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name: data-validation description: QA an analysis before sharing with stakeholders — methodology checks, accuracy verification, and bias detection. Use when reviewing an analysis for errors, checking for survivorship bias, validating aggregation logic, or preparing documentation for reproducibility.

Data Validation Skill

Pre-delivery QA checklist, common data analysis pitfalls, result sanity checking, and documentation standards for reproducibility.

Pre-Delivery QA Checklist

Run through this checklist before sharing any analysis with stakeholders.

Data Quality Checks

• Source verification: Confirmed which tables/data sources were used. Are they the right ones for this question?
• Freshness: Data is current enough for the analysis. Noted the "as of" date.
• Completeness: No unexpected gaps in time series or missing segments.
• Null handling: Checked null rates in key columns. Nulls are handled appropriately (excluded, imputed, or flagged).
• Deduplication: Confirmed no double-counting from bad joins or duplicate source records.
• Filter verification: All WHERE clauses and filters are correct. No unintended exclusions.

Calculation Checks

• Aggregation logic: GROUP BY includes all non-aggregated columns. Aggregation level matches the analysis grain.
• Denominator correctness: Rate and percentage calculations use the right denominator. Denominators are non-zero.
• Date alignment: Comparisons use the same time period length. Partial periods are excluded or noted.
• Join correctness: JOIN types are appropriate (INNER vs LEFT). Many-to-many joins haven't inflated counts.
• Metric definitions: Metrics match how stakeholders define them. Any deviations are noted.
• Subtotals sum: Parts add up to the whole where expected. If they don't, explain why (e.g., overlap).

Reasonableness Checks

• Magnitude: Numbers are in a plausible range. Revenue isn't negative. Percentages are between 0-100%.
• Trend continuity: No unexplained jumps or drops in time series.
• Cross-reference: Key numbers match other known sources (dashboards, previous reports, finance data).
• Order of magnitude: Total revenue is in the right ballpark. User counts match known figures.
• Edge cases: What happens at the boundaries? Empty segments, zero-activity periods, new entities.

Presentation Checks

• Chart accuracy: Bar charts start at zero. Axes are labeled. Scales are consistent across panels.
• Number formatting: Appropriate precision. Consistent currency/percentage formatting. Thousands separators where needed.
• Title clarity: Titles state the insight, not just the metric. Date ranges are specified.
• Caveat transparency: Known limitations and assumptions are stated explicitly.
• Reproducibility: Someone else could recreate this analysis from the documentation provided.

Common Data Analysis Pitfalls

Join Explosion

The problem: A many-to-many join silently multiplies rows, inflating counts and sums.

How to detect:

-- Check row count before and after join
SELECT COUNT(*) FROM table_a;  -- 1,000
SELECT COUNT(*) FROM table_a a JOIN table_b b ON a.id = b.a_id;  -- 3,500 (uh oh)

How to prevent:

• Always check row counts after joins
• If counts increase, investigate the join relationship (is it really 1:1 or 1:many?)
• Use COUNT(DISTINCT a.id) instead of COUNT(*) when counting entities through joins

Survivorship Bias

The problem: Analyzing only entities that exist today, ignoring those that were deleted, churned, or failed.

Examples:

• Analyzing user behavior of "current users" misses churned users
• Looking at "companies using our product" ignores those who evaluated and left
• Studying properties of "successful" outcomes without "unsuccessful" ones

How to prevent: Ask "who is NOT in this dataset?" before drawing conclusions.

Incomplete Period Comparison

The problem: Comparing a partial period to a full period.

Examples:

• "January revenue is $500K vs. December's $800K" -- but January isn't over yet
• "This week's signups are down" -- checked on Wednesday, comparing to a full prior week

How to prevent: Always filter to complete periods, or compare same-day-of-month / same-number-of-days.

Denominator Shifting

The problem: The denominator changes between periods, making rates incomparable.

Examples:

• Conversion rate improves because you changed how you count "eligible" users
• Churn rate changes because the definition of "active" was updated

How to prevent: Use consistent definitions across all compared periods. Note any definition changes.

Average of Averages

The problem: Averaging pre-computed averages gives wrong results when group sizes differ.

Example:

• Group A: 100 users, average revenue $50
• Group B: 10 users, average revenue $200
• Wrong: Average of averages = ($50 + $200) / 2 = $125
• Right: Weighted average = (100*$50 + 10*$200) / 110 = $63.64

How to prevent: Always aggregate from raw data. Never average pre-aggregated averages.

Timezone Mismatches

The problem: Different data sources use different timezones, causing misalignment.

Examples:

• Event timestamps in UTC vs. user-facing dates in local time
• Daily rollups that use different cutoff times

How to prevent: Standardize all timestamps to a single timezone (UTC recommended) before analysis. Document the timezone used.

Selection Bias in Segmentation

The problem: Segments are defined by the outcome you're measuring, creating circular logic.

Examples:

• "Users who completed onboarding have higher retention" -- obviously, they self-selected
• "Power users generate more revenue" -- they became power users BY generating revenue

How to prevent: Define segments based on pre-treatment characteristics, not outcomes.

Result Sanity Checking

Magnitude Checks

For any key number in your analysis, verify it passes the "smell test":

Metric Type	Sanity Check
User counts	Does this match known MAU/DAU figures?
Revenue	Is this in the right order of magnitude vs. known ARR?
Conversion rates	Is this between 0% and 100%? Does it match dashboard figures?
Growth rates	Is 50%+ MoM growth realistic, or is there a data issue?
Averages	Is the average reasonable given what you know about the distribution?
Percentages	Do segment percentages sum to ~100%?

Cross-Validation Techniques

1. Calculate the same metric two different ways and verify they match
2. Spot-check individual records -- pick a few specific entities and trace their data manually
3. Compare to known benchmarks -- match against published dashboards, finance reports, or prior analyses
4. Reverse engineer -- if total revenue is X, does per-user revenue times user count approximately equal X?
5. Boundary checks -- what happens when you filter to a single day, a single user, or a single category? Are those micro-results sensible?

Red Flags That Warrant Investigation

• Any metric that changed by more than 50% period-over-period without an obvious cause
• Counts or sums that are exact round numbers (suggests a filter or default value issue)
• Rates exactly at 0% or 100% (may indicate incomplete data)
• Results that perfectly confirm the hypothesis (reality is usually messier)
• Identical values across time periods or segments (suggests the query is ignoring a dimension)

Documentation Standards for Reproducibility

Analysis Documentation Template

Every non-trivial analysis should include:

## Analysis: [Title]

### Question
[The specific question being answered]

### Data Sources
- Table: [schema.table_name] (as of [date])
- Table: [schema.other_table] (as of [date])
- File: [filename] (source: [where it came from])

### Definitions
- [Metric A]: [Exactly how it's calculated]
- [Segment X]: [Exactly how membership is determined]
- [Time period]: [Start date] to [end date], [timezone]

### Methodology
1. [Step 1 of the analysis approach]
2. [Step 2]
3. [Step 3]

### Assumptions and Limitations
- [Assumption 1 and why it's reasonable]
- [Limitation 1 and its potential impact on conclusions]

### Key Findings
1. [Finding 1 with supporting evidence]
2. [Finding 2 with supporting evidence]

### SQL Queries
[All queries used, with comments]

### Caveats
- [Things the reader should know before acting on this]

Code Documentation

For any code (SQL, Python) that may be reused:

"""
Analysis: Monthly Cohort Retention
Author: [Name]
Date: [Date]
Data Source: events table, users table
Last Validated: [Date] -- results matched dashboard within 2%

Purpose:
    Calculate monthly user retention cohorts based on first activity date.

Assumptions:
    - "Active" means at least one event in the month
    - Excludes test/internal accounts (user_type != 'internal')
    - Uses UTC dates throughout

Output:
    Cohort retention matrix with cohort_month rows and months_since_signup columns.
    Values are retention rates (0-100%).
"""

Version Control for Analyses

• Save queries and code in version control (git) or a shared docs system
• Note the date of the data snapshot used
• If an analysis is re-run with updated data, document what changed and why
• Link to prior versions of recurring analyses for trend comparison