Design Enquiry Through Data: Appropriating a Data Science Workflow for the Design Process

Kun, Péter; Mulder, Ingrid; Kortuem, Gerd

doi:10.14236/ewic/hci2018.32

Cited by 15 publications

(12 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Jones selected design methods from different disciplines, for example, presenting interviewing users as a design method borrowed from social sciences, highlighting the ever-existing approach of designers to appropriate methods, techniques, and tools from adjacent disciplines to design. We explored similar appropriation of methods, techniques, and tools from end-user data communities to designers in earlier work [18]. While our previous study confirmed the general adaptability of such data practices to design work, it has remained unclear how the thinking process changes when appropriating data practices.…”

Section: Developing Design Methodsmentioning

confidence: 96%

“…This perspective was motivated by the similarity of data exploration and design inquiry following an opportunistic inquiry process. In an earlier study [18], we observed how designers appropriate a generic data science understanding of inquiry; an inquiry focused on answering questions about a phenomenon through inferences from a dataset, including data acquisition, if necessary. We highlighted that leveraging data approaches requires precise question-formulation due to the computational, automated characteristic of data techniques.…”

Section: Data Exploration For Design Inquirymentioning

confidence: 99%

“…To conclude, while the big data era has been triggering new approaches to inquiry in various fields, including design, designers still primarily approach data through data expert collaborators. Contrary to the previous approaches relying on data experts in the process, our previous work [18,19] has explored how designers as 'data non-experts' can leverage data. Our investigations confirm that new types of insights can be gained from 'big data approaches' to fuel the design process, and designers themselves are able to conduct data practices using non-expert tooling.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Developing a Design Inquiry Method for Data Exploration

Kun¹,

Mulder²,

Kortuem³

2020

IxD&A

Self Cite

View full text Add to dashboard Cite

The increasing availability of large-scale datasets such as sensor data or social media data and increasingly accessible data science tools create unique opportunities for design. However, the relationship between data science practices and design methods is still underdeveloped. In this paper, we propose that data exploration activities can be effectively embedded within a broader design inquiry framework and define a new design method, coined Data Exploration for Design, to support methodical designerly data exploration. The design method addresses the novice’s learning curve and supporting developing a data exploration inquiry mindset with procedures and curated tools. The empirical evaluation highlights support for producing exploration outcomes that are worth the additional technical effort. We close the paper by positioning the findings in design methodology literature and motivating data exploration principles for design inquiry.The principles urge to acknowledge biases in data collection, spending time with the data, using visualizations as a means-to-an-end, and designers being part of the data collection.

show abstract

Section: Developing Design Methodsmentioning

confidence: 96%

Section: Data Exploration For Design Inquirymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Developing a Design Inquiry Method for Data Exploration

Kun¹,

Mulder²,

Kortuem³

2020

IxD&A

Self Cite

View full text Add to dashboard Cite

show abstract

“…Waller and Fawcett (2013) summarized methods that can be used to address big data challenges in supply chain management. Similar overview studies have looked into the role of data science in the design of digital products and services (Kun et al, 2018), in cybersecurity (Sarker et al, 2020), in soft materials engineering (Ferguson, 2017) and in aviation (Chung et al, 2020). The definition by Waller and Fawcett (2013) highlighted the bonding between statistics and data science.…”

Section: Related Workmentioning

confidence: 96%

The disciplinary research landscape of data science reflected in data science journals

Hong¹,

Moen²,

Yu³

et al. 2020

IDD

View full text Add to dashboard Cite

Purpose This paper aims to selects 59 journals that focus on data science research in 14 disciplines from the Ulrichsweb online repository. This paper analyzes the aim and scope statement using both quantitative and qualitative methods to identify the research types and the scope of research promoted by these journals. Design/methodology/approach Multiple disciplines are involved in data science research and publishing, but there lacks an overview of what those disciplines are and how they relate to data science. In this study, this paper aims to understand the disciplinary characteristics of data science research. Two research questions are answered: What is the population of journals that focus on data science? What disciplinary landscape of data science is revealed in the aim and scope statements of these journals? Findings Theoretical research is mainly included in journals that belong to statistics, engineering and sciences. Almost all data science journals include applied research papers. Keywords analysis shows that data science research in computers, statistics, engineering and sciences appear to share characteristics. While in other disciplines such as biology, business and education, the keywords are indicative of the types of data to be used and the special problems in these disciplines. Originality/value This is the first study to use journals as the unit of analysis to identify the disciplines involved in data science research. The results provide an overview of how researchers and educators from different disciplinary backgrounds understand data science research.

show abstract

“…Prior work contributed to our understanding of the collaboration process between data scientists and UX designers [19,35,39,61], software engineers (SEs) [7,37,53], and people in business and other fields [38,52,62]. However, there has been little investigation of how members in different roles on the team communicate about ML, in particular about the quality of models.…”

Section: And Collaborationmentioning

confidence: 99%

How Teams Communicate about the Quality of ML Models: A Case Study at an International Technology Company

Almahmoud

DeLine

Drucker

2021

Proc. ACM Hum.-Comput. Interact.

View full text Add to dashboard Cite

Machine learning (ML) has become a crucial component in software products, either as part of the user experience or used internally by software teams. Prior studies have explored how ML is affecting development team roles beyond data scientists, including user experience designers, program managers, developers and operations engineers. However, there has been little investigation of how team members in different roles on the team communicate about ML, in particular about the quality of models. We use the general term quality to look beyond technical issues of model evaluation, such as accuracy and overfitting, to any issue affecting whether a model is suitable for use, including ethical, engineering, operations, and legal considerations. What challenges do teams face in discussing the quality of ML models? What work practices mitigate those challenges? To address these questions, we conducted a mixed-methods study at a large software company, first interviewing15 employees in a variety of roles, then surveying 168 employees to broaden our understanding. We found several challenges, including a mismatch between user-focused and model-focused notions of performance, misunderstandings about the capabilities and limitations of evolving ML technology, and difficulties in understanding concerns beyond one's own role. We found several mitigation strategies, including the use of demos during discussions to keep the team customer-focused.

show abstract

Design Enquiry Through Data: Appropriating a Data Science Workflow for the Design Process

Cited by 15 publications

References 27 publications

Developing a Design Inquiry Method for Data Exploration

Developing a Design Inquiry Method for Data Exploration

The disciplinary research landscape of data science reflected in data science journals

How Teams Communicate about the Quality of ML Models: A Case Study at an International Technology Company

Contact Info

Product

Resources

About