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MIS41500

Academic Year 2025/2026

Managing Data and AI (MIS41500)

Subject:
Management Information Systems
College:
Business
School:
Business
Level:
4 (Masters)
Credits:
7.5
Module Coordinator:
Dr Hippolyte Lefebvre
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

Data is a strategic asset for organizations, fueling artificial intelligence (AI) and informing decision-making across the whole organizational structure of diverse enterprises. Hence, it is no surprise that data management has evolved from a back-office IT concern to a strategic capability that creates competitive advantage.

This course integrates two perspectives on data management, enabling students to understand how organizations use, govern, and extract value from data:
1. Technical Data Management: focusing on managing Data Operations including topics such as data architecture and modeling, metadata management, data quality management
2. Managerial Data Management focusing on Data governance and Data strategy, including ethics and responsibility
Students will learn not only how to manage data effectively but also how to lead organizations in using data and AI strategically and responsibly.

The course leverages internationally recognized frameworks such as DAMA-DMBOK and incorporates key principles from emerging AI governance frameworks. There will be guest talks with subject matter experts from research and practice.

About this Module

Learning Outcomes:

Upon completion of the course, students will be able to demonstrate an understanding of:
1. How organizational data structures and data models support accurate, reliable, and scalable data use.
2. Traditional and AI-driven techniques for inspecting, cleaning, transforming, and assuring the quality of data.
3. Core data governance components, including roles (ownership, stewardship), policies, controls, and accountability mechanisms.
4. The role of metadata, documentation, and lineage in enabling data understanding, discovery, trust, and compliance.
5. Organizational processes and operating models that enable effective data management and data-driven innovation.
6. How data assets support organizational goals and contribute to value creation and performance.
7. The components of an effective data strategy.
8. The strategic and ethical challenges emerging with AI/GenAI and their implications for data management.

Indicative Module Content:

The course will cover data management at three levels that reflect the reality of organizations: data operations, data governance, and data strategy.

Student Effort Hours:
Student Effort Type Hours
Lectures

24
Specified Learning Activities

96
Autonomous Student Learning

60
Total

180

Approaches to Teaching and Learning:
This course adopts a practice-oriented, management-focused approach to teaching and learning, guiding students from foundational data operations to governance and finally strategy. Learning is structured around real-world challenges and supported by weekly readings, interactive discussions, hands-on exercises, and guest speakers who bring industry perspectives. Students progressively apply concepts through a group project analyzing an AI implementation case. The course emphasizes active participation, critical reflection, and the ability to connect theory to organizational realities, preparing students to become data- and AI-driven decision-makers.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Reflective Assignment: Weekly 200-word maximum reflections on assigned readings, summarizing key insights and linking them to course themes to support continuous learning and exam preparation. Week 1, Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10, Week 11, Week 12 Graded No
15
 No
Group Work Assignment: Group analysis of an AI implementation case, producing a short management report and presentation that apply course concepts to evaluate challenges and recommend actions. Week 7, Week 8, Week 9 Graded Yes
35
 Yes
Exam (In-person): A closed-book written exam assessing understanding of all course readings and materials through MCQs, testing students’ ability to integrate and apply key concepts. End of trimester
Duration:
2 hr(s)
 Graded Yes
50
 Yes

Carry forward of passed components
No
 

Resit In Terminal Exam
Summer Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring Lecture Offering 1 Week(s) - 26, 29, 30, 32 Mon 12:00 - 14:50
Spring Lecture Offering 1 Week(s) - 20, 21, 23, 24, 25, 33 Mon 13:00 - 14:50
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