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.@en

Comparable to the concept of a data(-driven) enterprise, the concept of a ggovernment as data (-driven) enterprise' is gaining popularity as a data strategy. However, what it implies is unclear. The objective of this paper is to clarify the concept of the government as data (-driven) enterprise, and identify the challenges and drivers that shape future data strategies. Drawing on literature review and expert interviews, this paper provides a rich understanding of the challenges for developing sound future government data strategies. Our analysis shows that two contrary data strategies dominate the debate. On the one hand is the data-driven enterprise strategy that focusses on collecting and using data to improve or enrich government processes and services (internal orientation). On the other hand, respondents point to the urgent need for governments to take on data stewardship, so other parties can use data to develop value for society (external orientation). Since these data strategies are not mutually exclusive, some government agencies will attempt to combine them, which is very difficult to pull off. Nonetheless, both strategies demand a more data minded culture. Moreover, the successful implementation of either strategy requires mature data governance - something most organisations still need to master. This research contributes by providing more depth to these strategies. The main challenge for policy makers is to decide on which strategy best fits their agency's roles and responsibilities and develop a shared roadmap with the external actors while at the same time mature on data governance.

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