Data and Service Design
Academic Year 2025-2026, 2nd semester
The course "Data and Service Design: Designing Data-centric Services" intends to be as a fundamental introduction to the notion of data for product-service system design students, providing them with conceptual and critical tools, as well as practice-oriented approaches and methods, to engage and work with data as a "material" in their design processes.
The understanding of the contemporary data landscape and its main topics will be developed through a double-track approach in which theoretical content and practical hands-on activities are carried out alongside each other.
Course perspective: Key Useful Concepts and Principles
Any course is a collective learning journey. We seek to build a common vocabulary and perspective toward the topic addressed: data and service design. In line with this, we propose two concepts:Data-Centric Services
Several organizations find themselves in the position of being data holders: as a by-product of their activities, they possess relevant data that can potentially be turned into value. However, effectively utilizing this data requires addressing significant barriers, including interoperability, accessibility, management, and representation, while also ensuring compliance with legal and technical frameworks.
The concept of data-centric services (Leoni et al., 2023)— as opposed to data-driven services — emphasises the importance of designing services by leveraging data generated across different organizations and their information systems, which is often not yet linked, collected, or used consistently. Product-service system designers can engage with these scattered data sources by bringing a perspective that is useful for making sense of them and creating value from them, while supporting data holders in their sense-making processes and in building inter-organizational data governance and partnerships.
Data practices
Future designers should acknowledge data not as a neutral entity, but as something situated within service systems and carrying strong democratic and ethical implications (Falk, 2023) — what is usually discussed under the label of data ethics. To recognise and critically address the contextual (social, political, organisational and material) conditions of data, theory alone is not sufficient: data must be engaged with in practice.
The concept of data practices for acknowledging data's non-neutral and biased nature. In contrast to data as abstracted entities, practice compels students (as well as researchers) to recognise data as something always actively made by someone or something (Burkhardt et al., 2022). Actively working with data helps to outline issues that go beyond service systems. For example: Is my data collection publicly acceptable? Are all populations equally represented in my data? What might be the ultimate effects of this data on users and beyond?
Together, these two concepts define the perspective the course adopts toward the topic. The three resulting guiding principles of our journey are:
- • Data is a central element of contemporary society
- • Data is contextual and inherently non-neutral
- • Data is a material for product-service system designing
Approach: Learning Framework, Goals and Outputs
Given this perspective, the course unfolds through a double-track approach developed in parallel, during the course.
Theoretical track
Primarily based on lectures by professors and guest speakers, it first introduces interpretative lenses that use “data” as a way to describe the contemporary world. It then focuses on the relationship between data and services, and from there discusses broader governance and ethical implications.
Practical track
Sequential and based on hands-on work in class: at each lesson, students will build upon what was done previously. This track is structured around three stages:
PT Stage 1: Data-driven research and discovery
It will focus on the basics of accessing and manipulating heterogeneous data sources, such as those available from the web and social media.
PT Stage 2: Data modeling, preparation, and insight generation
Focuses on manipulating data into structured, analyzable formats and on developing appropriate data models to support exploration and analysis.
PT Stage 3: Data-driven representations
Focuses on how to work with data from a representational perspective, and on the methods and tools that can be used to visualize complex data in ways that support exploration and decision-making.
Guest lectures
Andrea Benedetti - Università degli Studi di Milano
Title: "Data and service: designing in (un)desired futures"
Chiara Criscuolo - Banca d'Italia
Title: "Ethical Data Science: Challenges in a Responsible Innovation"
Davide Posillipo - Komebi Studio
Title: "Service Design and Data Engineering: an alliance towards impacts"
Federico Bartolomucci - Politecnico di Milano
Title: "Using data for good: opportunities and challenges in the era of AI"
Petter Falk - Karlstad University
Title: "Data and/in democracy. Power, governance and value in data-driven public services"
Faculty
Francesco Leoni
Postdoc research fellow at Politecnico di Milano's Design Department. He works at the Design Policy Lab and Polifactory, developing research in the field of "design for policy" by studying data-centric innovations in the public sector.
Mathyas Giudici
Postdoc research fellow at Politecnico di Milano's DEIB Department. His research activity focuses on the design and evaluation of Human-AI collaboration systems.
Teaching assistant
Maria Zerbola
Postgraduate student at Politecnico di Milano. She is pursuing the interdepartmental Internal Double Degree in Product Service System Design and Management Engineering.
