Data Readiness Checklist for Execs

Healthcare • ~7–9 min read • Updated May 1, 2025

You don’t need a perfect data estate to start creating value with AI. You need a minimal viable data posture that is safe, fast, and focused on the few use-cases that matter.

Why this matters now

Many programs stall trying to “finish the platform.” Meanwhile competitors ship AI into workflows with guardrails, iterating toward better data over time. Leaders win by sequencing data investments with the value roadmap rather than chasing generic perfection.

Our point of view

Adopt a use-case–first posture: prioritize the smallest set of data capabilities that unlock your top 3 outcomes in the next 2–3 quarters, then compound.

Executive data readiness checklist

1) Use-case framing

  • Top 3 AI use-cases with defined economic impact and risk profile.
  • Clear decision owners and success metrics (leading/lagging).

2) Data sourcing & quality

  • Identify critical tables/fields per use-case; tolerate “good enough” elsewhere.
  • Data contracts for producers; basic freshness & completeness checks on arrival.
  • PII/PHI handling patterns documented; masking/anonymization where required.

3) Access & governance

  • Role-based access with least privilege; auditable grants and revocation.
  • Approved model/data usage policy; human-in-the-loop points defined.
  • Model registry + change control; incident response for data/model issues.

4) Platform & integration

  • Single landing pattern for sources (batch/stream); standardized ingestion pipelines.
  • Feature storage pattern (warehouse/lakehouse + feature store) documented.
  • Secure connectivity to apps/workflows (APIs, ETL/ELT, event bus) with DX controls.

5) Monitoring & reliability

  • Data observability (freshness, volume, schema, lineage) on critical assets.
  • Model monitoring for drift, bias, performance with rollback paths.
  • Defined RTO/RPO and backup/restore tests for critical datasets.

6) Team & operating model

  • Named DRI for each use-case across data, model, and business ownership.
  • Quarterly capital gates tied to evidence (adoption, ROI, risk posture).
  • Engineering enablement: templates, code standards, and platform guardrails.

60–90 day starter plan

  1. Weeks 1–3: Lock top use-cases, map critical data, stub access policies.
  2. Weeks 4–8: Stand up ingestion + basic quality checks; seed feature store; pilot governance.
  3. Weeks 9–12: Ship first workflow; implement monitoring; run evidence gate for scale funding.

Closing

Good data beats perfect later. Start with the smallest set of assets and controls that unlock your top outcomes, then compound capability with each release.

Thought Leadership by Dr. Dodi Mossafer, DBA

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