Data extraction techniques, methods, and tools: a complete guide

Introduction

Automating data extraction marks the difference between what a business can accomplish with a variety of operational tasks. 

As we work with vast amounts of data, as well as generate, process and store different data formats, a core need of a digitized business revolves around using data extraction efficiently.

In fact, data extraction is the first step for operational excellence, granting the recognition and the pull of data in different formats.

This unlocks a proper distribution of the information, which entails a smoother decision making, other than the minimization of errors in critical operations like financial tasks and accounts payable activities.  

This guide covers key data extraction techniques, explores industry challenges, and highlights the best tools available to start leveraging data extraction.

What is data extraction?

Data extraction is the process of collecting and organizing information from various sources, including structured databases, semi-structured files (XML, JSON), and unstructured formats (scanned documents, emails, handwritten notes).

It serves as the foundation for data processing and analysis, as well as to empower document processing operations in industries like finance, e-commerce, logistics and healthcare.

With advancements in AI and automation, businesses can now extract data more accurately and efficiently than ever before: modern software rely on robust data extraction techniques to streamline operations, reduce errors, and ensure compliance with regulatory standards.

It’s important to note that the application of data extraction works at scale: small businesses can increase their operational accuracy and save resources, just like big corporations need legacy systems that can recognize and pull data in bulk.

The importance of data extraction

Effective data extraction plays a crucial role in streamlining business operations and enhancing decision-making. Some key benefits include:

• Automation and efficiency – reduces manual data entry, saving time and resources.
• Accuracy and compliance – ensures data integrity and regulatory adherence.
• Business intelligence – provides insights for data-driven decision-making.
• Integration with digital tools – facilitates seamless workflows across CRMs, ERPs, and cloud applications.

What are data extraction tools capable of?

We can dissect data extraction functions in its various techniques and areas of application.

Optical character recognition (OCR)

OCR technology converts printed or handwritten text into machine-readable formats, making it easier to process and analyze unstructured data from physical and digital documents.

An industry use case – Accounts Payable (AP) automation in financial services:

Banks and financial institutions handle thousands of invoices, contracts, and compliance documents daily, and OCR enables them to automatically scan and extract data from supplier invoices, reducing the need for manual entry and improving processing speed.

For instance, a multinational bank can use OCR to extract invoice numbers, supplier details, and tax amounts, integrating this data into their ERP system for automated payments and compliance checks.

If you want to check the most suitable OCR tool for your business, we have created a complete guide here.

Natural language processing (NLP)

NLP allows AI to understand, classify, and process text from complex documents, enabling automated categorization, entity recognition, and intent extraction.

An industry use case – Insurance claim processing:

Insurance companies receive a high volume of claim documents with varying formats and terminologies, and NLP helps in identifying and extract key policy details, claimant names, and incident descriptions, categorizing them for faster approval.

For example, a health insurance provider can automate claim assessments by using NLP to extract ICD-10 medical codes and match them with policy coverage terms, significantly reducing claim processing time from weeks to hours.

Machine learning algorithms

Machine learning- based software enhances accuracy by learning from past data extractions, adapting to different document layouts, and improving data validation over time.

An industry use case – Fraud detection in fintech:

Fintech companies process a high volume of financial transactions and documents, making fraud detection a priority. ML algorithms analyze patterns in financial statements, identifying anomalies that indicate potential fraud.
So, for instance, a digital lending platform uses ML to compare submitted bank statements with historical data, flagging inconsistencies that may indicate document tampering or fraudulent applications.

Automated data structuring

This process extracts relevant information, categorizes it, and distributes it into predefined fields such as invoice numbers, dates, customer names, and financial values.

An industry use case – Document processing for Online Travel Agencies (OTAs):

Travel agencies manage invoices from multiple airlines, hotels, and transport providers. Automated data structuring allows for seamless reconciliation of payments and expenses.

If we think of an OTA integrating an AI-driven system, data extraction would be automatic for data like booking IDs, customer names, and total amounts from various invoices, categorizing them into its financial database without manual input.

Integration capabilities

The integrability level of a data extraction software is its set of joints: the more it can connect with other systems, the more flexible it will be.

Seamless integration with ERPs, CRMs, and accounting software ensures that extracted data flows directly into existing business systems.

An industry use case – Financial reporting for enterprise accounting firms:
Accounting firms require data from multiple sources for audit and reporting. Integration capabilities enable them to pull structured financial data into their enterprise resource planning (ERP) software.
Thus, an accounting firm can connect an OCR-powered document processing tool to its ERPs automatically syncing extracted tax and expense data with its client financial reports.

With Procys, you can create custom integration with extreme ease and flexibility. Alternatively, users can leverage a wide array of pre-set integrations.

API-based extractions

API-based extractions allow businesses to customize how data extraction tools interact with their proprietary workflows or third-party platforms.

An industry use case – Real-time policy underwriting in insurance:

Let’s picture an insurance firm that needs to assess applicant information quickly for policy underwriting. API-based extractions allow the operational team to pull relevant customer data from government-issued documents, financial statements, and medical reports.

Business challenges of data extraction

Following up, let's identify which blockers are usually associated with data extraction and how to overcome them.

Handling unstructured data 

Extracting meaningful information from diverse formats is complex. For this, advanced software uses AI and ML-empowered OCR technology to be able to deal with different data formats.

Data security concerns 

Ensuring compliance with GDPR, HIPAA, and other security regulations is paramount, especially for industries where there’s sensitive data and financial numbers involved.

This is why modern tools must offer enterprise-grade security measures, encryption, and controlled access protocols into their AI-powered document processing and data extraction solutions.

Scalability issues 

Managing high volumes of data without performance degradation it’s easier said than done. Manual data entry or outdated OCR solutions struggle to scale efficiently, leading to delays, errors, and performance bottlenecks.

Both the auto-scaling enabled by intelligent, cloud-based systems and the integrability with other systems help with the expansion of the volume of operations and data extracted.

Integration with legacy systems

As we mentioned before, a system is as scalable as it can be flexible. Yet, let’s make an additional point here: a system must be malleable to adapt to specific business needs, so to become truly useful.

The compatibility with other ERPs and especially outdated enterprise applications it’s a challenge that can slow down the adoption of these accelerating technologies.

For this, businesses need software that can be adapted with pre-set connections, ad-hoc integrations and tailored APIs.

You can try out the data extraction system that responds efficiently to all of these challenges from this page.

Exploring data extraction tools

Several tools simplify data extraction, leveraging automation, AI, and integration capabilities.

We have created a thorough analysis in this article, identifying some of the most relevant tools in the game, and categorizing them by their prices, integrations, core functions and other criteria.

Nonetheless, here are some leading solutions, with a breakdown on how they apply data extraction and to which level of tech expertise they correspond.

Procys

Procys is an AI-powered document processing solution designed to accelerate data extraction and workflow automation, making it up to six times faster than manual processing.

With advanced OCR and machine learning capabilities, Procys ensures seamless data capture, transformation, and integration—whether the data is stored for reference or transferred to other systems.

This makes it an ideal solution for teams handling diverse document types, including invoices, receipts, vendor and client records, product catalogs, contracts, and official reports—streamlining operations with precision and efficiency.

Main features

• AI-powered optical character recognition (OCR) and natural language processing (NLP) for intelligent document processing.
• Seamless integration with QuickBooks, FreshBooks, Microsoft Dynamics, and Zoho CRM, ensuring smooth workflow connectivity.
• Machine learning-enhanced accuracy, continuously improving data extraction results.
• Secure, cloud-based storage for reliable data management and compliance.
• API and automation capabilities to optimize workflows and streamline document processing.

Best suited for

• Beginner-level users (Non-Technical)
  
  
  
  • Small business owners, accountants, and finance teams who need an easy-to-use document automation tool.
  • Users looking for a no-code solution that extracts, structures, and integrates data seamlessly.
• Intermediate users (business and IT professionals)
  
  
  
  • Finance, operations, and accounting teams who need configurable document processing workflows.
  • IT managers who want to automate document intake without heavy development work.
  • Businesses that need direct integration with ERP or CRM systems via low-code platforms like Zapier.
• Advanced users (developers and enterprises)
  
  
  
  • Companies requiring custom API integrations for high-volume data extraction.
  • Large enterprises with complex workflows that demand deep ERP/CRM system integration.
  • Fintech and InsurTech developers looking for AI-powered document processing with programmatic control over extraction logic.

Airtable

Airtable provides several methods for data extraction:​

• AI-Powered summarization, to summarize and extract insights from your data, assisting in tasks like summarizing meeting notes or feedback.
  
  
• Formula functions, it supports functions to extract specific text from fields, like a certain number of characters starting at a specific position within a string.
  
  
• Third-Party extensions, which enable exporting selected data to formats like JSON, XML, CSV, SQL insert queries, or ASCII tables directly from a database.

Tech expertise required

Beginner - intermediate

• No coding knowledge is required for basic data extraction using formulas.
• Intermediate users can leverage automation workflows and integrations with third-party tools like Zapier for enhanced data structuring.
• Developers can use Airtable’s API to extract and process data programmatically.

Best suited for

• Business professionals who want an easy-to-use tool for data management.
• SMEs looking to extract and organize structured data without technical complexity.

Monday.com

Monday.com offers data extraction capabilities through various features:

• Extract app, which automatically extracts content from board updates, such as emails and sender names, and adds them to your board items, streamlining workflows without manual data entry.
  
  
• API access, using APIs that allow users to extract data programmatically. For example, users can employ an API to extract data and use it in tools like Power BI for reporting purposes.
  
  
• Data export, so that users can export their entire account's data, including shareable and private boards, into a .zip file.

Tech expertise required

Beginner - advanced

• Non-technical users can perform basic exports and use built-in automation tools.
• Intermediate users can configure Extract App to automate data structuring.
• Advanced users (developers) can use Monday.com's GraphQL API to extract and manipulate data in real-time.

Best suited for

• Project managers and business teams who need an easy-to-use automation tool.
• Tech-savvy teams who want to extract and analyze data through API calls.
• Organizations that want to integrate monday.com with external reporting tools like Power BI.

AWS S3

Amazon S3 (Simple Storage Service) facilitates data extraction through several features:​

• S3 Select, which allows to retrieve a subset of data from an object using simple SQL expressions, enabling efficient data processing without the need to download entire objects.
  
  
• Integration with other AWS services, like AWS Lambda and Amazon Athena to automate data extraction and processing workflows.

Tech expertise required

Intermediate - advanced

• Requires knowledge of SQL for querying data via S3 Select.
• Intermediate users can configure AWS Lambda to automate data extraction workflows.
• Advanced users (DevOps, Data Engineers) can integrate S3 with other AWS services (Athena, Glue, Redshift) for large-scale data processing.

Best suited for

• Data engineers and cloud architects working with large datasets.
• Enterprises that need scalable and automated data extraction from stored files.
• Developers working with AI/ML models that require efficient data retrieval.

Holded

Holded incorporates data extraction functionalities to enhance business processes, including:

• AI-Powered extraction, to automatically input data from receipts, invoices, and other documents, streamlining data entry processes.
  
  
• Reporting and analytics, to generate real-time reports using the data entered into the system, providing up-to-date insights into various business operations.

Tech expertise required

Beginner 

• No technical expertise is required for standard data extraction.
• Holded’s AI automates data entry without manual setup.
• Basic navigation skills are needed for exporting and analyzing reports.

Best suited for

• Small business owners and finance teams looking for an automated accounting solution.
• Non-technical users who need AI-driven data extraction without coding.
• Accounting professionals managing financial data and reports.

Conclusion

Data extraction is a fundamental process for businesses seeking automation, accuracy, and compliance.

Organizations must assess their specific challenges and choose tools that streamline workflows, reduce manual intervention, and enhance decision-making. As AI and automation continue to evolve, the future of data extraction promises greater efficiency, accuracy, and scalability.

If you want to try how an advanced data extraction software works, you can register for free with Procys at this page.