Making sense of data within the context of user-centered design: A study on amateur running

Abstract

With the datafication of an ever-increasing range of human activities, Big Data, as a social, cultural and technological phenomenon has fundamentally altered various aspects of human life. As one of these aspects, sport activities have started being increasingly datafied, addressing not only professional, but also amateur and recreational athletes from a wide range of disciplines. Getting immensely popular in public life and being adopted by millions of people worldwide, running has also become highly engaged in data practices, especially with the increasing attention to self-tracking technologies. Considering this increasing presence of data in everyday life contexts, exploring how data becomes meaningful for the ones who generate, track, and record it has vital importance to understand the real value and potential of data in today's world, as well as in design studies. Hence, the aim of this study is to investigate data sense making practices of amateur runners from a user-centered design perspective. Defining sense making as an active process emerging through human-artifact interaction and embracing meaning as the central concept in human-centered design, it focuses on the ways data become meaningful and gain value in the context of running. In the first step of the study, a fieldwork is conducted by using three research methods. First, unobtrusive measures are embraced and used throughout the fieldwork to gain a general understanding about the topics under research. Also, an online qualitative survey is used to gain a comprehensive insight about runners, running communities and running experience in the broadest sense. Then, in the light of survey findings, in-depth semi-structured interviews are conducted to better understand both the runners' running experience and their interactions with the artifacts in the running context. After the fieldwork is completed, qualitative data gathered through interviews is analyzed by carrying out qualitative content analysis. Finally, all findings are interpreted accordingly. In this way, the act of data sense making of amateur runners is described. The findings of the study show that this act is complex and has a variety of elements involving physical, emotional, environmental, device-related and social ones. With this complex nature, the act of data sense making in amateur running is defined with two main categories: "data resolution" and "data response". Accordingly, the meaning, value and role of data in running are identified and explained under three main subjects: "data as a means in running", "data as an end in running" and "data as a burden in running". The findings of the study demonstrate that data have gained various meanings for different runners as they interact with them. Also, these meanings affect and shape the ways runners use and interact with their trackers, their tracking practices, their engagement with running itself and their self-perceptions and self-presentations in terms of their runner identities. In this sense, it is understood that their data and related practices have a significant role in runners' running experience, affecting its both performative and social dimensions. As the concluding remarks, some ideas are shared in terms of how the current findings can provide insights for design research and practice. Embracing data itself as an artifact and recognizing the fluid presence of data across different everyday life contexts are presented as the potential contributions to the design domain. Also, it is argued how the act of data sense making described in this study can be a design tool for future studies in the intersection of data and design. Moreover, some suggestions are made for future studies. These studies can examine data practices not only in various sports activities, but also in a variety of human activities that have been datafied in today's world. Also, examining these studies across generations can provide further perspectives in better understanding the presence of data in society and positioning it in design studies in a more inclusive way. As for the concept of self-tracking, artificial intelligence technologies can open up new perspectives in this regard.