Data Quality Checker

Pro v1.0.0 1 view

Automated data quality assessment across 5 dimensions with actionable fix recommendations

What You Get

Saves 4-6 hours per dataset by automating quality checks that would require manual inspection, generating prioritized issues with specific fix commands

The Problem

Data professionals spend hours manually checking datasets for quality issues before analysis. Missing values, duplicates, invalid formats, and inconsistencies are tedious to find and easy to miss, leading to unreliable analysis results.

The Solution

This skill generates and executes custom Python analysis scripts for your specific dataset structure. It automatically detects column types, applies appropriate quality checks across five dimensions (completeness, validity, consistency, uniqueness, accuracy), calculates weighted scores, identifies issues with row/column references, and produces actionable recommendations with fix commands.

How It Works

  1. 1 Load dataset and generate profile with row/column counts, data types, and sample records
  2. 2 Assess completeness by calculating missing value percentages and identifying patterns
  3. 3 Validate data validity by checking formats, outliers, and impossible values
  4. 4 Check consistency for contradictory values and format inconsistencies
  5. 5 Assess uniqueness by detecting duplicates and verifying key column uniqueness
  6. 6 Generate comprehensive quality report with scores, prioritized issues, and fix recommendations

What You'll Need

  • Dataset file in CSV or Excel format
  • Python 3 with pandas and numpy installed
  • File size under 500MB for full analysis (larger files use sampling)

Get This Skill

Requires Pro subscription ($9/month)

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Examples

Customer Database Quality Check

Validates a customer database CSV with common issues: missing email addresses, duplicate customer IDs, invalid email formats, and impossible dates. Demonstrates completeness, validity, and uniqueness dimension scoring with specific row-level issue identification.

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Employee Directory Referential Integrity Check

Validates an employee directory for referential integrity issues including circular manager references (employee manages themselves), orphaned manager IDs pointing to non-existent employees, and duplicate records. Critical for HR system migrations.

View example

Sales Transaction Calculation Validation

Analyzes a sales orders dataset to detect calculation mismatches where total doesn't equal quantity times price. Demonstrates the consistency dimension by catching arithmetic errors and missing values in transactional data.

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