All examples for Data Quality Checker

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.

Input

What you provide to the skill

Analyze quality: order_id,qty,price,total
101,2,10.00,20.00
102,3,15.00,44.00
103,1,25.00,25.00
104,,20.00,
105,2,10.00,20.00

Output

What the skill generates for you

DATA QUALITY HEALTH CHECK REPORT

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Dataset: orders_data.csv

Records: 5

Columns: 4

Analysis Date: 2025-12-03



OVERALL QUALITY SCORE: 86/100 - GOOD



DIMENSION SCORES:

  Completeness:     80/100  (Good)

  Validity:         100/100  (Excellent)

  Consistency:      52/100  (Poor)

  Uniqueness:       100/100  (Excellent)

  Accuracy:         100/100  (Excellent)



TOP 3 CRITICAL ISSUES (by impact)

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1. CRITICAL - Missing Values in qty

   Impact: HIGH | Affected: 1 rows (20.0%)

   Issue: Column 'qty' has 1 missing values (20.0%)

   Location: Column 'qty', 1 NULL values

   Example: Rows [3] have missing qty

   Fix: Investigate and fill missing values or remove rows with: df.dropna(subset=['qty'])



2. CRITICAL - Missing Values in total

   Impact: HIGH | Affected: 1 rows (20.0%)

   Issue: Column 'total' has 1 missing values (20.0%)

   Location: Column 'total', 1 NULL values

   Example: Rows [3] have missing total

   Fix: Investigate and fill missing values or remove rows with: df.dropna(subset=['total'])



3. CRITICAL - Calculation Mismatch: total != qty × price

   Impact: HIGH | Affected: 1 rows (20.0%)

   Issue: Total amount does not match quantity × price calculation

   Location: Rows [1]

   Example: Row 1 - qty:3.0, price:$15.00, total:$44.00 (should be $45.00)

   Fix: df['total'] = df['qty'] * df['price']



ACTIONABLE RECOMMENDATIONS

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IMMEDIATE ACTIONS (High Priority):



1. Missing Values in qty

   Command: Investigate and fill missing values or remove rows with: df.dropna(subset=['qty'])

   Impact: Resolves 1 affected rows



2. Missing Values in total

   Command: Investigate and fill missing values or remove rows with: df.dropna(subset=['total'])

   Impact: Resolves 1 affected rows



3. Calculation Mismatch: total != qty × price

   Command: df['total'] = df['qty'] * df['price']

   Impact: Resolves 1 affected rows



ESTIMATED IMPROVEMENT IMPACT

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Current Score:     86/100

Projected Score:   99/100 (after addressing critical issues)



Quality Gate Status: PASS
View all examples for Data Quality Checker

About This Skill

Automated data quality assessment across 5 dimensions with actionable fix recommendations

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