How do I analyse unstructured data?

AI techniques: NLP for text, computer vision for images, speech recognition for audio. Vector databases enable semantic search. LLMs can summarise, extract, and answer questions about unstructured content.

Can I query unstructured data like a database?

Not directly with SQL, but: full-text search, vector similarity search, or AI extraction can enable queries. RAG systems let you ask questions and get answers from document collections.

What is semi-structured data?

Between structured and unstructured: has some organisation but flexible schema. JSON, XML, logs are examples. Easier than unstructured but more flexible than rigid tables.

How much business data is unstructured?

Commonly cited as 80% of enterprise data. Includes: emails, documents, images, recordings. Much of this data goes unanalysed - AI is unlocking its value.

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Unstructured Data

Data without a predefined format or schema, such as text documents, images, audio, and video.

In-Depth Explanation

Unstructured data lacks a predefined data model or organised structure. It includes text, images, audio, video, and other formats that don't fit neatly into tables and databases.

Types of unstructured data:

Text: Emails, documents, social media, chat logs
Images: Photos, scans, diagrams
Audio: Calls, voice messages, podcasts
Video: Recordings, surveillance, meetings
Rich documents: PDFs with mixed content

Challenges:

No standardised schema
Difficult to search and query traditionally
Requires AI/ML for meaningful analysis
Storage and processing at scale
Quality and relevance assessment

AI techniques for unstructured data:

NLP for text analysis
Computer vision for images
Speech recognition for audio
Multimodal models for combined types
Vector embeddings for semantic search

Business Context

Unstructured data contains valuable insights (customer sentiment, visual defects, spoken feedback) but requires AI to unlock at scale.

How Clever Ops Uses This

We help Australian businesses unlock value from unstructured data using AI - turning documents, conversations, and images into actionable insights.

Example Use Case

"Analysing thousands of customer support emails to identify common issues, sentiment trends, and improvement opportunities."

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