COVID-19 is most evidently a potent respiratory virus at the heart of a global pandemic—neither of which are unheard of. What sets COVID-19 apart is that it is, at its heart, an unprecedented data crisis. By placing the emphasis on data as the source of truth, we obscure the political processes behind capturing, defining, and institutionalizing data, and downplay the power of the individual in judging and acting on data.
Is there truth in the COVID-19 data?
Data is at its core a contradiction, even when it creates valuable and significant knowledge. What we consider “data” is not an individual piece of knowledge, but the whole, an agglomeration of information presented as numerical insights. The contradiction here is that a single datum, or piece of data, is insignificant on its own, but gains more importance the more data there is. That one piece of information gains power by being understood in larger and larger contexts. So, data is both the annihilation of the individual, and its culmination in the group.
Despite the fact that data appears interchangeable with knowledge, “truth” in data is a moving target. Because the individual datum is inconsequential on its own, the “truth” of data resides in its interpretation and analysis. “Raw” data is indeed a valuable raw material that requires labor to form it into something meaningful. But because raw data is information (as opposed to some other raw material like cotton or ore), the work to form data into insights doesn’t “use up” the data, it can even endlessly multiply the data: for example interpreting a set of data against new variables, applying new formulas and algorithms, or defining the unit of data differently. There’s only so much cotton in a bushel…but data proliferates.
COVID-19 is a crisis in data because it brings the contradictions of truth in data to the forefront, and pushes the limits of data-driven decision making. Here I have two key examples: the tepid United States response to the pandemic and the admonition to “flatten the curve.” The first shows the inherent instability of “truth” in data, and the second shows the danger of believing the data is truth.
The United States response
Institutionally, COVID-19 shows how unstable a data-first analysis can be, because one’s point of view–measurements, chosen variables, applied formulas–define the data. As it spread across the world, we saw different countries’ health organizations struggle to analyze and communicate important information. And if key concepts differ between countries (for example, “confirmed cases”) we will get different pictures of the disease in each place. This also assumes that institutional standards and analyses are free of political influence, that everyone is simply “after the truth.” The fact remains that data analysis lends itself to politicization easily. How one researches, changes results. Those results might be overall similar for a layperson (thousands sick), but they will certainly affect public health priorities and policy decisions. This, combined with a capricious and insecure White House, led to a slow and often contradictory response from United States political leaders.
“Flatten the curve!”
Less formally, we hear “flatten the curve”: the slogan of every-day people to persuade others to act in light of the danger of COVID-19. The curve appears as the truth of the situation, be it calamitous or mild, but it requires the good faith (and good behavior) of the individual. Against the “curve” of millions, my individual actions bear statistical weight.To “flatten the curve” is a data-driven (based in fact, truth-bearing) moral argument about an individual’s social obligations to others. Such an argument conflates the factually-based with the inherently social, political, and moral realms of how to act toward others. This conflation contributes to a general moral panic and the social ostracization of those who are seen disobeying the new rules (not social distancing, visiting family, etc). It also serves to justify strong, centralized political institutions to enforce the moral law.
Thinking into the future of data
Ideally, data supports experienced and educated experts in making day-to-day judgment calls. In light of this, I consider OrbitalRX’s drug shortage management platform to be optimal: we find and organize important data around drug shortages and availability. But this data is not the answer: it serves to support hospital pharmacists in making their own practical decisions as the situation demands. We require this ethical fulcrum of human decision making in Healthcare, but it is the core of any meaningful data-decision.
For some, the lasting impact of COVID-19 will be tragedy. But for all, it will be a historical and political rupture, a sudden and unprecedented shift in governments and economies, that led to quick, extreme, and lasting social changes. I believe COVID-19 will come to define a crisis in our faith in data. The way beyond this crisis is not to search for greater truth in data, but to find the truth that data stands in for, and to attribute meaning to those who make it: the decision makers.
About the Author:
Juniper Alcorn PhD
Juniper received her PhD in Philosophy from The New School for Social Research in 2019, writing on new biotechnologies and their social impact. She works as a Software Developer at OrbitalRX.