Lilly Tech Systems
Enterprise Data Strategy Best Practices
Lessons learned from organizations that have successfully built AI-ready data foundations. These patterns apply across industries and company sizes.
Organizational Best Practices
- Executive sponsorship: Data strategy needs C-level backing. Without it, cross-functional alignment is impossible
- Data literacy programs: Invest in training business users to understand and use data. AI adoption stalls without data-literate stakeholders
- Embedded data engineers: Place data engineers within business units rather than in a centralized team. They build closer relationships and better understand domain data
- Data product thinking: Treat datasets as products with owners, SLAs, documentation, and consumers. This drives accountability and quality
- Community of practice: Create cross-team forums for data practitioners to share patterns, tools, and lessons learned
Technical Best Practices
- Schema-on-read with contracts: Store raw data in its original form but enforce schemas at consumption time through data contracts
- Immutable data: Never overwrite source data. Use append-only patterns and maintain full history for reproducibility
- Infrastructure as code: Define all data infrastructure (pipelines, storage, access controls) in version-controlled code
- Feature reuse: Build a feature store to prevent duplicate feature engineering across teams
- Metadata-driven pipelines: Use metadata to drive pipeline behavior rather than hard-coding transformation logic
Common Pitfalls
- Big bang migration: Trying to migrate all data to a new platform at once. Migrate incrementally by use case
- Ignoring data debt: Legacy data issues do not disappear. Budget time to address technical debt continuously
- Over-centralization: A single central data team becomes a bottleneck. Distribute ownership while maintaining standards
- Tool sprawl: Adopting too many tools creates integration complexity. Standardize on a core stack
- Measuring activity, not outcomes: Track business outcomes (model accuracy, time to insight) not vanity metrics (tables created, pipelines built)
Success Metrics
| Metric | What It Measures | Target |
|---|---|---|
| Time to data | How long it takes a new AI project to access needed data | < 1 week |
| Data quality score | Composite quality across dimensions | > 95% |
| Feature reuse rate | Percentage of features reused from the feature store | > 60% |
| Pipeline reliability | Percentage of pipeline runs that succeed | > 99% |
| Governance compliance | Percentage of datasets with proper classification and ownership | 100% |
Frequently Asked Questions
How long does it take to build a data strategy?
A foundational data strategy can be defined in 4-8 weeks. Implementation is ongoing — expect 6-12 months for the first phase covering your highest-priority AI use cases, with continuous improvement thereafter.
Should we build or buy our data platform?
Most enterprises use a combination. Buy managed services for infrastructure (Databricks, Snowflake) and build custom components for domain-specific data products and integrations. Avoid building what you can buy, but do not force-fit tools where they do not belong.
How do we get business buy-in for data strategy?
Connect data strategy to specific AI use cases with measurable business value. Show how poor data quality blocks those use cases. Start with a quick win that demonstrates value, then expand. Avoid leading with technology — lead with business outcomes.