DESIGN 2026 - Workshop 1

The Data Quality Playbook for Engineering Design

Location: Hotel Croatia, Cavtat, Croatia (Room TBC)
Dates: 18 May 2026 (Time TBC)
Chairs: Kostas Stylidis, Cyriel Diels and Bastian Quattelbaum
Organised by: Data-Informed-Design SIG

Workshop Description

Join us for a comprehensive workshop on “The Data Quality Playbook for Engineering Design”, where participants will explore a wide range of techniques and methodologies for effective data collection across different contexts, including academic research, industrial practice, and field studies. The workshop addresses both digital and analogue data collection methods, equipping participants with practical tools adaptable to their specific research and design needs.

A central focus will be placed on data quality criteria—such as accuracy, reliability, validity, and completeness—to support the creation and use of robust and trustworthy data sets. Through hands-on activities, participants will engage with best practices in data collection using surveys, interviews, observational studies, and digital tools such as mobile applications and cloud-based databases.

The workshop will also facilitate discussion on common challenges and practical solutions for maintaining data quality across different collection contexts. Finally, an outlook will be provided on how high-quality data sets can be connected to and leveraged by AI-based agents and tools in engineering design.

Objectives

• To familiarise participants with a variety of data collection methods suitable for different research and design contexts.
• To develop an understanding of key data quality criteria and how to apply them in practice.
• To promote best practices for ensuring high data quality across both digital and analogue data collection approaches..

Key Outcomes

• Identify how AI is currently impacting industrial and engineering design processes.
• Strengthen the SIG community of researchers and practitioners interested in design practice and design processes.
• Identify next steps for collaboration and future research and exploration.

Brief Overview

• Explore diverse data collection methods tailored to different research and design contexts.
• Understand and apply key data quality criteria: accuracy, reliability, validity, and completeness.
• Learn best practices for digital (e.g., surveys, mobile apps) and analogue (e.g., interviews, field notes) data collection.
• Participate in hands-on activities to support learning and practical application.
• Collaborate with peers to exchange experiences, insights, and challenges related to ensuring data quality.n