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What is data extraction? Everything you need to know

The key role of extracting data from documents and how automating this reduces cost, increases productivity and boosts customer satisfaction & revenue

Jan 29, 2024
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All organizations rely on certain data to run their business effectively. This data appears in many forms - be it structured or unstructured, physical documents or digital documents, native PDFs or scans.

Without data extraction, this information is ‘trapped’ and can’t deliver value.

In this blog we examine the essential role of extracting data from documents, highlight several extraction techniques and outline how automating this, reduces cost, increases productivity, boosts customer satisfaction and even increases revenue.

What is data extraction or data capture?

Data extraction is defined as a process or system for recognizing meaningful data in documents and extracting it so it can be stored and used effectively. Data extraction can be done in three different ways. (1) Manually by a data entry operator, (2) fully automated by using software or by (3) a human in the loop (HITL), which is a combination of the first two.

Why is data extraction important?

Data extraction whether done manually or automated is crucial to keep certain processes (such as invoicing) running. But it can also be the driver of benefits that are unreaped because this knowledge is stuck in documents.

  • Operational efficiency: Especially when more people are involved in a certain business process, extracting data can be responsible for triggering a further action that is necessary to complete a process.

    For example, when you’re receiving an invoice in a somewhat larger company and you don’t fill out the details in the company’s accounting system, the invoice - which is processed by another department - might never be paid. Especially when more stakeholders are involved in a process, data capture helps in streamlining the day-to-day operations.
  • Informed decision-making: By distilling voluminous and complex information into accessible formats, organizations can extract valuable insights, empowering decision-makers with the knowledge needed to steer the company in the right direction.

    Consider a shipbroker. As a broker you’re the middleman between oil producers (e.g. Shell, Cargill & BP) and shipowners (Maersk Tankers). The shipbroker receives tons of emails per day from shipowners, containing unstructured market insights about their fleet. Capturing information inside these emails and placing it in a central database, will give them an accurate overview which is necessary to make the right deal when Shell or BP are contacting the broker.
  • Regulatory compliance: With a simple method of routinely capturing and handling information from documents, your organization can easily implement compliance measurements.

Advantages of automating data extraction

Some major benefits of automating data extraction include (1) cost reduction, (2) faster turnaround times, and (3) scalability.

(1) Cost reduction. As many organizations must handle significant volumes of documents, this might be a costly process if done manually. An automated data entry solution can minimize human intervention and significantly decrease the operational cost of processes.

(2) Faster turnaround times. This aspect carries the potential for multiple positive outcomes and cannot be overstated. Consider, for instance, the impact on customer satisfaction, especially for industries like insurance. The swifter a claim can be processed and reimbursed, the more positive the customer experience, resulting in happy customers. On the flip side, accelerated processes also have the power to increase revenue. In the case of these same insurers, the ability to attract new customers becomes a tangible outcome, as the promise of swift and efficient services is a USP used in growing their customer base.

(3) Scalability (or increased productivity). When a company is growing, the number of documents from which information should be retrieved is obviously also increasing. In times of labor shortage and an aging population, it can be difficult to expand your team against reasonable cost. Automating data capture will help increase productivity per employee, allowing the company to execute more work without necessarily growing their current employee base.

Types of data (defining structured vs unstructured data)

Data in documents can be structured or unstructured, and cover many different domains. A single document might contain a mix of customer data, financial details, product information, order details, or other information like sensor measurements.

In each case, a reliable process is needed to extract and handle each type of data correctly.

Defining structured data vs. unstructured data

Structured data is the kind of data that comes from a datasource that already has a well-defined logical structure, making it suitable for extraction purposes. No preliminary adjustments or manipulations are required prior to initiating the data extraction process. Instances of such easily extractable formats include Excel, CSV and XML files.

Unstructured data represents 80 to 90% of all new enterprise data (according to Gartner). It is usually scattered amongst the “white noise” of non-useful data. Examples include e-mails, scans, images, social media posts, Teams conversations, physical documents. The reason more firms aren’t taking advantage of this great trove of information about their products or services is due to its very undefined, unformatted nature.

Data extraction techniques for Document Processing

Until today, manual methods were commonly used to extract data from unstructured data sources and put it into the right structured format. Now, AI-powered data extraction and other technologies can make this task more dependable and efficient, which enables fully automated document processing.

Manual data extraction

While manual data extraction can capture important text or other information from a small number of documents, this is not the best method when large volumes of documents need to be processed. There are two reasons for this: a high error rate, and a lack of speed.

A typical error rate for manual data entry is 1%. While many organizations don’t measure the cost of bad quality data, it’s clear that fat-finger errors can be very costly indeed.

Even the fastest typists in the world cannot compete with the speed (and accuracy) of modern data extraction software tools. The costs of slow, manual methods are increased by the invisible cost of slower processes, missed opportunities, and reduced innovation.

Technologies used for automated data extraction

There are several important technologies used to automatically extract data from documents. Optical Character Recognition (OCR) is the most important as it turns the text and numerical data from images (like scanned documents or photos) into machine-readable alphanumeric text.

Following data capture with OCR, there are a few different methods to correctly extract data from documents: rule based (REGEX), Templates and AI-based technologies.


Regular Expression (or REGEX) is a way to identify and capture specific patterns in textual data. This technique is often used in combination with OCR which converts images to text in the preceding step before applying REGEX. REGEX works well in certain situations, for instance when the raw data that you try to capture is uniform, and always follows a specific pattern. REGEX can quite easily identify invoice numbers, email-addresses, phone numbers, etc.

Depending on the use case one might also use REGEX to capture addresses in data. However, the difficulty lies in the diversity of address formats. Addresses can vary across regions and countries, and local conventions may introduce complexities. Crafting a comprehensive REGEX pattern that accommodates all possible variations can be challenging. It often requires iterative testing and refinement based on the specific dataset or application.

Template-based data extraction

This method uses a clear template for extracting the important parts from repetitive and consistent documents. An example of this is when you receive invoices with identical format and layout each time. Templates become less useful when documents come in multiple formats/layouts.

AI-powered data extraction

This extraction technique goes beyond setting up simple extraction rules. AI-based document processing looks at the whole context of the textual data before extracting the relevant information. In doing so it often combines technologies like Machine Learning (ML), Natural Language Processing (NLP), and Optical Character Recognition (OCR). The output of AI models go hand in hand with accuracy scores, which gives some kind of guidance in the certainty of an extracted value. One might also apply business rules on top of these outcomes to assure correct data processing. It's the most efficient way to handle the processing of very unstructured data as it reduces errors and increases overall processing speed.

Learn how to extract data from documents to speed up your business processes

The reliability of an AI data extraction tool like Send AI enables organizations to process documents in a fully automated way.

With this setup, each scan or photo is converted into text by OCR, analyzed and extracted using AI such as machine learning (ML) and natural language processing (NLP), and the validated data is then fed directly into digital processes. With advanced tooling like this, a scanned invoice can become a scheduled payment within seconds of being received.

However, to unlock this level of productivity, you first need a clear plan for how you can accurately extract the data from your documents. This is highly dependent on your unique situation and processes, but we’re here to help.

Want to see this in action? Get in touch for a free demo of Send AI, and discover how automated data extraction can benefit your organization.

Ready to start automating your Document Processing flow?

At Send AI, we empower you to fine-tune your own language models. Are you eager to start speeding up your document processing flow while keeping error rates low?