Data-driven government: Cross-case comparison of data stewardship in data ecosystems

Abstract

Government agencies are becoming more data-driven and need high-quality data to fulfill their roles in society. In the past, each agency organized its own data exchange system according to its own needs. Today, data is distributed over many organizations, and government agencies need to adopt an ecosystem approach for data exchange. Fundamental in the ecosystem approach is the dependence on other parties for the execution of stewardship strategies. Data-driven government agencies increasingly depend on other organizations for high-quality data and data stewardship across organizations is becoming more critical. While there is ample research on data stewardship within organizations, little is known about data stewardship in ecosystems. More specifically, it is unclear which data stewardship strategies government agencies can employ in ecosystems. The main goal of this explorative paper is to identify and compare data stewardship strategies used in empirical government-business ecosystems. Following an explorative case study approach, this paper reveals three different configurations of inter-organizational data stewardship: 1) the government-led ecosystem, 2) the government-business-led ecosystem, and 3) the regulation-led ecosystem. The case studies expose a wide array of data stewardship strategies across ecosystems. While the ecosystem approach provides advantages such as cost-sharing and innovation by private parties, government agencies become increasingly dependent on private parties to gain high-quality data and provide distributed infrastructure components. Maximizing the benefits and minimizing the risks of the ecosystem approach requires government agencies to be cautious when selecting a specific ecosystem configuration.