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

Author:

Marjanovic OliveraORCID,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

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.

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3