Abstract
This research identifies the opportunities for data science to support the service design processes through explorative development of a guide to data science methods for service designers. Designers and their teams search for data science techniques from their perspective as designers, while current literature is fragmented and technical. The present research explores methods that can help designers get started with data science. It evaluates if the techniques meet the designer's needs and fit the design process with user-centred activities; as a result, the methods contribute to the diversity of the designers' methods toolkit. These methods increase the validity of user research, make hidden information accessible with specialised user research tools and help designers in their creative process through relevant resources, inspiration and/or an alternative perspective. Together these results encourage organisations to mature data science resources for design projects so that their services benefit from more informed designers.
Keywords
Service Design; Data Science; Process mining; Mixed Methods
DOI
https://doi.org/10.21606/drs.2020.331
Citation
Kunneman, Y., and Alves da Motta Filho, M. (2020) Data Science for Service Design: An exploration of methods, in Boess, S., Cheung, M. and Cain, R. (eds.), Synergy - DRS International Conference 2020, 11-14 August, Held online. https://doi.org/10.21606/drs.2020.331
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Data Science for Service Design: An exploration of methods
This research identifies the opportunities for data science to support the service design processes through explorative development of a guide to data science methods for service designers. Designers and their teams search for data science techniques from their perspective as designers, while current literature is fragmented and technical. The present research explores methods that can help designers get started with data science. It evaluates if the techniques meet the designer's needs and fit the design process with user-centred activities; as a result, the methods contribute to the diversity of the designers' methods toolkit. These methods increase the validity of user research, make hidden information accessible with specialised user research tools and help designers in their creative process through relevant resources, inspiration and/or an alternative perspective. Together these results encourage organisations to mature data science resources for design projects so that their services benefit from more informed designers.