dataanalyseproces

Dataanalyseproces (data analysis process) is a structured workflow used to turn data into insights that inform decisions. It is typically iterative and non-linear, covering problem definition, data collection and preprocessing, exploratory data analysis, modeling, evaluation, interpretation, and communicating results, followed by deployment and monitoring when insights are applied in practice.

Framing and data collection are followed by cleaning and preprocessing to address quality issues. Exploratory data analysis summarises data and reveals patterns. Modeling and analysis apply statistical methods or machine learning to quantify relationships and make predictions. Evaluation checks accuracy, robustness, and fairness, often with held-out data.

Interpretation and communication translate results into actionable recommendations, including caveats. Deployment implements data products or dashboards and monitoring tracks performance, triggering retraining if needed.

Governance, ethics, privacy, and bias mitigation are integral, as are reproducibility and documentation through version control and clear methodology. The process is most effective when cross-functional teams collaborate among data scientists, domain experts, and decision makers to ensure relevance and accountability.