Module: Data Governance

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Data Governance
Module Code	CSC1151 (ITS: CA691)
Faculty	Computing	School	Engineering & Computing
NFQ level	9	Credit Rating	10

Description

This module aims to develop an understanding of modern data and AI governance practices needed in the GDPR and AI Act era. It will explore: (i) what decisions must be made to ensure effective management and use of IT systems for data processing; (ii) how to create structures and processes for decision making; (iii) assessing what technical and organisational measures are needed for data and AI to achieve quality, value, and regulatory compliance; and (iv) how to identify and assess challenges for assuring data ethics and trustworthy AI. This module will also develop an understanding of data governance standards, methods and tools. New challenges in data governance such as the increasing use of AI and the sensitivity of information will be investigated. The module will foster development of fundamental skills in modern practices for governance of data and technology with a view to achieving value through responsible innovation.

Learning Outcomes

1. Apply business process design and analysis methods to data processing systems
2. Contrast different approaches to AI and data governance and understand their relevance for compliance with GDPR and the AI Act
3. Demonstrate the principles underlying data quality, metadata management, data value and data discovery.
4. Apply data lifecycle, ethics, and trustworthy AI principles for planning projects and establishing principles within an organisation
5. Analyse the state of the art in data governance to identify relevant solutions for business problems.
6. Evaluate data protection, privacy and ethical issues associated with the storage, transfer, and processing of data.

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	36	3 hour lecture, class participation expected
Fieldwork	70	Preparatory readings for lectures, background readings for assessments
Independent Study	144	Research, reading, completing assignment
Total Workload: 250

Section Breakdown
CRN	20417	Part of Term	Semester 2
Coursework	0%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	Y
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Harshvardhan Pandit	Module Teacher	Alessandra Mileo, Boualem Benatallah, Renaat Verbruggen

Type	Description	% of total	Assessment Date
Assessment Breakdown
Report(s)	Individual research report outlining learning to date and delivered in the form of a presentation in class and a submitted document	10%	n/a
Group project	Practical group project on the topic of data governance. Assessed together with the individual report on group project for each individual.	25%	n/a
Report(s)	Individual Report outlining learnings and contributions within the larger group project as well as additional activities undertaken as an individual.	15%	n/a
Formal Examination	End-of-Semester Final Examination	50%	End-of-Semester

Reassessment Requirement Type
Resit arrangements are explained by the following categories; RC1: A resit is available for both^* components of the module. RC2: No resit is available for a 100% coursework module. RC3: No resit is available for the coursework component where there is a coursework and summative examination element. ^* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment

Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Case Studies and Literature Search
Motivational case study, student-directed literature search and presentations on data governance trends and topics.

Data Management – Baseline Techniques
Data Lifecycles, Data Quality, Master Metadata Management, Data Value, Security Management, Data Platforms, Data Catalogues, Data Curation, Data Uplift.

Modelling Organisations and Systems
Systems Analysis and Design Using UML, Business Process Modelling, Understanding Socio-Technical Systems

Designing Data Governance
Systems of Record, The Data Value Map, The Ethics Canvas, Data Governance Decision Domains, Operationalising Data Governance, Organisational Roles, Regulatory Compliance, Developing a Data Strategy

Data Governance Techniques
Data Controllers and Processors, Risk Assessments, Countermeasures, Data Protection Impact Assessments, Data Minimisation, Accountability and Auditing, Third Party Transfers, Consent, Fairness and Bias, Trustworthy AI

Data Governance Platforms
Collibra Data Governance Centre, the Data Governance Operating Model, Practical Case Studies

Data Governance Standards
DAMA, ISO, CMMI Institute and W3C standards.

Data Governance Trends and Emerging Topics
Federated, Open and Web Data, Systems of Engagement, Big Data, IoT, AI Governance, Data Citizens, Digital Trust, Supporting Data Scientists, Ethical Data Governance

Indicative Reading List

Books:

Ladley, J.: 2019, Data governance: How to design, deploy, and sustain an effective data governance program, Newnes,
Katherine O'Keefe, Daragh O Brien: 2018, Ethical Data and Information Management: Concepts, Tools and Methods, KoganPage,
DAMA International: 2017, DAMA-DMBOK: Data Management Body of Knowledge: 2nd Edition, 978-163462234

Articles:
None

Other Resources

None