The Messy Beauty of Warm Data
Reconnecting data to its messy human contexts can deliver unexpected beauty . . . and possibly Truth.
by Greg Sherwin
I first encountered the poetic elegance of math as an undergraduate engineering student studying physics in Chicago. Maxwell’s Equations are four differential equations made up of just 18 symbols in total, yet this compact, eloquent set of rules describes the universal behavior of electricity and magnetism. The equations can explain everything from mobile phones to electric vehicles to karaoke machines.
This proximity of truth and beauty in the laws that govern the known universe revealed itself further to me as my career progressed. Working on high-energy physics experiments at the Stanford Linear Accelerator Center, I collaborated with physicists from around the world — who often spoke in triple integrals and sported gravity-defying hairstyles — to build and operate what was literally an anti-matter factory. We helped fill in the blanks of the Standard Model, an audacious attempt by the world’s physicists to describe all the laws of matter and energy under a single unified idea made beautiful by its symmetry, harmony, coherence, and balance.
The pursuit of beauty, however, with its affinity for oversimplification and aversion to the messy and complex, can also lead us away from the truth. Theoretical physicist Sabine Hossenfelder points out this problem in her new book, Lost in Math: How Beauty Leads Physics Astray, by explaining how the pursuit of the ideal in mathematical elegance has prevented theoretical physics from developing new ideas for the past 50 years.
The risk of missing the truth in our pursuit of oversimplification extends to our growing use of, and dependency on, big data — which also happens to be the modern foundation for scientific discovery. The peril lies in our human tendency to deconstruct our complex world into smaller, digestible, independent parts, because these parts are much easier to understand in isolation rather than in their entangled, chaotic whole. Studying how a single Facebook account likes news stories and shares photos with friends tells you very little of how the service could help subvert democracy and contribute to genocide. Suffice it to say that the social consequences for missing this broader context can be extensive.
Fortunately, there has been a movement within the emerging field of big data to reconsider our reductionist tendencies and look at the bigger picture. Nora Bateson is a research designer, award-winning filmmaker, writer, and educator. While attending a session on big data at the SAS Institute in early 2012, she coined the term “warm data” to describe a need to complement the hyper-minutia of big data analytics with broader contextual thinking. For example, when big data researchers take a reductionist look at the discrete chemical and nutritional components of food, how might that information also relate to ecology, economics, climate, diet, culture, and even human psychology?
To help codify some basic practices for exploring the intercontextual relationships behind big data, the International Bateson Institute in Sweden has been conducting trainings and certifications around the concept of Warm Data Labs. Participants who previously only focused on the base-level nodes in a network now think about the qualities of their interconnections. For example, when studying the social dynamics of families, it can be critically important to conceive of mother-daughter, sister-brother, and various other inter-family relationships as unique and varied entities in themselves.
Not only do these practices open opportunities to learn greater truths, they also offer a very human upside. The human experience is often complicated and sometimes contradictory. However, a reductionist examination of our individual data “exhaust” can only lead to gross generalizations and stereotypes because each of us is more than our gender, our race, our religious beliefs, our medical conditions, and our favorite Netflix programs.
We might scoff at those who follow astrology for bucketing humanity among twelve possible “types” based on their birthdays, yet we eagerly base business decisions on gross assumptions about four or five groups of humanity (Gen Z and Millennials, for example) based purely on their birth year. This oversimplification problem is compounded by the likelihood that the big data about us is frequently inaccurate, according to a recent study by Deloitte.
The warm data approach suggests we might actually stand a chance of being seen more realistically, as we truly are: complex, situational, imperfect, and aspirational, neither the formulaic end-product of, nor dismal slaves to, the concept of homo economicus.
Beauty certainly exists in simplicity and coherence. But in regards to our best possible human future, it also exists in diversity, complexity, and even contradiction. Acknowledging the dynamic potential of human beings is one of the most humanizing stances we can take as we continue to use big data and our myriad machines to make assumptions about individuals and groups.
It may even be a stance that brings us closer to the truth.
Greg Sherwin serves on the Advisory Board of the House of Beautiful Business. When not aspiring to be Le Flâneur de Lisbonne, he prefers his data to be warmed but not hot to the touch.
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