The basic methodology of data—as collected by real-world institutions obeying real-world forces of economy and scale—systematically leaves out certain kinds of information.

there are so many important factors that are far harder to measure: happiness, community, tradition, beauty, comfort, and all the oddities that go into “quality of life.”

As Merry puts it, metrics and indicators require all kinds of political compromises and judgment calls to compress so much rich information into a single ranking. But the superficially simple nature of the final product—the metric—tends to conceal all kinds of subjectivity and politics

Not all kinds of knowledge, and not all kinds of understanding, can count as information and as data. Historian of quantification Theodore Porter describes “information” as a kind of “communication with people who are unknown to one another, and who thus have no personal basis for shared understanding.” In other words, “information” has been... See more

The process of making data portable also screens off sensitive, local, or highly contextual modes of understanding. In transforming understanding into data, we typically eliminate or reduce evaluative methods that require significant experience or discretionary judgment in favor of methods that are highly repeatable and mechanical.

So here is the first principle of data: collecting data involves a trade-off. We gain portability and aggregability at the price of context-sensitivity and nuance. What’s missing from data? Data is designed to be usable and comprehensible by very different people from very different contexts and backgrounds. So data collection procedures tend to... See more

I once sat in a room with a bunch of machine learning folks who were developing creative artificial intelligence to make “good art.” I asked one researcher about the training data. How did they choose to operationalize “good art”? Their reply: they used Netflix data about engagement hours.