Data Profiling Methods for Source System Assessment

Abstract

Data profiling methods are important for source system assessment because organizations must understand the structure, quality, completeness, and consistency of data before integration, migration, or warehouse loading. Source systems often contain hidden errors, missing values, duplicate records, inconsistent formats, invalid codes, and undocumented relationships that can affect downstream data processing. Existing literature highlights column profiling, pattern analysis, value distribution checks, dependency analysis, duplicate detection, null-value assessment, and referential integrity verification as major methods for evaluating source data. However, many enterprises still face challenges such as poor documentation, inconsistent field usage, unknown data rules, fragmented databases, and weak visibility into legacy data quality. This research is important because inaccurate source system assessment can lead to migration failures, ETL errors, reporting inconsistencies, and unreliable decision-making. This article discusses data profiling methods for source system assessment, focusing on metadata review, data structure analysis, completeness checking, uniqueness testing, relationship discovery, anomaly detection, and quality reporting. The study concludes that effective data profiling improves data understanding, reduces integration risk, strengthens data quality planning, and supports reliable enterprise data management.