Computers Were People Too: The Past, Present, & Future of Document Processing
For a long time, computers were people. From the early 17th century until as recently as the 1970s, the word “computer” meant “someone who does calculations”. In business, human computers were responsible for document data processing.
The problem with human computers is that they’re so human. This makes them less than ideal document processors when it comes to data extraction and data management. For example, an M-Files study shows that nearly half of employees surveyed believe they’re spending too much time searching for documents they need for work. At the same time, 83% of respondents say they’re constantly recreating documents because they can’t find them in their organization’s network.
We’re going to take a short trip back in time to look at the history and evolution of document processing. Then we’ll look into the future to see how the digitization of data capture solutions can benefit companies like yours.
This post should help you gain a deeper understanding of document processing and how profoundly it impacts your business. You’ll also discover the far-reaching benefits of cognitive data extraction and see the value that an intelligent document processing (IDP) solution can deliver to your company.
Before we get to the history of document processing, let’s look at its fundamental stages.
Document Processing: A Definition
Generally speaking, document processing is the act of extracting text contained in paper and digital documents and entering the extracted data into digital systems. This activity enables you to manage and carry out a vast range of critical business operations including purchasing, ordering, and payments.
Encyclopedia.com provides a simple example of a series of transactions that each require some form of document processing:
“When a customer submits an order to purchase a certain product, the order becomes a document for processing. The manufacturing company coordinates the activities of acquiring the raw materials, making the product, and finally delivering it to the customer with an invoice to collect payment—all by passing documents from one department to another, from one party to another.”
Failure to process data from the documents in this simple chain of events would result in lost orders, no raw materials, no product, no delivery conditions, no invoice, and no payment. In short, such a business would self-destruct.
Processing errors, such data entry typos or incorrect data classifications, can have the exact same effect on an organization as not processing documents at all. To ensure success, you need to make sure every step of this operation, from document collection to data storage, is efficient and accurate.
The 3 Stages of Document Processing
When done right, document processing can be an important contributor to your company’s long-term success. Get it wrong, and it can put your organization out of business in a keystroke.
To help you identify any existing or potential complications, we suggest you break document processing down into its three main stages, and examine these stages carefully. You may find opportunities to increase operational efficiencies through automation.
Document receipt and classification
Like most businesses, you probably get paper and digital documents from a wide range of sources, including email, post, productivity apps, and maybe even a faxing service. Before you can capture data, a person, team, or software solution needs to classify each document so the information they contain goes into the right systems.
An intelligent document processing (IDP) solution can help save time, money, and resources at this early stage. You could, for example, set up an inbox for your IDP software, which would receive digital documents, as well as scanned paper documents. To follow best practices, you could set up different inboxes for different document types, which would take care of classification without any margin for error.
Alternatively, you could use one IDP email address and task an employee with document sorting within the application itself. This allows for early document validation, which could be especially important for checking the legibility of data on paper documents.
Data capture can be the most expensive, time-consuming, and resource-intensive step of document processing. If you’re having data entered into your business systems manually, or you’re using a traditional optical character recognition (OCR) system, you might want to take the time to calculate the total cost of current approach.
As you examine the data extraction stage, you might also notice how the quality of the data is affecting other parts of your business. For instance, when processing invoices, you want accurate and timely data entry that ensures stable vendor-customer relationships and presents opportunities to save production costs through early payment discounts. These savings could then allow you to price your goods or services more competitively.
When reviewing this step, you first need to determine and specify exactly which data you need to capture – this analysis, and the resulting taxonomy of information you are going to process, is the key to a smooth data extraction process that does not require superfluous effort. To ensure precise data capture, you can quickly and easily integrate an IDP solution into your business systems. Look for a platform that uses machine learning technology to extract document data more accurately with regular use.
Data validation and export
The validation stage of document processing can easily fall by the wayside when you’re using manual data entry. While some specialists may check their work as they’re entering it, most see this as doubling down on the monotony of typing or copy-pasting data into a spreadsheet, accounting software, or enterprise resource planning (ERP) system.
On the other hand, using an automated IDP platform isolates data validation, giving it a greater sense of value. An employee can easily review and correct extracted information in minutes before exporting the data to your company’s network or business systems. As the platform’s AI learns to understand new document formats with continued use, it gets closer to automating this stage and your document processing procedure as a whole.
A Brief History of Data Processing
We’ve been processing data for millennia. As mentioned in the introduction, the term “computer” once meant a person who performed calculations on paper or with a calculator. A look back at Renaissance-era astronomy, for example, shows that Johannes Kepler got his start as a computer before going on to be a major contributor to the 17th-century scientific revolution.
Throughout history, bookkeepers were computers who kept track of transactions that enabled them to draw up balance sheets. From the 1930s to the 1970s, women worked as computers for NASA, making significant contributions to America’s efforts in the Space Race.
With some exceptions, human computing and data processing was generally regarded as menial labor. Though it required greater concentration than pure data entry, it could still be a cumbersome chore for computers whose sole motivation was a regular paycheck.
Automated data processing
The development of Herman Hollerith’s punched card equipment for the 1890 US census moved data processing towards automation. Replacing human computers with machines meant the Census Office could publish results in two to three years – a massive improvement over the seven to eight years it took to process all the data from the 1880 census. In addition to time savings, Hollerith’s system cut processing costs by $5 million in 1890 dollars (over $141 million in 2020 dollars) despite the fact that the 1890 census had double the questions of the 1880 census.
If we stick with the history of the US census to track the evolution of data processing, the use of electronic computers began in the 50s. The Census Office was the first organization to work with the legendary – and colossal – UNIVAC I.
Since then, technological advances in data processing have focused mainly on helping human operators enter document data into business systems more easily, though not necessarily more efficiently or accurately.
Data processing systems that require minimal to no human intervention are a relatively recent development. This could explain, in part, why many organizations are still using manual data extraction methods, or traditional OCR solutions that require a significant amount of human input.
Overcome Today’s Challenges With Intelligent Document Processing
Organizations that use manual and traditional OCR data capture methods have to deal with issues on several fronts. Achieving a balance of speed and accuracy is perhaps the most obvious challenge your organization faces when processing documents. The amount of time your employees spend on this task could be reducing their productivity. If you’re working with paper documents, storage might be another issue. And you need to dedicate some time, money, and resources to the organization and management of digital and physical documents.
Document processing doesn’t have to be problematic. You can replace or minimize the human element with automation to ensure accurate document data goes into your business systems. Digitization solutions for document processing may also present you with opportunities to streamline and scale operations. Furthermore, when you integrate an IDP solution into your business processes, you can manage documents, and the data they contain, more easily.
According to a Seagate-sponsored IDC study, individuals and organizations will have produced 1 trillion gigabytes of data by 2025. Businesses will make and manage most of this data, which will come primarily from the processing of unstructured documents.
The machines we now call computers can capture, export, and store data faster and more accurately than people can. But we have the advantage of comprehending the meaning of document data better than machines. We can skim and understand information contained in an invoice, purchase order, receipt, or any other business document unaided.
Until recently, computers simply captured data without interpreting its meaning. Even with traditional OCR solutions, document processing is still a slow and costly process. The time saved on manual data entry is spent developing rules and templates for every single document format the solution processes. More unstructured documents means more time lost training the OCR system.
Now is the ideal time to look at IDP solutions that understand the data they’re capturing. A simple tool that captures data is no longer enough. To be effective, IDP platform must be able to learn to recognize and classify data in unstructured documents with minimal human training. The solution should also be able to scale so it can handle the high volume of documents that will increase as your business grows.
To get an idea of how automated document processing can transform your business, you can access an IDP solution that works out of the box right now. Use it to process a batch of invoices in less time than it takes to make a cup of instant coffee – that’s a lot less time than it would take a person to either process the documents manually or set up rules and templates for an OCR solution to capture the data.