Document management is an essential part of an organization’s business operations. From contracts to invoices and forms, documents are crucial to maintaining records, addressing grievances, and conducting day-to-day operations. Over the past few decades, businesses have invested in digitization efforts to automate data capture and entry into their systems of record. The biggest drivers of this trend have been an increasing focus on data security and achieving better process efficiency.
With data at the center of modern businesses, digitization is key to leveraging that data in a meaningful way. However, despite digitization having been around for a while, paper-based manual business processes are still very much around, even in highly automated industries like manufacturing and logistics.
As we head deeper into a data-first world, decision makers rely on accurate reporting and data. Data informs how businesses respond to change, achieve their strategic objectives, and serve their customers. Given how businesses grow based on the velocity of their data, manual data processes simply can’t keep up.
Automated data capture systems allow businesses to tap into this data and bridge the gap between data and insights. In this article we’ll explore what automated data capture systems are, how they work, and some of the most common use-cases.
Automated data capture systems (ADCS) are a combination of software and hardware that mechanize the entry of information into a system of record. They have been at the center of digitization efforts and have helped businesses streamline data entry. Today, data entry processes are increasingly being run with artificial intelligence (AI) and machine learning (ML) technologies.
With data volumes exploding, manual processes are becoming more complicated and expensive. Consider this — businesses typically lose between 5-15% of all paper documents and 30% of employees’ time is spent trying to locate these documents. Add to that the fact that employees spend 35% of their time creating, storing, searching, and managing paper documents, and here’s where AI and ML come in.
ML models excel at document classification (sorting). These models are trained on data sets that feature variations in different types of documents — enabling algorithms to recognize similarities and differences that ultimately allow them to successfully classify documents at scale. ML models greatly cut down on the manual effort required in document classification.
AI models, on the other hand, are effective in extracting the contextual information from the document. ML models trained to understand context can extract crucial information like invoice number, bill amount, and vendor names regardless of whether this information is structured (digital-first documents) or unstructured (handwritten notes). Over the past few years AI algorithms have matured to the point where they can analyze different formatting and patterns to extract information even without the need for labeling.
AI’s emerging capabilities in this area are underscored by the growing demand for intelligent data capture solutions, projected to hit $3.84 billion globally by 2024. These solutions are critical across industries that rely on document management for information, intelligence, and compliance. Sectors like banking, healthcare, government, and legal services are expected to drive immediate and long-term demand for automated data capture solutions.
Organizations are examining the link between data capture and process automation to make sure that the right information is being recorded. While it’s no secret that automation excels at helping businesses scale repetitive tasks and improve employee productivity, ADCS offers a host of other benefits too.
Here are three ways ADCS enables businesses to do more with less:
Even the most seasoned data operators make mistakes. And these mistakes can quickly escalate to thousands or hundreds of thousands of dollars in missed revenue opportunities or even expensive lawsuits resulting from inaccurate data. Automated data capture systems can greatly minimize the risks associated with human errors.
AI and ML algorithms can not only help you scale data capture and extraction but also validate the correctness of the data collected. A 2009 paper by researchers at the University of Nevada and Anderson University found that automated data capture systems had an average mistake rate of 0.38 errors per 30 sheets of data versus 10.23 errors for humans against the same dataset.
AI goes a step further and validates the data extracted from a document against existing information in other systems. For instance, when processing paper invoices, intelligent data capture systems can match the vendor name against the existing data in the accounting software.
We’ve outlined in sections above how manual processes can become bottlenecks in data collection and analysis. Expert data entry operators typically type at 15,000 keystrokes per hour. However, that speed can come down dramatically when operators deal with complex data that requires comprehension before entry. So, an expert operator entering 400 units of data would take anywhere between 6-8 minutes.
What happens when the data volume is higher? The turnaround time increases proportionally.
ADCS can scale data capture seamlessly because unlike human operators, these systems don’t need to examine every single version of a document to extract relevant information. Remember how we mentioned ML’s applications in data classification? ML models combined with robotic process automation (RPA) concepts can be used to create AI engines that employ a dynamic variance network that allows an ADCS to compare every component of a document with each other.
In other words, the software calculates all vectors between characters in the document against the target fields to promote data capture at scale.
While common sense would dictate that costs associated with manual data processes are primarily labor-related, the truth is that the real cost of manual data entry is the error of correction. Errors have a more damaging impact on the bottom line than the actual cost of labor. Incorrect data costs businesses at least 30% or more of their revenue. Manual data verification and error correction costs are often overlooked.
A Goldman Sachs report found that the direct and indirect costs of manual, paper-based invoicing costs businesses $2.7 trillion annually.
Automation is not only more cost effective but also helps businesses realize significant improvements in customer satisfaction, brand perception, and employee satisfaction. Additionally, ADCS systems also enable organizations to improve their document management practices, resulting in better efficiency across all departments.
The workflow for a typical automatic data capture processes involves five major steps:
While technologies like optical mark recognition (OMRs) and barcode scanners have been around for over five decades now, AI- and ML-based solutions are driving the modern shift to automated data collection. Let’s look at some examples of automated data capture systems in use today.
OCR is perhaps the most commonly found functionality for data capture automation today. OCR algorithms are able to convert a photo of a document into a fully editable digital file. It offers near instant document digitization capability for businesses. With OCRs, businesses can process, manage, store, and share the most important data without human intervention.
OCR algorithms have been adapted for multiple business use-cases for printed text. The technology is relatively simple and highly scalable — two reasons why it has found widespread adoption across businesses of all sizes.
IDC is an evolution of OCR technology and combines image recognition capabilities with data interpretation, which allows businesses to gather more meaningful insights from data.
An example of IDC you’re likely to have come across is Google Lens. It will not only tell you what the image or photo is but also provide meaningful content from it. For instance, if you scan a bill using IDC, you’ll not only get useful filing information like the document data but also additional context such as bill due date.
IDCs rely on deep learning or neural networks that have been trained on large sets of annotated data. IDC leverages smart parsing to structure data from images or printed text. This results in massive productivity gains as businesses no longer need as many humans in the loop.
If you’ve ever used an intelligent virtual assistant like Siri or Alexa, you’ve experienced the power of voice recognition technology. The ease-of-use and mobile availability of voice search has resulted in rapid adoption of voice recognition technology. It’s estimated that in 2020, half of all searches were performed using voice, and the trend is only growing.
Voice recognition also has massive applications in the world of business — auto transcription and note-taking are some of the most popular use-cases for voice recognition software. The software is powered by deep learning models that have been trained with large data sets around vocal patterns and speech elements. Labelling or annotating big data for context is also an important step towards developing voice-recognition software.
While facial recognition software is ubiquitous across consumer technologies like handheld mobile devices, it also has numerous applications in business. At its core, facial recognition software leverages image recognition algorithms to detect, capture, and match a user’s face against a database.
Facial recognition data collection enables software to collect biometric information like the spacing of eyes, bridge of the nose, contour of the lips, ears, and chin. Automated systems then use this information to authenticate and identify people.
What makes facial recognition software so important is that it finds application in day-to-day consumer technologies such as the iPhone, as well as business and law enforcement biometrics. It can be used in dynamic and unstable environments (like a crowded airport). Emerging business applications of facial recognition include security/verification, match databases, biometric ID, and sentiment analytics.
Manual data entry is no longer efficient. Paper workflows are damaging for businesses as valuable time is lost in creating, converting, and safely storing data. Automated data collection addresses productivity and cost concerns around document management. Businesses capturing data with automated methods have realized multiple benefits such as decreased security risks and higher customer satisfaction scores, ultimately leading to revenue growth.
However, building automated data capture systems is far from easy, particularly when designing custom solutions for specific business needs.
If you’re looking to leverage data collection to reduce human error, get accurate insights, and improve your staff’s productivity, let’s talk!
