IDEAS home Printed from https://ideas.repec.org/a/spr/infsem/v19y2021i1d10.1007_s10257-020-00500-5.html
   My bibliography  Save this article

Enterprise architecture management as a solution for addressing general data protection regulation requirements in a big data context: a systematic mapping study

Author

Listed:
  • Georgios Georgiadis

    (Ghent University)

  • Geert Poels

    (Ghent University)

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.

Suggested Citation

  • Georgios Georgiadis & Geert Poels, 2021. "Enterprise architecture management as a solution for addressing general data protection regulation requirements in a big data context: a systematic mapping study," Information Systems and e-Business Management, Springer, vol. 19(1), pages 313-362, March.
  • Handle: RePEc:spr:infsem:v:19:y:2021:i:1:d:10.1007_s10257-020-00500-5
    DOI: 10.1007/s10257-020-00500-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10257-020-00500-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10257-020-00500-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Alharthi, Abdulkhaliq & Krotov, Vlad & Bowman, Michael, 2017. "Addressing barriers to big data," Business Horizons, Elsevier, vol. 60(3), pages 285-292.
    2. Lee, In, 2017. "Big data: Dimensions, evolution, impacts, and challenges," Business Horizons, Elsevier, vol. 60(3), pages 293-303.
    3. Irving Fisher Committee, 2017. "Big Data," IFC Bulletins, Bank for International Settlements, number 44, July.
    4. Sauer, Chris & Willcocks, Leslie, 2003. "Establishing the Business of the Future:: the Role of Organizational Architecture and Information Technologies," European Management Journal, Elsevier, vol. 21(4), pages 497-508, August.
    5. Cuquet, Martí & Fensel, Anna, 2018. "The societal impact of big data: A research roadmap for Europe," Technology in Society, Elsevier, vol. 54(C), pages 74-86.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Arnold, René & Hildebrandt, Christian & Taş, Serpil, 2020. "Europäische Datenökonomie: Zwischen Wettbewerb und Regulierung. Endbericht," Study Series, WIK Wissenschaftliches Institut für Infrastruktur und Kommunikationsdienste GmbH, number 251537.
    2. Umit Can & Bilal Alatas, 2017. "Big Social Network Data and Sustainable Economic Development," Sustainability, MDPI, vol. 9(11), pages 1-19, November.
    3. Parmar, Rashik & Leiponen, Aija & Thomas, Llewellyn D.W., 2020. "Building an organizational digital twin," Business Horizons, Elsevier, vol. 63(6), pages 725-736.
    4. Amit Kumar Gupta & Harshit Goyal, 2021. "Framework for implementing big data analytics in Indian manufacturing: ISM-MICMAC and Fuzzy-AHP approach," Information Technology and Management, Springer, vol. 22(3), pages 207-229, September.
    5. Brave, Scott A. & Butters, R. Andrew & Fogarty, Michael, 2022. "The perils of working with big data, and a SMALL checklist you can use to recognize them," Business Horizons, Elsevier, vol. 65(4), pages 481-492.
    6. Arnold, René & Hildebrandt, Christian & Taş, Serpil, 2020. "European data economy: Between competition and regulation. Final report," Study Series, WIK Wissenschaftliches Institut für Infrastruktur und Kommunikationsdienste GmbH, number 251538.
    7. Tabesh, Pooya & Mousavidin, Elham & Hasani, Sona, 2019. "Implementing big data strategies: A managerial perspective," Business Horizons, Elsevier, vol. 62(3), pages 347-358.
    8. Paulo Ferreira & Éder J.A.L. Pereira & Hernane B.B. Pereira, 2020. "From Big Data to Econophysics and Its Use to Explain Complex Phenomena," JRFM, MDPI, vol. 13(7), pages 1-10, July.
    9. Hayes, Darren R. & Cappa, Francesco, 2018. "Open-source intelligence for risk assessment," Business Horizons, Elsevier, vol. 61(5), pages 689-697.
    10. Maniyassouwe Amana & Pingfeng Liu & Mona Alariqi, 2022. "Value Creation and Capture with Big Data in Smart Phones Companies," Sustainability, MDPI, vol. 14(23), pages 1-22, November.
    11. Pei Zhang & Peiran Chen & Fan Xiao & Yong Sun & Shuyan Ma & Ziwei Zhao, 2022. "The Impact of Information Infrastructure on Air Pollution: Empirical Evidence from China," IJERPH, MDPI, vol. 19(21), pages 1-17, November.
    12. Francis Aboagye‐Otchere & Cletus Agyenim‐Boateng & Abdulai Enusah & Theodora Ekua Aryee, 2021. "A Review of Big Data Research in Accounting," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(4), pages 268-283, October.
    13. Acharya, Abhilash & Singh, Sanjay Kumar & Pereira, Vijay & Singh, Poonam, 2018. "Big data, knowledge co-creation and decision making in fashion industry," International Journal of Information Management, Elsevier, vol. 42(C), pages 90-101.
    14. Certomà, Chiara & Corsini, Filippo & Frey, Marco, 2020. "Hyperconnected, receptive and do-it-yourself city. An investigation into the European “imaginary” of crowdsourcing for urban governance," Technology in Society, Elsevier, vol. 61(C).
    15. Tiago Carneiro & Winnie Ng Picoto & Inês Pinto, 2023. "Big Data Analytics and Firm Performance in the Hotel Sector," Tourism and Hospitality, MDPI, vol. 4(2), pages 1-13, April.
    16. Ms. Longmei Zhang & Ms. Sally Chen, 2019. "China’s Digital Economy: Opportunities and Risks," IMF Working Papers 2019/016, International Monetary Fund.
    17. Jean-Luc Pradel Mathurin Augustin & Shu-Yi Liaw, 2020. "Exploring the Relationship between Perceived Big Data Advantages and Online Consumers’ Behavior: An Extended Hierarchy of Effects Model," International Business Research, Canadian Center of Science and Education, vol. 13(6), pages 1-73, June.
    18. Haitham Nobanee & Mehroz Nida Dilshad & Mona Al Dhanhani & Maitha Al Neyadi & Sultan Al Qubaisi & Saeed Al Shamsi, 2021. "Big Data Applications the Banking Sector: A Bibliometric Analysis Approach," SAGE Open, , vol. 11(4), pages 21582440211, December.
    19. Sundarakani, Balan & Ajaykumar, Aneesh & Gunasekaran, Angappa, 2021. "Big data driven supply chain design and applications for blockchain: An action research using case study approach," Omega, Elsevier, vol. 102(C).
    20. Bell, Frances & Fletcher, Gordon & Greenhill, Anita & Griffiths, Marie & McLean, Rachel, 2014. "Making MadLab: A creative space for innovation and creating prototypes," Technological Forecasting and Social Change, Elsevier, vol. 84(C), pages 43-53.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:infsem:v:19:y:2021:i:1:d:10.1007_s10257-020-00500-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.