Lessons Learned to Improve the UX Practices in Agile Projects Involving Data Science and Process Automation

TL;DR

This study explores practices and challenges in UX integration within agile data science and automation projects, emphasizing the importance of end-user participation and communication strategies for better problem understanding.

Contribution

It provides empirical insights into UX practices, challenges, and perceptions in agile data science projects, highlighting the need for improved user-team communication methods.

Findings

Prototypes and stakeholder meetings are commonly used practices.

Using Lean Inceptions aids in problem understanding.

End-user participation is critical for defining problems and overcoming barriers.

Abstract

Context: User-Centered Design and Agile methodologies focus on human issues. Nevertheless, agile methodologies focus on contact with contracting customers and generating value for them. Usually, the communication between end users and the agile team is mediated by customers. However, they do not know the problems end users face in their routines. Hence, UX issues are typically identified only after the implementation, during user testing and validation. Objective: Aiming to improve the understanding and definition of the problem in agile projects, this research investigates the practices and difficulties experienced by agile teams during the development of data science and process automation projects. Also, we analyze the benefits and the teams' perceptions regarding user participation in these projects. Method: We collected data from four agile teams in an academia-industry collaboration focusing on delivering data science and process automation solutions. Therefore, we applied a carefully designed questionnaire answered by developers, scrum masters, and UX designers. In total, 18 subjects answered the questionnaire. Results: From the results, we identify practices used by the teams to define and understand the problem and to represent the solution. The practices most often used are prototypes and meetings with stakeholders. Another practice that helped the team to understand the problem was using Lean Inceptions. Also, our results present some specific issues regarding data science projects. Conclusion: We observed that end-user participation can be critical to understanding and defining the problem. They help to define elements of the domain and barriers in the implementation. We identified a need for approaches that facilitate user-team communication in data science projects and the need for more detailed requirements representations to support data science solutions.