Table I.

Characteristics and examples of unstructured and structured data

	Unstructured data	structured data
Characteristics	Difficult to query and search Cannot be easily classified or formatted From heterogenous sources (eg, photographs, videos, free-form text, scanned documents, e-mails) Reside in multiple applications and warehouses in heterogenous formats Apt target for analysis with big data technologies (eg, natural language processing) to understand patterns and behaviors of data The vast majority of extant clinical information	Highly organized and easier to query and Search Ideally resides in data warehouses and typically formatted in tables Usually managed with structured query language Usually text or numbers format
Examples	Chief complain and subjective concerns of the patient Free-form narrative physician’s notes Subjective treatment response Communication with patients (eg, e-mails, phone calls) Photographs of patient disease Descriptive biopsy results	Demographic information Diagnostic and procedural codes Medications and doses Standardized disease metrics and symptom scales Standardized clinical photographs with associated metadata Numeric laboratory test results Data from wearable devices Metadata about features in a clinical photograph or narrative block of text