Data Monetization & Open Data: Designing a Tool for Selective Data Revealing
The creation and possession of vast amounts of data through e.g. social media and Industry 4.0 enables organizations to create value. Given the lack of skills and capacity to process this data, however, poses a major challenge to organizations to extract value from data. Hence, firms are looking for novel ways to extract value from their data assets. In recent years, for-profit organizations have started to share part of their data for free as so-called open data.Open Data enables the co-creation of value in digital ecosystems by leveraging the skills and capabilities of multiple actors. However, at this point, little is known about how to operationally enable and strategically position Open Data to create and capture value.
The objective of this thesis is to develop a better understanding of the process that organizations go through to select data to share externally. This so-called selective revealing represents a critical trade-off between sharing the right type and quantity of data to spark innovation while at the same time protect the firm’s competitiveness. Based on a design science research (DSR) approach, the student will conduct interviews and derive a preliminary framework to support organizations in selecting open data. The thesis is part of a broader research project at IISM/KSRI and will build upon existing results.