Common Data Quality Issues in SAP Systems

Working with data extracted from SAP systems is often the starting point for analysis and AI/ML projects. While SAP is a robust platform for business processes, the data it contains is not always perfectly clean or ready for direct use in analytical models. Data quality issues are common and can significantly impact the reliability and accuracy of any insights derived or models built.

These data quality challenges stem from various factors inherent in large, complex enterprise resource planning (ERP) systems like SAP. They can arise from manual data entry errors, system configurations across different modules, historical data migrations, or simply the evolution of business processes over many years. Understanding these common issues is the crucial first step in preparing your SAP data for Python-based analysis.