Why the Data Marketplaces of the Future Will Sell Insights, Not Data

As director of MIT’s Institute for Data, Systems, and Society (IDSS), Munther Dahleh oversees multiple research projects that use AI and data science to tackle challenges in energy, finance, health care, and urban environments. He views data as an important asset across many domains, including business, but argues that we’re lacking sound ways to verify data’s value.

Dahleh, who is also the William A. Coolidge Professor for Electrical Engineering and Computer Science at MIT, says the technology for a better data marketplace is already under development. The IDSS is creating a cloud-based “Data Lab” that will store data sets securely and offer data processing services to individuals who want to make use of those data sets. This same Data Lab architecture can be used for future commercial data marketplaces, Dahleh says.

MIT Sloan Management Review spoke with Dahleh about why current data marketplaces fall short, how a more efficient one would work, why businesses may overestimate the value of their big-data caches, and how the financial value of specific business insights will determine what a collection of data is really worth. MIT SMR’s Elizabeth Heichler conducted the interview, and what follows is an edited and condensed version of their conversation.

MIT Sloan Management Review: You’ve said that the current market for data is inadequate — that pricing is illogical, and there’s no verification that particular data has value. What do you consider the hallmarks of an efficient market for data?

Dahleh: A market has two sides — buyers and sellers. Prices are decided based on demand and supply, an equilibrium of some sort. If the market is designed well, then you trust the pricing of the market.

Today, there’s no data market that operates like the stock market, or the online ads market, based on auction strategies. And for this to happen, people have to buy into the market, and share their data, and trust that the market maker will actually compensate them appropriately for that data.

How would a business that needs data for decision-making use the data marketplace you envision?

Dahleh: Here’s an example: You might come into the market because you’re a retailer and you’re trying to predict your inventory. You describe the prediction you need, choose the prediction algorithms you want to use, and place a bid. The market evaluates your request and informs you what relevant data is available.