Towards understanding inter-organizational data sharing practices and technical tools needs
Malysh, Konstantin LU
(2025)
In Licentiate thesis
1:2025.
- Abstract
- Background: Data quickly emerges as a core driver of businesses across the world
due to rapidly expanding use in different areas, such as Machine Learning. In order
for the data-related organizational operations to be efficient, the topic of data shar-
ing has to be explored. This thesis focuses on understanding inter-organizational
data sharing by exploring practices and challenges that are present within data-
driven businesses.
Aim: The objective of this thesis is to explore inter-organizational data sharing
practices and assess the need for governance and tools to support the related pro-
cesses. We aim to identify and fill the research gaps on the data sharing needs and
challenges in order to... (More) - Background: Data quickly emerges as a core driver of businesses across the world
due to rapidly expanding use in different areas, such as Machine Learning. In order
for the data-related organizational operations to be efficient, the topic of data shar-
ing has to be explored. This thesis focuses on understanding inter-organizational
data sharing by exploring practices and challenges that are present within data-
driven businesses.
Aim: The objective of this thesis is to explore inter-organizational data sharing
practices and assess the need for governance and tools to support the related pro-
cesses. We aim to identify and fill the research gaps on the data sharing needs and
challenges in order to produce more specific requirements that would help organi-
zations conduct their data sharing efficiently.
Research Methodology: We conducted the research in several steps according to
the design science paradigms. We performed literature analysis with the goal of
exploring the practices and challenges related to data sharing in existing research.
Afterwards, we performed an empirical interview study with the representatives
of data-driven companies located in Sweden to collect our own data on challenges
and practices. Finally, we performed a case study in order to extend knowledge on
data sharing by diving deep into one specific case of industrial data sharing in an
industry-academia collaboration context, and validating our findings by exploring
several adjacent cases.
Results: We present the results as two papers in this thesis. We observed and cate-
gorized the core challenges and aspects that come along with data sharing process,
with privacy being considered one of the most important topics. We also presented
a conceptual model of organizational entities involved in data sharing processes.
Afterwards, we focused on Open Data Ecosystems and platforms, and observed
and assessed incentives, challenges, governance and functionalities that are to be
present in order for the ecosystem function effectively.
Conclusion: Inter-organizational data sharing is an important and complex con-
cept that needs to be investigated. The process of ensuring the quality of data
sharing practices should be done on a case-by-case scenario in order to satisfy the
needs of all the actors involved, so that the challenges are minimized and the pro-
cesses flow as efficiently as possible. Within our research, this will be achieved by
having a mature governance model, being supported by a well maintained tech-
nological platform. Future work will include separately exploring the topics of
technological tools and governance within Open Data Ecosystems and platforms
with goal of obtaining a clear set of requirements needed for both aspects to be
addressed effectively as a part of data sharing process. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/947dffae-5778-41b2-bab3-bca42a095755
- author
- Malysh, Konstantin
LU
- supervisor
-
- Per Runeson LU
- Johan Linåker LU
- organization
- publishing date
- 2025
- type
- Thesis
- publication status
- published
- subject
- in
- Licentiate thesis
- volume
- 1:2025
- pages
- 60 pages
- ISSN
- 1652-4691
- ISBN
- 978-91-8104-496-6
- 978-91-8104-495-9
- language
- English
- LU publication?
- yes
- id
- 947dffae-5778-41b2-bab3-bca42a095755
- date added to LUP
- 2025-03-31 06:32:12
- date last changed
- 2025-04-23 03:24:22
@misc{947dffae-5778-41b2-bab3-bca42a095755,
abstract = {{Background: Data quickly emerges as a core driver of businesses across the world<br/>due to rapidly expanding use in different areas, such as Machine Learning. In order<br/>for the data-related organizational operations to be efficient, the topic of data shar-<br/>ing has to be explored. This thesis focuses on understanding inter-organizational<br/>data sharing by exploring practices and challenges that are present within data-<br/>driven businesses.<br/><br/>Aim: The objective of this thesis is to explore inter-organizational data sharing<br/>practices and assess the need for governance and tools to support the related pro-<br/>cesses. We aim to identify and fill the research gaps on the data sharing needs and<br/>challenges in order to produce more specific requirements that would help organi-<br/>zations conduct their data sharing efficiently.<br/><br/>Research Methodology: We conducted the research in several steps according to<br/>the design science paradigms. We performed literature analysis with the goal of<br/>exploring the practices and challenges related to data sharing in existing research.<br/>Afterwards, we performed an empirical interview study with the representatives<br/>of data-driven companies located in Sweden to collect our own data on challenges<br/>and practices. Finally, we performed a case study in order to extend knowledge on<br/>data sharing by diving deep into one specific case of industrial data sharing in an<br/>industry-academia collaboration context, and validating our findings by exploring<br/>several adjacent cases.<br/><br/>Results: We present the results as two papers in this thesis. We observed and cate-<br/>gorized the core challenges and aspects that come along with data sharing process,<br/>with privacy being considered one of the most important topics. We also presented<br/>a conceptual model of organizational entities involved in data sharing processes.<br/>Afterwards, we focused on Open Data Ecosystems and platforms, and observed<br/>and assessed incentives, challenges, governance and functionalities that are to be<br/>present in order for the ecosystem function effectively.<br/><br/>Conclusion: Inter-organizational data sharing is an important and complex con-<br/>cept that needs to be investigated. The process of ensuring the quality of data<br/>sharing practices should be done on a case-by-case scenario in order to satisfy the<br/>needs of all the actors involved, so that the challenges are minimized and the pro-<br/>cesses flow as efficiently as possible. Within our research, this will be achieved by<br/>having a mature governance model, being supported by a well maintained tech-<br/>nological platform. Future work will include separately exploring the topics of<br/>technological tools and governance within Open Data Ecosystems and platforms<br/>with goal of obtaining a clear set of requirements needed for both aspects to be<br/>addressed effectively as a part of data sharing process.}},
author = {{Malysh, Konstantin}},
isbn = {{978-91-8104-496-6}},
issn = {{1652-4691}},
language = {{eng}},
note = {{Licentiate Thesis}},
series = {{Licentiate thesis}},
title = {{Towards understanding inter-organizational data sharing practices and technical tools needs}},
volume = {{1:2025}},
year = {{2025}},
}