šÆ Audit Parameters
Audit Progress
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Overall Score
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AI Readiness
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Critical Issues
š Data Audit Checklist
Data Quality Assessment
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Data Accuracy Assessment
Evaluate the accuracy and correctness of data across critical datasets
How to Assess:
ā¢ Sample key datasets and validate against source systems
ā¢ Check for logical inconsistencies and outliers
ā¢ Compare with external reference data where available
ā¢ Measure error rates in business-critical fields
ā¢ Check for logical inconsistencies and outliers
ā¢ Compare with external reference data where available
ā¢ Measure error rates in business-critical fields
Success Criteria:
ā¢ >95% accuracy in critical business fields
ā¢ Documented validation processes
ā¢ Regular accuracy monitoring in place
ā¢ Documented validation processes
ā¢ Regular accuracy monitoring in place
Data Completeness Analysis
Assess missing values and incomplete records across datasets
How to Assess:
ā¢ Calculate missing value percentages by field
ā¢ Identify patterns in missing data
ā¢ Assess impact on ML model training
ā¢ Review data collection processes
ā¢ Identify patterns in missing data
ā¢ Assess impact on ML model training
ā¢ Review data collection processes
Success Criteria:
ā¢ <5% missing values in critical fields
ā¢ Clear handling strategy for missing data
ā¢ Sufficient complete records for ML training
ā¢ Clear handling strategy for missing data
ā¢ Sufficient complete records for ML training
Data Consistency Validation
Check for consistency across systems and data formats
How to Assess:
ā¢ Compare same data across different systems
ā¢ Validate format consistency and standards
ā¢ Check referential integrity
ā¢ Assess naming conventions and schemas
ā¢ Validate format consistency and standards
ā¢ Check referential integrity
ā¢ Assess naming conventions and schemas
Data Freshness & Timeliness
Evaluate data currency and update frequency for AI requirements
How to Assess:
ā¢ Map data update cycles and lag times
ā¢ Assess real-time vs batch processing needs
ā¢ Validate timestamp accuracy
ā¢ Review data retention policies
ā¢ Assess real-time vs batch processing needs
ā¢ Validate timestamp accuracy
ā¢ Review data retention policies
Data Accessibility & Discovery
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Data Catalog & Discovery
Assess data discoverability and cataloging systems
How to Assess:
ā¢ Inventory all data sources and repositories
ā¢ Evaluate metadata quality and completeness
ā¢ Test data discovery capabilities
ā¢ Assess search and filtering functionality
ā¢ Evaluate metadata quality and completeness
ā¢ Test data discovery capabilities
ā¢ Assess search and filtering functionality
Data Access Methods & APIs
Evaluate programmatic access and integration capabilities
How to Assess:
ā¢ Review available APIs and connectivity options
ā¢ Test data access performance and reliability
ā¢ Assess authentication and authorization
ā¢ Evaluate real-time vs batch access capabilities
ā¢ Test data access performance and reliability
ā¢ Assess authentication and authorization
ā¢ Evaluate real-time vs batch access capabilities
Self-Service Data Access
Assess business user data access capabilities
How to Assess:
ā¢ Evaluate self-service analytics platforms
ā¢ Test user interface accessibility
ā¢ Assess training and documentation quality
ā¢ Review approval workflows for data access
ā¢ Test user interface accessibility
ā¢ Assess training and documentation quality
ā¢ Review approval workflows for data access
Data Architecture & Infrastructure
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Data Storage Architecture
Evaluate storage systems and data lake/warehouse design
How to Assess:
ā¢ Review current storage architecture
ā¢ Assess scalability and performance
ā¢ Evaluate cloud vs on-premise strategy
ā¢ Check data partitioning and optimization
ā¢ Assess scalability and performance
ā¢ Evaluate cloud vs on-premise strategy
ā¢ Check data partitioning and optimization
Data Integration Capabilities
Assess ETL/ELT processes and data pipeline architecture
How to Assess:
ā¢ Map current ETL/ELT processes
ā¢ Evaluate integration platform capabilities
ā¢ Test data transformation and validation
ā¢ Assess error handling and monitoring
ā¢ Evaluate integration platform capabilities
ā¢ Test data transformation and validation
ā¢ Assess error handling and monitoring
AI & ML Readiness
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Labeled Training Data Availability
Assess availability and quality of labeled datasets for supervised learning
How to Assess:
ā¢ Inventory existing labeled datasets
ā¢ Assess label quality and consistency
ā¢ Evaluate data volume for ML training
ā¢ Review labeling processes and tools
ā¢ Assess label quality and consistency
ā¢ Evaluate data volume for ML training
ā¢ Review labeling processes and tools
Feature Engineering Readiness
Evaluate data structure and format for ML feature creation
How to Assess:
ā¢ Review data types and formats
ā¢ Assess feature derivation possibilities
ā¢ Evaluate categorical vs numerical data
ā¢ Check for feature engineering tools/platforms
ā¢ Assess feature derivation possibilities
ā¢ Evaluate categorical vs numerical data
ā¢ Check for feature engineering tools/platforms
Data Volume & Diversity
Assess data volume sufficiency and diversity for AI training
How to Assess:
ā¢ Calculate dataset sizes for ML requirements
ā¢ Assess data diversity and representation
ā¢ Evaluate class balance and distribution
ā¢ Check for bias in data collection
ā¢ Assess data diversity and representation
ā¢ Evaluate class balance and distribution
ā¢ Check for bias in data collection
š” Recommended Actions
High Priority
Implement Data Quality Framework
Establish automated data quality monitoring and validation processes to improve accuracy and completeness.
Medium Priority
Deploy Data Catalog Solution
Implement enterprise data catalog to improve discoverability and metadata management.
High Priority
Create AI-Ready Data Pipelines
Build dedicated pipelines for ML feature engineering and model training data preparation.
š„ Export Audit Results
š Comprehensive Report
Full audit report with findings and recommendations
ā Checklist Export
Interactive checklist for ongoing tracking
š Action Plan
Prioritized remediation roadmap
š¾ Save Progress
Save current audit state