How critical is the Automation of Extraction from Documents
Ever thought about how long it takes to process incoming documents manually?
Our back-offices are impeded with such legwork slowing down processes. Automating these documents using today's technology can be extremely helpful to accelerate business processes, therefore, enabling companies to automate much higher levels of advanced data processing.
For starters, what is data extraction?
The retrieval of structured/unstructured data from documents that would further be processed or used as storage is termed data extraction.
Now, why is this important?
With the help of technology, any documents can be converted into structured data using OCR, artificial intelligence and automation. The quality of document extraction is the biggest challenge in automation.
Machines typically process structured data. For instance, once the invoice is registered into a systematically configured ERP tool like SAP, the completion of payments can be automated, and records can be prepared automatically. What is time consuming here is entering data from the invoice into the ERP. This is where data extraction and automated data input to the system comes into play.
Automation of data extraction always remains a significant obstacle to back-end offices since manually retrieving data is time consuming and error prone.
Since the cost of data extraction is prohibitive, most businesses/organizations choose to extract only specific fields.
Because of the restrictive cost of data extraction, organizations extract only required fields from documents, contributing to just a small percentage of the total information available in the document. This limited information permits automation of the most vital business processes, for example, invoice processing. Nevertheless, other essential functions such as compliance validations, reconciliation of accounts, or VAT remain hand-operated as the required data may not extracted from these documents.
Extracting data manually or using template-based solutions are the most extensively used substitutes. With the help of template-based solutions, organizations can formulate templates that pull data from documents using a particular format. Taking into consideration the extensive categories of documents, achieving higher automation rates is quite back-breaking. On the other hand, manually processing data has its imperfections as it is costly, slow, fallacious, and overwhelming.
Over the years, with the rise in technology, the quality of automated data extraction has grown significantly, making it a preferred option compared to template-based or manual data extraction.
All in all, with this relentless drift, businesses need to automate data extraction to obtain the advantages of automated data extraction from documents and facilitate elevated automation in the processing of documents.