Module overview
This module studies how data is generated, valued, and monetised within digital ecosystems, as well as the ethical, legal, and technical challenges surrounding data ownership, privacy, and regulation.
For example, how can we manage a music dataset produced by artists and used to train a generative AI model? What are the technical solutions to support selling and profit distribution of the generated model? What are the ethical and legal implications for artists and other actors involved?
The module covers the data value chain, from collection and storage to integration, analysis, distribution, and monetisation, and the data governance issues associated with it.
Aims and Objectives
Learning Outcomes
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- The use of data privacy languages and algorithms in data sharing.
- The use of AI-based data pricing and monetisation algorithms.
- The technical foundations of data-driven economies and data processing pipelines, including data marketplaces.
- The application of data governance and regulation frameworks like Data Protection Act (GDPR), Data Governance Act, AI legislation and their impacts on systems and algorithms.
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Apply concepts of data monetisation, including data as an asset and resource.
- Develop skills in data privacy engineering, including algorithms for automated compliance.
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- Evaluate the privacy, security, and ethical challenges associated with data use.
- Analyse the value creation, pricing, and business models of data-driven companies.
Subject Specific Practical Skills
Having successfully completed this module you will be able to:
- Design compliant and secure architectures for data sharing and data interoperability.
- Implement and optimise algorithms for data collection, processing, search and storage.
Syllabus
1.Introduction to Data Economy technologies
2.Data, collection and publishing models and algorithms.
3.Data integration and exchange solutions.
4.Data warehouse and data catalogues. Data Trusts.
5.Data privacy languages and algorithms. Data anonymization and security.
6.Data governance and legislations. Data consent and compliance technologies.
7.Data pricing and monetisation algorithms.
Learning and Teaching
Teaching and learning methods
-Reading and preparation, including lecture notes and selected research papers.
-8 hours of labs (weekly clinics to support the project)
-Lectures: Three hours per week during the teaching weeks.
| Type | Hours |
|---|---|
| Specialist Laboratory | 8 |
| Wider reading or practice | 50 |
| Lecture | 28 |
| Completion of assessment task | 50 |
| Revision | 14 |
| Total study time | 150 |
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Final Assessment | 60% |
| Continuous Assessment | 40% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Set Task | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
| Method | Percentage contribution |
|---|---|
| Set Task | 100% |