Explains rule enforcement mechanisms and platform enforcement

K-356 · Knowledge knobit · Activity: Document data rules as SBVR fact types and rule statements · Competence: Data modelling

Schema constraint, data quality check, application or ETL, and which platforms enforce declared constraints.

Can AI do this?

AI does this well Most of the tested AI models master this. 10 of 10 models master it.

Mastered means a probability of at least 90% that the model has this knobit, estimated from its graded answers. “Not shown” means the answers do not settle it either way. How the models are tested.

What it looks like in practice

Expected result: Schema constraint, data quality check, application or ETL, and which platforms enforce declared constraints

How it is assessed: Assigns sample rules a mechanism and says what each target platform actually enforces

Related knobits

Needed for

• K-310 Recognises a constraint claimed by a rule and missing from the schema
• K-311 Recognises a uniqueness rule with no unique key behind it
• K-312 Recognises a rule assigned to a missing data quality check
• K-313 Recognises a declared constraint the target platform does not enforce
• K-314 Recognises a data-update rule with no enforcing or detecting mechanism
• K-703 Recognises a constraint deferred without a check
• K-705 Recognises history kept without its constraints checked
• K-709 Recognises a check unable to fail
• K-808 Recognises a CHECK constraint parsed and ignored

All 28 knobits of Document data rules as SBVR fact types and rule statements.

Source: Data-management practice used by the stage 3 reference and review checklist