Governing Data for Artificial Intelligence: A qualitative study of the human factors that affect governance of client-data for IT-consulting firms
(2025) INFM10 20251Department of Informatics
- Abstract
- As organisations increasingly seek to leverage data for artificial intelligence (AI) analytical use-cases, one of the main challenges towards value realisation is effective use of data governance. Existing research on the topic of data governance focuses on technical and structural aspects of data governance, while this thesis identifies human-centred organisational success factors for data governance, in a consulting context. These include clearly defined roles and responsibilities, sustained communication, top management support, and a strong focus on data security. The findings further reveal the importance of data governance initiatives to be seen as continuous practices. In a consulting context, long-term governance outcomes depend on... (More)
- As organisations increasingly seek to leverage data for artificial intelligence (AI) analytical use-cases, one of the main challenges towards value realisation is effective use of data governance. Existing research on the topic of data governance focuses on technical and structural aspects of data governance, while this thesis identifies human-centred organisational success factors for data governance, in a consulting context. These include clearly defined roles and responsibilities, sustained communication, top management support, and a strong focus on data security. The findings further reveal the importance of data governance initiatives to be seen as continuous practices. In a consulting context, long-term governance outcomes depend on the extent to which clients are empowered to sustain these practices beyond the engagement. By highlighting the organisational and human dimensions of data governance, this study contributes to a broader understanding of how data can be effectively governed to support AI initiatives and create long-term value. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9203533
- author
- Erander, Linn and Granat, Elias
- supervisor
- organization
- course
- INFM10 20251
- year
- 2025
- type
- H1 - Master's Degree (One Year)
- subject
- keywords
- Data, Governing data, Artificial Intelligence, Consultancy
- language
- English
- id
- 9203533
- date added to LUP
- 2025-06-19 21:44:16
- date last changed
- 2025-06-19 21:44:16
@misc{9203533,
abstract = {{As organisations increasingly seek to leverage data for artificial intelligence (AI) analytical use-cases, one of the main challenges towards value realisation is effective use of data governance. Existing research on the topic of data governance focuses on technical and structural aspects of data governance, while this thesis identifies human-centred organisational success factors for data governance, in a consulting context. These include clearly defined roles and responsibilities, sustained communication, top management support, and a strong focus on data security. The findings further reveal the importance of data governance initiatives to be seen as continuous practices. In a consulting context, long-term governance outcomes depend on the extent to which clients are empowered to sustain these practices beyond the engagement. By highlighting the organisational and human dimensions of data governance, this study contributes to a broader understanding of how data can be effectively governed to support AI initiatives and create long-term value.}},
author = {{Erander, Linn and Granat, Elias}},
language = {{eng}},
note = {{Student Paper}},
title = {{Governing Data for Artificial Intelligence: A qualitative study of the human factors that affect governance of client-data for IT-consulting firms}},
year = {{2025}},
}