Like oil, data must be refined to be useful—but like sunlight, data will be everywhere
have long quarrelled over whether data should be singular or plural . A better question is why are data so singularly plural? That is, why do they have so many different faces?
This in turn means that there is not just one data economy, but three more or less distinct ones, each with its own ideology. And the big question is whether one will come to dominate, or whether the mirror world will be as much of a mixture as the real one. Offering insights from mining data can be very profitable, too. On Kaggle, a website owned by Google that hosts machine-learning contests, thousands of teams of data scientists compete against each other to see who can come up with the best algorithms to predict a building’s energy consumption or to detect “deepfake” videos, with prizes sometimes exceeding $1m. That is also Facebook’s and Google’s way to make money.
As for personal data, defining property rights is tricky, because much information cannot be attributed to one person. Who, for instance, owns the fact that a dating site has matched a couple? The couple themselves? Or the service? Complicating matters, data have plenty of externalities, both positive and negative, meaning that markets often fail.
This line of argument has already given birth to what is known as the “open-data” movement. Its champions push organisations and universities to give away their data so they can be widely used, for instance by startups. Today, most governments, national or otherwise, boast an open-data project, although the quality of the data made available varies greatly.
Like the oil comparison, however, the data-as-sunlight analogy breaks down: open data, too, can go only so far. For personal data, the main limitation is increasingly strict privacy laws, such as the’s General Data Protection Regulation , as well as the California Consumer Privacy Act , which will start being enforced in July. For corporate data the checks are economic in nature: generating good data is expensive and they can reveal too much about a firm’s products.
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