microveldt

the irreducible part of any problem is that people need to be part of its solution

Category: Nonprofits

  • Small, Deep, Loosely Networked

    For the past 20 years, funders have encouraged nonprofits to focus on growth and scalability. These were understood to be the keys to sustainability and impact. But what if that was wrong?

    It’s time for nonprofits, especially those serving vulnerable communities, to see themselves as part of the resistance and to retool for that reality. It’s time to build small, deep, loosely networked communities of service as a way of:

    1. Safeguarding vulnerable communities
    2. Mitigating legal risk
    3. Building a different kind of sustainability and impact

    What does fundraising look like?

    1. It looks like cultivating genuine, long-term relationships built on values and mission
    2. It is a quiet, principled, sustained support that doesn’t require effusive and public thanks
    3. It is a practice that realizes that others in the space are allies, not competitors.

    What does impact data look like?

    1. It is anonymized
    2. It is right-sized
    3. It shows complexity and interdependence.

    It’s high time for funders to recognize this new reality and invest in the success of this “nonprofit as resistance” model.

    For upwards of ten years, funders have asked nonprofits, and certainly the vulnerable populations they serve, to be resourceful, resilient, and develop grit. Now, before it’s too late, funders, it’s your turn to show us what you’ve got.

  • The Story of Storytelling Data

    Storytelling data is a kind of test data used for humane database training and development. Storytelling data is data that tells a story to the user about the purpose and functions of a database. Ideally, it allows users to envision their work in a database, and understand its core functionality without needing a lot of technical documentation.

    Creating storytelling data is itself a way of exploring the database your team is developing. The data is a jumping-off point for team conversations about how to make the database better, easier to use, more helpful to the customer, more equitable and inclusive, giving you deeper respect and understanding of the data you are managing.

    When you begin creating storytelling data, think about the purpose of the data, your database customers create, and why they are managing it. What problems are they trying to solve? What are they trying to achieve in the world? What kind of data are they collecting, and why?

    If the data is about people, think about those people, not in the abstract, but individuals with plans and projects of their own. If the data is about things or systems of things. think about those things and systems in relation to individual people.

    As with any toolset, it’s the user’s commitment to specific values that will determine how the toolset will perform. Some of the ideas and concepts I’m working with include:

    • If you suspect that the UI is brittle, build storytelling data to expose and explore the brittleness that can help the team see where more work is needed.
    • If you think that your development is missing needed nuance in DEI, write data to show that weakness. Consider getting input on your storytelling data from the groups you suspect would be impacted and pay them equitably for their time sharing their lived experiences with you. This can include, but is not limited to, members from the disability, BIPOC, LGBTQ, parolee populations.
    • Better still, hire representatives from marginalized groups to help you build the storytelling data and encourage them to educate your team about the issues they face in seeing stories like theirs represented in your database.

    When I first began working with storytelling data, I was just trying to save time composing screenshots for documentation. As I continued to build it out, my team had conversations about using storytelling to model rich data to expose brittle UI so that the team could make the UI more flexible to accommodate more data and more complex data. Additionally, we discussed how storytelling data could show where our working assumptions could be made more equitable and inclusive. It’s the conversations that are sparked by the use of storytelling data that are the generative power of the tool.