datagathering

Datagathering is the process of collecting information from diverse sources for analysis, decision making, monitoring, or forecasting. It encompasses raw data and structured observations and is applied in research, business intelligence, software telemetry, journalism, and public policy.

Data can be primary or secondary. Primary data are collected directly by the practitioner through surveys, interviews, experiments, observations, or sensors. Secondary data come from existing datasets, records, publications, or archives. Data are qualitative (descriptions, themes) or quantitative (numbers, measurements) and may be structured, semi-structured, or unstructured.

Planning and methods: define objectives, design data collection plans, select sources or samples, and choose instruments. Common methods include questionnaires, interviews, experiments, field observations, sensor streams, log files, APIs, and web scraping. Sampling aims to represent the population and reduce bias. Data quality controls include validation rules, calibration, and provenance tracking.

Governance and ethics: privacy and consent considerations, anonymization, and compliance with data protection laws. For human subjects, institutional review or ethics approvals may apply. Data ownership, licensing, and stewardship are important, along with metadata and documentation to support reproducibility and interoperability.

Challenges and considerations: bias, missing data, measurement error, cost, time, access restrictions, and legal constraints. Ensuring security, proper storage, and long-term preservation are also key. Transparency about methods helps evaluators assess reliability.

Outcomes and use: Collected data supports analysis, modeling, reporting, and decision making. When appropriate and legal, datasets may be shared or published in standard formats such as CSV or JSON, with clear licensing and provenance.