Data Quality Considerations for ERP Implementation: Techniques for Effective Data Management

Data Quality Considerations for ERP Implementation: Techniques for Effective Data Management

연구 분야: Databases

학회: International Conference for Information and Communication Technologies

Most industrial companies implement enterprise resource planning systems to enrich their digital transformation. ERP systems are databased technologies that employee big data, machine learning, data science and automation. Data is important to deploy such centralized systems, and to improve business decisions. ERP system can merge data from warehouse, sales orders, marketing commands, finance, human resources and management offices. It helps enterprises to get advantages of digital ecosystems and competitive rank in the market. Data quality issues in ERP implementation can have significant consequences for businesses success and their competitive positions. To mitigate these consequences, enterprises must prioritize data quality during ERP implementation. Implementing robust data governance practices, conducting data cleansing and validation, ensuring data accuracy and completeness, and establishing data quality monitoring mechanisms are crucial steps to enhance data quality and minimize the negative impact on business operations. This paper focuses on some data issues that may face implementers during data migration phase, in addition, it proposes some solutions. The work reviews current related studies that help companies to overcome lack of quality data and related problems that may cause delay, budget deficit and collapse of ERP projects. It discusses three concepts namely, data integration, ETL process and data integrity in order to help ERP project managers distinguish these activities. Additionally it provides technical recommendations to manage data quality for success of ERP implementations.