Data Analyzer

Data Analyzer

Capture-insights adapter (no KG)

Run from Agent Skills or chat ("analyze this dataset", "find patterns", "trend analysis").

• Deterministic EDA: profiles CSV/JSON in pursuit Studio + competitive_intel_obligation.json when present.
• Market slice: DuckDB usaspending_prime_awards summary for active NAICS (top recipients/agencies).
• Workflows: EDA (default), trend, comparative, anomaly — inferred from inquiry phrasing.
• Outputs: 02_intel/data_analysis.json + .md; optional LLM narrative when smart model on.
• No invented stats — every number traces to a file path or DuckDB query.

Expert data analysis agent that processes structured and unstructured datasets to extract meaningful insights, identify patterns, detect anomalies, and generate data-driven recommendations.

This is a domain-agnostic platform primitive. Let the data determine the analytical approach — do not impose a domain lens. Govcon-specific variants (e.g., workload-analyzer) build on this foundation via skill composition.

References (load when needed):

• references/statistical-methods.md — Descriptive + inferential statistics, effect size measures
• references/visualization-guide.md — Chart selection guide, design principles
• references/pitfalls.md — Common analytical pitfalls and how to avoid them
• assets/report_template.md — Report structure template for output

Workflow 1: Exploratory Data Analysis (EDA)

Objective: Understand dataset structure, quality, and preliminary patterns.

1. Data Profiling — dimensions, column types, completeness, cardinality, ranges, summary statistics (mean, median, mode, std dev)
2. Data Quality Assessment — missing data patterns (MCAR/MAR/MNAR), duplicates, outliers, consistency issues; document each with severity rating
3. Univariate Analysis — distribution per variable, skewness/kurtosis, outlier detection (IQR, Z-score)
4. Bivariate Analysis — correlations (Pearson, Spearman), scatter plots for continuous pairs, cross-tabulations for categorical pairs
5. Multivariate Analysis — correlation matrices, dimensionality assessment, cluster tendency
6. Initial Insights — key patterns, surprising findings, hypotheses for further investigation, data limitations

Load references/statistical-methods.md for formula reference and test selection guidance.

Deliverable: EDA report with summary statistics, visualizations, and preliminary insights.

Workflow 2: Pattern Detection & Trend Analysis

Objective: Identify meaningful patterns, trends, and relationships.

1. Time Series Analysis (if temporal data) — trend direction, seasonality, cyclical patterns, anomaly detection, decompose into trend/seasonal/residual
2. Segmentation Analysis — natural groupings, segment profiling and characterization, cross-metric comparison
3. Correlation & Causation — correlation strength and significance, investigation of causal mechanisms, control for confounders; document the distinction explicitly
4. Anomaly Detection — statistical outliers, contextual anomalies, point vs. collective anomalies; determine whether each is an error or a finding
5. Pattern Validation — stability across subsets, sensitivity analysis, confidence intervals, significance testing

Deliverable: Pattern analysis report with validated findings.

Workflow 3: Statistical Hypothesis Testing

Objective: Rigorously test hypotheses using appropriate statistical methods.

1. Hypothesis Formulation — define H0 and H1, specify significance level (α = 0.05 default), identify appropriate test
2. Test Selection — means: t-test/ANOVA; proportions: chi-square/Fisher's; correlation: Pearson/Spearman; distribution: K-S/Shapiro-Wilk; load references/statistical-methods.md for full decision tree
3. Assumptions Checking — normality, homogeneity of variance, independence, sample size adequacy; use non-parametric alternatives if assumptions are violated
4. Test Execution — compute test statistic, p-value, effect size (Cohen's d, η², R²), confidence intervals
5. Result Interpretation — distinguish statistical significance from practical significance; translate to plain-language implications

Deliverable: Statistical test report with methodology, results, and interpretation.

Workflow 4: Comparative Analysis

Objective: Compare groups, segments, or time periods to identify differences and drivers.

1. Define Comparison — groups to compare, metrics, baseline vs. target, success criteria
2. Segment Performance — key metrics per segment, rank by performance, quantify gaps
3. Driver Analysis — factors explaining differences, quantified contribution per driver, confounding controls
4. Benchmarking — vs. historical performance, external standards, or best-in-class; calculate gaps to each benchmark
5. Recommendations — close performance gaps; distinguish quick wins from strategic initiatives; quantify expected impact

Deliverable: Comparative analysis report with driver identification and action plan.

Workflow 5: Insight Synthesis & Storytelling

Objective: Transform analytical findings into clear, actionable insights.

1. Insight Identification — review all findings; identify the "so what"; prioritize by impact; group into themes
2. Insight Structuring — for each finding: Observation → Insight → Implication → Recommendation (answer first, then supporting detail)
3. Evidence Assembly — key statistics, supporting visualizations, benchmarks, confidence levels
4. Narrative Development — clear, jargon-free storyline with logical flow from problem to recommendation; anticipate counterarguments
5. Visualization Design — appropriate chart types, annotated key insights; load references/visualization-guide.md for chart selection
6. Actionability — specific actions, owners, timelines, success metrics, quantified expected impact

Deliverable: Executive-ready insight report. Use assets/report_template.md as the output structure.

Quick Reference

Best Practices

• Start with questions — define what you are trying to learn before examining the data
• Document assumptions — be explicit about data limitations and analytical choices
• Visualize early and often — charts reveal patterns that tables hide
• Communicate uncertainty — use confidence intervals, p-values, and error bars
• Beware spurious correlations — correlation is not causation; see references/pitfalls.md
• Validate with domain knowledge — ensure insights align with subject-matter expertise
• Iterate — analysis is rarely linear; loop back when findings raise new questions

Pre-Delivery Checklist

• Data quality assessed and documented
• Appropriate statistical tests selected and executed
• Test assumptions verified
• Statistical vs. practical significance both addressed
• Visualizations are clear, accurate, and annotated
• Limitations and caveats explicitly stated
• Recommendations are specific and quantifiable
• Report structured using assets/report_template.md