Transparent Peer Review By Scholar9
Asset Master Data Management: Ensuring Accuracy and Consistency in Industrial Operations
Abstract
The problems of data quality are topical nowadays, and the tendencies of growing amounts of data bring certain challenges to data management strategies. Also, technical tools of today allow for data that is more than most firms can administrate and different business solutions can result in increased data density. Master data management is truly experiencing globalisation at an exponential rate because its relevance or a link to the results or income does not have a direct connection to the operation of an organisation. In the contemporary world that is characterised by stiff economy competition, proper management of master data is crucial to making right strategic decisions and management of an organisation for efficient functioning. This paper aims at studying the topic of Master Data Management (MDM) taking into account the enhancement of dependability, consistency and quality of numerous fields of activity. Focusing on a single scheme of Master Data Management and the integration of various data sources, it describes MDM frameworks and implementation methods. The study reviews different types of MDM solutions—operational, analytic, and enterprise—and discusses key phases and approaches for successful MDM implementation. Furthermore, it investigates current research trends and gaps in MDM literature, highlighting the need for adaptive and scalable MDM architectures tailored to evolving organizational needs.
Aravind Ayyagari Reviewer
11 Sep 2024 04:29 PM
Approved
Relevance and Originality:
The Research Article addresses the pressing issue of data quality and management in the context of growing data volumes and business needs. The focus on Master Data Management (MDM) and its importance for strategic decision-making and organizational efficiency is highly relevant. The exploration of different MDM solutions and their integration is original and timely, reflecting the current challenges and trends in data management.
Methodology:
The abstract does not provide specific details about the methodology used in the study. While it mentions the review of MDM frameworks, implementation methods, and types of MDM solutions, it lacks information on the research design, data sources, and analysis techniques. Including these details would be essential to assess the study’s thoroughness and approach.
Validity & Reliability:
The abstract highlights the review of various MDM solutions and implementation strategies but does not specify how validity and reliability are ensured. Details on how the findings are validated, any criteria for evaluating MDM frameworks, and how the study ensures consistent results would be important for evaluating the robustness of the research.
Clarity and Structure:
The abstract is generally clear and well-structured, presenting the problem, the focus of the study, and the areas covered. It describes the MDM frameworks and types of solutions considered. However, a more detailed outline of the study's objectives, methodology, and specific findings would enhance clarity and help readers understand the scope and contributions of the research.
Result Analysis:
The abstract does not provide specific results or detailed analysis. While it mentions the investigation of current research trends and gaps in MDM literature, including concrete findings, data, or examples of successful MDM implementations would strengthen the result analysis and provide a clearer picture of the study's impact on the field.
IJ Publication Publisher
Ok Sir
Aravind Ayyagari Reviewer