To an increasing degree, data is a driving force for digitization, and hence also a key asset for numerous companies. In many businesses, various sources of data exist, which are isolated from one another in different domains, across a heterogeneous application landscape. Well-known centralized solution technologies, such as data warehouses and data lakes, exist to integrate data into one system, but they do not always scale well. Therefore, robust and decentralized ways to manage data can provide the companies with better value give companies a competitive edge over a single central repository. In this paper, we address why and when a monolithic data storage should be decentralized for improved scalability, and how to perform the decentralization. The paper is based on industrial experiences and the findings show empirically the potential of a distributed system as well as pinpoint the core pieces that are needed for its central management.

Increasingly common open data and open application programming interfaces (APIs) together with the progress of data science -such as artificial intelligence (AI) and especially machine learning (ML) -create opportunities to build novel services by combining data from different sources. In this experience report, we describe our firsthand experiences on open data and in the domain of marine traffic in Finland and Sweden and identified technological opportunities for novel services. We enumerate five challenges that we have encountered with the application of open data: relevant data, historical data, licensing, runtime quality, and API evolution. These challenges affect both business model and technical implementation. We discuss how these challenges could be alleviated by better governance practices for provided open APIs and data.

The field of software ecosystems is rapidly maturing and significant numbers of articles are published each year to further develop our understanding of this concept and support innovation through it. The growth of the field also brings along challenges, such as findability and reusability of research results, coordination of research initiatives, and significant review pressure on members of the community. In this mapping study of empirical research methods in the field, we show that few studies do a good job of reporting their research methods and results. Using data from the study, we provide guidelines for performing empirical research in software ecosystems.

Increasingly common open data and open application programming interfaces (APIs) together with the progress of data science -such as artificial intelligence (AI) and especially machine learning (ML) -create opportunities to build novel services by combining data from different sources. In this experience report, we describe our firsthand experiences on open data and in the domain of marine traffic in Finland and Sweden and identified technological opportunities for novel services. We enumerate five challenges that we have encountered with the application of open data: relevant data, historical data, licensing, runtime quality, and API evolution. These challenges affect both business model and technical implementation. We discuss how these challenges could be alleviated by better governance practices for provided open APIs and data.