Data science vs. data analysis: What's the actual difference

Data analysis and data science overlap in data sources and tools but serve different purposes: analysis primarily explains what happened and why through reports, dashboards, segmentation, and diagnostic work, while data science uses statistics, machine learning, and production models to predict outcomes, recommend actions, and support experimentation. Organizations generally use analysis for existing-data questions that need answers within hours or days, and data science for forecasting, risk scoring, anomaly detection, recommendations, and causal modeling that can require weeks or months. The article argues that growing demand for routine business insights often exceeds analyst capacity, leading employees to rely on spreadsheet exports or personal AI tools that can undermine data lineage, security, auditability, and accuracy. It presents Sigma as a self-service analytics platform intended to address this bottleneck by allowing business users to query live cloud-warehouse data through spreadsheet-style and natural-language interfaces while retaining warehouse security policies, governance controls, and traceability.

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