Manual Data Documentation
Manual Data Documentation is the process of creating and maintaining descriptive records about datasets, including metadata, data dictionaries, and usage guidelines, without relying on automated tools. It involves manually documenting data sources, structures, transformations, and business logic to ensure data quality, consistency, and accessibility for teams. This practice is crucial for data governance, enabling stakeholders to understand, trust, and effectively use data in decision-making and analysis.
Developers should learn and use Manual Data Documentation when working with complex or legacy datasets, in environments with limited automation tools, or to complement automated documentation for critical data assets. It is essential in data engineering, analytics, and science projects to prevent data misinterpretation, facilitate collaboration, and comply with regulatory requirements like GDPR or HIPAA. Use cases include documenting ETL pipelines, creating data dictionaries for APIs, and maintaining metadata for machine learning models.