Designing Data Governance in Platform Ecosystems

As platform ecosystems such as Facebook or Twitter are rapidly growing through platform users’ data contribution, the importance of data governance has been highlighted. Platform ecosystems, however, face increasing complexity derived from the business context such as multiple parties’ participation. How to share control and decision rights about data assets with platform users is regarded as a significant governance design issue. However, there is a lack of studies on this issue. Existing design models focus on the characteristics of enterprises. Therefore, there is limited support for platform ecosystems where there are different types of context and complicated relationships. To deal with the issue, this paper proposes a novel design approach for data governance in platform ecosystems including design principles, contingency factors and an architecture model. Case studies are performed to illustrate the practical implications of our suggestion

A Contingency-Based Approach to Data Governance Design for Platform Ecosystems

Data today is regarded as a new type of fuel of platform ecosystems. It enables sustainable growth of platform ecosystems through users’ data contribution. Data governance is necessary to safely manage platform data and succeed in business. Platform ecosystems, however, encounter a complicated business context and environment since there are multiple participating groups, different strategies, goals, and different levels of market regulations. How to adopt appropriate data governance dealing with the concerns has been neglected, and therefore there is little research on this topic. To respond to the challenge, we introduce a novel design approach which can address the various contingencies, characteristics and governance goals of platform ecosystems. We present a case study and use case to illustrate the implications and support the implementation of the approach in practice

Data Governance for Platform Ecosystems: Critical Factors and The State of Practice

Recently, “platform ecosystem” has received attention as a key business concept. Sustainable growth of platform ecosystems is enabled by platform users supplying and/or demanding content from each other: e.g. Facebook, YouTube or Twitter. The importance and value of user data in platform ecosystems is accentuated since platform owners use and sell the data for their business. Serious concern is increasing about data misuse or abuse, privacy issues and revenue sharing between the different stakeholders. Traditional data governance focuses on generic goals and a universal approach to manage the data of an enterprise. It entails limited support for the complicated situation and relationship of a platform ecosystem where multiple participating parties contribute, use data and share profits. This article identifies data governance factors for platform ecosystems through literature review. The study then surveys the data governance state of practice of four platform ecosystems: Facebook, YouTube, EBay and Uber. Finally, 19 governance models in industry and academia are compared against our identified data governance factors for platform ecosystems to reveal the gaps and limitations

Data Governance Decisions for Platform Ecosystems

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

Data governance for platform ecosystems

Platform ecosystem has become an information system research subject after many years of industry success. The concept facilitates fast and self-growing of a platform by encouraging data contribution and consumption of multiple networks. It is essential to understand how data should be managed in platform ecosystems where there are complicated relationships. However, this topic has been rarely addressed in both industry and academia. Prior research is still in early-stage. To respond to this, we proposed a novel data governance framework based on a design science research approach which delivers a set of guidelines and practical disciplines to support right decision making. A contingency model is proposed as an operating model of the framework to support designing and implementing data governance. It describes the influence of the context and characteristics of a platform ecosystem on data governance design. In this study, we used qualitative research within a design science approach which is mainly based on a literature review and case studies. The case studies are carried out to understand how and if industry platforms are addressing the theoretically important factors of the proposed framework in reality. Through the case studies, we confirmed that industry platforms consider data governance as a set of important decision areas. We have also found there are some limitations of the implementation of practices such as a lack of consideration of various types of data or proactive monitoring mechanisms. For the next steps, we willconsider further case studies as there are limited cases are examined in this study

QB4AIRA: A Question Bank for AI Risk Assessment

The rapid advancement of Artificial Intelligence (AI), exemplified by ChatGPT, has raised concerns about the responsible development and utilization of AI systems. To address these concerns, AI ethics principles have been established, emphasizing the need for risk assessment frameworks to ensure adherence to ethical and societal considerations. However, existing frameworks lack a comprehensive and organized synthesis of dedicated AI risk assessment questions. Coping with this limitation, we present a novel question bank for AI Risk Assessment (QB4AIRA), which is developed through the collection and refinement of questions from five globally-known AI risk frameworks, categorized according to Australia AI ethics principles. QB4AIRA consists of 291 questions, covering a wide range of AI risk areas and providing a standardized approach to AI risk assessment. The questions are prioritized based on their importance and level of detail, facilitating effective AI risk assessment. QB4AIRA serves as a valuable resource for various stakeholders to assess and manage potential risks associated with AI systems. By promoting responsible and ethical AI practices, QB4AIRA contributes to the responsible deployment of AI systems and mitigates potential risks and harms