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Data governance: A conceptual framework, structured review, and research agenda

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  • Abraham, Rene
  • Schneider, Johannes
  • vom Brocke, Jan

Abstract

Data governance refers to the exercise of authority and control over the management of data. The purpose of data governance is to increase the value of data and minimize data-related cost and risk. Despite data governance gaining in importance in recent years, a holistic view on data governance, which could guide both practitioners and researchers, is missing. In this review paper, we aim to close this gap and develop a conceptual framework for data governance, synthesize the literature, and provide a research agenda. We base our work on a structured literature review including 145 research papers and practitioner publications published during 2001-2019. We identify the major building blocks of data governance and decompose them along six dimensions. The paper supports future research on data governance by identifying five research areas and displaying a total of 15 research questions. Furthermore, the conceptual framework provides an overview of antecedents, scoping parameters, and governance mechanisms to assist practitioners in approaching data governance in a structured manner.

Suggested Citation

  • Abraham, Rene & Schneider, Johannes & vom Brocke, Jan, 2019. "Data governance: A conceptual framework, structured review, and research agenda," International Journal of Information Management, Elsevier, vol. 49(C), pages 424-438.
    • Handle: RePEc:eee:ininma:v:49:y:2019:i:c:p:424-438
    DOI: 10.1016/j.ijinfomgt.2019.07.008
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    Citations

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    Cited by:

    1. Hongmei Chen & Songlin Cai, 2023. "Research on Evaluation and Influencing Factors of Regional Digital Innovation Ecosystem Resilience—Empirical Research Based on Panel Data of 30 Provinces and Cities in China," Sustainability, MDPI, vol. 15(13), pages 1-20, July.
    2. Anassaya Chawviang & Supaporn Kiattisin & Montree Thirasakthana & Theeraya Mayakul, 2023. "A Smart Co-Operative Management Framework Based on an EA Concept for Sustainable Development," Sustainability, MDPI, vol. 15(9), pages 1-22, April.
    3. Shamim, Saqib & Yang, Yumei & Ul Zia, Najam & Khan, Zaheer & Shariq, Syed Muhammad, 2023. "Mechanisms of cognitive trust development in artificial intelligence among front line employees: An empirical examination from a developing economy," Journal of Business Research, Elsevier, vol. 167(C).
    4. Anassaya Chawviang & Supaporn Kiattisin, 2022. "Sustainable Development: Smart Co-Operative Management Framework," Sustainability, MDPI, vol. 14(6), pages 1-25, March.
    5. Meng Li & Jiqiang Liu & Yeping Yang, 2024. "Automated Identification of Sensitive Financial Data Based on the Topic Analysis," Future Internet, MDPI, vol. 16(2), pages 1-17, February.
    6. Nunan, Daniel & Di Domenico, MariaLaura, 2022. "Value creation in an algorithmic world: Towards an ethics of dynamic pricing," Journal of Business Research, Elsevier, vol. 150(C), pages 451-460.
    7. Rene Abraham & Johannes Schneider & Jan vom Brocke, 2023. "A taxonomy of data governance decision domains in data marketplaces," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-13, December.
    8. Struijk, Mylène, 2023. "IT Governance in the digital era : Insights from meta-organizations," Other publications TiSEM a6f02085-ff68-427f-b65a-8, Tilburg University, School of Economics and Management.
    9. Charlotte Ducuing & René Herbert Reich, 2023. "Data governance: Digital product passports as a case study," Competition and Regulation in Network Industries, , vol. 24(1), pages 3-23, March.

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