Business processes for data monetization: an exploratory analysis and proposition of design patterns

Abstract

Purpose The data monetization market is valued at $1.5 billion, with an expected annual growth rate of 25%. This growth presents significant opportunities for companies to expand their revenue streams. However, many companies struggle to extract value from their data due to existing challenges and need for more knowledge. While existing studies describe and classify dimensions of the phenomenon, there is a need to explore causality relations that can help the structuring of data monetization processes. This study aimed to support the structuring of the data monetization process. Design/methodology/approach Proposing causality relations is important to explore the data monetization phenomenon. Therefore, empirical knowledge about data monetization was organized into design patterns using the context-intervention-mechanism-outcome (CIMO) logic. The effectiveness of these patterns was then assessed through an exploratory case study conducted at a leading Brazilian academic institution where data monetization is central to its business model. Findings The study yields six design patterns that address various aspects such as data pricing, data-driven business models and best practices for data monetization. Additionally, it presents a comprehensive understanding of the data monetization process through a value-added chain framework. Originality/value The findings contribute to the advancement of knowledge in the field, the proposition of causality, and offer valuable insights into organizations that wish to structure their resources and capabilities and leverage data.

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