A passenger's name and loyalty number appear slightly differently across Meridian's booking system, the loyalty database, and the customer-service chatbot's records, causing the fraud-detection model to sometimes miss that all three refer to the same person. Which practice should the PM push the team to implement to fix this?

Master data management establishes a single, authoritative 'golden record' for core entities like customer identity, reconciling inconsistent representations across multiple source systems such as booking, loyalty, and chatbot records. This directly addresses Meridian's problem: the fraud-detection model is fed the same real person as three near-duplicate identities, degrading its ability to link suspicious activity to one account. Data augmentation, to generate additional synthetic passenger records, addresses a different problem entirely — increasing training-data diversity or volume, not resolving conflicting identity records; it would not fix the underlying data quality issue. Model retraining alone, without addressing the underlying identity records, treats the symptom of model performance without addressing the root cause of inconsistent master data, so the same issue would persist after retraining. Deleting two of the three systems' passenger records to force consistency is an operationally destructive non-solution that would break legitimate business systems rather than reconcile records. For the exam, recognize master data management as the named practice for cross-system identity/entity consistency, distinct from lineage, which tracks transformation history, and stewardship, which covers ownership and accountability.