The idea that many businesses rely heavily on data to produce or market goods and services is not new.1 Indeed, even in 2018, four of the six top companies in market valuation — Amazon, Alphabet, Facebook, and Alibaba2 — based their business models on the use of data to optimize advertising. However, data differs greatly from traditional factors of production, such as capital and labor. For instance, to achieve scale, companies need data about large numbers of customers — especially when algorithms are used in advertising and other revenue-generating models. Given that scale, data interacts with personal privacy — even national security — in ways that other factors of production do not. These special attributes of data hinder its efficient and transparent trade in data markets, keep it in closed silos despite its digital nature, and often stop organizations from maximizing its value.
But the conception of big data as a silo managed by single entities is giving way to the notion of shared data. We are interested specifically in data exchanges — shared platforms where data is gathered and curated from many different sources (all the individuals and organizations that voluntarily share it), allowing third parties to gain insights from it. As those insights start to move freely, securely, and confidentially in the market, they will greatly enhance data-based value generation. But for that potential to be realized, managers must become familiar with the unique characteristics of data and with how data exchanges can capitalize on them while mitigating threats.
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What Makes Data Unique
Appreciating the full potential of shared data starts with understanding how data is unlike other factors of production:
Data is non-fungible. Distinct units of data can be used differently by the same company. For example, when a business receives, say, $1 of investment, it is irrelevant which specific U.S. dollar of the many in circulation the business receives and whether the investor pays this dollar as one note, four quarters, or 100 cents. That’s because capital is interchangeable. However, should a company receive one unit (say, a megabyte) of data to develop a specific algorithm, not all units of data (health-related data, financial data, geolocation data, and so on) will serve the organization equally well in that effort.
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3. C.I. Jones and C. Tonetti, “Nonrivalry and the Economics of Data,” working paper 26260, National Bureau of Economic Research, September 2019.
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