What is Good Feedback in Big Data Projects for Cyberinfrastructure Diffusion in e-Science?

摘要

This paper investigates the role of feedback in big data projects for cyberinfrastructure (CI) diffusion in e-science. For many of these projects, large-scale and heterogeneous datasets, multidisciplinary and dispersed experts, and advanced technologies are brought together to harness analytic insights. However, without effective CI and computational tools, the accuracy and meaningfulness of analytics results are compromised. In fact, without CI tools, raw data remain raw with hidden insights, as data analytics cannot be executed at all. In order to improve such tools for meaningful results, we argue to conceptualize the communication mechanism of `feedback' in agile software development, with the goal of producing CI tools that are responsive to users. Based on a grounded analysis of interview data, we concluded that feedback helps developers in big data projects understand users' needs, makes tools user-friendly, prevents emergencies, and is better for developers than no feedback. Furthermore, good feedback is often structured, specific, actionable, timely, generalizable, and delivered in a tactful way. Despite the limitation of the findings being exploratory and yet to be evaluated experimentally, we argued that they still can motivate developers to be proactive seekers of feedback for their tools, productively guide developers' communication with users, and ultimately promote further adoption and diffusion of CI tools in e-science.