Warm Data
Consider people’s experiences and concerns around the system, across many contexts.
we can create an Inventory of Centers and a Relationship Map to get a broad overview of how a system operates, but if that system involves humans (and almost all important ones do), we will need to go beyond our tidy diagrams and embrace complexity.
We like mechanistic models of how systems work because they’re easier to understand, but people are not simple machines.
-
So far, these relationships have been largely mechanistic in how we think of them. We’re used to systems that are machines, so it’s easy to think in those terms. But more complex systems, like living systems embody equally complex relationships. For example, as soon as you add human beings to a system, relationships get complex very quickly. In a complex system like a family, there are behaviors and expectations that are passed down for many generations, with people taking on various roles that change over time and in different contexts.
-
Trying to understand these kinds of complex relationships, not just between people, but between people and social constructs like marriage or money or race or gender roles, is not easy, but it’s important work that needs doing.
-
The term “warm data” comes from Nora Bateson’s work. My best understanding is that warm data tries to get at a “trans-contextual” understanding of a system by considering the experiences of different kinds of people in different contexts. It is not “hard” data in the sense that it can be aggregated apart from its context and quantified. I wonder if it is more Portrait kinds of data. In the image above, I’m imagining it integrated into a Flowchart.
-
I need to learn more about exactly how warm data is collected and processed such that it leads to useful insights. My best guess is that its is similar to how Takashi Iba creates pattern languages: by gathering people with direct experience of a system, from as many contexts as possible and writing down experiences, ideas, insights and more onto Post-It notes. The post-it notes are then looked at later and grouped using some process that is similar to Affinity Mapping.
Therefore:
Consider people’s experiences and concerns around the system, across many contexts.
Use Affinity Mapping to find important patterns in a complex set of warm data