In a blockchain-based system, data and the consensus-based process of recording and updating them over distributed nodes are central to enabling the trustless multi-party transactions. Thus, properly understanding what and how the data are stored and manipulated ultimately determines the degree of utility, performance, and cost of a blockchain-based application. While blockchains enhance the quality of the data by providing a transparent, immutable, and consistent data store, the technology also brings new challenges from a data management perspective. In this paper, we analyse blockchains from the viewpoint of a developer to highlight important concepts and considerations when incorporating a blockchain into a larger software system as a data store. The work aims to increase the level of understanding of blockchain technology as a data store and to promote a methodical approach in applying it to large software systems. First, we identify the common architectural layers of a typical software system with data stores and conceptualise each layer in blockchain terms. Second, we examine the placement and flow of data in blockchain-based applications. Third, we explore data administration aspects for blockchains, especially as a distributed data store. Fourth, we discuss the analytics of blockchain data and trustable data analytics enabled by blockchain. Lastly, we examine the data governance issues in blockchains in terms of privacy and quality assurance.INDEX TERMS Analytics, blockchain, databases, data governance, data handling, distributed data management, distributed databases, software architecture, transaction databases.

As platform ecosystems such as Facebook

Platform ecosystem has become an information system research subject after many years of industry success. The concept of platform ecosystem facilitates fast and self-growing of a platform by encouraging data contribution/consumption of multiple networks, and thus the importance and value of data in platforms is accentuated. It is essential to understand how data should be managed in platform ecosystems where there is complicated relationships between multiple participating groups. However, this topic has been rarely addressed in industry and academia. Industry governance frameworks focus on organizational data, and prior research on platform ecosystem is still in early-stage. To response to the limitation, we propose critical data governance decisions for platform ecosystems, and discuss how they have to be implemented in practice. This study supports right decision making about data, and facilitates a secure platform ecosystem. We perform a case study to illustrate the practical implications of this study.

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