Enterprise Data Foundations for AI
A ~36-minute foundations course for CDOs, data platform leaders, architects — most AI projects fail at the data step. 5 pillars, data product pattern, data contracts, vendor reality, 18-month roadmap.
8
Chapters
~36 min
Duration
Advanced
Level
No
Certification
Who this is for
For chief data officers, data platform leaders, and architects making the platform decisions today that will define AI capability for the next five years.
How this course works
- 8 audio-narrated slide chapters · ~36 min of focused content
- Capstone with interactive Markdown builder you take to your team
- Trust trip-wires on every play — what not to cross
- Free verifiable certificate on completion
What you'll walk out with
Specific outcomes from this course — no fluff.
- Most AI projects fail at the data step — recognise the pattern and stop it
- 5 pillars of data foundations — quality · lineage · governance · privacy · platform — scored independently
- 3 anti-patterns that waste budgets — buy platform without fixing data, ocean-boil cleanup projects, disconnected data team
- Apply the DAMA-DMBOK quality framework with 4 dimensions (accuracy · completeness · freshness · consistency)
- Build defensible lineage — EU AI Act Art 15 + NIST AI RMF + GDPR Art 30 traceability
- Choose the right architecture — lakehouse + federated-with-central-platform is durable for most enterprises
- Adopt the data product pattern — 3-5x AI team speed when data is published as curated, versioned, owned products
- 18-month rollout with 4 trust trip-wires you don't cross, plus an interactive builder for your CDO/CIO conversation
Course content
8 chapters · ~36 min
Welcome
A 1-minute orientation — what the course covers, how to navigate, and what you walk out with. No audio on this screen.
The data foundations landscape
Most AI projects fail at the data step · 5 pillars · 3 anti-patterns (buy-platform · ocean-boil · disconnected team) · what this course covers.
Data quality
DAMA framework · 4 dimensions (accuracy · completeness · freshness · consistency) · 3 failure modes (entity-gap · silent-drift · quality-assumed) · 3 patterns that work.
Data lineage
3 reasons (explainability · regulation · incident response) · 3 layers (source-to-sink · transformation · semantic) · EU AI Act Art 15 + NIST + GDPR Art 30.
Data governance
4 components (ownership · access · retention · deletion) · multi-jurisdiction (GDPR + DPDPA + CCPA + PIPL) · 3 failure modes.
Data privacy techniques
3 techniques (masking · tokenisation · differential privacy) · matched to 4 scenarios · 3 failure modes.
The platform pattern
2 architecture choices (lakehouse · federated-with-central) · data product pattern (3-5x AI team speed) · data contracts as reliability layer.
Vendor + tooling reality
2026 landscape (Databricks · Snowflake · Fabric · Collibra · Atlan · Monte Carlo) · 3 build/buy principles · consolidation · AI-specific contracts.
Making it stick: your data foundations roadmap
3-phase 18-month rollout · 4 trust trip-wires · interactive Markdown builder.
Want this delivered inside your organisation?
The course is the starting point. The same content powers a 4-week pilot, an org-wide rollout, or a continuous build engagement — set up on your data, with your team, by Gennoor Tech.