In recent years, a significant number of manufacturing enterprises globally have adopted Enterprise Resource Planning (ERP) systems as a strategic step toward digital transformation, leveraging advancements in cloud-based technologies. ERP systems, characterized by their comprehensive database structures, support advanced capabilities such as Artificial Intelligence (AI), Big Data analytics, Machine Learning (ML), and process automation. Given their integrative potential, these systems effectively consolidate essential business functions, including Sales, Accounting, Manufacturing, Human Resources, and overall management.
Data quality emerges as a critical factor and one of the foundational pillars for the successful implementation of ERP systems. The relevance of high-quality data in ERP deployments is underscored by its direct influence on operational efficiency, departmental integration, and informed decision-making at executive levels. Poor data quality during ERP implementation can result in significant adverse effects, disrupting interdepartmental coordination, and leading to flawed strategic decisions.
This review addresses key data quality issues commonly encountered during the data migration phase, transitioning from legacy systems to modern ERP infrastructures. It highlights prominent data quality challenges, including data inconsistencies, duplication, incompleteness, and misalignment across disparate data sources. Additionally, the paper explores various methodologies and best practices for enhancing data quality, such as rigorous data cleansing, robust governance frameworks, and systematic validation procedures during migration.
Furthermore, this study emphasizes the criticality of maintaining data integrity throughout ERP implementation phases and identifies effective ERP project management practices as vital to ensuring successful system deployment. Insights drawn from recent literature and empirical case studies illustrate the strategies employed to mitigate data quality risks, ensuring the realization of anticipated ERP system benefits.
Rushabh Mehta, Strategic Integration of ERP and Manufacturing Information Systems: Overcoming Implementation Challenges and Driving digital transformation , International journal of data science and machine learning: Vol. 5 No. 01 (2025)
Copyright (c) 2025 Rushabh Mehta
This work is licensed under a Creative Commons Attribution 4.0 International License.
inLibrary — is a scientific electronic library built on the paradigm of open science (Open Science), the main tasks of which are the popularization of science and scientific activities, public quality control of scientific publications, the development of interdisciplinary research, a modern institute of scientific review, increasing the citation of Uzbek science and building a knowledge infrastructure.
CONTACTS:
