Capture and process automation solutions makes life easy for companies. The right information can lead businesses to the right decisions. Even though data is everywhere, it is difficult to capture this data consistently to derive meaningful insights. Many businesses lack the basic tools to harness data from different document-dependent processes like invoice processing, medical claims processing, customer sales order processing, etc. A large proportion of entities rely on manpower and manual effort to access and process data. More often than not, manual effort introduces errors, inefficiencies, and inconsistencies when performing document processing and data entry work. Poor data cultivation and management can lead to inaccurate results or data. Inaccurate or bad data is not an option when companies are trying to chart future growth trajectories and drive higher performance standards based on business data.
Today, in the age of digital transformation and industry 4.0, data has become a key asset for business progression of any kind. Without data, businesses will cease to function. But the key thing to note here is that not all data is useful. Data must be cultivated, accessed, processed, and made useful for business. By useful, we mean data must be made relevant and actionable, and have a positive impact on a company’s decisions, growth forecast, business development, and even costs.
We’ll discuss automated data capture solutions that companies, both SMBs as well as large corporations must avail, as part of their cost control and business acceleration efforts, or simply as part of their long-term digital transformation initiatives. Advanced automation is not the only consideration when deciding on modern technology solutions for your business. Most businesses are stuck with legacy systems and elaborate IT infrastructure that are difficult to revamp in their entirety. It is important that new business applications and automation software firstly integrate well with the existing IT setup in an organization, if they are to make any positive impact on business.
Data capture technology has evolved over the years. Companies have always resorted to some form of data acquisition mechanism. Data entry and scanning are possibly the simplest, more traditional data acquisition methods that many companies still use to date. Even before the Big Data phenomenon, companies have always felt the need to use data to understand the way their business is performing and make amends accordingly. An example would be the simple task of bookkeeping, where manual data entries on the company's purchases, expenses, revenues, and even payroll would give companies a record of the day-to-day business transactions undertaken by a company, and a decent understanding of their finances. A consistent recording of business transactions such as these help companies monitor their finances and either exercise caution or engage in more bold investment opportunities. But today’s advanced data capture solutions offer a more nuanced way of acquiring, processing, and understanding data. Much of the technological progress in the field of data capture has been to service the customer better. How a customer thinks and acts can help companies engage them better, and data plays a big part in that engagement.
Much of the data and corresponding insights related to that data is hidden in unstructured and semi-structured documents. We find these documents as part of daily business transactions. In fact, any time a company interacts with its external stakeholders including customers and vendors, there are a ton of business documents that are created. These are mostly unsaturated, in that data in these documents is not neatly organized in rows and columns, like in a spreadsheet. This makes it difficult for companies to immediately retrieve or extract information from these documents for better understanding and inference. Transaction or source documents are generated when a company does some form of business with its stakeholders. Examples of transaction documents include credit memos, vendor invoices, customer sales orders, statements, purchase orders, bills of lading, packing slips, receipts, etc. All these documents come in unstructured or semi-structured form, and businesses have traditionally resorted to manual effort to perform data entry from these documents and into their corporate spreadsheet or other form of database for further business use. Manual data entry and document processing work including document sorting, document classification, data validation including checking for things like if the data entered into a corporate database or spreadsheet is correct, and data verification, all entail a lot of manual labor, costs, and time.
Another critical point to remember is that it is hard to capture transaction documents, especially with the rate and frequency with which these documents are created by a company. Just take the case of a simple purchase for raw materials made by a company. The company receives an invoice corresponding to the purchase from a vendor. This invoice could reach the company at its site office rather than its corporate office, where most of the administrative and back-office work is carried out. Capturing transactions documents at the source, relaying them to a central repository in say, a corporate office, and then processing these documents including validating and verifying the purchase, before approving final payments to the vendor, all takes a lot of time and manual effort. Not to mention, in instances where the vendor invoice goes missing or is not immediately reported or recorded by a company, there could be ether a delay in processing invoices and making final payments to the vendor, or in worst cases, the company fails to pay up at all, having had no knowledge or record of pending payments.
Advanced data capture solutions help companies capture transaction documents at the source, including from channels like emails, FTPs, MFPs, scan, post, fax, etc., greatly lowering the chances of companies defaulting on payments due to missing invoices or delaying them due to manual data entry and document processing.
Data capture software solutions today employ digital transformation technologies like artificial intelligence, machine learning, business process automation, robotic process automation, etc. to enable processing of complex document types including unstructured and semi-structured formats. These solutions enable companies to accelerate the rate at which transaction data enters an organization and can be made useful or actionable. This is vastly different from the simple scan and upload feature, and lowers the dependence on manpower to process complex document types.
Modern intelligent capture solutions are designed to read, understand, and capture data from complex documents, much like a human would, but without entailing any of the inefficiencies and costs associated with manual data entry and document processing work.
The need of the hour for companies has been to lower the manpower required to perform data entry and document processing, while also accelerating the fforet to acquire data from transaction documents at record speed, in order to facilitate timely business application and decision-making. Intelligence-based data capture solutions offer this, enabling straight-through document processing and accurate data availability from unstructured or semi-structured document-dependent processes.
Also, capture methods can be of two types: a) template-based and b) self-learning based. Templatized capture technology is most useful when you have a limited set of incoming documents with defined templates that do not change over time. An example would be a company dealing with the same set of venros over time, in which case, the invoice documents received by the company from these vendors have the same standard format. The capture logic in this case, where the document structure does not change over time, is usually built-in with standard rules on how to capture the document. Self-learning based capture technology becomes useful when, say, a company is dealing with a wide variety of new and existing vendors, where the invoice documents received from some may greatly vary from your standard invoice format. Here, the software is made to learn the new document format before capturing it. Self-learning can be found in advanced capture technologies, needed by those companies that deal with diverse document types, and where document-variability is a constant.
Different companies have different data capture needs, and the range of capture technology employed may vary from one business to the next depending on whether they want to scale operations like processing more vendor invoices in a day, or lower the labor costs of handling paperwork in the accounts payable division, or simply to adhere to the strict data management and compliance standards, as required by law. Capture requirements can vary: