/ˌæn.əˈlɪt.ɪks/
noun — "turning piles of data into excuses for decisions."
Analytics in information technology is the systematic examination of data to discover patterns, draw conclusions, and support decision-making. It includes statistical analysis, predictive modeling, and data visualization to extract actionable insights from raw or processed data.
Technically, Analytics involves:
- Descriptive analytics — summarizing historical data to understand what happened.
- Diagnostic analytics — investigating why something occurred using correlations and patterns.
- Predictive analytics — using statistical and machine learning models to forecast future events.
- Prescriptive analytics — recommending actions based on predicted outcomes and optimization techniques.
Examples of Analytics include:
- Analyzing user engagement metrics to optimize website performance.
- Using predictive models to forecast sales trends and inventory needs.
- Detecting anomalies in network traffic for fraud detection or cybersecurity.
Conceptually, Analytics is the lens through which raw data gains meaning—it transforms numbers into insight, guiding strategic and operational decisions.
In practice, Analytics is applied using tools like Python, R, SQL, and business intelligence platforms, often combined with data analysis pipelines and dashboards for visualization and reporting.
See Data, Data Analysis, Business Intelligence, Fraud Detection, Machine Learning.