Data Management
Governance, quality, and standards
Data management spans governance (ownership, stewardship, policy, decision rights), quality (completeness, accuracy, timeliness, consistency, uniqueness), lifecycle (capture, storage, retention, destruction), and interoperability. Know master data management and patient/provider identity, metadata and data lineage, analytics architecture (warehouse, lake, lakehouse), and the standards landscape: HL7 v2 messaging, FHIR for RESTful APIs and resources, DICOM for imaging, X12 for claims, plus terminologies - ICD-10 for diagnoses and procedures, SNOMED CT for clinical concepts, LOINC for laboratory observations, RxNorm for medications.
Traps: confusing data governance (who decides and what the policy is) with data management (executing it); assuming FHIR immediately replaces older standards in every interface; mixing up terminology systems, for example using SNOMED for lab result identifiers; and overlooking that secondary use of data for research or population health requires its own approvals and careful de-identification.
Rule of thumb: ask "wrong field, wrong definition, or wrong time?" Before blaming the reporting tool, identify which quality dimension is violated, which steward owns that data domain, and whether a terminology mapping is missing. Most analytics failures are definition and mapping problems, not tooling problems.