Capturing Value from Data Complementarities: A Multi-Level Framework

Abstract

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 dataset 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).