Data & AI Delivery Checklists
Key takeaway
Data and AI delivery checklists are short verification lists teams use at kickoff, before launch, and during quarterly reviews. They cover four areas: strategy readiness, data governance, platform readiness, and AI model readiness. Each confirms essentials such as defined KPIs, named data owners, pipeline monitoring, tested recovery plans, and drift monitoring before work moves forward.
Use these checklists at project kickoff, before launch, and during quarterly reviews.
Included Checklists
Strategy Readiness Checklist
- Business outcomes and KPIs defined
- Use cases prioritized by value and feasibility
- Executive sponsor and product owner assigned
- Funding and delivery timeline confirmed
Data Governance Checklist
- Data owners and stewards identified
- Data quality SLAs defined for critical datasets
- Privacy, security, and access policies in place
- Metadata and lineage captured for key data products
Platform Readiness Checklist
- Ingestion and transformation standards documented
- Monitoring and alerting for pipelines and SLAs
- Cost and performance guardrails implemented
- Backup and recovery plan tested
AI Model Readiness Checklist
- Training data validated for quality and bias risk
- Model purpose, limitations, and intended use documented
- Evaluation across segments completed
- Monitoring for drift and performance in place
Next Steps
Ready to move from strategy to measurable impact?
Sources
Frequently asked questions
What should be on a data governance checklist?
A data governance checklist should confirm that data owners and stewards are identified, data quality SLAs are defined for critical datasets, privacy, security, and access policies are in place, and metadata and lineage are captured for key data products. These items establish accountability and traceability before data is widely used.
How do you know an AI model is ready for production?
An AI model is ready for production when its training data has been validated for quality and bias risk, its purpose, limitations, and intended use are documented, it has been evaluated across segments, and monitoring for drift and performance is in place. Missing any of these items increases operational risk.
What does a data platform readiness checklist include?
A data platform readiness checklist includes documented ingestion and transformation standards, monitoring and alerting for pipelines and SLAs, cost and performance guardrails, and a backup and recovery plan that has actually been tested. These checks confirm the platform can run reliably and recover from failure before production workloads depend on it.
What should be in place before starting a data and AI project?
Before starting a data and AI project, confirm that business outcomes and KPIs are defined, use cases are prioritized by value and feasibility, an executive sponsor and product owner are assigned, and funding and the delivery timeline are confirmed. This strategy readiness check prevents starting work without a clear owner or goal.
