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Paper Type
Complete
Abstract
Data ecosystems offer great potentials for companies, but there are many challenges that prevent companies from fully realizing them. To provide guidance and enhance existing literature, we propose milestones for the development of data ecosystems. These milestones are particularly important in the birth and expansion phases of a data ecosystem. Our research is based on two case studies of two data ecosystems that were developed from birth to expansion. A total of 17 companies were part of the ecosystems. From these two case studies we derived ten findings and the following three milestones: 1. internal customer focused and domain specific ecosystem, 2. internal customer focused and cross-domain ecosystem, and 3. external customer and cross-domain-specific ecosystem. These milestones are particularly useful in traditional non-data-driven industries where they were initially observed.
Paper Number
1802
Recommended Citation
Weber, Patrick; Hiller, Simon; Kurrle, Sven; Werling, Maxmilian; and Werth, Daniel, "Evolutionary Milestones in the Development of Data Ecosystems" (2024). AMCIS 2024 Proceedings. 4.
https://aisel.aisnet.org/amcis2024/data_eco/data_eco/4
Evolutionary Milestones in the Development of Data Ecosystems
Data ecosystems offer great potentials for companies, but there are many challenges that prevent companies from fully realizing them. To provide guidance and enhance existing literature, we propose milestones for the development of data ecosystems. These milestones are particularly important in the birth and expansion phases of a data ecosystem. Our research is based on two case studies of two data ecosystems that were developed from birth to expansion. A total of 17 companies were part of the ecosystems. From these two case studies we derived ten findings and the following three milestones: 1. internal customer focused and domain specific ecosystem, 2. internal customer focused and cross-domain ecosystem, and 3. external customer and cross-domain-specific ecosystem. These milestones are particularly useful in traditional non-data-driven industries where they were initially observed.
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