Huan Tang on the financial power of data-sharing among firms
In her study, ‘Data as a Networked Asset,’ assistant professor of finance at Wharton Huan Tang finds that companies are connected not only by products or supply chains, but by the information they exchange.
2 min. read
What is the scope of data-sharing across firms, and what are the economic implications? In her study, “Data as a Networked Asset,” supported by the Stevens Center for Innovation in Finance, Huan Tang, assistant professor of finance at Wharton, uncovers a hidden network of inter-firm data flows—revealing that companies are connected not only by products or supply chains, but by the information they exchange. This research shows that this data network influences how firms perform, how they respond to shocks, and how they are valued in the market.
“As firms become increasingly connected through data sharing, it’s important to understand how they use data, how they exchange it, and what the broader consequences of this sharing are,” says Tang. “Our research sheds light on this by uncovering the data-sharing network among firms, offering insights for companies seeking data-driven growth, for investors evaluating firm value, and for policymakers designing data privacy laws that could reshape the structure of the data economy in ways we don’t yet fully grasp.”
“People often say, ‘data is the new oil.’ That’s not far off—as data becomes a core input in production. But unlike physical capital, data is intangible, difficult to measure, and behaves in unique ways.”
Tang explains what motivated her research is the unprecedented scale of the data economy. “Our paper is a first step in measuring and characterizing data as a networked asset. The data network we construct, along with measures of centrality and connectedness, could help researchers and practitioners better understand how data-driven firms are valued and how shocks propagate in the data economy.”
“Future work could build on this by asking: How do firms choose their data peers? What types of network structures lead to the most productivity in the data economy? And how can we design policies that balance consumer privacy with innovation and growth in the data economy?”
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