Data Capture: Definition, Methods, and Benefits

Data capture converts information from the physical world into digital data for analysis. In this guide, we’ll discuss the definition of data capture, methods, benefits, and practical applications.

A simple data capture definition is that data capture is the process of collecting and converting unstructured information from the physical world into a structured, digital format. More recently, smart data capture has automated data capture processes and expedited decision making by embedding intelligence at the point of capture.

Any data pipeline where the data relates to physical products or processes fundamentally relies on accurate, repeatable, and scalable data capture. Business data capture is essential for streamlining operations, improving accuracy, and enabling real-time decision-making.

While the roots of data capture date back to ancient civilizations (such as, markings on bones, papyrus scrolls, or clay tablets), data capture as we know it took hold from the 1970s onwards, when electronic data capture began.

This is also around the time when people began to realize the full potential of the barcode. The barcode was patented in 1952 (based on Morse code) and became the foundation for modern data capture.

Early data capture tools were limited to basic barcode scanners, but today’s data capture technology uses AI, computer vision and mobile devices. Advanced data capture techniques like AI-powered barcode scanning, optical character recognition (OCR), ID scanning, and object recognition accelerate workflows and reduce operational costs in shelf management, inventory, logistics, travel, and more.

There are many methods of data capture, including OCR, barcodes, RFID, ID scanning, and object recognition. The physical world is complex and variable, and data capture tools vary widely from industry to industry and workflow to workflow. Different data capture methods are better suited to different industries and situations:

Optical character recognition (OCR)

Optical character recognition, or OCR, extracts printed or handwritten text into a machine-readable format. Initially developed by IBM in the 1950s, today OCR is primarily used to digitize printed and handwritten text so that it can be accessed digitally.

Aside from digitizing scanned documents, OCR is also used to automate data entry, convert printed materials into formats for visually impaired people, for text recognition in images, and for translating text into other languages. Healthcare and finance are two common industries where OCR is used.

Intelligent character recognition (ICR)

Intelligent character recognition is a more advanced version of OCR, which reads more diverse handwriting styles and more complicated text formats. Compared to OCR, ICR is more efficient, more adaptable and more accurate.

It's commonly used in education, finance, and healthcare fields for its ability to automatically process checks and bank statements, digitize handwritten prescriptions, and more.

Optical mark recognition (OMR)

Optical mark recognition systems scan physical documents and detect differences based on light reflection. OMR systems can read marked data fields, which makes them a popular solution in educational environments, especially for standardized tests.

It's also a data capture method that's widely used in market research, as it can process responses from feedback forms and surveys.

Barcode and QR code technology

Barcodes and QR codes (QR codes are actually a type of barcode) are used just about everywhere today. Think of a store associate checking stock, a delivery driver dropping off a parcel, a conductor checking a train ticket, or a pharmacist scanning medication. All these workflows, and many more, depend on barcode scanning.

Printed or digital barcodes consist of lines, squares, dots, and spaces that encode relevant data. Barcode scanning software then converts this pattern of light into electrical signals that are then processed into machine and/or human readable characters.

Advanced AI-powered barcode scanning software can adapt to context, capture multiple barcodes simultaneously, and add augmented reality (AR) overlays for actionable insights.

Object recognition

As the name implies, object recognition involves the use of visual data recognition to detect certain objects or features. For instance, some common applications where object recognition may be used include security surveillance, analyzing medical images or in retail analytics.

Scandit’s AI-powered shelf intelligence solution ShelfView uses object recognition to scan retail shelves and identify products that are not on shelf, have incorrect prices, or are in the wrong place in store.

RFID

RFID (Radio Frequency Identification) is a data capture technology that uses electromagnetic fields to automatically identify and track tags attached to objects. These tags contain electronically stored information that can be read from a distance by RFID readers, without requiring line-of-sight.

RFID is used as an alternative to barcode scanning in retail point-of-sale (particularly in fashion) and in RFID wristbands for hospital patients.

ID scanning

ID scanning is used to quickly and accurately verify the authenticity of passports, driver’s licenses, military IDs and more. ID scanning and verification solutions can extract data from the document, determine whether the document is real or not, and confirm that the person presenting the ID is the rightful owner.

Different industries use different types of ID scanning and verification software depending on the level of assurance they require, level of control and customization they need, among other factors.

The data capture process involves four main steps: acquiring raw, unstructured data, extracting data from it, validation and integration into business systems, and finally utilization of the data. Automated data capture reduces manual entry errors and improves processing accuracy and speed.

1. The first step is acquiring the raw, unstructured data — for example, from a camera feed or a document scan.
2. Using the appropriate data capture technology, the data is then extracted and processed. It’s not uncommon for businesses to combine several data capture techniques to provide a more accurate, complete, and useful view. For example, Scandit’s advanced barcode scanning solution MatrixScan Pick can be used for exception handling when RFID misses items.
3. Following extraction, data is checked, validated and then transformed so that it can be integrated into the appropriate business system.

From here, the data is utilized — for operational, analytical, or strategic decision-making. With smart data capture technology, real-time insights such as stock levels are delivered back almost instantaneously.

Automated data capture transfers tedious manual tasks involved in capturing data from people to technology. There are different levels of automation. Basic automation replaces wholly manual (pen-and-paper) data capture methods with technology. At the other end of the scale, advanced solutions automate complete end-to-end workflows such as inventory counting.

From improved data accuracy to reduced costs and enhanced security, there are a number of benefits associated with automating data capturing.

Improved data accuracy and efficiency

Perhaps the biggest benefit of automated data capture is improved data accuracy and data completeness.

Human error in data capture doesn't just have the potential to sap efficiency. Mistakes can impact a business financially and reputationally. For example, errors in identity checks for age-restricted goods may lead to non-compliance with legal requirements. This can result in fines or the loss of a license.

Cost savings and operational efficiency

Automated data capture improves operational efficiency and decision-making through real-time insights. Because operational efficiency is higher, the overall cost of data management tends to be much lower.

Enhanced data security

Automation often comes with heightened security, compared to manual data capture, thanks to encrypted data, on-device capture, and more secure overall protocols. This helps reduce the risk of any data breaches or data theft.

Improved employee satisfaction

Studies indicate that productivity and employee happiness and satisfaction go hand in hand. Automated data capture solutions reduce tedious manual tasks, and streamline productivity. This can lead to an overall increase in employee satisfaction, happiness and morale.

What industries leverage data capture?

From banks to hospitals, retail businesses to logistics companies, many industries utilize data capture. Any business that has physical operations has to do some sort of data capture.

Here are a few data capture examples showing real-world uses: scanning barcodes during inventory counts, using OCR to digitize forms, capturing IDs for passenger verification, and using object recognition to detect shelf gaps.

Banking

Some of the most popular uses of data capture in banking involve processing bank statements and verifying customer identities.

Travel

​In the travel industry, data capture technologies streamline processes such as check-in, ticket validation, and baggage handling. For instance, scanning passports or IDs during online check-in means passengers arrive at the airport “ready to fly”, accelerating passenger processing.

Manufacturing

​In manufacturing, data capture technologies facilitate real-time tracking from receipt through production to dispatch. This minimizes errors, ensures precise assembly, and streamlines inventory management.

Additionally, augmented reality (AR) overlays can provide instant information during quality checks, aiding compliance and reducing rework.

Supply Chain

Data capture is essential in distribution operations to track the movement of goods across tasks like truck loading, inventory checks, proof of delivery, and returns. Workers often use smart devices to scan multiple barcodes quickly, verify shipments, and update inventory in real time.

This reduces manual errors, shortens processing times, and ensures accurate tracking of goods throughout the supply chain.

Field Service

In field services, data capture technologies help technicians work more efficiently by enabling accurate asset tracking, inventory management, and real-time access to information. With smart devices, they can quickly scan barcodes to verify parts, update stock, and view service histories.

AR overlays provide instant access to manuals or instructions, supporting faster, more accurate repairs.

Healthcare

In healthcare, data capture technologies are vital for enhancing patient safety and operational efficiency. Healthcare professionals use smart devices to scan patient wristbands, ensuring accurate identification and access to medical records.

During medication administration, scanning barcodes on medications verifies the correct drug and dosage, reducing errors. Additionally, these devices assist in tracking medical supplies and specimens, maintaining accurate inventory and supporting timely reordering.

Retail and logistics

In retail, data capture plays a key role in helping staff handle tasks more quickly and accurately. Associates can use smart devices to scan items for inventory checks, pricing, order picking, or restocking — reducing the chance of mistakes and saving time.

AR overlays provide visual cues that highlight shelf gaps or show product details on screen, supporting faster decision-making on the shop floor. These tools also help provide better customer service by enabling on-the-spot assistance without switching devices. The result is smoother day-to-day operations and a more responsive in-store experience.

Like any technology or innovation that has staying power, data capture will continue to advance over time.

AI is already deeply embedded in data capture, but is poised to have even greater impact. For example, at Scandit we view AI capabilities in barcode technology across three distinct levels. These range from barcode scanning which is AI in name only, through to context-aware scanning that understands not just barcode data but environment and user intent.

The future of data capture is also unlikely to be about any single data capture technology, but about smart data capture platforms that combine multiple different types of data capture, integrate these with analytics, and can evolve and scale. The move from thinking of data capture as the point-in-time process of capturing a single type of data to a holistic approach is one of the biggest trends influencing the future of data capture.

What is the difference between OCR and ICR?

OCR and ICR are similar technologies, but the main difference is that ICR is a more advanced version of OCR that uses machine learning and AI to read more diverse handwriting styles and more complicated text formats.

How does data capture improve business efficiency?

Data capture improves business efficiency by improving data accuracy and workflow efficiency. Automated solutions are fast and work to reduce the likelihood of human error during data entry.

What are the security risks associated with data capture?

The biggest security risks associated with data capture come not from data capture itself but from poor management of the data that has been captured. One area to be particularly aware of is whether a data capture solution performs processing on end-user devices, or needs to send information to the cloud for decoding.

Cloud-based solutions can be secure but generally come with higher risks. In an example such as ID scanning and verification where you are processing personally identifiable information (PII), solutions that scan IDs and passports on end-user devices may be simpler and lower-risk.

Can data capture technology be integrated with existing business systems?

Yes, modern data capture technology is designed to easily integrate into existing systems, such as ERPs, WMS or other systems of record.

How does Scandit’s barcode scanning improve data capture?

Scandit's AI-powered barcode scanning improves data capture by reducing manual tasks, enhancing efficiency and building customer loyalty. Its solutions utilize computer vision technology and augmented reality to facilitate smart data capture on mobile devices, streamline workflows and reduce the likelihood of human error.

What types of businesses benefit most from data capture?

Any business that has to track or manage physical items or processes benefits from (indeed must have) data capture. Some of the industries that Scandit solutions empower include retail and logistics, healthcare, manufacturing, travel, field service and technology.

What are the cost implications of implementing automated data capture?

Implementing automated data capture will always (like any new technology) require an initial investment cost. However, this is generally more than made up for based on the significant return on investment from these solutions that can be realized. From reduced errors to increased operational efficiency, the long-term benefits largely outweigh any upfront or ongoing costs.