Module: Data Governance

Module Specifications.

Current Academic Year 2024 - 2025

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Date posted: September 2024

Module Title	Data Governance
Module Code	CA691 (ITS) / CSC1151 (Banner)
Faculty	Engineering & Computing	School	Computing
Module Co-ordinator	Alessandra Mileo
Module Teachers	Boualem Benatallah, Renaat Verbruggen
NFQ level	9	Credit Rating	10
Pre-requisite	Not Available
Co-requisite	Not Available
Compatibles	Not Available
Incompatibles	Not Available

Repeat examination
A resit is available for all components of this module.

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

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

Assessment Breakdown
Continuous Assessment	50%	Examination Weight	50%

Type	Description	% of total	Assessment Date
Course Work 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

Reassessment Requirement Type
Resit arrangements are explained by the following categories: Resit category 1: A resit is available for both* components of the module. Resit category 2: No resit is available for a 100% continuous assessment module. Resit category 3: No resit is available for the continuous assessment component where there is a continuous assessment and examination element.
* ‘Both’ is used in the context of the module having a Continuous Assessment/Examination split; where the module is 100% continuous assessment, there will also be a resit of the assessment
This module is category 1

Indicative Reading List

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

Other Resources

None