analytics

Analytics is the systematic process of discovering, interpreting, and communicating meaningful patterns in data to support decision making. It encompasses techniques from statistics, data analysis, and operations research, and covers both data-driven insight generation and optimization.

It is often categorized into descriptive analytics (what happened), diagnostic analytics (why it happened), predictive analytics (what is likely to happen), and prescriptive analytics (what actions to take). Techniques include statistical analysis, data mining, machine learning, forecasting, optimization, and data visualization.

Data sources include transactional systems, logs, sensors, surveys, and external data. Analytics relies on data quality, integration, and governance, and typically uses data warehouses, data lakes, and ETL processes, along with dashboards and reporting tools.

Applications span business intelligence, marketing optimization, customer analytics, supply chain and operations, financial risk analysis, healthcare analytics, energy, sports analytics, and public sector forecasting.

Privacy, security, ethics, and transparency are central concerns, as analytics often involves sensitive data. Regulations and governance frameworks guide data usage, consent, and bias mitigation.

The term originated in statistics and business contexts; modern analytics blends data science, machine learning, and decision science. It is related to data analysis, data analytics, business analytics, and data science, but focus and scope vary by discipline and industry.