Open Data Ecosystems -- an empirical investigation into an emerging industry collaboration concept

TL;DR

This paper investigates Open Data Ecosystems (ODE) as a new industry collaboration model, exploring their key features, governance, and evolution through empirical data from practitioners and case studies.

Contribution

It introduces an initial conceptual model of ODEs' value, intrinsics, governance, and evolution, providing a foundation for future research and practice.

Findings

ODE must be value driven.

Data type, meta-data, and legal frameworks influence openness.

Ecosystem initiation and organization characteristics are differentiating.

Abstract

Software systems are increasingly depending on data, particularly with the rising use of machine learning, and developers are looking for new sources of data. Open Data Ecosystems (ODE) is an emerging concept for data sharing under public licenses in software ecosystems, similar to Open Source Software (OSS). It has certain similarities to Open Government Data (OGD), where public agencies share data for innovation and transparency. We aimed to explore open data ecosystems involving commercial actors. Thus, we organized five focus groups with 27 practitioners from 22 companies, public organizations, and research institutes. Based on the outcomes, we surveyed three cases of emerging ODE practice to further understand the concepts and to validate the initial findings. The main outcome is an initial conceptual model of ODEs' value, intrinsics, governance, and evolution, and propositions for practice and further research. We found that ODE must be value driven. Regarding the intrinsics of data, we found their type, meta-data, and legal frameworks influential for their openness. We also found the characteristics of ecosystem initiation, organization, data acquisition and openness be differentiating, which we advise research and practice to take into consideration.