Descriptive Analytics: Summarizing the Past
This is the foundational step in data analysis. Its purpose is to summarize historical data to understand past events, patterns, and trends. It answers the question, "what happened?" and forms the bedrock for all more advanced analyses.
Diagnostic Analytics: Explaining "Why It Happened"
This stage goes beyond description to uncover the root causes of outcomes. It examines data to determine "why" certain events occurred by identifying patterns, trends, and connections, bridging the gap between knowing what happened and predicting what might happen next.
Predictive Analytics: Forecasting the Future
This advanced form of analytics uses historical and current data to forecast future outcomes. By integrating data analysis, machine learning, and AI, it identifies patterns that can reliably predict "what might happen next," enabling proactive strategies.
Prescriptive Analytics: Recommending Optimal Actions
This is the pinnacle of data analytics. It moves beyond prediction to recommend the optimal course of action. It answers "what should we do next?" by using optimization and simulation to guide strategic decision-making.