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Why You Should Use Intelligent Data Extraction

Learn how businesses use intelligent data extraction to classify, sort, and process data at scale with greater speed and accuracy.

Why You Should Use Intelligent Data Extraction

What are the Benefits of Intelligent Data Extraction?

Intelligent data extraction can help optimize workflows and boost efficiency for many different companies across industries. By taking advantage of automation, organizations not only free up their employees to work on other areas, but they can extract critical information far faster and with greater accuracy than by the equivalent manual processes.

As an example, intelligent data extraction is widely used in industries such as healthcare. Within healthcare, many important systems may still use paper-based documents. Without automation systems in place, these documents may need to be manually transcribed, which takes time and potentially introduces errors into the system in the form of transcription errors and other mistakes.

Intelligent document processing automates much of the process, increasing workflow efficiency while also reducing the error rate. Optical character recognition (OCR) technology is used to scan and extract text from documents, which can then automatically identify and extract the key data needed from within the document. In a healthcare system, this can be used to automatically identify prescriptions, patient transfer forms, physician notes and more. This works whether the text is handwritten or typed, saving both time and money over manual processes.

Why is Data Extraction so Difficult Without AI?

Artificial intelligence (AI) is not necessary in order to automate data extraction. It’s certainly possible to run ‘dumb’ data extraction across documents, but there are a number of issues that present a significant challenge in doing so. Without AI, dumb processes are generally fairly rigid - they depend on a consistent data structure or look for specific, hard-coded keywords to extract. Unstructured data presents a challenge, as do synonyms or misspellings. 

Due to these inconsistencies, errors are much more frequent and much more time needs to be dedicated to validation in order to catch these issues before the data is propagated into the wider system. The digital transformation of paper-based systems may also require training staff on new processes, which can be completely bypassed with automated systems.

What is the Best Way to Automate Data Extraction?

Intelligent data extraction represents a digital transformation in how documents are consumed by businesses. There are a number of solutions to automate this process, but one such way is to combine OCR technology with text classification and named entity recognition. These technologies allow for paper documents to be scanned and have the content understood in a digital format. Both text classification and named entity recognition are forms of intelligent data extraction and are used to recognize the type of document it is (e.g an invoice or purchase order) and then be able to automatically pull the key information (such as a list of the items ordered and their price). This can then be fed into other automated systems to quickly and efficiently handle paper-based processes in an automated manner.

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How is Automated Data Extraction Being Used?

Many different industries are already using traditional OCR technology and automated data extraction to deal with document analysis, data extraction, validation, processing, and more wide-ranging functions. These include banking and finance, healthcare, legal professionals and plenty more. Below are some concrete real-world use cases of how businesses across industries are utilizing this technology to improve operational efficiency and cut costs on manual document processing.


While much of banking and finance has been moving towards paperless systems, there are nevertheless still a lot of systems in place in finance that make use of paper-based documents. Invoices, purchase orders, loan and mortgage application forms, as well as accompanying documents, are all examples of paper-based documents that must be processed every day by banks at scale. In order to meet this demand in a cost-efficient manner, automated data extraction is used to identify and categorize documents, as well as extract key data from them that can be digitized and used within a bank’s wider electronic systems.

Legal services

The legal services industry is incredibly document-driven, with many of these still being printed on paper. Everything from depositions to court summons, warrants, litigation filings, court orders, and contracts are printed on paper, but all need to be kept track of. This is a monumental amount of paperwork generated on a daily basis that must be accounted for. Intelligent data extraction represents a digital revolution in how this mountain of paperwork is sorted, analyzed, and stored. 

Supply Chain Services

Supply chain management is another industry that generates large volumes of paperwork, with much of it being unstructured or sorted into many different file formats. Everything from invoices to purchase orders and customs declaration forms needs to be handled, with different clients and countries providing their own formats for these documents. Intelligent data extraction is used to quickly and easily categorize and process these documents without requiring the manual homogenization of many different formats for invoices or purchase orders.

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