International Journal of Science, Engineering and TechnologyAuthors: Nagender Yamsani
Abstract: Engineering trustworthy enterprise data has become a central concern for organizations operating complex, transaction intensive environments where data quality failures directly translate into financial, operational, and regulatory risk. This study examines how structured validation and cleansing controls can be systematically engineered to improve data reliability, consistency, and downstream usability within large scale enterprise systems. Focusing on data quality operations observed within Elavon, the paper analyzes how rule based validation frameworks, matching logic, and controlled cleansing workflows collectively contribute to sustained data trust across heterogeneous data sources and consuming applications. The study adopts a qualitative, architecture driven research approach, combining design analysis, process mapping, and operational pattern evaluation to identify how validation rules are defined, governed, executed, and monitored across the data lifecycle. Empirical patterns suggest that embedding data quality logic as a first class engineering concern, rather than a post processing activity, significantly improves defect detection rates, reduces remediation latency, and strengthens auditability. This study argues that the disciplined separation of validation logic, cleansing execution, and governance oversight enables scalability while preserving transparency and control. The findings contribute a practical yet theoretically grounded perspective on enterprise data quality engineering, offering a reusable conceptual framework that bridges technical implementation and governance accountability. By articulating how structured controls translate into measurable trust outcomes, this research provides a foundation for future studies on resilient data architectures in regulated and high volume enterprise environments.
DOI: http://doi.org/10.5281/zenodo.18936262
