Year: 2024
Type: article
Abstract: In today’s digital landscape, organizations face the challenge of managing vast amounts of data characterized by high volume, variety, and velocity. Without consistent data management practices, data becomes fragmented and unreliable, making it difficult to achieve high data quality and seamless integration. This paper addresses the role of Master Data Management (MDM) in overcoming these challenges, focusing on its impact on simplifying operations, enhancing strategic decision-making, and enabling scalability by showing the practical architecture of how MDM is created. This paper presents an approach to MDM implementation, showcasing practical architectures and real-world examples that bridge the gap between theoretical concepts and applied data management. The research introduces centralized data models and standardized quality control mechanisms, demonstrating how MDM serves as the backbone of modern data governance. The paper also explores the integration of MDM with Artificial Intelligence and Machine Learning for data quality optimization. The paper also examines the role of data stewards in supporting MDM initiatives. Findings indicate that well-implemented MDM strategies not only enhance operational efficiencies but also contribute to significant cost savings and improved regulatory compliance. By providing a roadmap for successful MDM implementation, this research offers valuable insight for data engineers, business leaders, and academics, contributing to the development of robust, scalable, and effective data management ecosystems in the ever-evolving digital era.
Author: Arpit Sharma
Language: English
Topic: Data Quality and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4403514597