Framework
AI Readiness Framework
A practical model for improving semantic integrity, explainability, and readiness for AI in analytics systems.
Crosswalk logic
Semantic Integrity
Ensures every metric and term has one clear meaning, so AI and humans compute the same truth.
Context Stability
Keeps filter context predictable so the same question produces the same answer every time.
Analytical Explainability
Makes answers auditable and reasoned: not just numbers, but explanations and drivers.
AI Readiness & Interoperability
Ensures the model is AI‑readable, well‑governed, and compatible with different AI tools.
Diagnostic map
Reliability domains
Data Movement Reliability
The reliability domain focused on whether data is ingested, transformed, and delivered as intended before downstream semantics and decisions depend on it.
Semantic Reliability
The reliability domain focused on whether models, metrics, joins, and definitions continue to preserve intended business meaning over time.
Execution Reliability
The reliability domain focused on runtime behavior: whether workloads, queries, and analytical systems behave predictably under changing data volume, latency, and operational pressure.
Change Reliability
The reliability domain focused on whether changes to code, configuration, models, and metadata introduce hidden analytical regressions.
Failure modes
Execution Drift
A runtime failure mode where query or pipeline behavior changes materially under load, latency, retries, or deployment shifts even though the system still appears available.
Freshness Failure
A failure mode where data arrives too late, updates too slowly, or becomes stale relative to the decision window the system is expected to support.
Intent Mismatch
A failure mode where the system answers a plausible question, but not the one the user or downstream agent actually meant.
Metric Definition Drift
A gradual change in metric meaning caused by hidden logic changes, shifting business rules, or uncontrolled local variants.
Reconciliation Failure
A mismatch between upstream and downstream state that leaves teams debating which system is correct instead of using the platform to decide.
Schema Drift
An unplanned structural change to data that alters downstream behavior, often without immediately breaking the pipeline or the user interface.