Enhancing Data Quality for Operational Efficiency in Company X

Abstract

This thesis evaluates the quality of auxiliary service data maintained by Company X, a global leader in the metal industry, and proposes strategies for enhancing data accuracy to facilitate a smooth transition to a new ERP system. It emphasizes the critical link between data quality and operational efficiency, highlighting how accurate data minimizes error correction time, enables informed decision-making, and fosters innovation.

The research identifies initial deficiencies in the company’s data quality, characterized by unreliable reporting and gaps in technical information. By implementing a structured verification process and establishing new methodologies for data maintenance, the study reveals improvements in data accuracy and reporting reliability.

Furthermore, it discusses the importance of ongoing data quality assessments and collaboration among stakeholders to sustain improvements and adapt to evolving business needs. Ultimately, the findings underscore the necessity of high-quality data as a cornerstone for sustainable business growth and operational success.