Data activities and technologies in modern organisations are becoming more diverse and complex, and continuously operate, change, and evolve as an enterprise data ecosystem (EDE). This fast-changing environment presents both challenges and opportunities for many computing and design concepts and approaches and raises the questions whether and how they can be continuously and effectively applied. This paper analyses operation features and complexity of EDE and delves into design considerations for data products and Data-as-a-Service (DaaS) to play their roles as solutions for data practice management improvement, integration, interoperability and design of services in data space.

The significant increase in data generation across various sectors has prompted the development of concepts such as Data Product and Data Economy (DE) to enhance organizational productivity. Concurrently, advancements in AI models have heightened data privacy concerns, particularly as typical AI model training methods often involve data collection and storage in centralized databases, which are exposed to misuse. In response, Federated Learning (FL) has emerged as a promising approach, enabling the collaborative training of AI models without the direct sharing of data. This paper examines the potential of FL in the initial stages of data generation and throughout the data product design process. It further explores how FL can facilitate the generation of data products, providing a range of practical applications across different industries to address privacy concerns effectively in modern AI solutions.

Smooth Inter-organization or cross-domain data flows are increasingly demanded when more organizations are becoming data-driven, which will eventually lead to the rise of a new form of the world economy, that is, data economy (DE). Through exploring relevant concepts, technologies, issues and challenges facing DE, this paper discusses the operational concepts of DE ecosystem and its key elements, particularly data products and data marketplaces, and the trends of their developments. It then presents directions for research and development of DE ecosystem and its key elements, in order to help organizations and industry to prepare for the arrival of DE and identify areas for innovations and disruptive technologies. The paper also discusses the requirements of the data product design in the enterprise data ecosystem (EDE) for the rising data economy and the gaps that exist in the current data engineering and technologies, particularly examining the role of service technology in the development and practice of data economy ecosystems.