Unstructured data

Unstructured data is information that does not conform to a predefined schema or tabular format, making it impossible to query directly with standard relational database tools without first applying extraction or transformation processes. It includes text (customer reviews, support tickets, product descriptions, contracts, emails), images (product photos, scanned documents, user-generated content), audio (call center recordings, voice search queries), video (in-store surveillance, product demonstration clips), and semi-structured formats like JSON logs or HTML pages that have internal structure but no fixed schema. Unstructured data constitutes the majority of all data generated — estimates commonly place it at 80–90% of total enterprise data volume.

In commerce, unstructured data contains some of the highest-value signals available: a customer's review explaining why a product disappointed them, a support chat transcript revealing a recurring fulfillment failure, a product image from which dimensions and materials can be inferred, or a voice query revealing purchase intent that a category browse would never surface. Historically, this data was largely ignored because it required expensive manual processing to interpret. Large language models and multimodal AI have dramatically changed this calculus: modern NLP models can classify, summarize, and extract structured information from text at scale; computer vision models can tag and search product images automatically; speech-to-text systems transcribe call recordings for sentiment analysis. Organizations that unlock their unstructured data assets gain access to a category of insight that competitors relying on structured data alone simply cannot replicate.