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Master Data Management: A Conceptual Framework For Successfully Supporting Data-Driven Transformation And Corporate Success

Author

Listed:
  • MATTHIAS SCHMUCK

    (Doctoral School of Economics and Business Administration of „Alexandru Ioan Cuza“ University – Iași, Romania)

  • EMANUEL RIEDER

    (Doctoral School of Economics and Business Administration of „Alexandru Ioan Cuza“ University – Iași, Romania)

Abstract

Digital transformation is reshaping business and public sector structures, increasingly relying on high-quality, strategically usable data. In this context, Master Data Management (MDM) emerges as a critical enabler of data-driven transformation. This study presents a framework – developed using the Design Science Research methodology – that illustrates how MDM, supported by effective change management, contributes to organizational success. A systematic literature review (SLR) identified key success factors, grouped into four dimensions (management, technology, people and organization, and support), and common pitfalls (CP). Change Management (CM) serves as the central link/bridge between MDM and business impact. A combined approach using CM models by Kotter, Hiatt, and Lewin is proposed to support transformation at organizational and individual levels. Using the success chain concept, the transformational framework shows how improved master data capabilities – driven by the success factors and change initiatives – leads to psychological and behavioural changes in data users and, ultimately, to enhanced corporate success like decision-making efficiency, process optimization or technology adaption. MDM is positioned not as a technical tool, but as a strategic asset in digital value creation and corporate success. The paper is part of a series of articles on the topic of “Measuring the Success of MDM†.

Suggested Citation

  • Matthias Schmuck & Emanuel Rieder, 2025. "Master Data Management: A Conceptual Framework For Successfully Supporting Data-Driven Transformation And Corporate Success," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 35, pages 177-191, June.
  • Handle: RePEc:aic:revebs:y:2025:j:35:schmuckm
    DOI: 10.47743/rebs-2025-1-0011
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    JEL classification:

    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • O10 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - General
    • O25 - Economic Development, Innovation, Technological Change, and Growth - - Development Planning and Policy - - - Industrial Policy
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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