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A Comprehensive Guide To Data Capture – Methods, Benefits, And Trends
Enterprises deal with a high volume of documents daily, such as invoices and purchase orders. Data capture lets businesses extract valuable information from these unstructured documents for informed decision making.
Data capture technologies utilize advanced techniques like optical character recognition (OCR) and intelligent document processing (IDP) to automate extracting relevant information from unstructured documents.
In this blog, we explore data capture and how it has evolved over time.
What is Data Capture?
Data capture refers to extracting and converting data from various sources, such as physical or digital documents, into a format that computer systems can easily process, analyze, and utilize. It involves capturing relevant information, such as text, numbers, images, or codes, and transforming it into a structured, machine-readable format.
The process is used regularly in multiple industries. For example, retailers receive hundreds of invoices daily. They can use technologies, such as OCR, to extract key information from these invoices, such as invoice number, date, amount, and address.
How does Data Capture Work?
Traditionally, data entry specialists used to pour over sheets of paper and manually enter information into a table.
Today, modern tools enable analysts to automate the entire process. They also allow users to schedule workflows so that the data is extracted automatically and sent to the relevant destination once a document arrives. Here is how the data capture process works:
- Document Intake: The process begins when the organization receives a document, such as an invoice or purchase order. This document can be in various formats, including paper, electronic files (PDF, Word), or scanned images.
- Document Analysis: The data capture software analyzes the document to identify and locate the key fields that contain the relevant information to be extracted. This analysis can involve OCR technology to convert scanned or photographed documents into machine-readable text.
- Field Extraction: Once the key fields are identified, the software automatically extracts the relevant data from those fields either through predefined templates that match the layout and structure of the documents or through Intelligent algorithms that can understand the context and meaning of the data.
- Data Validation: The extracted data is validated to ensure accuracy and completeness. Every business has its own rules based on which it can check the correctness of the data.
- Workflow Automation: Modern data capture tools often have workflow automation capabilities that allow users to define rules and configure workflows to streamline the process. For example, once a document arrives, the data extraction process can be triggered automatically, and the extracted data can be sent to the relevant destination, such as an enterprise resource planning (ERP) system or a database.
- Integration and Data Delivery: Data capture solutions integrate with other systems and applications within the organization’s technology ecosystem. The extracted data can be seamlessly delivered to downstream systems, such as visualization tools or data warehouses.
The Various Methods of Data Capture
Manual Data Capture: Manual entry involves copying information into a digital form by hand – across hundreds of pages of documents. This method is suitable for small teams with a limited budget. It is also a cost-effective option when the amount of incoming data is low. However, this method is error-prone and time-consuming, which is why automated data entry tools were developed.
Automated Data Capture: This method helps businesses improve efficiency, increase worker satisfaction, and reduce costs. There are multiple ways in which this method works:
- OCR and ICR: Optical Character Recognition converts digitized text to a machine-friendly format. It processes scanned documents, such as invoices and insurance forms. ICR, or Intelligent Character Recognition, takes OCR a step further. ICR software can detect typed and handwritten text in different fonts – making it a more powerful technique for data capture.
- IDP: IDP stands for Intelligent Document Processing. It is a rapidly evolving technology used by more and more teams across different verticals and industries. IDP uses AI and automation to extract data from many documents with different sizes and structures. IDP often utilizes other technologies – such as OCR and Machine Learning – for a robust data extraction solution.
There are also other types of data capture methods not exclusive to documents.
Image and Video Capture: These methods employ AI to identify and extract accurate information about individuals. It is valuable for real-time analysis and has applications in workplace security, database matching, security scanning, and biometric identification.
Web Scraping: This involves using web bots or web crawlers to search and scrape the internet. Web scraping collects dynamic info such as news updates, policy changes, price fluctuations, forex rates, weather updates, stock market data, and more.
QR codes and barcodes: Barcode technology contains encrypted information in 1D barcodes, which can be read using a barcode scanner. It is used to track inventory or employee logs on shop floors accurately, check patient details in hospitals, print bank passbooks, and more.
Quick Response (QR) codes, also known as 2D barcodes, are in the form of shapes, such as hexagons, and can capture various types of information, such as documents and webpages. QR codes are commonly used in shops, courier services, and restaurants and can easily be captured by smartphones.
Benefits of Automated Data Capture
Automated data capture is the future of document data extraction. It is a highly scalable process and easily manageable. It also reduces time to insight, enabling enterprises to make decisions quickly. Businesses that employ this method can reap several benefits:
Significantly Reduced Chance of Errors
Copying info manually can cause errors, such as missing figures and incorrect entries. Such errors can be very expensive – each can take $10 to correct for every record. Overall, poor data can set back a company by millions of dollars.
Data capture software offer high accuracy that remains strong across hundreds of documents. They reduce the need for human supervision. Plus, they provide automatic data validation to catch missing/incorrect data in real time.
Streamlined Business Processes
With teams capturing data efficiently, it allows them to process documents faster. This helps businesses carry out essential tasks such as vendor payments and claims processing on time. This streamlined approach improves relationships with vendors and customers which can provide multiple financial benefits.
Improved Employee Morale
Pouring over documents, both paper and digital, can be exhausting. Automated tools allow teams to focus on more value-added tasks, increasing their morale and productivity. Consequently, businesses due to improved employee satisfaction and higher output.
Lower Costs
Automated data capture is not just more practical. It only costs around one-third of its manual alternative. Additionally, the cost of ignoring manual data entry errors can be as high as $100 per record. The cost savings of data capture automation are tremendous and add up over time.
Better Security
Digitization helps store documents online securely, enabling users to retrieve them quickly. It is a better alternative to storing and managing thousands of papers in your office. Automated capture methods can also reduce fraud, by detecting fake signs, for example.
Future Trends in Data Capture – From AI to Virtual Reality
Technological advancements continue to shape how we extract and process information from paper and digital documents. Here are the most exciting future trends:
Intelligent Document Extraction Systems
Intelligent systems leverage AI and machine learning advancements to recognize and extract text from documents accurately. Traditional technology has improved significantly, allowing for efficient data extraction from various sources, including paper documents and digital scans. Future systems will further enhance accuracy by incorporating advanced algorithms that can handle complex document layouts, recognize handwriting, and process multilingual content.
Natural Language Processing (NLP)
NLP techniques enable systems to understand and process unstructured text, improving data capture from documents such as emails, reports, and social media posts. NLP algorithms analyze language patterns, syntax, and semantics to extract meaning and identify key entities and relationships within the text. Future tools will leverage NLP to interpret contextual information, detect sentiment, and derive insights from unstructured textual data.
Blockchain for Data Capture
Blockchain can create an immutable record of captured data by building a decentralized and tamper-proof ledger of document transactions. Integrating blockchain with data capture will allow businesses to enhance security and trustworthiness, preventing unauthorized alterations and maintaining a transparent audit trail.
Internet of Things (IoT) Integration
Data capture will integrate with IoT devices and sensors as this technology expands. IoT-generated documents, such as sensor readings, maintenance records, and reports, can be captured and processed to extract relevant information. Data capture systems can analyze and interpret this data to derive insights, trigger automated actions, and enable predictive maintenance.
Augmented Reality (AR) and Virtual Reality (VR)
AR and VR technologies can potentially transform data capture by providing immersive interfaces and visualization capabilities. AR can overlay digital information onto physical documents, enabling interactive capture and analysis. VR can create virtual environments where users can navigate and interact with digital documents and data. These technologies offer opportunities for enhanced information visualization, interactive data capture from physical objects, and virtual document analysis.
It’s 2023 – The Perfect Time to Adopt Automation
Data capture technology continues to evolve with innovations like AI and Machine Learning. These new features will make data capture more robust and efficient. So, businesses that still use manual data entry methods should look to switch to automated options to stay competitive.
Astera ReportMiner is an automated data extraction software – that does more than automate data entry. ReportMiner uses AI to classify documents and extract key fields automatically. It enables firms to automate their entire document management process, from extraction to validation.
Authors:
Junaid Baig
