A framework to assist in the governance and management of data in the digital ecosystem

University of Technology Sydney. Faculty of Engineering and Information Technology.The digital ecosystem (DE) continues to grow with the proliferation of new digital offerings every day, a trend that is expected to accelerate rapidly in the next few years. The digital ecosystem involves several players, platforms and industries that provide solutions based on advanced technologies such as the Internet of Things (IoT), cloud computing, analytics and artificial intelligence. The data-driven digital ecosystem provides organisations with the information they need to make better insightful decisions for monetary benefits. However, there are a few challenges. There is limited guidance available on how to effectively establish integrated data governance and management for the data-intensive digital ecosystem. The existing approaches focus on individual organisations rather than the ecosystem. There is a need to look beyond the boundary of a single enterprise. To address data governance and management concerns, this thesis applies the design science research (DSR) approach to develop a framework that can be utilised to create an integrated data governance and management capabilities for a focal enterprise in the DE. Rather than having a fixed one-size-fit-all approach, the framework focusses on the adaptability approach to address the changing business and regulatory landscape. The framework has three major components: Drivers, Elements and Stages. The driver has four key purposes (e.g., Data Compliance, Data Protection, Monetisation and Operational Efficiency). Drivers provide justification to conduct data governance and and data management activities. The element component comprises of six elements (e.g., Data Asset, Data Risk, Guidance, Processes and Procedures, Ecosystem Actors and Technology) and the underlying attributes. Elements provide stakeholders key toolkits to govern and manage data. There are four key stages: 1) Identify, 2) Insulate, 3) Inspect and 4) Improve. The stages provide guidance to achieve objectives of drivers with the elements. The framework is evaluated through scenario-based testing and survey. The results indicate that the framework is reasonably suited to support integrated data governance and management activities in different organisational contexts

A Review of General Data Protection Regulation for Supply Chain Ecosystem

© 2020, Springer Nature Switzerland AG. The data-intensive digital supply chain management (SCM) ecosystems seem to be impacted by the recent changes in the regulations and advancement in technologies such as Artificial Intelligence, Big Data, Analytics, Networking, IoT including proliferation of less expensive hardware devices. There is limited guidance available on how to govern the logistics sector, particularly from a regulatory compliance perspective. Through this paper, we investigate the impact of General Data Protection Regulation (GDPR) on digitized SCM. The key questions are: What are the GPDR specific legal obligations? What is the best approach to manage data access, quality, privacy, security and ownership effectively in SCM? This research paper aims to assist researchers and practitioners to understand the impact of GDPR on SCM, provide the 4I (Identify, Insulate, Inspect, Improve) Framework and its applicability to streamline the GDPR compliance activities