Rapid Recall Key 62–91

1. Manage DQ Rules; Measure/Monitor DQ; Manage Data Issues; Establish DQ SLAs; Data Quality Response.
2. Capture/discover, approve, document, version/change, implement, maintain, and gather feedback on rules.
3. Detailed rule-execution results and aggregated results such as scorecards.
4. Measurable; business relevant; threshold-linked; accountable/stewarded; controllable/actionable; trendable.
5. Percentage of tested data conforming to the rule/requirement.
6. Count/percentage violating the rule/requirement.
7. Statistical/pattern exploration of content, distributions, nulls, uniqueness, ranges, frequencies and relationships.
8. Profiling discovers conditions/potential issues but does not determine business requirements or root causes by itself.
9. Vocabulary/classification; assignment; escalation; workflow; diagnosis/RCA; remediation options; business decision; implementation/verification/history.
10. Impact, duration, urgency, risk and SLA/Governance commitments.
11. Trace symptoms/lineage, isolate entry point, assess process/environment/external contributors, identify potential root cause.
12. Process fixes; system modifications; prevention; added monitoring/inspection; direct controlled correction; or no action when cost/value does not justify it.
13. Covered elements, business impact, dimensions/rules, methods, thresholds, notifications, response/remediation deadlines, escalation, possible rewards/penalties.
14. Scorecards; trends; SLA metrics; issue status; Governance/policy conformance; business effects of improvements.
15. Profiling; Business Rule Templates/Engines; Parsing/Formatting; Transformation/Standardization; Enrichment; Incident Management.
16. Parsing decomposes/recognizes components; standardization changes them to approved representation.
17. Adding data/context to improve usefulness: geocode, demographics, timestamps, lineage/audit context, reference vocabulary, etc.
18. Pareto analysis, fishbone diagram, track-and-trace, process analysis, Five Whys.
19. Fully automated; manually-directed; manual.
20. Automation uses confidence/scoring, but uncertain or sensitive cases require human/Steward review.
21. They bypass normal safeguards and are high risk; controlled interfaces with edits/audit are preferred.
22. Hybrid: top-down sponsorship/consistency/resources plus bottom-up discovery/incremental wins.
23. Current understanding/pain; actual state of data; risks in creation/processing/use; cultural/technical readiness for scalable monitoring.
24. Status-quo resignation; silo politics/blame; hero culture.
25. Metadata formalizes DQ expectations/rules and stores results/issues; DQ measurements themselves become Metadata.
26. They provide critical, trusted domains/parents supporting validity, consistency, uniqueness and integrity.
27. Data movement can introduce defects; inspection between stages isolates corruption earlier and preserves DQ evidence.
28. Governance sets priorities/decision rights, coordinates participation/access, makes measurement lead to action, and resolves conflicts.
29. ISO 8000 supports portable/application-neutral data meeting stated requirements and standards-based definition/request/verification of quality.
30. SPC seeks unexpected/special-cause variation relative to a statistically stable baseline; control limits are not business acceptance thresholds.

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