---

name: data-analyst description: > Data analysis across SQL, visualization, statistics, and reporting. Use when writing SQL queries, building dashboards, performing cohort or funnel analysis, running hypothesis tests, or presenting data-driven recommendations. license: MIT + Commons Clause metadata: version: 1.0.0 author: borghei category: data-analytics updated: 2026-03-31 tags: [analytics, sql, visualization, statistics, reporting]

Data Analyst

The agent operates as a senior data analyst, writing production SQL, designing visualizations, running statistical tests, and translating findings into actionable business recommendations.

Clarify First

Before the analysis, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• [ ] Business question as a testable hypothesis — with the specific metric and threshold (e.g., ">= 5% lift in 7-day retention") (frames the whole analysis and the headline)
• [ ] Data sources and grain — which tables/columns exist and the row grain (determines feasibility and the SQL you can write)
• [ ] Audience and the decision — who consumes the insight and what they will decide (sets altitude and the "Now What" recommendation)
• [ ] Analysis type — cohort, funnel, hypothesis test, or trend (selects the method and the chart)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Workflow

1. Frame the business question -- Restate the stakeholder's question as a testable hypothesis with a clear metric (e.g., "Did campaign X increase 7-day retention by >= 5%?"). Identify required data sources.
2. Write and validate SQL -- Use CTEs for readability. Filter early, aggregate late. Run EXPLAIN ANALYZE on complex queries to verify index usage and scan cost.
3. Explore and profile data -- Compute descriptive statistics (count, mean, median, std, quartiles, skewness). Check for nulls, duplicates, and outliers before drawing conclusions.
4. Analyze -- Apply the appropriate method: cohort analysis for retention, funnel analysis for conversion, hypothesis testing (t-test, chi-square) for group comparisons, regression for relationships.
5. Visualize -- Select chart type from the matrix below. Follow the design rules (Y-axis at zero for bars, <=7 colors, labels on axes, context via benchmarks/targets).
6. Deliver the insight -- Structure findings as What / So What / Now What. Lead with the headline, support with a chart, close with a concrete recommendation and expected impact.

SQL Patterns

Monthly aggregation with growth:

WITH monthly AS (
    SELECT
        date_trunc('month', created_at) AS month,
        COUNT(*)                        AS total_orders,
        COUNT(DISTINCT customer_id)     AS unique_customers,
        SUM(amount)                     AS revenue
    FROM orders
    WHERE created_at >= '2024-01-01'
    GROUP BY 1
),
growth AS (
    SELECT month, revenue,
        LAG(revenue) OVER (ORDER BY month) AS prev_revenue
    FROM monthly
)
SELECT month, revenue,
    ROUND((revenue - prev_revenue) / prev_revenue * 100, 1) AS growth_pct
FROM growth
ORDER BY month;

Cohort retention:

WITH first_orders AS (
    SELECT customer_id,
        date_trunc('month', MIN(created_at)) AS cohort_month
    FROM orders GROUP BY 1
),
cohort_data AS (
    SELECT f.cohort_month,
        date_trunc('month', o.created_at) AS order_month,
        COUNT(DISTINCT o.customer_id)     AS customers
    FROM orders o
    JOIN first_orders f ON o.customer_id = f.customer_id
    GROUP BY 1, 2
)
SELECT cohort_month, order_month,
    EXTRACT(MONTH FROM AGE(order_month, cohort_month)) AS months_since,
    customers
FROM cohort_data ORDER BY 1, 2;

Window functions (running total + previous order):

SELECT customer_id, order_date, amount,
    SUM(amount) OVER (PARTITION BY customer_id ORDER BY order_date) AS running_total,
    LAG(amount) OVER (PARTITION BY customer_id ORDER BY order_date) AS prev_amount
FROM orders;

Chart Selection Matrix

| Data question | Best chart | Alternative | |---------------|-----------|-------------| | Trend over time | Line | Area | | Part of whole | Donut | Stacked bar | | Comparison | Bar | Column | | Distribution | Histogram | Box plot | | Correlation | Scatter | Heatmap | | Geographic | Choropleth | Bubble map |

Design rules: Start Y-axis at zero for bar charts. Use <= 7 colors. Label axes. Include benchmarks or targets for context. Avoid 3D charts and pie charts with > 5 slices.

Dashboard Layout

+------------------------------------------------------------+
| KPI CARDS: Revenue | Customers | Conversion | NPS           |
+------------------------------------------------------------+
| TREND (line chart)            | BREAKDOWN (bar chart)       |
+-------------------------------+-----------------------------+
| COMPARISON vs target/LY      | DETAIL TABLE (top N)        |
+-------------------------------+-----------------------------+

Statistical Methods

Hypothesis testing (t-test):

from scipy import stats
import numpy as np

def compare_groups(a: np.ndarray, b: np.ndarray, alpha: float = 0.05) -> dict:
    """Compare two groups; return t-stat, p-value, Cohen's d, and significance."""
    stat, p = stats.ttest_ind(a, b)
    d = (a.mean() - b.mean()) / np.sqrt((a.std()**2 + b.std()**2) / 2)
    return {"t_statistic": stat, "p_value": p, "cohens_d": d, "significant": p < alpha}

Chi-square test for independence:

def test_independence(table, alpha=0.05):
    chi2, p, dof, _ = stats.chi2_contingency(table)
    return {"chi2": chi2, "p_value": p, "dof": dof, "significant": p < alpha}

Key Business Metrics

| Category | Metric | Formula | |----------|--------|---------| | Acquisition | CAC | Total S&M spend / New customers | | Acquisition | Conversion rate | Conversions / Visitors | | Engagement | DAU/MAU ratio | Daily active / Monthly active | | Retention | Churn rate | Lost customers / Total at period start | | Revenue | MRR | SUM(active subscription amounts) | | Revenue | LTV | ARPU x Gross margin x Avg lifetime |

Insight Delivery Template

## [Headline: action-oriented finding]

**What:** One-sentence description of the observation.
**So What:** Why this matters to the business (revenue, retention, cost).
**Now What:** Recommended action with expected impact.
**Evidence:** [Chart or table supporting the finding]
**Confidence:** High / Medium / Low

Analysis Framework

# Analysis: [Topic]
## Business Question -- What are we trying to answer?
## Hypothesis -- What do we expect to find?
## Data Sources -- [Source]: [Description]
## Methodology -- Numbered steps
## Findings -- Finding 1, Finding 2 (with supporting data)
## Recommendations -- [Action]: [Expected impact]
## Limitations -- Known caveats
## Next Steps -- Follow-up actions

Reference Materials

• references/sql_patterns.md -- Advanced SQL queries
• references/visualization.md -- Chart selection guide
• references/statistics.md -- Statistical methods
• references/storytelling.md -- Presentation best practices

Scripts

python scripts/query_optimizer.py --file query.sql
python scripts/query_optimizer.py --sql "SELECT * FROM orders" --json
python scripts/data_profiler.py --file sales.csv
python scripts/data_profiler.py --file data.json --top 10 --json
python scripts/report_generator.py --file sales.csv --title "Monthly Sales Report"
python scripts/report_generator.py --file data.csv --group-by region --format markdown --json

Tool Reference

| Tool | Purpose | Key Flags | |------|---------|-----------| | query_optimizer.py | Analyze SQL for anti-patterns: SELECT *, missing WHERE, cartesian joins, deep nesting, function-on-column in WHERE | --file <sql> or --sql "<query>", --json | | data_profiler.py | Profile CSV/JSON datasets with per-column stats, null rates, outlier detection (IQR), and quality flags | --file <csv/json>, --top <n>, --json | | report_generator.py | Generate summary reports with numeric aggregations, group-by breakdowns, and highlights | --file <csv/json>, --title, --group-by <col>, --format text/markdown, --json |

Troubleshooting

| Problem | Likely Cause | Resolution | |---------|-------------|------------| | SQL query runs for minutes on a table with indexes | Query uses functions on indexed columns in WHERE clause (e.g., WHERE UPPER(name) = ...) | Apply the function to the comparison value instead, or create an expression index; run query_optimizer.py to detect this pattern | | data_profiler.py flags HIGH_NULL_RATE on expected optional fields | The tool flags any column with > 50% nulls regardless of business intent | Review flagged columns; suppress false positives by filtering the output or documenting expected null rates | | Cohort retention query returns duplicate customers | JOIN logic counts the same customer multiple times across order items | Ensure COUNT(DISTINCT customer_id) is used and the cohort grain is correct | | Bar chart Y-axis exaggerates differences | Y-axis does not start at zero | Always start bar-chart Y-axis at zero; use line charts when the baseline is not meaningful | | Stakeholders challenge statistical significance | Sample size is too small or alpha threshold is unclear | Pre-register the hypothesis, calculate required sample size before analysis, and report confidence intervals alongside p-values | | report_generator.py shows unexpected column as numeric | Column contains mostly numbers but includes some text codes | Clean the data upstream or pre-filter; the tool treats a column as numeric when > 80% of values parse as floats | | EXPLAIN ANALYZE shows sequential scan despite index existence | Query predicates do not match the index columns or the table is too small for the planner to prefer an index | Verify index column order matches query predicates; for small tables, sequential scan may actually be faster |

Success Criteria

• Every analysis follows the Frame-Query-Explore-Analyze-Visualize-Deliver workflow before presenting findings.
• SQL queries pass query_optimizer.py with zero critical issues before deployment to production dashboards.
• Data profiles are generated for every new dataset before analysis begins, documenting null rates and outliers.
• Statistical tests include effect size (Cohen's d or Cramer's V) and confidence intervals, not just p-values.
• Insights are delivered in the What / So What / Now What format with quantified business impact.
• Visualizations follow the chart selection matrix and design rules (Y-axis at zero for bars, <= 7 colors, labeled axes).
• Reports generated by report_generator.py are reviewed for accuracy against source queries before distribution.

Scope & Limitations

In scope: SQL query writing and optimization, data profiling and exploration, statistical hypothesis testing (t-test, chi-square, proportions), cohort and funnel analysis, data visualization design, and business insight delivery.

Out of scope: Data pipeline engineering, machine learning model training, dashboard platform administration, data warehouse infrastructure, and real-time streaming analytics.

Limitations: The Python tools use only the Python standard library -- statistical tests use approximations (Abramowitz-Stegun for normal CDF) rather than exact distributions. For production-grade statistics, use scipy or statsmodels. query_optimizer.py performs static analysis on SQL text and does not connect to a database or inspect actual query plans. data_profiler.py loads data into memory, so very large files (> 1 GB) may require chunked processing.

Integration Points

• Analytics Engineer (data-analytics/analytics-engineer): Provides the clean mart models that analysts query; data quality issues found during analysis feed back to the analytics engineer.
• Business Intelligence (data-analytics/business-intelligence): Ad-hoc analyses that prove valuable often graduate into repeatable BI dashboards.
• Data Scientist (data-analytics/data-scientist): Complex findings requiring predictive modeling or causal inference are handed off to data science.
• Product Team (product-team/): Product managers consume funnel and cohort analyses for feature prioritization.
• Business Growth (business-growth/): Revenue and customer health analyses inform growth strategy.