Diagnostic analytics is a form of data analysis that goes beyond describing what happened to explore why it happened. It investigates the root causes behind trends, anomalies, or outcomes identified through descriptive analytics by using techniques such as drill-down analysis, correlation analysis, data segmentation, and statistical modeling. The goal is to uncover relationships, dependencies, and explanations that inform deeper business understanding.
For data professionals, diagnostic analytics is a bridge between reporting and forecasting. It is commonly used in root cause analysis, customer churn investigation, operations breakdowns, and campaign performance reviews. It requires clean, well-structured data and is often performed using BI platforms, SQL queries, statistical tools, or advanced analytics frameworks. By providing insights into causal factors, diagnostic analytics helps organizations take targeted action rather than relying solely on intuition.