International Journal of Science, Engineering and TechnologyAuthors: Ajay Kumar Kota
Abstract: In enterprise Business Intelligence (BI) environments, maintaining clarity around data assets is a critical yet often overlooked challenge. This article explores how metadata-driven data dictionaries can bridge the gap between raw data structures and business understanding. Traditional, manually curated dictionaries are error-prone, quickly outdated, and difficult to scale. By contrast, automated, metadata-driven approaches leverage structured metadata extracted from BI tools, databases, and ETL pipelines to generate and maintain up-to-date data dictionaries across the enterprise. The article covers foundational concepts of metadata, the benefits of automated data documentation, the architecture of a metadata-driven framework, and integration strategies with leading BI platforms such as Power BI, Tableau, and Qlik.
