Data is not only a way of measuring and categorizing information, it’s a way of seeing the world. In the digital humanities, we often speak about data as something quantifiable – in terms of engagement, clicks and likes. But data is so much more than that, and always requires context.
I’ve been looking into some new data approaches and concepts that you might find interesting.
One of them is warm data.
Warm data is a concept by Nora Bateson and implemented further by The International Bateson Institute. I first read about it in this interview with Brian Massumi, a Deleuzian philosopher who never fails to inspire. In the article, Massumi describes an alter-economy based on cryptocurrency that works as an affect-o-meter. The business model is based on qualities, intensities and relationships, rather than the quantifiable values that are common in our economy.
‘The key, once again, is finding workable solutions to the problem of how to use qualitative analysis to register movements of creative intensity—how to coax numbers into an alliance with qualities of experience. There is a new concept being developed by Nora Bateson that she calls “warm data” that has a similar goal, in relation to basic science, that we’d like to hook into.’
That idea of warm data immediately intrigued me and reminded me of my own research. It evokes ideas about lived experiences and relationships that are so fundamental in queer theory, phenomenology, ethnography and narratives. Warm data, from my point of view, could be about bodies, humans, spaces, relationships. It could be about how we share the world.
The Bateson Institute frames the concept in detail and also applies it to cases and in “warm data labs” all over the world. This lens offers a mix of social research, critical thinking, system thinking. Yes, this is about qualitative data, but a specific type of it: ‘Our work is to look in other ways so that we might find other species of information and new patterns of connection not visible though current methodologies.’
Warm data is about uncovering what’s happening in the world in all its complexity, and analyzing all the parts of a system. It’s data that is hard to study, and often not done justice too. You can read more here. Of course there is other data that we must think about and conceptualize too. To some extent, there is a bias for quantified data emerging in the digital humanities that I am not in favor of. In an upcoming collection for Palgrave, I write about algorithms and hidden data. The data that we can’t see, that is never uploaded, filtered out, or rewritten, and can only be studied in qualitative ways (e.g. through reverse engineering and qualitatively probing how an algorithm works).
I summarized the qualities of warm data and added some of my own in this table.
It seems to me, for instance, that such data can’t be readily sampled but that it needs to be probed, sensed, and experienced (what I dubbed sensing). It seems to me that it strives for depth, rather than large patterns, and emphasizes complex systems over labeling, boxing and categorizing.
The concept of warm data intrigues me. It can point to intensities, relationships, depth. But it also evokes a very phenomenological, intuitive idea of research, which I love, and fits those of us doing ethnography, interviewing, and other methods. Of course warm and cold data could mix which can raise interesting insights as well.
This dichotomy has similarities with other concepts, like…..
- Close-reading versus distant reading, as discussed by Moretti
- Thick data, by Tricia Wang and based on the ideas of Clifford Geerz and his thick description
- Deep data, a concept used more in the professional field, but spreading widely by now
All of these are about emotions, people, and gathered in different way. Warm data is akin to all of these, but puts the emphasis on relationships and systems rather than individuals. This is an interesting twist to thick data, which is about emotion and stories.
There’s more room for new methodologies in our current academia. Still, can any of these concepts easily compartmentalize this scientific reality? Data is messy, hidden, socially constructed. Data is a process rather than an artifact or product. It’s not easily captured in these dichotomies of either/or.
Anyway, what do you think about this concept? Do you find it helpful?
Nicolle Lamerichs 