Leveraging Master Data Management in IT for Improved Data Governance, Operational Efficiency, and Strategic Decision-Making

Not Approved

Relevance and Originality:

The research addresses a highly relevant and significant issue in modern business operations—the management of data through Master Data Management (MDM). As organizations increasingly rely on data for decision-making, improving data quality and governance is crucial for business success. This study fills an important gap by providing insights into the role of MDM frameworks in improving operational efficiency and data governance, areas that are often underexplored in-depth. The originality of the research lies in its practical application through case studies, along with quantitative data analysis, making it relevant for both academic and industry audiences. However, it would benefit from exploring emerging technologies in data management that may impact MDM frameworks in the future.

Methodology:

The research employs a mixed-method approach, integrating qualitative case studies with quantitative data analysis, which is appropriate for investigating complex and multidimensional topics like MDM. The use of case studies from leading firms in the IT sector allows the research to draw from real-world examples, enhancing its practical relevance. However, the study would benefit from a clearer explanation of how the firms were selected, as this would help to establish the representativeness of the data. Additionally, more details on the quantitative analysis methods, such as sample size and statistical tools used, would strengthen the credibility of the findings.

Validity & Reliability:

The findings appear to be robust, with the research providing a clear link between the implementation of MDM frameworks and improvements in data accuracy and operational efficiency. The use of both qualitative and quantitative methods helps to reinforce the validity of the conclusions. However, the generalizability of the results may be limited due to the focus on leading IT firms, which might not represent the broader spectrum of organizations across different industries. Expanding the sample size or including diverse industry sectors could enhance the study’s generalizability and applicability to a wider audience.

Clarity and Structure:

The organization and readability of the research article are commendable, with a logical flow from introduction to conclusion. The argumentation is clear, and the structure allows for an easy understanding of the research objectives and results. However, certain sections could benefit from more detailed explanations, particularly the methodology, which could be better explained in terms of sampling methods and data collection processes. Overall, the clarity of the writing is good, but a more comprehensive discussion of the research process would improve the reader's understanding.

Result Analysis:

The results are presented with a clear connection to the research questions, and the analysis of MDM frameworks is thorough. The study successfully identifies key challenges and benefits related to MDM, such as integration complexity and data silos. However, the depth of the analysis could be enhanced by discussing the implications of these challenges in more detail and how organizations can specifically overcome them. Furthermore, while the results are significant, more in-depth interpretation could be provided regarding the long-term impact of MDM on strategic decision-making and business intelligence capabilities. The conclusions are supported by the results, but a more nuanced discussion of the challenges faced during MDM implementation would add greater value.