The Ultimate Guide to Evaluating Data Lineage for AI-Native Governance
21 questions to assess data lineage – and the gotchas to watch out for.
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Inside the guide
Discover the key questions and watch-outs for assessing data lineage.
What does AI lineage actually look like?
Why lineage is necessary for proving AI systems are accurate and trustworthy.
Lineage is the backbone of AI defensibility
How lineage powers AI-critical use cases, from drift detection to impact analysis.
3 variables that define AI-ready data lineage
Evolving data lineage from documentation to infrastructure.
21 questions for data lineage – and gotchas to avoid
The questions to ask, why they matter, watch to look for, and what to avoid.
How data lineage drives business value
Practical use cases from CME Group, DigiKey, and HelloFresh.
From lineage to AI-native governance
The platform approach to activating lineage to power AI at scale.
21 questions to give you confidence and direction.
Evaluate lineage across 4 dimensions, so you can make the best decision for your team and your tech stack.
The Problem with Traditional Lineage
Our Approach
Core Lineage Capabilities
Foundational technical requirements
AI Context & Provenance
Emerging requirements for AI deployment
Extensiveness & Integration
Coverage for your entire data estate
Governance Activation & Outcomes
Ability to drive real governance outcomes
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