Why Data Is Never Raw
On the seductive myth of information free of human judgment
Source/Fonte: University of London
Nick Barrowman
A curious fact about our data-obsessed era is that we’re often not entirely sure what we even mean by “data”: Elementary particles of knowledge? Digital records? Pure information? Sometimes when we refer to “the data,” we mean the results of an analysis or the evidence concerning a certain question. On other occasions we intend “data” to signify something like “reliable evidence,” as in the saying “The plural of anecdote is not data.”
In everyday usage, the term “data” is associated with a jumble of notions about information, science, and knowledge. Countless reports marvel at the astonishing volumes of data being produced and manipulated, the efficiencies and new opportunities this has made possible, and the myriad ways in which society is changing as a result. We speak of “raw” data and laud it for its independence from human judgment. On this basis, “data-driven” (or “evidence-based”) decision-making is widely endorsed. Yet data’s purported freedom from human subjectivity also seems to allow us to invest it with agency: “Let the data speak for itself,” for “The data doesn’t lie.”
Out of this quizzical mix, it is perhaps unsurprising that near-magical thinking about data has emerged. In the 2015 book Digital Destiny: How the New Age of Data Will Transform the Way We Work, Live, and Communicate, Shawn DuBravac describes a collection of “properties of data” and expresses them in anthropomorphic terms. DuBravac, former chief economist at the Consumer Electronics Association and a self-styled futurist and “trendcaster,” claims that data “seeks permanence,” “wants to replicate,” “seeks instantaneity,” “wants to be understood,” and “seeks movement.”
Data is immediate.... When data comes into being, when it is first tracked, captured, or copied, it wants to immediately be utilized — to exert force and influence.... Data constantly moves toward efficiency. It removes barriers; it closes distances; it destroys the moments between recognition and understanding. Because data wants to be understood, it abhors friction.
This projection of human-like qualities onto data is ostensibly metaphorical, but it can muddle our thinking. It seems aimed at obscuring how intertwined is the production of data with human judgment, and the use of data with human agency. And once our agency has been obscured, it is not hard to imagine that data has a mind of its own, that to solve our great problems we have only to collect the data and set the computers running.
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