AI Data Validation - Quality Assessment Tool

Comprehensive Quality Checks

• Structural Validation: Ensures consistent row and column structure
• Completeness Analysis: Identifies missing values and empty columns
• Consistency Assessment: Validates data format and type consistency
• Duplicate Detection: Finds duplicate rows and headers
• Size Adequacy: Evaluates dataset size for ML training requirements

Critical Issue Detection

• Empty Columns: Identifies completely empty columns that provide no value
• Inconsistent Structure: Detects rows with varying column counts
• High Missing Value Rates: Flags columns with excessive missing data (>50%)
• Duplicate Headers: Finds duplicate column names that could cause processing errors
• Data Type Conflicts: Identifies columns with mixed or inconsistent data types

ML Readiness Assessment

• Training Suitability: Evaluates if dataset is suitable for ML model training
• Size Requirements: Checks if dataset meets minimum size requirements (100+ rows recommended)
• Quality Thresholds: Assesses against professional ML data quality standards
• Preprocessing Needs: Identifies required data cleaning and preparation steps

Structural Integrity

• Column Count Consistency: All rows must have the same number of columns
• Header Validation: Column names must be unique and non-empty
• Data Boundaries: Proper start and end of data ranges

Data Completeness

• Missing Value Analysis: Percentage of missing values per column
• Empty Column Detection: Columns with no data across all rows
• Sparse Data Identification: Columns with very low data density

Data Consistency

• Format Consistency: Consistent data formats within columns
• Type Consistency: Consistent data types within columns
• Encoding Consistency: Proper character encoding throughout dataset

Dataset Adequacy

• Size Requirements: Minimum row counts for reliable ML training
• Feature Adequacy: Sufficient number of meaningful columns
• Target Variable Presence: Identification of potential target variables

Overall Quality Assessment

The validation provides an overall quality assessment:

• ✓ Excellent: No critical issues, minimal warnings
• ⚠ Good: Minor issues that don't prevent ML use
• ⚠ Fair: Some issues that should be addressed
• ❌ Poor: Critical issues that must be fixed before ML use

Specific Quality Metrics

• Completeness Score: Percentage of non-missing data
• Consistency Score: Structural and format consistency
• Uniqueness Score: Ratio of unique to total rows
• Overall Quality: Weighted combination of all metrics

Critical Issues

Empty Columns

• Problem: Columns with no data provide no value for ML
• Solution: Remove empty columns from dataset
• Prevention: Clean data before analysis

Inconsistent Row Structure

• Problem: Rows with different column counts cause processing errors
• Solution: Ensure all rows have the same number of columns
• Prevention: Use consistent data entry practices

Duplicate Headers

• Problem: Multiple columns with the same name cause confusion
• Solution: Rename duplicate columns with unique identifiers
• Prevention: Use descriptive, unique column names

Quality Warnings

High Missing Value Rates

• Problem: Columns with >50% missing values may not be useful
• Solution: Consider imputation strategies or remove problematic columns
• Prevention: Improve data collection processes

High Cardinality

• Problem: Columns with too many unique values may not be useful for ML
• Solution: Group similar values or use embedding techniques
• Prevention: Design appropriate categorical variables

Small Dataset Size

• Problem: Datasets <100 rows may not provide reliable ML results
• Solution: Collect more data or use data augmentation techniques
• Prevention: Plan for adequate sample sizes

Pre-Validation Preparation

• Clean Obvious Issues: Fix apparent problems before running validation
• Standardize Formats: Ensure consistent date, number, and text formats
• Remove Test Data: Exclude test entries and placeholder data
• Verify Headers: Ensure column names are descriptive and unique

Post-Validation Actions

• Prioritize Critical Issues: Address critical problems first
• Document Changes: Keep records of data cleaning actions
• Re-validate: Run validation again after making changes
• Plan Preprocessing: Use results to plan ML preprocessing steps

Ongoing Quality Management

• Regular Validation: Validate data quality regularly as new data arrives
• Quality Monitoring: Track quality metrics over time
• Process Improvement: Use validation results to improve data collection
• Team Training: Educate team members on data quality best practices

Workflow Integration

1. Validate First: Always run validation before other AI tools
2. Address Issues: Fix critical problems identified by validation
3. Generate Insights: Use AI Data Insights for detailed analysis
4. Prepare for ML: Use Prepare Data for AI for export
5. Get Recommendations: Use AI Model Recommender for model selection

Quality-Driven Workflow

• Validation → Insights → Preparation → Modeling
• Each step builds on the quality foundation established by validation