Data Warehouse Source System Assessment for ETL Plannings

Abstract

Data warehouse source system assessment is an important activity in ETL planning because the quality, structure, availability, and reliability of source data directly affect warehouse design and loading accuracy. In enterprise environments, source systems may include transactional databases, legacy applications, spreadsheets, flat files, CRM systems, ERP modules, and external data feeds. Poor assessment of these sources can lead to missing fields, inconsistent formats, duplicate records, incorrect mappings, delayed extraction, and weak data quality in the warehouse. This article discusses how structured source system assessment supports effective ETL design by examining data structures, field definitions, data volume, update frequency, primary keys, referential integrity, business rules, and extraction constraints. It also highlights common challenges such as incomplete metadata, undocumented transformations, poor data ownership, inconsistent coding standards, and limited access to legacy systems. A structured assessment approach is presented to improve source-to-target mapping, data cleansing, load scheduling, and validation planning. The study concludes that effective source system assessment improves ETL reliability, reduces integration risk, and supports accurate data warehouse implementation.