6 Automated Data Capture techniques For Business Development
Digitization has now pervaded all aspects of the business. Individuals generate 2.5 quintillion bytes of data every day, the majority of which is unorganized. Big data, whether audio, video, or text, can provide economic value when properly gathered, recognized, and processed using cutting-edge technologies.
However, no matter how sophisticated robots are, they are incapable of acquiring automated data collection process through their senses in the same manner that people do. Data in digitized form is required for training machine learning and deep learning algorithms. As a result, AI-based solutions would be able to process and understand it.
The automated data collection process
Using technology with innovative ways to capture data enables machines to translate it into relevant insights is known as automated data capture. The type of business and strategic goals influence the automated data-collecting technologies used. Here are some data automation examples of how big data is critical for organizations to succeed.
Another phrase we may utilize to explain how automated data capture systems work is automated data collection methods. The automated data collection process is enabled by technology that recognizes and collects data. Businesses benefit from automated data capture systems because they automate processes.
The following are some of the advantages of data capture technology examples:
Costs and paperwork are reduced.
There is less chance of human error.
What Do OCR, OMR, and ICR Mean?
OCR (optical character recognition) is a well-known and well-proven technique. After disrupting the traditional method to document management, technology is still as vital as ever.
It’s a popular choice for digitizing massive amounts of paper and electronic information in industries including logistics, health, finance, banking, government, and more. Multipurpose OCR systems efficiently minimize data capture costs, automate typical manual activities, and take over repetitive jobs for human personnel. Despite the fact that the solution necessitates human evaluation – notably, when dealing with official documentation and financial reports – it is a must for cost-effective document management.
Statistics back up the technology’s efficacy. The global OCR market is expected to reach a value of over 25,000 dollars by 2025. It will be 14.8% higher than last year. North America has the greatest number of adopters, followed by Europe.
Optical mark recognition is another method of document management (OMR). The technology is frequently used to quicken up and simplify the capture of data that has been marked by humans. Polls, multiple-choice tests, customer feedback, and surveys are data automation examples. The technology finds the location and recognizes hand-written marks several times quicker than human workers after scanning the documents. The tech-based strategy encourages firm workflow automation by enabling machines to execute routine tasks in less time and with fewer resources.
Intelligent character recognition (ICR) is used to tackle more difficult situations. Teaching machines can now process raw, hand-written materials thanks to new technologies. Depending on typefaces and styles, block letters, or cursive handwriting, the level of accuracy could range from 50% to 70%. This ratio could be improved by further training the algorithm on big datasets of specialized data.
For a Large Number of Documents, IDR
In the financial and shipping industries, for data capture technology examples, complex business processes generate large volumes of unstructured documents. Data can be extracted from any part of a document, including the meta description, using IDR. The technology not only uses optical character recognition but also improves it. It can recognize the beginning and conclusion of documents and classify them into groups based on patterns, tables, and content in both paper and electronic formats. Any necessary data can then be extracted and prepared for storage in a database or used in business applications.
IBM Watson, for example, recently released an IDR-driven functionality for working with business and governmental documents. These kinds of business solutions are in high demand these days since they help prevent risks, save time and money, and find new company perspectives.
Detect QR Codes to Improve Customer Service
Because data exists in numerous formats by definition, such as being encrypted into QR codes, automated data capture techniques can be carried out using a variety of techniques. The number of connected use cases in retail and in-store payment is growing at an exponential rate. Among others, Walmart, Starbucks, and Amazon have turned the technology into an inventive solution for recording data about things and processing payments.
Amazon Go is expected to disrupt the typical purchasing experience. To make purchases in a checkout-free store, each visitor is prompted to download the app and scan the QR code presented at the door. It’s just one of the automated data collection methods that the scan-and-go system employs.
Starbucks places QR codes around in-store locations and on fliers to encourage customers to scan the codes and be redirected to coffee product information, including expert opinions and even music from the region where coffee is grown, in order to promote coffee roast and allow coffee lovers to learn more about their favorite beverages.
Walmart, the largest retailer in the United States, uses QR codes to allow self-service. Several in-store registers in large shopping malls enable customers to scan the code using a mobile app and pay for products with their cellphones.
For Smooth Interactions, Use Voice Recognition
According to Econsultancy, voice search will account for 50% of all web searches by 2020. Natural language processing (NLP) technology is used in voice assistants like Google Assistant, Siri, Alexa, and Cortana. “Hey, Google” or “Alexa” are simple commands that wake up the system, which decodes the command and then obeys or generates an answer.
Deep learning algorithms are used in cutting-edge NLP approaches to recognize and interpret voice patterns, digitize human speech, and analyze large volumes of data in order to react in a human-like manner.
NLP can be incorporated in electronic data capturing systems, in addition to other prominent areas of technology adoption such as interpreting, support services, marketing, and security. Clinical trial data or patient data that can be input manually or by voice can be aggregated with such technologies.
Important Points to Remember
Time-saving and cost-effective methods of data gathering and processing with little to no human participation, innovative ways to capture data to recover insight and foresee hazards or reveal viewpoints, improved user experience, facilitated search, and more are all advantages of automated data capture techniques. When it comes to drawbacks, automated data capturing systems aren’t perfect, but the error rate is steadily decreasing.
Any business must establish a method for collecting and analyzing massive data. They can become owners of top-tier solutions that fulfill niche-specific criteria by collaborating with a dependable automated data collection methods provider. It is, without a doubt, the key to success in today’s extremely competitive economy.
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