Depending on the situation, data is either like a liquid (data streams), a solid (data mining), or a gas (the cloud). Why and how these metaphors get used when they do is not immediately obvious. There are tons of alternatives: Data could be stored in a “data mountain,” or data could be made useful through a process of “data desalination.”
The metaphors we use matter, because metaphors have baggage. Metaphors are encumbered with assumptions, and when people use metaphors, they embed those assumptions in the discussion. These assumptions are the residue of the physical analogues from which the metaphors draw. Referring to “data exhaust”—a term sometimes used to describe the metadata that are created in the course of day-to-day online lives—reinforces the idea that these data, like car exhaust, are unwanted byproducts, discarded waste material that society would benefit from putting to use. On the other hand, calling data “the new oil,” carries strong economic and social connotations: Data are costly to acquire and produced primarily for commercial or industrial ends, but bear the possibility of big payoffs for those with the means to extract it.
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What’s more, metaphors matter because they shape laws and policies about data collection and use. As technology advances, law evolves (slowly, and somewhat clumsily) to accommodate new technologies and social norms around them. The most typical way this happens is that judges and regulators think about whether a new, unregulated technology is sufficiently like an existing thing that we already have rules about—and this is where metaphors and comparisons come in.
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And in all our talk about streams and exhaust and mines and clouds, one thing is striking: People are nowhere to be found. These metaphors overwhelmingly draw from the natural world and the processes we use to draw resources from it; because of this, they naturalize and depersonalize data and its collection. Our current data metaphors do us a disservice by masking the human behaviors, relationships, and communications that make up all that data we’re streaming and mining. They make it easy to get lost in the quantity of the data without remembering how personal so much of it is. And if people forget that, it’s easy to understand how large-scale ethical breaches happen; the metaphors help us to lose track of what we’re really talking about.
- More Here
The metaphors we use matter, because metaphors have baggage. Metaphors are encumbered with assumptions, and when people use metaphors, they embed those assumptions in the discussion. These assumptions are the residue of the physical analogues from which the metaphors draw. Referring to “data exhaust”—a term sometimes used to describe the metadata that are created in the course of day-to-day online lives—reinforces the idea that these data, like car exhaust, are unwanted byproducts, discarded waste material that society would benefit from putting to use. On the other hand, calling data “the new oil,” carries strong economic and social connotations: Data are costly to acquire and produced primarily for commercial or industrial ends, but bear the possibility of big payoffs for those with the means to extract it.
[---]
What’s more, metaphors matter because they shape laws and policies about data collection and use. As technology advances, law evolves (slowly, and somewhat clumsily) to accommodate new technologies and social norms around them. The most typical way this happens is that judges and regulators think about whether a new, unregulated technology is sufficiently like an existing thing that we already have rules about—and this is where metaphors and comparisons come in.
[---]
And in all our talk about streams and exhaust and mines and clouds, one thing is striking: People are nowhere to be found. These metaphors overwhelmingly draw from the natural world and the processes we use to draw resources from it; because of this, they naturalize and depersonalize data and its collection. Our current data metaphors do us a disservice by masking the human behaviors, relationships, and communications that make up all that data we’re streaming and mining. They make it easy to get lost in the quantity of the data without remembering how personal so much of it is. And if people forget that, it’s easy to understand how large-scale ethical breaches happen; the metaphors help us to lose track of what we’re really talking about.
- More Here
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