Capturing Value from Data Complementarities: A Multi-Level Framework

Data—as a specific form of digital resource distinct from software—has become strategically important for individual firms and for supply chains, ecosystems, and platforms. Data is by nature nonrival; it does not lose value when shared, and in technical terms, data can be infinitely disseminated, combined, and used. Indeed, a particular data set often gains in meaningfulness and value when combined and aggregated into actionable bundles such as “data objects” (e.g., user profiles, simulation models) or “information goods” (e.g., adverts)—a phenomenon we conceptualize as data complementarities. However, as data resources also entail competitive, legislative, and technical challenges—especially with regard to their mobility—the question of who captures value from data complementarities (and how) is a relevant concern. This chapter describes a multi-level model for capturing value from four types of data complementarity: internal (hierarchy), relational (bilateral contractual relationship), supermodular (platform ecosystem), and unbounded (data markets).