Research: Profiting From Data Commons

ManuFuturew Today is exploring how we might generate and share data among smaller manufacturers, in order to speed their digital transformation. Large companies have an internal infrastructure to capture and analyze data. Smaller manufacturers don’t. How can we pool data among smaller manufacturers to that we can speed their journey.

Recently Ali Shacouri sat down with a leading MIT researcher, Eric von Hippel, to explore these issues.

What is a “data commons”? How can it accelerate innovation? These topics and others are covered in a new scholarly paper: “Profiting from Data Commons: Theory, Evidence, and Strategy Implications”. We have posted the paper in our community.

You can read the Abstract here:

“We define data commons as repositories of freely accessible, “open source” innovation-related data, information, and knowledge. Data commons are and can be a significant resource for both innovating and innovation-adopting firms and individuals. First, the availability of free data and information from such commons reduces the innovation-specific private or open investment required to access the data and make the next innovative advance. Second, the fact that the data are freely accessible lowers transaction costs substantially. In this paper, we draw on the theory and empirical evidence regarding innovation commons in general and data commons in particular. Based on these foundations, we consider strategic decisions in the private and public domain: how can individuals, firms, and societies profit from data commons? We first discuss the varying nature of and contents of data commons, their functioning, and the value they provide to private innovators and to social welfare. We next explore the several types of data commons extant today and their mechanisms of action. We find that those who develop innovation-related information at private cost already have, surprisingly often, an economic incentive to freely reveal their information to a data commons. However, we also find and discuss important exceptions. We conclude with suggestions regarding needed innovation research, data commons “engineering”, and innovation policymaking that could together increase private and social welfare via enhancement of data commons.”