Qualitative aspects to scaling PersonalData.IO

We are against what someone here has described as a “challenge of an unfathomable scale”. I don’t necessarily agree, given the success of much harder projects like OpenStreetMap (you need eyeballs on the ground…) and, I must acknowledge, the track record accumulated (which opens up opportunities more quickly). Still, we need to have a proper strategy in place.

This is an open thread to invite contributions on qualitative aspects of scaling the impact of PersonalData.IO, focused on making sure we account for all the dimensions of this problem. It’s a purely ideation thread. Let’s exchange for now ideas about these dimensions. We can refine each dimension later, quantify them, decide which are synergetic, decide whether we need money, decide which to implement.

My reason to frame the question qualitatively first is this idea that “given the capacity of increasing dimensions of a rectangle by an absolute value, say 1 meter, the area of the rectangle will increase most when you make it closer to a square”.

Applied to our scaling problem, this “rectangle intuition” means that if you already have a successful collaboration with journalists, expand very little of your finite resource (i.e. time) creating new such collaborations (don’t work on making the rectangle longer). Instead work on creating collaborations with academics, for instance (make it thicker).

So, here are the facets of the problem I am aware of:

  • by objective (get data, get corporate policy to change, get enforcement action going, for strategic partnership, etc);
  • by target controller type;
  • by methodology (i.e. complexify the insights we gain);
  • by types of data consumers (journalists, academics, labor unions, etc);
  • by type of data subject (are they a minority? weaker in some way?);
  • by numbers of data contributors/data consumers/data subjects;
  • by market of data contributors/data consumers/data subjects;
  • whether the energy goes bottom up or top down;
  • with or without money;
  • by “justice quality” (how much does our action contribute to mitigating injustice derived from information asymmetry?).

From these dimensions, we can then search for use cases that are very beneficial in some way to our overall goals, and the growth opportunities each use case affords to us (where growth is only pursued insofar it builds capacity necessary for furthering our later goals).

1 Like

In my opinion changing corporate policy is a big fish. There are good reason to change to ethical data policies, see e.g. the announcements of IKEA (https://www.youtube.com/watch?v=j1MsEl9cTRc). Addressing big consumer brands (realistically outside the pure adtech martech universe) may be worth exploring. Mapping success stories of ethical data practices (another one is e.g. the Dutch public broadcasters, see https://www.emerce.nl/nieuws/ster-helemaal-cookieloos-adverteren). Influencing corporate investment decisions is the direct route to the summit. This may open doors to some funding on the side.