What is semi-structured data? A comprehensive guide

Every-day business documents like invoices, Purchase Orders, insurance claims, delivery notes, customer forms, contracts, and more can contain semi-structured data.

In fact, organizations rely on this type of information to run financial, operational, and compliance-critical workflows. However, because semi-structured data doesn’t follow a rigid predefined model, it is notoriously difficult to handle them with traditional software or manual processes.

In this guide, we break down what semi-structured data is, why it matters, where it appears in real business workflows, and how modern AI solutions make it easier to extract, validate, and operationalize at scale.

What is semi-structured data?

Semi-structured data is information that does not follow a strict, fixed database schema but still contains organizational elements, such as tags, separators, key-value pairs, or predictable patterns that make it more structured than free-form text.

Positioned between structured data (like ERP databases) and unstructured data (like plain text emails or images), semi-structured data is flexible for humans but difficult for traditional rule-based systems or basic OCR tools to interpret consistently at scale.

They may include:

• Irregular formatting: fields appear in different places or layouts depending on the document source.
• Variable schema: information exists, but its structure changes across templates or vendors.
• Human-readable design: documents are built for people, not machines.
• Presence of identifiers: labels such as “invoice number,” tags like <amount>, or consistent separators that AI can learn from.

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Examples of semi-structured data

Semi-structured data may appear across almost every department and industry, especially in organizations dealing with finance, operations, logistics, customer management, and compliance-heavy workflows.

Unlike structured databases, these documents come in many layouts, formats, and templates, but still contain identifiable elements that AI can extract.

Below are the most common and business-critical examples.

Invoice documents

Invoices are one of the most widespread forms of semi-structured data.

Each supplier uses a different layout, logo placement, field order, and line-item structure, but the core information remains consistent (invoice number, issue date, total amount, VAT, line items, supplier details).

Thus, modern organizations look for precise invoice data extraction tools that can scan through unstructured or semi-structured data without losing efficiency.

Purchase Orders

Purchase Orders follow similar logic to invoices.

They include recognizable elements like order numbers, item descriptions, quantities, and delivery dates, but formats vary across vendors, ERPs, and regions.

This makes Purchase Order data extraction potentially unreliable and slow, unless powered by precise, AI-based systems.

Receipts and Point-Of-Sale outputs

Receipts generated by POS systems, restaurants, hotels, or retail stores also count as semi-structured data.

They contain transaction details, taxes, payment methods, and timestamps, but layouts differ significantly depending on the provider or country.

Even here, accounting and financial departments usually suffer from lack of efficiency and look to up their receipt data extraction game. 

Shipping documents

Logistics workflows rely heavily on semi-structured formats such as:

• Bills of lading
• Packing lists
• Delivery notes
• Customs documents

These files are highly regulated but rarely standardized, making manual processing error-prone, one of the biggest challenges for freight and supply chain operators .

Financial statements and reports

Bank statements, account summaries, card transaction reports, and reconciliation statements contain repeating elements, but their structure changes by bank, region, or system.

If you’re a business manager, CFO, COO, or CTO of a small-medium business, you should be prepared to manage huge document volumes, face strict regulatory pressure, and depend on accurate data for reporting, customer experience, and decision-making.

See how easy it can be to reach top precision in document management and data extraction by trying Procys for free.

Customer-facing forms

Web forms, insurance claim forms, application forms, surveys, and rental agreements usually contain labeled fields combined with free text. They are semi-structured because the structure exists, but is not fully standardized across providers.

Machine-generated documents

Files like XML, JSON, and some PDF exports include tags or hierarchical data, making them semi-structured. They are structured enough for AI to process, yet flexible enough for businesses to customize.

Looking to swap file type while maintaining consistency? Try the free converters from PDF to Excel and from PDF to OCR.

Are semi-structured data better than other data formats?

Semi-structured data can unlock significant business value when organizations have the right tools to extract and operationalize it.

Instead of treating these documents as a source of manual work, companies can transform them into a strategic asset that fuels automation, analytics, and real-time decision-making.

Greater flexibility than structured data

Semi-structured data adapts to different formats, vendors, and systems, which makes it easier for organizations to exchange information without fully standardizing every document.

This flexibility supports workflows where hundreds or thousands of suppliers and, therefore, thousands of templates are involved. 

Enhanced automation opportunities

Semi-structured data is the foundation for automating entire processes, not just data extraction.

After the data extraction process, the information can trigger workflows in AP automation, AR automation, procurement, logistics, and more. 

Fragmentation is more common

Semi-structured data often lives across email inboxes, shared drives, PDF folders, and disparate systems. This fragmentation prevents organizations from achieving:

• Unified financial insights
• Real-time reporting
• Smooth AP/AR workflows
• Consolidated vendor management
• Central audit trails

Structured data, meanwhile, has already passed this step and lives in more unified, or even more accessible places.

Inconsistent formats across vendors and systems

If not organized, semi-structured documents rarely follow one standard layout. Invoices, POs, receipts, statements, and shipping forms can - among other things - vary by:

• Vendor
• Country and tax regime
• Document version
• Internal department workflows

One main goal for Intelligent Document Processing (IDP) platforms here is to organize this information so users can exploit automation capabilities and minimize manual work.

Difficulty integrating with legacy systems

Many organizations still rely on older ERPs, accounting software, or POS systems that cannot natively handle semi-structured formats. This creates costly integration gaps, forcing teams to switch between tools or rely on spreadsheets- one of the key challenges identified for retail, hospitality, and logistics operations that depend on multiple disconnected systems .

Lack of accuracy with traditional OCR tools

When managing semi-structured data, basic OCR solutions struggle with:

• Non-standard layouts
• Low-quality scans
• Images or photos
• Multilingual documents
• Handwritten notes
• Misaligned fields
• Mixed templates

How AI extract and work with semi-structured data

Traditional OCR systems rely on fixed templates and rigid rules, making them unreliable for real-world documents that vary across suppliers, formats, and regions.

Modern AI and intelligent document processing solutions solve this challenge by learning patterns, structures, and relationships within semi-structured data, regardless of layout.

Below is a breakdown of how advanced AI-powered extraction works.

Good to know

Because semi-structured data contains signals that AI can interpret, businesses can drastically reduce manual data entry. This leads to:

• Fewer errors in invoice processing
• Cleaner financial records
• Faster AP/AR reconciliation
• Improved compliance reporting

Optical Character Recognition (OCR) enhanced by machine learning

The best OCR software is AI-driven and goes beyond simple text detection.

Using Machine Learning, systems like this can:

• Recognize text in multiple languages
• Detect fonts, stamps, tables, and line items
• Interpret low-quality scans and photos
• Identify fields even when their positions change

Natural Language Processing (NLP) for understanding context

NLP helps AI understand what each extracted value represents.

For example, let’s look at situations where different values all represent the same field:

• “Invoice number,” “factura nº,” and “inv. no.”
• “Due date” vs. “payment deadline” vs. “fecha de vencimiento”
• “VAT” vs, “IVA”

This contextual intelligence is essential for companies working across multiple markets and languages.

Continuous learning and improvement

AI models refine accuracy over time by learning from user corrections and repeated patterns. The more documents the system processes, the better it becomes (unlike template-based OCR, which stagnates).

This is one of the reasons organizations shift from competitors’ rigid solutions to more modern, AI-first IDP platforms, as reflected in your marketing priorities and USP research 

Integrations with existing systems

AI-based data extraction software must rely on integrations to operate with modern businesses.

A powerful Intelligent Document Processing system can usually integrate with:

• Accounting software 
• ERPs (Dynamics, Salesforce Commerce Cloud)
• CRMs
• Productivity and workflow automation tools

In this sense, flexible integrations reflect real-world needs from your partner and integration ecosystem, as documented in your platform materials.

Optimizing business operations with semi-structured data

When organizations can reliably extract, validate, and operationalize semi-structured data, they unlock measurable improvements across finance, operations, logistics, and customer-facing workflows. 

Thinking about the big picture for your business, you can achieve:

• Real-time cash flow forecasting
• Inventory and supply chain optimization
• Monitoring of vendor performance
• Detection of anomalies or potential fraud

Try Procys for free and work with semi-structured data efficiently and with precision, no credit card required!