Data Extraction: First Step to Automated Document Processing

A significant portion of back-office tasks involves processing incoming paperwork, which can be automated with the help of modern technology. Automated document processing is the first step in this direction. Data extraction is necessary for document processing and gets better as more and better data are extracted. Therefore, improved document data extraction enables businesses to automate more sophisticated processing at higher levels.

‍What is Data Extraction?

Retrieving information from documents and other data sources is known as data extraction. A slightly more formal definition may be found on Wikipedia, which states that “data extraction is the act or process of obtaining data out of (often unstructured or badly constructed) data sources for further data processing or data storage.”

Why is Data Extraction Important?

Most documents can now be processed automatically if they are transformed into structured data, thanks to technological advancements. Therefore, the biggest barrier to automating back-office tasks worth trillions of dollars is the quality of data extraction.

In a back office, structured data is often processed by machines. For instance, payments can be made immediately, and system records can be created after an invoice is put into a well-configured Enterprise Resource Planning (ERP) application like SAP.

Back-office automation is still hampered by data extraction because manual data extraction is insufficient. Companies only extract important fields from documents due to the high cost of data extraction, capturing a small percentage of the entire amount of information included in documents. Automated Document Processing is one of the most important operations, such as payment in the event of an invoice, which is made possible by little information. However, since the required data is not extracted from documents, other crucial operations like VAT compliance checks or account prediction continue to be done manually.‍

What are the Types of Data Extraction Techniques?

The two methods for extracting data are logical extraction and physical extraction

1. Logical Extraction

The method that is most frequently utilized is logical extraction. There are two subtypes of it

• Full Extraction: All data is fully extracted simultaneously without the need for additional (technological) knowledge. When the data is extracted and loaded for the first time, full extraction is the technique employed. It reflects the information that is currently available in the source system.
• Incremental Extraction: The source data changes that have occurred since the last successful data extraction (identified by a timestamp) are tracked. Then, incremental loads and extractions of these changes are made.

2. Physical Extraction

Applying physical extraction techniques is the only approach to obtain this data if logical extraction methods are impossible to use to extract data from restricted or expired data storage systems. Physical extraction is divided into two categories

• Online Extraction: The final archive and the source system are directly connected. When employing the online extraction method, the extracted data is more structured than the original data.

Offline Extraction: The actual data extraction happens away from the system of origin. The data in offline extraction operations are either already structured or will be structured using extraction techniques.

How to choose an Automated Data Extraction Solution that Complies with your Company’s Needs?

When choosing a data extraction solution for your organization, you should be cautious about the features that various platforms offer because what might work for one corporation might not work for another. Therefore, when making a purchase selection, you must keep the following factors in mind

• Intelligent Data Capturing

The data extraction tool must be able to extract data from various document kinds, including contracts, delivery notes, accounts payable, and more, without losing any information and classify them according to their corresponding designs

• Accuracy in Results

Businesses prefer a data extraction tool that produces quick results, but it also needs a high accuracy level. The information must remain in the extracted output, and the tool must be able to extract tables, typefaces, and essential parameters without affecting the layout

• Storage Options

Select a data extraction platform that provides seamless backup choices and secure storage. Thanks to cloud-based extraction, you may quickly and whenever you want to extract data from websites

Data about a single computer can be quickly extracted using cloud servers. The speed of automated web data extraction influences how quickly you can respond to sudden developments that impact your business

• Simplistic UI and Robust Features 

Advanced automatic data extraction software needs a straightforward user interface. The software interface’s design must be straightforward enough to guide you through carrying out a laborious operation at launch. The platform must not only offer a simple UI experience but must also maintain all necessary functionalities

• Price

Pricing is a thoughtful concern, even though it may not be the most important one. Purchasing expensive software with capabilities that do not pertain to your business or picking the incorrect pricing structure might not be the best action. While ensuring the price is within your budget, consider examining the software’s features.

How to Automate Data Extraction at your Company?

The requirement for automating data extraction is clear given the availability of high-performance technologies and the potential advantages of automation. The majority of large businesses, however, deal with hundreds of different forms, so it’s critical to decide which procedures to automate first. An initial data extraction project that yields significant results quickly can persuade management to automate other procedures and lead to an improvement in business productivity

The best place to start automating is with large volume, complicated documents that require the most sophisticated processing stages and are available as off-the-shelf solutions. Formally speaking, the metrics to focus on are

• The current cost of data extraction: It depends on the volume and complexity of the document, though it can be difficult to determine precisely. You may estimate the most expensive documents for your firm based just on those two indicators.
• The current cost of advanced document processing: What follows the data extraction for your team? Data extraction followed by additional manual document processing stages is a strong indicator of how much time was spent on advanced document processing. For instance, after thorough data extraction, the normal processing stages for invoices include checking for VAT compliance and projecting accounts for invoices that cannot be matched to POs.
• Availability of data extraction solutions: There is probably a solution for that document if it is one that practically every organization receives, such as a receipt, invoice, order, pay slip, etc. For each of these groups, we have solutions. At the same time, we collaborate with businesses to create individual machine learning models for their documents when we do not yet have a solution. 

Final Words

Data extraction is a critical step in automating the collection of structured data so that it can be used for additional analysis. Make sure the automated data extraction solution your company plans to deploy is flexible enough to adapt to your use case and significantly influence workflow.

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