What is Cognitive Capture?
Data capture is the process of extracting relevant information from both structured or unstructured documents and transforming it into machine-readable data. Artificial intelligence has transformed the way data capture is being done today. Cognitive capture is an advanced stage of machine learning and cognitive data is now pivotal for many successful business processes.
Defining Cognitive Data Capture
The term ‘Cognitive” refers to the process used to recognize, process, store, or retrieve information to make informed decisions. In terms of artificial intelligence, it relates more to machine learning and the capability to make cognitive data instantaneous to improve business process efficiency.
Machine learning has been in existence for several decades and has constantly evolved by leaps and bounds. Cognitive capture is the height of innovation and uses what can be termed as ‘intelligent processing’.
The Science Behind Cognitive Data Capture
What exactly do we mean by intelligent processing in cognitive data capture?
Cognitive data capture systems use machine learning by mimicking human capabilities. This technology works in the same way as humans do. It recognizes, extracts, classifies, and stores both structured and unstructured data, without the use of templates or any other rules. Deploying OCR techniques and natural language processing (NLP), it can look for words, phrases, or patterns and sorts data contextually and accurately.
Cognitive data keeps complex data simple and easy to use as document processing is not bound by types, fonts, or formats. Huge volumes of data whether structured data such as names, addresses, numbers or unstructured data such as audio, video, images, emails can be handled smoothly to make data access logical and simple.
Other Types of Data Capture
Data capture has been in existence for decades now. Collecting information from paper or electronic documents has been integral to organizational processes. What started with manual data capture has been enhanced phenomenally by the use of technology.
Though many types of data capture exist, some of the popular ones are elaborated here.
- Manual Data Capture: One of the initial and popular methods used to collect data, manual data capturing is used in many businesses today. It refers to the method of collecting data from physical forms by manually entering them into software for access. This method is tedious, labor-intensive, time-consuming, and less efficient, which is why businesses are fast transitioning to using automated data capture.
- Automated Data Capture: The current trend of using computerized technology to automatically convert any data from forms, emails, etc into readable digital data enables instant access anywhere, anytime. Businesses nowadays rely on using automated data capture to efficiently manage data requirements, reduce project and manpower costs, and enhance productivity. Many different forms of automated data capture exist, some of which are detailed in this section.
- OCR: Optical Character Recognition is a technology used by machines to recognize and read specific typefaces on printed documents including PDF files, scanned documents, forms, etc., and digitize the data for use. OCR is commonly used in verticals such as insurance, finance, healthcare, and more, where there are high volumes of documentation involved and the information is very similar.
- ICR: Intelligent Character recognition is an advanced version of OCR. This technology can read hand-printed characters written in different fonts and styles that people use and process them digitally for relevant use. ICR is used extensively by banks and financial institutions for automating customer information into their software.
- Barcodes: Barcodes are encoded data made of black bars and white spaces in varying widths that are read by a scanner. This technology is efficiently used to classify inventory and collect data automatically, which speeds up the process to save time and effort. It is commonly used in most retail outlets and healthcare.
- Magnetic Stripe Cards: Acting on the principle of automated data transfer, magnetic stripes contain encrypted data that are decoded by reader devices to receive information wirelessly. Credit/ debit cards, access cards are classic examples of magnetic swipe card technology.
- Voice and Image Capture: If you are using Alexa or Siri, you are probably familiar with the voice capture technology. It uses speech recognition technology where signals are processed to interpret speech.
Biometric security systems use image capture for identifying relevant data. This kind of data capture uses technology to convert images or sounds into digital files that are cross-verified for authorization into secured systems. You can find biometrics being used at banks, airports, workplaces, and on mobiles for granting access.
Benefits of Cognitive Data Capture
Cognitive data can optimize business workflows and ensure the smooth functioning of business processes. Here are some of the main reasons why using cognitive capture can be ideal for businesses.
1. Lowered costs
Cognitive data solutions can help in reducing business costs and yet maintain accurate data. By eliminating the need for manual labor and optimizing data capturing capabilities, it can lower operational costs efficiently.
2. Compatibility
Cognitive data capture intelligently processes any document. The technology is compatible across multiple document types and can automatically recognize and extract data from PDFs, forms, invoices, customer orders, emails, etc, to streamline business processes and enhance productivity.
3. Speedy processes
The use of NLP combined with OCR techniques makes cognitive data capture faster and easier to use. With no set rules or templates, capturing data is faster and more efficient. Such technologies also facilitate instant data access to ensure the smooth flow of business processes.
4. Accuracy
Cognitive data capture is highly accurate and error-free. It can analyze an unlimited amount of data, be it structured or unstructured, and automatically extract specific information required for successful business transactions.
Cognitive Data Capture Use Cases
Cognitive data capture can be greatly beneficial to various industries. Below are some of the use case scenarios where cognitive data can be effective.
- Healthcare - Maintaining detailed and diverse medical data in patient records. For example, every patient’s data is collected from various sources such as the registration desk, doctor consultations, lab investigations, pharmacy, other referred departments, radiology, etc. This means a lot of information is being extracted and recorded for use. This process applies to numerous patient data across various departments. Cognitive capture technology offers a wholesome solution to streamline data collection for easy availability and use resulting in effective patient management.
- Insurance - Preventing frauds in claims processing. What this means is cognitive data capture can make the insurance claim process effortless and help insurance agents make conclusive decisions. AI-powered intelligent processing makes it possible to have hassle-free form-filling. Reviewing and verifying claims becomes easier as digital images and data extraction can be carried out on various documents yet making it relevant and accurate to the smallest detail. Billing errors are detected easily and cross-verification before a settlement can be automated to prevent fraud or false claims.
- Finance/ Accounting - Help with multiple invoice processing in the Accounts Payable (AP) department. To elaborate, AP departments in large organizations may receive invoices from multiple suppliers in the form of PDFs, emails, or as hard copies. Classifying and extracting data can become an error-prone and humongous task. Using cognitive data capture technology can help in automating invoice capture/processing and simplifying the entire process. Extracting key information from any kind of document, cross-checking with appropriate Goods Received Note (GRN), delivery notes, classifying invoices for approvals all help in faster and error-free invoice processing.
- Banking - Maintain customer records and help with individual loan processing. For example, banks handle thousands of customers who have different requirements like opening accounts, applying for loans, cash/cheque/ wireless transactions, and many more. Banks use a lot of data from different forms starting from a Know-your-customer (KYC) form, loan applications, challans, etc. that can be manually time-consuming and exhaustive. Cognitive capture can minimize time, effort, and labor to make these varied processes easier. By recording, classifying, and maintaining individual records of customers, it can provide instant access to banks to process applications, verify and sanction loans, update and transfer amounts between accounts ensuring smooth flow of operations.
- Logistics and Transportation - Logistics involves dealing with a vast amount of unstructured data such as billings, third-party vendor information, location, dates, and numerous other details. Ai-based cognitive capture can deal with these massive data to extract vital information needed to complete a particular transaction. In transportation, it can provide access to shipping data, help in optimization of routes to lower shipping costs, generate corresponding information for validating invoices, and track delivery completion.
- Education: The education sector is huge and uses a huge amount of data relating to various institutions, universities, subjects, students in its processes. Cognitive capture can help automate document-driven processes and make them simpler to use. For example, it can help institutions manage student information, maintain reports, prepare or validate student transcripts and centralize the data across centers to streamline processes and lower operational costs.
Conclusion
Cognitive data capture has changed the way data is available to make business processes smooth and effective. Using a combination of techniques to recognize, extract and classify data has potentially paved the way for error-free maintenance of data that spurs expertise and efficient decision-making. It is wise to acknowledge that cognitive data capture solutions open a multitude of possibilities for business management and have become the need of the hour.
Here's a slide summarizing the findings in this article.
