Visual Analytics: Transferring, Translating and Transforming Knowledge from Analytics Experts to Non-technical Domain Experts in Multidisciplinary Teams-Reference-Cited by-同舟云学术

Visual Analytics: Transferring, Translating and Transforming Knowledge from Analytics Experts to Non-technical Domain Experts in Multidisciplinary Teams

Published:2022-07-13 Issue: Volume: Page:
ISSN:1387-3326
Container-title:Information Systems Frontiers
language:en
Short-container-title:Inf Syst Front

Author:

Marjanovic Olivera^ORCID,Patmore Greg,Balnave Nikola

Abstract

Abstract Today’s complex problems call for multidisciplinary analytics teams comprising of both analytics and non-technical domain (i.e. subject matter) experts. Recognizing the difference between data visualisaion (DV) (i.e. static visual outputs) and visual analytics (VA) (i.e. a process of interactive visual data exploration, guided by user’s domain and contextual knowledge), this paper focuses on VA for non-technical domain experts. By seeking to understand knowledge sharing from VA experts to non-technical users of VA in a multidisciplinary team, we aim to explore how these domain experts learn to use VA as a thinking tool, guided by their knowing-in-practice. The research described in this paper was conducted in the context of a long-term industry-wide research project called the ‘Visual Historical Atlas of the Australian Co-operatives’, led by a multidisciplinary VA team who faced the challenge tackled by this research. Using Action Design Research (ADR) and the combined theoretical lens of boundary objects and secondary design, the paper theorises a three-phase method for knowledge transfer, translation and transformation from VA experts to domain experts using different types of VA-related boundary objects. Together with the proposed set of design principles, the three-phase model advances the well-established stream of research on organizational use of analytics, extending it to the emerging area of visual analytics for non-technical decision makers.

Funder

Australian Research Council

University of Technology Sydney

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Information Systems,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s10796-022-10310-4.pdf

Reference97 articles.

1. Abbasi, A., Sarker, S., & Chiang, R. H. (2016). Big data research in information systems: Toward an inclusive research agenda. Journal of the Association for Information Systems, 17(2), i–xxxii.

2. Alavi, M., & Leidner, D. E. (2001). Review: Knowledge management and knowledge management systems: Conceptual foundations and research issues. Management Information Systems Quarterly, 25(1), 107–136.

3. Andrienko, G., Andrienko, N., Drucker, S., Fekete, J-D., Fisher, D., et al. (2020). Big data visualization and analytics: Future research challenges and emerging applications. BigVis 2020 - 3rd International Workshop on Big Data Visual Exploration and Analytics, Mar, Copenhagen, Denmark.

4. Australian Institute (2012). Who Knew Australians Were so Co-operative? The Size and Scope of Mutually Owned Co-ops in Australia, Australian Institute, Bruce: ACT.

5. Australian Senate. (2017). Australian Government response to the Senate Economics Reference Committee Report, Australian Government, Available from: https://www.aph.gov.au/Parliamentary_Business/Committees/Senate/Economics/Cooperatives/Government_Response. Accessed 29 Sept 2021.