International Journal of Science, Engineering and TechnologyAuthors: Meena Pillai
Abstract: High-quality data is essential for accurate analytics, regulatory compliance, and informed decision-making. However, modern datasets often suffer from errors, inconsistencies, and incompleteness, leading to operational inefficiencies and unreliable insights. This review examines the implementation of robust scripts for maintaining clean, accurate, and reliable data. Key aspects include understanding data quality dimensions, addressing common challenges, applying scripting techniques for profiling, cleansing, and validation, and leveraging both open-source and enterprise tools. The review also highlights best practices for script design, automation, and integration into data pipelines. Case studies across finance, healthcare, and e-commerce demonstrate measurable improvements, while emerging trends such as AI-driven quality checks, real-time validation, and alignment with data governance frameworks indicate the future direction of scalable, intelligent data quality management. The insights provided aim to guide data engineers, analysts, and organizations in establishing resilient and effective data quality practices.
DOI: http://doi.org/10.5281/zenodo.17278995
