Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-13T06:00:08.821Z Has data issue: false hasContentIssue false
  • English
  • Français

THE IMPACT OF DATA ON THE ROLE OF DESIGNERS AND THEIR PROCESS

Published online by Cambridge University Press:  27 July 2021

Jiahao Lu ,
Alejandra Gomez Ortega ,
Milene Gonçalves  and
Jacky Bourgeois
Jiahao Lu*
Affiliation:
Delft University of Technology
Alejandra Gomez Ortega
Affiliation:
Delft University of Technology
Milene Gonçalves
Affiliation:
Delft University of Technology
Jacky Bourgeois
Affiliation:
Delft University of Technology
*
Lu, Jiahao, Delft University of Technology, Design, Organisation and Strategy (DOS), Netherlands, The, lujiahao1994@gmail.com

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

With the advance of the Internet and the Internet of Things, an abundance of 'big' data becomes available. Data science can be incorporated in design, which brings forward various opportunities for designers to benefit from this new material. However, the designer's perspective and their role remains unclear. How do they think about and approach data? What do they want to achieve with this data? What do they need to take ownership of designing with data? In this paper we take a design perspective to map the opportunities and challenges of leveraging large data-sets as part of the design process. We rely on a survey with 75 participants across a Faculty of Industrial Design Engineering and in-depth reflective interviews with a subset of 9 participants. We discuss the impact of data on the roles designers can adopt as well as an approach to designing with data. This work aims to inform on educational support, data literacy and tools needed for designers to take advantage of this new era of design digitalisation.

Keywords

Big dataDesign educationDesign processDesigners' rolesData literacy
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 3021 - 3030
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

References

Beghelli, A., Huerta-Cánepa, G. and Segal, R. (2019), “Data materialisation: A new undergraduate course for a data driven society”, Proceedings of the International Conference on Engineering Design, ICED, Vol. 2019-Augus No. August, pp. 2061–2070, http://doi.org/10.1017/dsi.2019.212.CrossRefGoogle Scholar
Bogers, S., Frens, J., Van Kollenburg, J., Deckers, E. and Hummels, C. (2016), “Connected baby bottle: A design case study towards a framework for data-enabled design”, in: DIS 2016 - Proceedings of the 2016 ACM Conference on Designing Interactive Systems: Fuse, DIS ’16, ACM, New York, NY, USA, pp. 301–311, http://doi.org/10.1145/2901790.2901855.CrossRefGoogle Scholar
Bourgeois, J. and Kortuem, G. (2019), “Towards responsible design with internet of things data”, in: Proceedings of the International Conference on Engineering Design, ICED, Vol. 2019-Augus, pp. 3421–3430, http://doi.org/10.1017/dsi.2019.349.Google Scholar
Bourgeois, J., Van Der Linden, J., Kortuem, G., Price, B.A. and Rimmer, C. (2014), “Using participatory data analysis to understand social constraints and opportunities of electricity demand-shifting”, in: ICT for Sustainability 2014, ICT 4S 2014, pp. 392–401, http://doi.org/10.2991/ict4s-14.2014.49.Google Scholar
Bryman, A. and Burgess, R.G. (1994), “Reflections on qualitative data analysis”, Analyzing qualitative data, pp. 216–226.CrossRefGoogle Scholar
Tie, Chun, Birks, Y., and Francis, M., K. (2019), “Grounded theory research: A design framework for novice researchers”, SAGE Open Medicine, Vol. 7, p. 205031211882292, http://doi.org/10.1177/2050312118822927.Google Scholar
Cila, N., Giaccardi, E., Caldwell, M., Rubens, N. and Tynan-O'Mahony, F. (2015), “Listening To an Everyday Kettle : How Can the Data Objects Collect Be Useful for Design Research?”, Proceedings of the 4th Participatory Innovation Conference, pp. 500–506.Google Scholar
Creswell, J.W. and Creswell, J.D. (2017), Research design: Qualitative, quantitative, and mixed methods approaches, Sage publications.Google Scholar
Crilly, N., Blackwell, A.F. and Clarkson, P.J. (2006), “Graphic elicitation: Using research diagrams as interview stimuli”, Qualitative Research, Vol. 6 No. 3, pp. 341–366, http://doi.org/10.1177/1468794106065007.CrossRefGoogle Scholar
Design Council (2006), “Double diamond design process model”,. http://www.designcouncil.org.uk/designprocessGoogle Scholar
Dove, G., Halskov, K., Forlizzi, J. and Zimmerman, J. (2017), “UX Design Innovation”, Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems - CHI ’17, pp. 278–288, http://doi.org/10.1145/3025453.3025739.Google Scholar
Dove, G. and Jones, S. (2014), “Using data to stimulate creative thinking in the design of new products and services”, Proceedings of the 2014 conference on Designing interactive systems - DIS ’14, pp. 443–452, http://doi.org/10.1145/2598510.2598564.CrossRefGoogle Scholar
Feinberg, M. (2017), “A design perspective on data”, Conference on Human Factors in Computing Systems - Proceedings, Vol. 2017-May, pp. 2952–2963, http://doi.org/10.1145/3025453.3025837.Google Scholar
Girardin, F. and Lathia, N. (2017), “When User Experience Designers Partner with Data Scientists”, Aaai 2017 Spring Symposia, pp. 376–381.Google Scholar
Gorkovenko, K., Burnett, D.J., Thorp, J.K., Richards, D. and Murray-Rust, D. (2020), “Exploring the Future of Data-Driven Product Design”, Conference on Human Factors in Computing Systems - Proceedings, pp. 1–14, http://doi.org/10.1145/3313831.3376560.Google Scholar
Gray, J., Gerlitz, C. and Bounegru, L. (2018), “Data infrastructure literacy”, Big Data and Society, Vol. 5 No. 2, pp. 1–13, http://doi.org/10.1177/2053951718786316.CrossRefGoogle Scholar
King, R., Churchill, E.F. and Tan, C. (2017), “Designing with data: Improving the User Experience with A/B Testing”, DIS 2016 Companion - Proceedings of the 2016 ACM Conference on Designing Interactive Systems: Fuse, p. 369.Google Scholar
Klein, P., van der Vegte, W.F., Hribernik, K. and Klaus-Dieter, T. (2019), “Towards an approach integrating various levels of data analytics to exploit product-usage information in product development”, Proceedings of the International Conference on Engineering Design, ICED, Vol. 2019-Augus No. August, pp. 2627–2636, http://doi.org/10.1017/dsi.2019.269.CrossRefGoogle Scholar
Kollenburg, J.v. and Bogers, S. (2019), Data-enabled design : a situated design approach that uses data as creative material when designing for intelligent ecosystems, Technical report, Technische Universiteit Eindhoven.Google Scholar
Laney, D. et al. (2001), “3d data management: Controlling data volume, velocity and variety”, META group research note, Vol. 6 No. 70, p. 1.Google Scholar
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.l., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D. and Alstyne, M.V. (2009), “Computational Social Science”, Vol. 323 No. February, pp. 721–724.Google ScholarPubMed
Lycett, M. (2013), “’Datafication’: Making sense of (big) data in a complex world”, European Journal of Information Systems, Vol. 22 No. 4, pp. 381–386, http://doi.org/10.1057/ejis.2013.10.CrossRefGoogle Scholar
Pavliscak, P. (2015), Data-informed Product Design, O'Reilly Media.Google Scholar
Ribeiro, E., Raposo, D. and Menezes, M. (2020), Perspective on Design - Research, Education and Practice.Google Scholar
Sanders, E. and Stappers, P. (2013), Convivial design toolbox: generative research for the front end of design., Laurence King Publishing.Google Scholar
Seemann, J. (2012), “Hybrid Insights : Where the Quantitative Meets the Qualitative”, Rotman Magazine, pp. 58–61.Google Scholar
Speed, C. and Oberlander, J. (2016), “Designing from, with and by Data: Introducing the ablative framework”, DRS2016: Future-Focused Thinking, Vol. 8, http://doi.org/10.21606/drs.2016.433.CrossRefGoogle Scholar
Stappers, P.J., Kistemaker, N., Kistemaker, S., Von Ahlen, J., Guldner, S., Schram, J., Van Veelen, J., Veenenbos, K., Vlot, D. and Van Der Lelie, C. (2010), context mapping put into practice Rich Insights!, Technical report.Google Scholar
Suryadi, D. and Kim, H. (2019), “Automatic identification of product usage contexts from online customer reviews”, Proceedings of the International Conference on Engineering Design, ICED, Vol. 2019-Augus No. 1, pp. 2507–2516, http://doi.org/10.1017/dsi.2019.257.CrossRefGoogle Scholar
Thorpe, J.R., Forchhammer, B.H. and Maier, A.M. (2017), “Sensing behaviour in healthcare design”, Proceedings of the 21st International Conference on Engineering Design (ICED17), Vol. 3: Product, Services and Systems Design, Vol. 3, pp. 171–181.Google Scholar
Wolff, A., Gooch, D., Cavero Montaner, J., Rashid, U. and Kortuem, G. (2017), “Creating an understanding of data literacy for a data-driven society”, Journal of Community Informatics, Vol. 12 No. 3.Google Scholar
Yang, Q., Scuito, A., Zimmerman, J., Forlizzi, J. and Steinfeld, A. (2018), “Investigating How Experienced UX Designers Effectively Work with Machine Learning”, Proceedings of the 2018 on Designing Interactive Systems Conference 2018 - DIS ’18, pp. 585–596, http://doi.org/10.1145/3196709.3196730.CrossRefGoogle Scholar
Zakaria, A.F. and Lim, S.C. (2018), “Design and development of a training module for data-driven product-service design”, IEEE International Conference on Industrial Engineering and Engineering Management, Vol. 2017-Decem, pp. 1149–1153, http://doi.org/10.1109/IEEM.2017.8290072.Google Scholar