Every enterprise AI initiative I've seen fail had one thing in common: they tried to build AI on top of data chaos.
You can have the most sophisticated models, the best engineering team, and unlimited compute. If your data isn't ready, your AI won't work.
The Data Product Mindset
A data product is data that's managed with the same rigor as software:
- Documented: Clear schemas, definitions, and lineage
- Versioned: Changes are tracked and reversible
- Owned: Someone is accountable for quality
- Discoverable: Users can find and understand what's available
- Reliable: SLAs for freshness, completeness, and accuracy
This isn't just good data governance. It's the foundation that makes AI possible.
Why AI Needs Data Products
1. Training Data Quality
Garbage in, garbage out isn't just a cliché—it's the primary failure mode for AI:
- Inconsistent labels create confused models
- Missing data introduces blind spots
- Stale data embeds yesterday's reality
- Biased samples perpetuate harmful patterns
2. Feature Engineering at Scale
Production ML requires reliable features:
- Features need consistent computation
- Historical features need time-travel capability
- Feature drift needs monitoring
- Retraining needs reproducibility
3. RAG and Knowledge Systems
Retrieval-augmented generation depends on content quality:
- Documents need consistent formatting
- Metadata needs to be accurate
- Updates need to flow through
- Duplicates need resolution
Building the Foundation
Start with Data Contracts
Define explicit agreements between data producers and consumers:
- Schema expectations
- Quality thresholds
- Freshness requirements
- Breaking change policies
Implement Data Quality Gates
Don't let bad data enter your AI pipelines:
- Automated validation on ingestion
- Anomaly detection for drift
- Blocking alerts for critical issues
- Quality dashboards for visibility
Build the Catalog
You can't use data you can't find:
- Comprehensive metadata
- Lineage tracking
- Usage analytics
- Access controls
Establish Ownership
Every data product needs:
- A product owner who's accountable
- A team that maintains it
- A roadmap for improvements
- A process for handling issues
The Payoff
When you have solid data products:
- AI projects accelerate because data is ready
- Model quality improves because inputs are reliable
- Trust increases because results are explainable
- Iteration speeds up because changes are manageable
My Approach
I've led data office initiatives across major enterprises. The pattern is consistent:
- Inventory existing data assets
- Prioritize based on AI use cases
- Define data product standards
- Build the infrastructure (catalog, quality, lineage)
- Migrate critical datasets to product standards
- Iterate based on AI team feedback
It's not glamorous work. But it's the work that makes everything else possible.
AI strategy without data strategy is just slideware.