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Notes on AI data readiness
Data governance for AI, compliance deadlines that land on your data team, and an honest take on what a readiness score can and cannot tell you.
Is your BigQuery data AI-ready? Auditing the sprawl.
BigQuery estates grow by team, not by design, and the ungoverned tail is where the exposure lives. Scoring readiness from INFORMATION_SCHEMA, policy tags, and labels, with two IAM roles and zero row access.
Is your Snowflake data AI-ready? The metadata already knows.
Snowflake ships the best governance surface in the industry and the median account uses a fraction of it. What ACCOUNT_USAGE, tags, and masking policies reveal about readiness, without reading a row.
The AI data readiness checklist: 12 checks before you build.
Most readiness checklists are aspirations wearing a checkbox costume. Twelve concrete checks, every one verifiable from warehouse metadata, grouped by the six factors that actually fail.
AI readiness is not a one-time score: monitoring data drift.
A readiness score is a photograph of a moving thing. Why the number decays as data drifts, how scheduled metadata-only re-scans catch it, and the materiality gate that means you only hear about it when something real changes.
How MortarIQ scores AI readiness without reading your data.
The thing that stalls most readiness checks is the security review. Why a metadata-only assessment reads the structure around your data and never a single row, what it can and cannot see, and how the connection is locked down.
EU AI Act Article 10: the data governance deadline is August 2, 2026.
The high-risk obligations become enforceable in about seven weeks, and Article 10 is aimed at your data team, not your legal team. What it requires, why it cannot be retrofitted as paperwork, and what to do in the time left.
Is your data ready for AI? Now you can know in minutes.
Most AI projects stall on data, not models. Why we built a readiness scanner that scores your warehouse across six factors from metadata alone, and what a real scan finds.