Introduction Director's Critical Incident Reports (DCIRs) represent high-priority events that could impact an agency's mission, personnel, or reputation. Analyzing the temporal patterns and impacts of these incidents requires sophisticated statistical methods that can identify meaningful changes across multiple metrics while maintaining scientific rigor. Current approaches often require extensive manual analysis, lack reproducibility, and struggle to handle diverse data types and missing values systematically. Problem Statement Organizations need efficient, standardized methods to assess the impact of critical incidents across heterogeneous datasets. Traditional analysis approaches are time-intensive, prone to human error, and difficult to scale across multiple incidents and metrics. Additionally, existing tools often fail to address common challenges including missing data, multiple comparison problems, and the need for both parametric and non-parametric statistical approaches. There is a critical need for an automated, configurable system that can rapidly generate defensible insights while maintaining statistical rigor. Methodology We developed a lightweight, data-agnostic analytics pipeline requiring minimal user input: file paths, date columns, and an intuitive time configuration (WINDOW_PERIOD ∈ {D, W, M, Y} and WINDOW_UNIT ∈ ℕ) that defines aggregation cadence and symmetric pre/post windows around each incident. The pipeline performs multi-stage analysis: (1) aggregates numeric features into common time buckets and generates cross-dataset correlation heatmaps for rapid signal scanning; (2) compiles comprehensive tabular reports for each incident, including pre/post statistics (mean, median, min, max, sample sizes) for numeric fields and value counts for categorical fields; (3) applies two-sample Kolmogorov-Smirnov (KS) testing for non-parametric detection of distributional shifts; (4) generates color-blind-safe visualizations including distribution plots and comparative bar charts; (5) performs automated clustering analysis for event density assessment to identify like-metrics and temporal groupings; and (6) optionally applies Benjamini-Hochberg false-discovery-rate control for multiple comparison correction. Results The pipeline successfully produces automated, repeatable correlation analyses with multiple statistical outputs per incident. For numeric metrics, it delivers comprehensive pre/post comparisons with Kolmogorov-Smirnov p-values indicating significant distributional changes. Categorical fields receive detailed value count analyses with explicit gap tallying for missing-date coverage. The cross-dataset correlation heatmap enables rapid identification of related metrics while avoiding within-dataset artifacts. Visual outputs facilitate rapid triage through intuitive pre/post comparisons. The clustering analysis reveals temporal patterns and metric groupings that inform strategic response planning. Discussion This pipeline addresses critical gaps in incident analysis by combining statistical rigor with operational efficiency. The testing provides robust non-parametric change detection, while the modular design supports extension to advanced causal inference methods including interrupted time series, panel event-studies, and difference-in-differences with staggered adoption. By emphasizing configuration over code changes and explicitly handling missing data, the system enables operations teams and analysts to move from raw incidents to defensible insights quickly and transparently. The low computational cost and data-agnostic architecture ensure broad applicability across diverse organizational contexts, making it a valuable tool for evidence-based decision-making in critical incident response.