Data Management
What this domain covers
Data management covers record hygiene, deduplication, normalization, and data flow between systems. Know how duplicate records are identified and merged, how field values are standardized (for example, country and state picklists), and how data is imported with proper field mapping. Understand list membership, static versus smart lists, and how segmentation uses data fields. Also know the role of data hygiene campaigns that clean and enrich records.
Traps to watch
Questions often test merge behavior: which record survives, which fields win, and what happens to list memberships and campaign history. Another trap is import mapping - a column mapped to the wrong field silently corrupts data. Watch for confusion between a smart list (dynamic) and a static list (fixed membership). Also expect questions on normalization: free-text fields cause segmentation and reporting problems, so picklists are preferred where values are constrained.
Rule of thumb
Treat data quality as a pipeline: validate at capture (forms and picklists), normalize on import (mapping and standardization), and deduplicate continuously (scheduled hygiene campaigns).
Worked example
Two records exist for the same person, one with a phone number and one with a recent activity history. Before merging, decide which record should be the surviving record so that the richer history and the correct field values are preserved. Merging blindly can discard the better data.