For the past few months, I’ve been struggling with where to go next with Makers All. That’s mostly because of how rapidly the world has changed.
From Black Lives Matter to the Coronavirus epidemic to the dramatic transformation of political discussions in tech — especially in AI — we’re living in a radically different context than where we were a year ago, when the Makers All report first came out.
As a result, I don’t think the name “Makers All” fits with where I think our communities and emerging tech need to go. And I’m not sure what the right name is — or for that matter, what the right scope of this project is.
- I don’t think it makes sense to talk about how to open the doors to good paying jobs in emerging tech without simultaneously talking about how to turn crappy paying jobs in care work, first response, and Ghost Work into good paying jobs that have security and respect
- Given how any Green New Deal — which we need if we’re going to survive — is going to fundamentally change the economy on a scale comparable to emerging tech, I don’t think it makes sense to talk about emerging tech jobs & wealth in isolation from talking about Green New Deal jobs￼
At the same time, I don’t want to completely give up my focus on emerging tech. The opportunities and dangers are too critical. And I think there are lessons we can learn from emerging tech that could be incredibly useful for how we think about the transformations we need to make in the rest of the economy — especially if we’re going to use this opportunity to help folks from Harlem to Harlan County make their communities whole.
So while I’m doing a lot of thinking and writing right now, I’m going to hold off posting anything until I have at least a rough idea of what sort of butterfly this project is going to transform into and what its new name will be.
Since I published Makers All’s flagship report last May, I’ve been talking with people in the tech world and the community organizing world about what in the report worked for them and what didn’t. And I have been running several small pilot projects to start testing out some of the ideas in the report.
I’ve also been struggling to deal with the loss of my mom. My mom died a few months before the report came out. I had many wonderful years with her, and after a long fight with cancer she was in bad enough shape that it was time for her to leave us. And I’m in my mid-50s, a point in your life when it’s common to start losing your parents. But I had no idea how brutal losing a parent can be no matter how old you are or what shape they are in.
It’s taken me a while to right myself and figure out where it makes sense to go next. But I feel like I finally have clarity.
For the next phase of Makers All, I’m going to focus on two areas, one that focuses on the nuts & bolts of tech, one that focuses on the big picture:
- I’m going to use the burgeoning – and awkwardly named – field of machine learning intelligibility to test out and rethink Makers All’s Smoothing the Learning Curve. Given the rise of “Auto ML,” where tech companies are taking their very first steps towards making machine learning more accessible to staff who aren’t data scientists, as well as the beginning efforts to combat bias in AI/ML, I think there’s room to do some work that’s useful for ML and that could have an impact on other areas of emerging tech.
- I’m going to change up Makers All’s work on making emerging tech jobs accessible to marginalized communities so that it fits with some other big economic changes we need to wrap our heads around, especially green jobs and the Green New Deal. By getting “democratizing tech” out of its silo, I think there’s an opportunity to have a much bigger and deeper impact – and make truly democratizing tech much easier to pull off.
In future posts, I’ll give a quick overview of why these two new projects and what I hope to accomplish.
Over the past decade, the tools that let staff who aren’t coders transform and analyze data have gotten more and more powerful. But if an organization just rolls out these tools to lots of power users without having a broader strategy about how the pieces will fit together, you’re likely to create a hot mess.
Data Chefs is a framework for helping you tap the power of these tools in a way that’s scalable and sustainable by growing ecosystem of power users.
Data Chefs is a mashup of my decades of experience training, supporting, and nurturing power users plus the research I did for Makers All’s flagship report. In the report, I argue there’s a way for corporations and large nonprofits to design their tech training/retraining strategy so it also benefits marginalized communities:
Between waves of automation and waves of new tech, corporations are going to face a never-ending need to train and retrain their staff. Currently, most simply aren’t equipped to do so.
Staff in corporations and other large organizations don’t have the same needs as people in the community. But there are many areas where they may overlap. As communities are developing a rich ecosystem for their members, it may be worth exploring if there are ways to jointly address their needs.
But bridging the gap between corporations and communities can be pretty daunting. After talking with a lot of folks in tech companies and the community after I published the report, I realized that I needed to figure out shrink the gap.
At the same time, my sabbatical working on launching Makers All was coming to an end and I needed to find a job. It’s been a long time since I’ve looked for a job, and as I started working on cover letters and figuring out my shtick, I spent some time thinking about the work my team and I did when I was the Decision Support Services manager at ASHA, where we created an earlier version of Data Chefs, as well as some of my previous jobs.
What I realized was that if I focused on trying to help corporations and other large organizations get really good at supporting their power users, we could end up building ecosystems inside these organizations that could be bootstrapped into an ecosystem across organizations. And that would make the gap between companies and communities a lot more manageable.
I also realized that if you took a lot of the ideas I’d developed in the report and scaled them down, they’d make a great place to start growing thriving corporate ecosystems for power users. Add in my frustrations with how Agile software development gets talked about and rolled out in so many organizations, and voilà: Data Chefs!
One final note. Although this approach to Data Chefs is pretty different from the original version we developed at ASHA, I wouldn’t have come up with it if it weren’t for that first version. So thanks, Ian and Morgana – it was such a pleasure working with and learning from you!
For archived blog posts, please visit the old Makers All site