Beyond Accuracy: Rethinking Data Quality as a Strategic Pillar in ERP Implementation

Аннотация

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. KEY WORDS: Data Quality, Data Integrity, Quality Control, ERP, Quality Assurance. 1. INTRODUCTION -Enterprise Resource Planning (ERP) systems are software packages composed of several modules, such as human resources, sales, finance and production, providing cross- organization integration of data through embedded business processes. Many employees in different divisions like accounting and sales use the same information for their various needs. It is not practical forcing employees to maintain separate databases and spread sheets that must be manually merged to generate reports in a real-time business process. So, ERP software Offer some degree of synchronized reporting and automation. Also give a chance to organization members to pull reports from one https://www.academicpublishers.org/journals/index.php/ijdsml 400 AMERICAN ACADEMIC PUBLISHER system. Having a portal or Dashboard which gives a chance to understand the performance of the business environment is the main feature of the ERP systems. It Help to get quick overview of current business situation. Companies need Data Quality to help make better decisions. Data Quality is generally defined as accuracy, completeness, timeliness or accessibility. Data Quality issues can take place by both conceptual and operational problems. -Data quality can be succinctly defined as the measure of suitability or fitness of a specific dataset when applied to achieve a particular purpose or objective. It is widely recognized as a multidimensional and multifaceted concept, encompassing various attributes and characteristics that determine its usefulness. Within databases or information systems, data itself does not inherently possess quality or intrinsic value; rather, it holds potential value that is actualized only through meaningful usage or application. Thus, the concept of data quality is context-dependent, becoming fully realized and measurable when data supports informed decision-making, analytical processes, or operational activities. - The traditional approach to data quality has predominantly focused on isolated, departmental-level practices, emphasizing data accuracy within discrete units such as Sales, Accounting, or Manufacturing. However, such an isolated approach is insufficient for enhancing decision-making at the strategic or enterprise level. Maintaining high data quality solely within individual departments does not inherently support comprehensive strategic insight or facilitate a robust cross-functional collaboration. Due to inherent limitations in data integration and consistency, such fragmented practices often lead to compromised accuracy, ultimately hindering informed and cohesive decision-making across the organization. Therefore, contemporary research advocates for a holistic and integrative data quality framework, emphasizing enterprise-wide governance, comprehensive data integration strategi