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Derives data quality rules from the data rules and the physical model

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https://knobit.org/id/outcome/QO-3

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dcterms:descriptionStage 3 (Data quality rules) of the data-quality skill: Derives data quality rules from the data rules and the physical model; produces 03-dq-rules/dq-rules.yaml. Formerly stage 7 of the data-modelling skill (data-modelling LO-7). en
dcterms:identifierQO-3
dcterms:licensehttps://creativecommons.org/licenses/by/4.0/
has prerequisiteDocuments data rules in SBVR and resolves rule anomalies
includes knobitExplains current value, history and point-in-time reproducibility
includes knobitRecognises a declared constraint the target platform does not enforce
includes knobitRecognises an update anomaly in a logical data model
includes knobitRecognises a stored derivable attribute
includes knobitExplains the insertion, update and deletion anomalies
includes knobitExplains third normal form
includes knobitExplains derived data and point-in-time snapshots
includes knobitRecognises an unmanaged duplicate column
includes knobitRecognises a stored derived or summary value
includes knobitRecognises an overwrite where history is required
includes knobitRecognises inconsistent validity periods in a versioned table
includes knobitRecognises a reference to a versioned table by business key alone
includes knobitExplains controlled denormalisation
includes knobitExplains null semantics and three-valued logic
includes knobitExplains history strategies and validity-period conventions
includes knobitRecognises history kept without its constraints checked
includes knobitExplains SQL dialect differences
includes knobitRecognises a rule assigned to a missing data quality check
includes knobitRecognises a data-update rule with no enforcing or detecting mechanism
includes knobitRecognises a constraint deferred without a check
includes knobitRecognises a derived or copied column without a check
includes knobitRecognises a check with no rule behind it
includes knobitRecognises a check outliving its rule
includes knobitRecognises a check unable to fail
includes knobitRecognises a check on the wrong object
includes knobitRecognises a check weaker than its rule
includes knobitRecognises a check stronger than its rule
includes knobitRecognises a check measuring the wrong population
includes knobitRecognises nulls left to chance in a check predicate
includes knobitRecognises a past event judged against present data
includes knobitRecognises a set rule written as a row rule
includes knobitRecognises a check expression unable to run
includes knobitRecognises a misfiled data quality dimension
includes knobitRecognises duplicate checks for one rule
includes knobitRecognises a threshold at odds with its rule
includes knobitRecognises disagreeing severity, threshold and failure action
includes knobitRecognises a rejection destroying more than it protects
includes knobitRecognises a check failure with no capable owner
includes knobitRecognises a check scheduled at the wrong moment
includes knobitRecognises an aggregate hiding a total failure
includes knobitRecognises an empty population reported as a pass
includes knobitExplains data quality dimensions
includes knobitExplains traceability from data rule to check
includes knobitExplains the population, measure and threshold of a check
includes knobitExplains row-level and set-level checks
includes knobitExplains severity, threshold and failure action
includes knobitExplains scheduling checks against loads and use
includes knobitExplains segmented data quality reporting
includes knobitExplains ownership of data quality failures
owl:sameAshttps://bitbucket.org/reffa/ontology/id/outcome/data-quality/03-dq-rules
rdfs:labelDerives data quality rules from the data rules and the physical model en
rdfs:labelTuletab andmekvaliteedi reeglid andmereeglitest ja füüsilisest andmemudelist et
related topicData quality (DAMA-DMBOK2 chapter 13)

Used by

assesses learning outcomeLoan application data rules (data rule anomaly skills)
assesses learning outcomeDeposit guarantee single customer view - profiling and assessment of critical data elements (profiling and assessment anomaly skills)
assesses learning outcomeDeposit mart data quality rules derived from the data rules and the Snowflake model (DQ rule derivation)
assesses learning outcomeLoan application data quality rules on Snowflake (DQ rule anomaly skills)
assesses learning outcomeCustomer and payment data quality rules read against their profile and run results (DQ rule anomaly skills)
has prerequisiteProfiles and assesses critical data elements over stated populations
includes learning outcomeLearning path: Data quality
related learning outcomeStage 3 — Data quality rules
related learning outcomeData quality rule anomaly checklist
schema:itemhttps://knobit.org/id/path/data-quality/QO-3