Abstract In recent decades, the proliferation of data and advances in information technology have led organizations to value data more highly and aim to build a data culture that is suitable for promoting and sustaining data‐related strategic outcomes. However, what a “good” data culture comprises is often expressed abstractly and there is no consensus about how such a culture should manifest in practice. This study explores the key dimensions and attributes of an ideal data culture, as perceived by expert practitioners in large, data‐rich public sector organizations. Using a two‐stage Delphi method, we engaged with 14 data management experts from Aotearoa New Zealand to understand their views on achieving “Data Nirvana” in practice, focusing on the attributes that explain an ideal data culture. Five categories of ideal data culture are identified: strategic agility, ethical use, human centricity, capability, and controls and discipline. These are linked through two unifying themes: trust and trustworthiness, and value integration. The resulting framework for data culture comprises seven elements. The study provides insights into the aspirational potential of data and the realities of organizational data practice, contributing to a deeper understanding of data culture.

Abstract Data culture/s as a research topic has begun to attract attention from a wide range of disciplines, albeit with inconsistent application of definitions, dimensions, and applications. This work builds on a call to investigate data culture/s within the information studies domain as a topic related to, but distinct from, information culture. The purpose of this study is to explore what is known about data culture/s in greater depth. We apply a retroductive approach to select and consider likely dimensions, inputs, and aspects of data culture/s in order to further map this construct to the literature, and thereby highlight gaps and opportunities to add to this body of knowledge. The initial candidate dimensions explored below include data‐related skills and attitudes, data sharing, data use/reuse, data ethics and governance, and a specific focus on Indigenous perspectives to provide insights on why and how a group may contest the emergent dominant discourse of data culture/s. Our conclusion highlights areas needing further research to fully define and examine the dimensions, inputs, and aspects of data culture/s, and calls for greater understanding and engagement with data culture/s from the information studies community.

Abstract It has been argued that emergent AI systems should be viewed as working partners rather than tools. Building on this perspective, this study investigates the process through which academics develop a working partnership with generative AI chatbots using a relationship lens and collaborative autoethnographic methods. Based on a collaborative autoethnographic analysis of our experiences of working with AI, we identify five stages of relationship development in building a successful working partnership with generative AI: Playing Around, Infatuation, Committing, Frustration, and Enlightenment and Readjustment. In reporting each stage, we provide vignettes to illustrate the emotional and cognitive challenges and rewards involved, the developing skills, and the experienced anthropomorphic dimension of this emerging relationship. Drawing on prior theory, we identify and examine three types of work that are required in developing this working partnership: articulation work, relationship work, and identity work. We analyse how each type of work unfolds with the stages of relationship development and consider implications for research and practice.

Information systems development (ISD) is an integral part of organizational agility in today’s competitive business environment. High turnover, agile ways of working, and fluid work environments pose challenges for ISD. This paper explores the erosion of knowledge retention (KR) arising from ISD staff churn in a New Zealand-based financial organization in the aftermath of a major earthquake. In this exploratory study, the authors develop a causal model of KR in the ISD context, which articulates the challenges to and consequences of ineffective KR at the routine and exiting stages of KR. The model identifies four challenges—coordination complexity, insufficient resources for knowledge retention, insufficient attention to knowledge retention, and slow staff replacement and handover processes—that can affect the loss of ISD knowledge when routine and exiting KR fall into disarray. This study also reveals that role stress and reduced ISD agility reinforce the cycle of knowledge loss.