and of ways to work with this data. The trend of “dataification” has also impacted the community sector. The project “Naviguer l’univers des données” aims to identify opportunities and challenges that come with this development, specifically from an anti-oppression approach.

Training workshops

In 2019 we facilitated a workshop as part of the ateliers/C which allowed us to better understand the questions that community organisations have around data. Based on this experience, we created a training plan for a series of 5 interactive, practical workshops tailored to the needs and realities of community organizations aimed at demystifying concepts in the data universe and making groups more comfortable working with data. At the heart of the process is access to tools and the application of new knowledge in practical exercises. In line with our values an anti-oppression lens is applied in all stages of the process. This means i.e. asking questions like “For whom might this fail?” and centering the value of equity when examining methods. For more see also the section on “Facilitated Data Approach”.

The data cycle

As part of the workshop we developed a tool to talk about the data cycle. Although there are many models of cycles, these normally use a language that is very far from the realities of community organisations. We are currently working on a audio-visual explainer of this tool.

Working with groups

The challenges community organizations face with data are often very specific and vary greatly. In order to account for this, we privilege an accompaniment approach which allows us to tailor our support to the needs of the group. We are currently prototyping this accompaniment service offer with several community organizations.

Working internally

One of our principles at COCo is to apply our practices to ourselves and to share these experiences with the community. In terms of data, we have analysed several areas of our organization, such as overtime accrual, participation in ateliers/C and the evaluation of contract work.

Facilitated Data Approach

When an organization asked COCo to submit a proposal on how to measure organizational health according to a anti-racism framework, Nadia Chaney and Mich Spieler started to develop an approach to combine arts-based, creative facilitation and emergent, self-reflexive data practices. The elements of this WIP-approach can be summarized as follows:

• Community-Data Literacy: an understanding of how to translate data solutions for the very different values, practices and human dynamics of community organizations and, vice versa, translating for community practitioners the potential languages, processes and impacts of seemingly neutral data solutions.
• Embracing Complexity: Tech, data and AI solutions tend to focus on a reduction of complexity. Sometimes the implication is that this flattening is itself a solution. We prefer to work with human and organizational complexity as it is, alive and responsive.
• Data Humanism: Following the work of Georgia Lupi we see data collection as human, playful, observational, relational and curious. A way to connect more deeply to the world and its peoples.
• Narrative Data: Rather than squeezing people’s lives into excel sheets and charts, we believe in centering the stories that bring those numbers into a living context, and take the lived experiences that underlie them into account. Our stories are lived in the context of bigger social stories that can dictate the wellbeing of communities.
• Purposeful Data Work: collecting data with the intent of transformation by asking what data is being collected and why, as a consistent and reiterative part of the process of designing and implementing data solutions.
• Setting Limits with Data: the importance of setting limits with data collection, learning to discern when investigations and collections are not appropriate and can even cause more harm than good.

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Other project activities

• Participation in the 2019 and 2020 editions of TechAide’s AI4GOOD hackathon
• Collaboration in the research project Artificial Intelligence (AI) for the Rest of Us, building a new model of public (civic)engagement in government decision making processes that are increasingly automated with AI.
• Presentation in a workshop at the ACM FAccT Conference 2021
• Facilitation of a workshop in the context of the Laboratory of Learning Organizations.
• Exchanges with several organisations working at the intersection of data and community.