AI-Ready Data: Complete Checklist for 2025

AI and machine learning are only as good as the data that powers them. Yet 80% of ML projects fail due to poor data quality and preparation. At Data Gardeners.ai, we've built AI-ready data foundations for Fortune 500 companies—here's exactly what you need.

The AI-Ready Data Framework

AI-ready data requires excellence across five dimensions:

• Quality: Accurate, complete, consistent data
• Accessibility: Easy to discover and access
• Governance: Secure, compliant, traceable
• Features: Engineered for model training
• Operations: Versioned, monitored, automated

✅ Data Quality Checklist

1. Completeness

• ❏ No critical missing values (< 5% null rate for key features)
• ❏ Complete historical data (minimum 12-24 months for patterns)
• ❏ All required features available
• ❏ Documented data gaps with mitigation strategies

2. Accuracy

• ❏ Data validated against source systems
• ❏ Outliers identified and handled appropriately
• ❏ Business rules enforced (e.g., age > 0, price >= 0)
• ❏ Regular accuracy audits scheduled

3. Consistency

• ❏ Standardized formats (dates, currencies, units)
• ❏ Consistent naming conventions
• ❏ Unified customer/product IDs across sources
• ❏ No conflicting values in master data

4. Timeliness

• ❏ Data freshness meets model requirements
• ❏ Automated pipelines for real-time features (if needed)
• ❏ Latency SLAs defined and monitored
• ❏ Batch processing schedules optimized

✅ Data Accessibility Checklist

5. Discoverability

• ❏ Data catalog with searchable metadata
• ❏ Clear documentation for each dataset
• ❏ Example queries and use cases documented
• ❏ Data lineage tracked and visualized

6. Accessibility

• ❏ Self-service access for data scientists
• ❏ APIs for programmatic access (Python, R)
• ❏ Notebooks integrated with data platform
• ❏ Feature store for reusable features

7. Performance

• ❏ Optimized for ML query patterns (columnar formats)
• ❏ Data partitioned appropriately
• ❏ Caching strategy for frequent features
• ❏ Training data retrieval < 5 minutes for 1M rows

✅ Data Governance Checklist

8. Security & Privacy

• ❏ PII identified and protected
• ❏ Role-based access control (RBAC) implemented
• ❏ Data encryption at rest and in transit
• ❏ GDPR/CCPA compliance for personal data
• ❏ Anonymization for sensitive fields

9. Compliance

• ❏ Audit logs for all data access
• ❏ Data retention policies enforced
• ❏ Consent management for personal data
• ❏ Regular compliance audits scheduled

10. Data Lineage

• ❏ Complete lineage from source to model
• ❏ Transformation logic documented
• ❏ Impact analysis capability (what changes affect which models)
• ❏ Lineage visualization available

✅ Feature Engineering Checklist

11. Feature Store

• ❏ Centralized feature repository implemented
• ❏ Features versioned and documented
• ❏ Online and offline feature serving
• ❏ Feature freshness monitoring

12. Feature Quality

• ❏ No data leakage (future information)
• ❏ Training-serving skew prevented
• ❏ Feature drift monitored
• ❏ Feature importance tracked

13. Feature Types

• ❏ Numerical features scaled appropriately
• ❏ Categorical features encoded (one-hot, embedding)
• ❏ Time-based features engineered (day of week, hour, seasonality)
• ❏ Aggregation features computed (rolling averages, counts)
• ❏ Interaction features created where valuable

✅ MLOps Integration Checklist

14. Data Versioning

• ❏ Training datasets versioned (DVC, MLflow)
• ❏ Feature definitions versioned in Git
• ❏ Reproducible data snapshots for experiments
• ❏ Rollback capability for data changes

15. Monitoring & Observability

• ❏ Data quality metrics tracked over time
• ❏ Feature distribution monitoring (drift detection)
• ❏ Data freshness alerts configured
• ❏ Pipeline health dashboards

16. Automation

• ❏ Automated feature pipelines
• ❏ Scheduled data quality checks
• ❏ Automatic retraining triggers on data drift
• ❏ CI/CD for data pipelines

✅ Architecture Checklist

17. Lakehouse Foundation

• ❏ Delta Lake or equivalent for ACID transactions
• ❏ Medallion architecture (Bronze/Silver/Gold)
• ❏ Unified batch and streaming
• ❏ Schema evolution support

Learn more about implementing this in our Lakehouse Implementation Guide.

18. Scalability

• ❏ Handles current data volume + 3x growth
• ❏ Auto-scaling compute clusters
• ❏ Distributed processing (Spark, Ray)
• ❏ GPU support for deep learning workloads

19. Cost Optimization

• ❏ Storage tiering strategy
• ❏ Compute right-sizing
• ❏ Spot instances for training
• ❏ Data lifecycle management

See our complete guide: Reduce Data Lake Costs by 40%

Real-World Implementation: Fortune 500 Case Study

We recently helped a Fortune 500 retail company prepare their data for AI:

The Challenge

• 500TB of data across 50+ sources
• 70% data quality issues
• 6-month lead time to prepare data for new models
• No feature reuse across teams

Our Solution

• Implemented Medallion architecture with Delta Lake
• Built centralized feature store (Feast)
• Automated data quality checks at each layer
• Created self-service data catalog
• Established MLOps pipelines

Results After 16 Weeks

• 95% data quality (up from 30%)
• 2-week feature development time (down from 6 months)
• 80% feature reuse across teams
• 4x faster model training with optimized data access
• $1.2M annual savings from cost optimization

Common Pitfalls to Avoid

1. Data Leakage

Using future information in training data. Always split data chronologically, never randomly for time-series problems.

2. Training-Serving Skew

Different feature computation in training vs production. Use the same code/logic for both with a feature store.

3. Label Quality

Poor labels = poor models. Invest in label quality—consider multiple labelers, consensus methods, and regular audits.

4. Ignoring Data Drift

Data distributions change over time. Monitor drift and retrain models when significant drift detected.

5. Over-Engineering

Start simple. You don't need a feature store on day one. Build incrementally as needs grow.

Tools and Technologies

Recommended Stack

• Storage: Delta Lake on S3/ADLS
• Processing: Apache Spark, Databricks
• Feature Store: Feast, Tecton, or Databricks Feature Store
• Data Catalog: Unity Catalog, AWS Glue, Datahub
• ML Platforms: MLflow, Kubeflow, SageMaker
• Monitoring: Great Expectations, Evidently AI, Datadog
• Versioning: DVC, LakeFS

Getting Started: 90-Day Plan

Month 1: Foundation

• Week 1-2: Data quality assessment and baseline metrics
• Week 3-4: Implement Bronze layer with raw data ingestion

Month 2: Quality & Access

• Week 5-6: Build Silver layer with data cleaning and validation
• Week 7-8: Deploy data catalog and establish governance

Month 3: Features & MLOps

• Week 9-10: Create Gold layer with curated features
• Week 11-12: Implement feature store and MLOps pipelines

Conclusion: AI Success Starts with Data

AI-ready data isn't a one-time project—it's an ongoing discipline. The companies winning with AI have invested in data foundations that prioritize:

• Quality over quantity: Clean, reliable data beats massive volumes of messy data
• Accessibility: Self-service empowers data scientists to move fast
• Governance: Security and compliance built-in from day one
• Automation: Manual processes don't scale
• Monitoring: Continuous validation catches problems early

At DataGardeners.ai, our AI Enablement services help companies build these foundations. We guarantee your data will be AI-ready within 90 days—or we keep working until it is.