Today’s enterprises produce a huge amount of documents. E.g. Vendors send invoices, and companies exchange purchase orders between departments. Companies sign contracts and keep them indefinitely. KYC forms are kept by banks, and bank statements are processed continuously. Many of the documents have critical business information in them, but most of this is unstructured, either in PDF format, scanners, e-mails and/or photographs. That is where Data Extraction Tools come into play to help convert the raw document into a structured format and provide businesses with usable and structured data to make decisions based upon and perform operations based upon that data.
What Is Data Extraction?
The use of automated data extraction to convert all unstructured and/or semi-structured documents into structured digital form allows companies to avoid the costs involved with manually entering the data into systems, thereby significantly improving speed and reducing error rates.
Manual data entry to capture data from the thousands or millions of documents processed by many companies each month creates a significant bottleneck in operations. The result is that businesses typically continue to use the same manual methods of entering data because there is no alternative available.
Common Enterprise Document Types
When businesses extract data from a variety of document types such as invoices, purchase orders, delivery receipts, contracts, KYC forms, bank statements, insurance claims, and regulatory filings, the data extraction tool must be able to accommodate the specific layout of each document type. Businesses will perform several data extraction processes for each document type in order to ensure proper compliance and consistency across all data entered into their systems.
Types of Data Extraction Tools
OCR-Based Extraction
“I OCR data extraction tools can take scanned images or PDFs and turn them into text that computers can read. However, traditional OCR software works well with clear, printed documents but does not handle complex layouts, tables, handwritten fields or low-quality scans very well. Because OCR only extracts text from the document and does not provide any context or meaning to the extracted text, most enterprises will also require additional logic to interpret the meaning of the OCR output.
Template-Based Extraction
Template-based systems extract data from documents by comparing those documents against a set of predefined layouts or templates. This type of product works well in scenarios where documents follow an established format (such as standard invoices from one company). However, these products do not work when the formats of the documents change, new vendors provide documents, or the documents are provided in an unexpected format. Template-based systems also require a significant amount of maintenance and are therefore not well-suited for use in dynamic enterprise environments.
AI/ML-Based Extraction (Intelligent Document Processing)
More and more enterprises are now using AI-based products for data extraction from documents, which have become commonly referred to as Intelligent Document Processing (IDP). IDP products leverage artificial intelligence (AI), combined with OCR, machine learning (ML), and natural language processing (NLP) to understand the contents of a document. As opposed to relying on rigid templates, IDP products use AI-based models to not only identify and extract relevant data, but also to learn patterns in each document type, to learn how to identify fields, extract tables, and adapt over time to changes in the data, thereby providing a better solution for large enterprises.”
What Enterprises Require from Extraction Tools
Enterprise document processing does not consist of reading text. Enterprises will look for high levels of accuracy, speed in which documents can be processed, and the ability to scale to support peak document volumes. The requirement for audit trails for regulatory compliance (in finance, banking, or other highly regulated industries) requires a viable audit trail. Security continues to be an important component; many documents include private or confidential financial information or personal information. Security measures need to include controlled access, encryption, and role-based permissions in order to protect this sensitive information.
A true enterprise-ready data extraction tool will meet all of these requirements without breaking existing workflows.
What Happens After Data Extraction?
Data extraction is just the first step in the process. After data has been extracted, most of the time, a validation process will take place to validate the extracted data and to review any exceptions to the validated data. Any extracted data that has been approved during the validation process will flow through the business processes, such as approvals, reconciliation, or compliance. Lastly, the data that is extracted will be integrated back into enterprise systems such as ERP, accounting software, CRM, or DMS. Without this end-to-end process, the process of extraction alone has limited value.
How to Evaluate Data Extraction Tools
Enterprises should not just evaluate basic OCR accuracy when assessing extraction solutions but also consider field-by-field accuracy (total, tax values, and dates) and table extraction capabilities (required for invoices, statements, and reports). Exception handling (no system is perfect) can be evaluated by an enterprise’s ability to efficiently process low-confidence fields. Enterprises should also evaluate the system’s ability to learn; as AI systems process documents, the systems should continuously improve the accuracy of document extraction.
Business Value of Data Extraction
The impact of automation on the business operation of an enterprise is enormous. Automated extraction process eliminates the need for manual entry of data, reduces the error rate associated with the inputting of data into a system, and speeds up the process of getting data into the respective systems. This allows team members to spend time analysing and making decisions rather than doing repetitive tasks. Faster turnarounds create better business partnerships, improved cash flow visibility, and improved compliance readiness. Over time, enterprises will benefit from better control, transparency, and scalability of their processes that are document-driven.
Real-World Example: Invoice Processing Flow
A common invoice processing workflow starts with an invoice being received either in PDF format or as a scanned image, through the use of an OCR tool, which reads the document and uses AI models to extract the vendor name, invoice number, invoice date(s), a breakdown of each line item on the invoice, and any taxes associated with it. After extraction, the data is validated, and any exceptions will be flagged and sent for approval. After approval, the information is automatically transferred to either an accounting system or ERP. Previously, this took days of labour to process; now it takes minutes, with improved accuracy.
Final Thoughts
As companies grow larger, they experience more incoming documents and greater document complexity. In addition, basic OCR or template-driven tools do not meet the needs of modern companies, which require intelligent, adaptive, and secure data extraction methods that integrate into the enterprise document workflow processes and systems.
Snoh Fusion (artificial intelligence-powered IDP) can automate the extraction of data from the invoice-to-data end-to-end and convert every document into structured and reliable data for the company at scale.
FAQs
What are data extraction tools?
The purpose of a data extraction system is to allow users to easily extract information from unstructured documents (i.e. PDFs, scanned documents, images and emails) into a digital format with little to no manual entry.
How does data extraction work?
By using technology called optical character recognition (OCR) and artificial intelligence (AI) modelling, data extraction systems read and process documents to create structured digital data that can be integrated into existing enterprise business systems.
What is OCR data extraction?
OCR is commonly used for processing scanned or image-based documents and converting the scanned or image-based content into machine-readable formats so that users can extract important data such as numbers, names, dates and other relevant attributes.
What is Intelligent Document Processing (IDP)?
Intelligent document processing (IDP) integrates OCR, AI and machine learning to provide users with greater insights about their documents and allows users to extract complex data from documents while continuing to improve the accuracy of the extracted data.
What types of documents can be processed using data extraction tools?
Enterprise organisations typically extract and process invoices, purchase orders, contracts, ‘Know Your Customer’ (KYC) forms, bank statements, insurance claims and regulatory records.