analítics

Analytics is the systematic analysis of data to extract insights and inform decision making. It employs methods from statistics, data mining, machine learning, and data visualization to turn raw data into actionable information. The field has grown from traditional business intelligence toward broader applications in science, government, and everyday operations.

Analytic approaches are commonly described as descriptive, diagnostic, predictive, and prescriptive. Descriptive analytics summarizes past events, diagnostic analytics investigates causes, predictive analytics models likely futures, and prescriptive analytics recommends actions and optimizes outcomes. Real-time or streaming analytics extends these ideas to live data.

Analytics relies on a data lifecycle that includes collection, cleaning, transformation, storage, analysis, and interpretation. Data sources range from transactional databases and logs to sensors, social media, and public datasets. Tools include programming languages such as Python and R, querying with SQL, data visualization software, and business intelligence platforms.

Practical applications span marketing optimization, financial risk assessment, manufacturing quality control, healthcare analysis, and sports analytics. Good governance and ethics are essential, covering data privacy, consent, security, bias mitigation, transparency, and accountability in model development and deployment.

Common challenges include data quality and integration, data silos, scalability, and model interpretability. The field is increasingly influenced by AI-assisted analytics, automated workflows, and edge analytics, with emphasis on explainability and responsible use of insights.