This lesson develops Matching, Clustering, Duplicate Review, and Survivorship within Oracle Enterprise Data Quality 12c: Profile, Audit, and Operate Ed 1.
The activity is anchored to a customer-data quality program measuring defects, standardizing records, identifying duplicates, and operationalizing controls, so each design, configuration, analysis, or support decision is tied to a realistic operating outcome.
The instructor will analyze and reconcile match identifiers, comparisons, algorithms, weights, and threshold bands in the context of a customer-data quality program measuring defects, standardizing records, identifying duplicates, and operationalizing controls. The demonstration captures commands, configuration, transactions, output, logs, plans, or screenshots that prove both the intended behavior and the control boundary.
Learners complete a controlled exercise covering candidate selection, clustering, one-to-many and many-to-many implications and match review, false-positive and false-negative analysis, and tuning, then introduce a realistic exception involving master-record and survivorship principles, decision evidence, and exception governance. They record assumptions, exact steps, expected and observed results, a reversible correction, and the evidence required for operational acceptance.
Acceptance for Matching, Clustering, Duplicate Review, and Survivorship requires the result to be reproducible, traceable to approved inputs, consistent with the relevant Oracle or platform behavior, and supported by checks for security, data integrity, reliability, performance, and recoverability.
When the result differs from expectation, learners isolate the smallest proven cause across match identifiers, comparisons, algorithms, weights, and threshold bands, candidate selection, clustering, one-to-many and many-to-many implications, match review, false-positive and false-negative analysis, and tuning; they avoid speculative production changes, preserve evidence, apply the lowest-risk correction, rerun the test, and document prevention.
Complete and defend a validated result for Matching, Clustering, Duplicate Review, and Survivorship with a reusable implementation record or runbook.