Data strategy alignment - ensuring data governance, quality pipelines, and feature stores support the AI use cases on the roadmap

1. List the top 3 features you hypothesize will predict churn (e.g., usage frequency, support tickets, billing issues). 2. For each feature, define its source system, ownership, required quality checks (e.g., completeness, timeliness), and governance classification (PII, internal). 3. Design a simple diagram showing the flow from source to a hypothetical feature store table for this model.

1. For each use case, define the critical data dependencies and performance requirements (e.g., latency < 100ms for feature serving). 2. Use a data catalog tool (e.g., Alation, Atlan) or manual audit to assess the current state of required datasets: quality, documentation, accessibility. 3. Deliver a findings report with a prioritized list of 'data debt' items blocking the roadmap, and a remediation plan with owners and timelines.

1. Define a federated ownership model (e.g., platform team owns infra, domain teams own business logic). 2. Architect a feature platform schema that includes metadata for lineage, quality scores, access controls, and semantic definitions. 3. Create a cross-functional governance council process to vet, publish, and deprecate features, ensuring alignment with the evolving AI roadmap. 4. Present a TCO analysis showing reduction in redundant pipelines and improved time-to-market for new models.