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