Manual Data Collection
Manual data collection is a process where data is gathered directly by humans through observation, surveys, interviews, or manual entry, rather than automated systems. It involves hands-on techniques to capture information from various sources, such as physical documents, field observations, or direct human interactions. This method is often used when data is unstructured, requires human judgment, or automation is impractical.
Developers should learn manual data collection when working on projects that involve initial data gathering for machine learning models, data migration from legacy systems, or qualitative research where automation is insufficient. It is crucial in scenarios like data labeling for AI training, digitizing paper records, or collecting user feedback through interviews, as it ensures data quality and contextual understanding that automated tools might miss.