Data Governance as Success Factor for Data Science

Paul Brous*, Marijn Janssen, Rutger Krans

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

4 Citations (Scopus)
15 Downloads (Pure)


More and more, asset management organizations are introducing data science initiatives to support predictive maintenance and anomaly detection. Asset management organizations are by nature data intensive to manage their assets like bridges, dykes, railways and roads. For this, they often implement data lakes using a variety of architectures and technologies to store big data and facilitate data science initiatives. However, the decision-outcomes of data science models are often highly reliant on the quality of the data. The data in the data lake therefore has to be of sufficient quality to develop trust by decision-makers. Not surprisingly, organizations are increasingly adopting data governance as a means to ensure that the quality of data entering the data lake is and remains of sufficient quality, and to ensure the organization remains legally compliant. The objective of the case study is to understand the role of data governance as success factor for data science. For this, a case study regarding the governance of data in a data lake in the asset management domain is analyzed to test three propositions contributing to the success of using data science. The results show that unambiguous ownership of the data, monitoring the quality of the data entering the data lake, and a controlled overview of standard and specific compliance requirements are important factors for maintaining data quality and compliance and building trust in data science products.

Original languageEnglish
Title of host publicationResponsible Design, Implementation and Use of Information and Communication Technology - 19th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2020, Proceedings
EditorsMarié Hattingh, Machdel Matthee, Hanlie Smuts, Ilias Pappas, Yogesh K. Dwivedi, Matti Mäntymäki
PublisherSpringer Open
Number of pages12
ISBN (Print)9783030449988
Publication statusPublished - 2020
Event19th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2020 - Skukuza, South Africa
Duration: 6 Apr 20208 Apr 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12066 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference19th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2020
CountrySouth Africa

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Asset management
  • Big data
  • Data governance
  • Data lake
  • Data quality
  • Data science
  • Digital transformation


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