 Enterprise architecture management as a solution for addressing general data protection regulation requirements in a big data context: a systematic mapping studyGeorgios Georgiadis () and
Geert Poels ()
Information Systems and e-Business Management, 2021, vol. 19, issue 1, No 11, 313-362 Abstract: Abstract Context Big Data Analytics is a rapidly emerging IT practice whose applications offer benefits for a wide variety of business areas across an organisation. Given the wide scope of applications, the many types of processing involved, including those for purposes not yet foreseen, and the inherent privacy concerns resulting from collecting and storing personal data, the newly introduced General Data Protection Regulation (GDPR) poses specific challenges for safeguarding the security and protection of big data. These challenges are not limited to the IT function but extend across the entire organisation. This raises the question whether Enterprise Architecture Management (EAM), as an approach for ensuring the coherence, strategic alignment and focus on value creation of all organisational resources, offers guidance for addressing those challenges in a holistic manner, and thus provides a fruitful ground for developing an approach for complying to GDPR requirements in a Big Data context. Objective This study surveys the state-of-the-art in research on security, privacy, and protection of big data. The focus is on investigating which specific issues and challenges have been identified and whether these have been linked to GDPR requirements. Further, it examines whether previous research has investigated the potential of EAM in addressing those challenges and what the main findings of those studies are. Method We used Systematic Mapping Review (SMR), which is a methodology for literature review aimed at surveying the state-of-the-art in a research field as it is documented in the scientific literature. Further, we used Template Analysis, which is a thematic analysis technique, for coding the texts of the selected papers, classifying the research studies, and interpreting the different themes addressed in the literature. Results Our study indicates that only few researchers have explored the use of EAM practices in relation to data security and protection in a Big Data context. We further identified seven trends within the areas under consideration that could be subjects for further research. Conclusions Our study does not invalidate the potential of EAM to help addressing GDPR requirements in a Big Data context. However, how EAM practices may contribute to risk management and data governance in environments where big data are being processed, is still a huge research gap, which we intend to address in our future research. Keywords: Big data; Data protection; Data protection directive; Enterprise architecture management; General data protection regulation; Governance; Information security; Privacy; Systematic literature mapping (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:infsem:v:19:y:2021:i:1:d:10.1007_s10257-020-00500-5 Ordering information: This journal article can be ordered from DOI: 10.1007/s10257-020-00500-5 Access Statistics for this article Information Systems and e-Business Management is currently edited by Jörg Becker and Michael J. Shaw More articles in Information Systems and e-Business Management from Springer |
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