Data drives a lot of the business processes today; be it vendor invoice data for performing accounts payable operations and reconciling vendor payments, or data from medical claims forms needed to process medical claims and decide on reimbursements. Businesses generate and interact with data all the time. A lot of the enterprise business applications like Acumatica Cloud ERP, QuickBooks accounting software, and Microsoft Dynamics 365 run on data. Usually, data entry staff is responsible for keying in important transaction information from say, vendor invoices into these applications for performing accounts payable operations and related payment reconciliations. Anytime a company interacts with its stakeholders, be it vendors, partners, or customers, data is generated. Capturing this continuous outflow of data from business transactions and a plethora of other sources is challenging. Human effort can go only so far in acquiring raw data from unstructured, semi-structured, and structured documents, processing and preparing that data, and converting it into actionable information for business use.
Technology, on the other hand, removes a lot of the inconsistencies, errors, inefficiencies, and delays that come with manual data entry and document processing work. The idea is to bring all business data onto a single platform that can be accessed, shared, distributed, and centrally managed within an organization. This data also needs to be accurately fed to databases and business applications without delays — a delay in posting vendor data in an ERP can delay validation of corresponding incoming invoice data, and subsequently, the processing of vendor invoices and payments reconciliations. Human effort typically introduces such operational delays. What businesses need is an intelligent automation system that emulates human actions to perform these very functions, but without the inefficiencies and costs associated with human engagement.
The first step in intelligent automation of data entry and document processing is data extraction. Automatic data extraction greatly lowers the risk of delaying data availability to business applications, databases, and in general, business implementation of any kind. A good data extraction tool must be able to automatically capture and extract data from different unstructured, semi-structured, and structured document types.
Most transaction or source data comes in unstructured or semi-structured form. Data extraction helps align and consolidate all kinds of business information centrally, where it can be managed, shared, and processed to make it actionable and useful for business. Actionable data means the data must add value and inference to the ongoing process, function, system, or department.
Data on a sales order form, when processed properly and verified for validity and authenticity, should tell the company there is a new customer order that needs to be fulfilled. Accurate sales order information, when fed into a business application like a CRM or ERP system, will notify all users of the system about the new order and help them collaborate effectively to fulfill that order. Data extraction helps companies have access to important business information on time — time being a critical component during the extraction process. With ordinary data entry methods, usually performed by human keystrokes, there are bound to be countless errors, leading to more delays in data availability. Apart from timely data availability, it must also be complete to facilitate the uninterrupted implementation of a process, system, function, or department.
If a database or ERP system is not updated with current data on a new purchase order by a company, it becomes very difficult to verify an incoming vendor invoice for that order, and delay vendor payments. Complete and reliable data on a transaction, process, or function can help businesses implement the next steps quickly and arrive at or achieve their target within set deadlines. Complete data also gives companies a holistic picture of the state of their business, the interactions with various external stakeholders, as well as the state of processes and definitions that help meet customer demand. With a powerful data extraction tool, businesses will be able to capture data at the source. This is especially useful and critical to avoid instances of missing, lost, or delayed data. Consider manual data entry work — mostly, it requires the collection, sorting, and filing of unstructured or semi-structured transaction documents before performing data entry for each document. These are typical paper documents that can be damaged, lost or missing from the files due to manual error. In this case, it is difficult to draw out the right, accurate data needed to operate a business. With automatic data extraction however, data from transaction or source documents can be captured right at the source like a remote site office or an inventory unit. This helps acquire and upload that data immediately into a central data management system or business application, helping eliminate instances of data or paperwork going missing.
Manual data entry does not do justice, given how error-prone and slow the process is. Data extraction using intelligent automation tools is more viable in the long-run, as there is scope to not just extract and upload the data into an intelligent automation workflow, but also easily route or transmit that data to an ERP system or similar business application.
in this data extraction method, the entire contents of a document including the header-footer and line-item details are captured. There are no capture constraints or limits assigned during the extraction method. Full page extraction is made possible using traditional OCR technology, where almost all the data fields from documents are extracted without any constraints or conditional logic.
zonal extraction is more useful when you need to extract data only from selected parts of a document like the ‘SUM TOTAL’ or ‘ITEMS’ data fields on a source document like an invoice or purchase order. Zonal extraction can be achieved through either template-based capture and extraction like using Zonal OCR or through intelligent data capture technology. Zonal OCR works well where the data fields that need to be captured are in the same position in all documents. The extraction or capture logic is pre-built and programmed to locate specific fields on a document. Template-based data extraction is most useful when the document variability does not change and documents are mostly in structured or semi-structured form, for which the document structure and field placements are already known. In the case of unstructured documents or even semi-structured documents that are highly complex, zonal OCR fails to do the job. Template-based data extraction is highly contextualized, meaning the capture logic is rendered useless when dealing with new document types or diverse documents where the field placements vary greatly with every document.
Intelligent Data Capture is a more advanced extraction technology that could come in handy when dealing with document variability. The ‘intelligence’ in an intelligent data capture software is backed by AI and machine learning, enabling it to ‘self-learn’ different document types for effective capture. Rather than create a capture logic for every new document type, which is not feasible, intelligent capture makes use of cognitive technologies to human keystrokes performed during data entry and emulates those actions to process all subsequent similarly formatted documents. The self-learning mechanism exhibited by intelligent data capture bots helps handle a variety of document types.
Advantages of Intelligent Data Capture