Designing Compliant Data Products (1 cr)

Assessment criteria

A passing grade requires that the student obtains at least 50 percent of the points from the assignments in the course.

Evaluation scale

Approved/Failed

Content scheduling

User-Centric View on Compliance Considerations When Designing Data Products: You will develop an understanding of how to incorporate compliance requirements, such as data privacy, consent, and transparency, into the user experience. The Role of Legal Design in Internal Communication to Help Address Data Product Compliance Challenges: You will be introduced to the concept of legal design, with a focus on how it can improve internal communication between product teams and legal professionals. Continuous Compliance Throughout the Data Product Lifecycle: You will learn how to implement continuous compliance strategies, ensuring your data products align with evolving legal standards.

Objective

Student is able to: - include a user-centric view on compliance considerations when designing data products - understand the role of legal design in internal communication to help address data product compliance challenges - ensure continuous compliance throughout the data product lifecycle

Location and time

The course is completed at your own pace in the learning environment built in Canvas

Materials

Anwar, M. J., Gill, A. Q., & Beydoun, G. (2018): A review of information privacy laws and standards for secure digital ecosystems Bendiek, A., & Römer, M. (2019): Externalizing Europe: the global effects of European data protection Cennamo, C., Kretschmer, T., Constantinides, P., Alaimo, C., & Santaló, J. (2023): Digital platforms regulation: An innovation-centric view of the EU’s Digital Markets Act Daggubati, L. S. (2024): Designing Digital Payment Experiences: The Crucial Role of User-Centered Design and Effective User Feedback Integration. Mitchell, A. D., & Mishra, N. (2023): Cross-Border Data Regulatory Frameworks: Opportunities, Challenges, and a Future-Forward Agenda Mujawar, J. S.: Creating Value through Data: The Definitive Exploration of Product Management for Data Products Stepanov, I. (2020): Introducing a property right over data in the EU: the data producer’s right – an evaluation Svantesson, D. (2020): Data localisation trends and challenges: Considerations for the review of the Privacy Guidelines Turillazzi, A., Taddeo, M., Floridi, L., & Casolari, F. (2023): The digital services act: an analysis of its ethical, legal, and social implications Voss, W. G. (2021): The CCPA and the GDPR are not the same: why you should understand both.

Teaching methods

The course is completed entirely independently according to the instructions in the learning environment built in Canvas. The course requires self-direction. The course does not include joint meetings or pair or group work. You will study the entirety in the learning environment built in Canvas, which includes study materials and assignments/tests. No personal feedback is given on the assignments: feedback is based on checking the correctness of the assignments and sharing model answers after the assignment submission.

Exam schedules

The course is completed at your own pace in the learning environment built in Canvas.

Completion alternatives

According to the degree regulations (Tutkintosääntö 2024), “students have the right to apply for recognition of prior learning regardless of where, how, and when the knowledge was acquired. At Laurea, recognition of prior learning is implemented through two different procedures: a) credit transfer (knowledge acquired in studies at another higher education institution) and b) competence demonstration (knowledge acquired in other ways). The initiative for recognition of prior learning is made by the student. It is the student’s responsibility to demonstrate and verify their competence.” More information about credit transfer and competence demonstration can be found on the student intranet.

Student workload

The workload for the course is designed so that achieving the learning objectives of the course, one credit corresponds to an average of 26.7 hours of student work. The actual time required varies individually, depending on factors such as prior knowledge.