Data Lineage
Tracking where training and inference data came from.
"If you can't trace the data, you can't trust the model."
What is Data Lineage? Tracking where training and inference data came from. Compliance, quality, debugging.
Standard requirement in regulated industries.
Compliance, quality, debugging.
Engineer lineage tracking from day one.
Pick what to reward the model for.
Tracking where training and inference data came from. In the wild: Standard requirement in regulated industries.
Pick a lever. There are no neutral ones — every incentive funds a behavior somewhere.
Pick a reaction to Data Lineage
One tap. We'll point you at the most useful next surface based on how this hits.
The full taxonomy entry
Every concept in the Atlas uses the same structure — so Data Lineage can be compared, recombined, and cited like an element on a periodic table.
- Business
- Leadership
- Government
- Healthcare
- Education
- Sales
- Marketing
- AI
- Negotiation
- Media
- Public Policy
- Relationships
- Where in our org would Data Lineage most often show up unnoticed?
- Which metric, ritual, or contract clause quietly rewards Data Lineage?
- If we removed every payoff for Data Lineage, what behavior would replace it?
- Who benefits when Data Lineage persists — and who pays the cost?
- People defend the status quo using the language of data lineage.
- Decisions cluster around the easiest narrative rather than the strongest evidence.
- New data changes the slide deck but not the decision.
- Anyone naming the pattern is treated as the problem.
Every Atlas entry is a node in a knowledge graph. See the related rail below to follow the connections.
See Data Lineage through 4 lenses
Each layer of the Incentives OS reframes this concept with its own thinkers, vocabulary, and diagnostic question.
- Layer 8Organizational Psychology
What is the org actually rewarding — versus claiming to reward?
- Layer 11Economics & Mechanism Design
Who pays, who is paid, and what does the price signal hide?
- Layer 13Leadership
What kind of leadership move does this situation actually require?
- Layer 15AI & Alignment
What proxy reward is the AI optimizing — and what is it ignoring?
Do you actually know Data Lineage?
Three quick questions. Result is saved into your review streak — come back when the term is due to lock it in.
Which best describes Data Lineage?
Worked example, counter-example & concept map
On-demand AI analysis grounded in the Lab's research. Cached on your device after first run.
When you encounter Data Lineage, your prefrontal cortex has to do extra work to override the automatic response — and that override budget is finite.
Executive control, planning, impulse override, working memory, System 2. First thing to go offline under stress, fatigue, or low blood sugar. Why your 4pm decisions are worse than your 9am ones.
See Prefrontal in the Brain Atlas →Picked for you, from the Atlas
Ranked by shared learning paths, overlapping chips, and what you've saved.
Written rules about how AI may be used internally.
Logging of AI inputs, outputs, and decisions.
Inventory of models, data, tools, and dependencies in an AI system.
Tracing AI components for risk and compliance.
Cross-functional governance body for AI decisions.
Agents deployed before anyone owns the consequences.
Send the card, not just the link
A pre-rendered social card with the title, eyebrow, and URL. Copy the link, post it anywhere, or download the SVG for slides.
More definitions to follow
Every term in the Atlas connects to a dozen others. Pick any of these and see where it takes you.
We overvalue things we built ourselves.
Customers become trapped in a product due to switching costs or network effects.
Underestimating the probability of bad outcomes — especially to us.
A behavioral relaxation of Nash: players choose better strategies more often, but not always — errors are smooth, not binary.
Institutions will try to preserve the problem to which they are the solution.
A controlled scenario, often impossible, used to isolate a variable and test an intuition or theory.
Innovators → early adopters → majority → laggards, AI-specific.
A story repeats until it becomes obviously true.
How options are presented changes which ones get chosen.
Countering hostile narratives with truthful, well-timed alternatives.
Aggressive enforcement and complex eligibility turn a safety net into a liability trap.
Specialized training on domain data.