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smart data transformation
Gurpreet Singh Arora
Gurpreet Singh Arora Posted on Apr 11, 2022   |  5 Min Read

Digitization penetrates almost every sphere of the company today. From likes to shares, tweets to swipes, data is being generated at a break-neck speed—oozing out of the devices we use every day, it shows no sign of slowing down. The digital world is exploding with massive volumes of data that are predominantly unstructured.

Consider these surprising figures – the total amount of data created and replicated globally has increased from 2 zettabytes to 64.2 zettabytes over the last decade and it’s estimated to reach 181 zettabytes by 2025. (Source: Aren’t these actually mind-blowing?

Meticulously collecting, recognizing, and processing—whether it is text, video, audio, or big data—can generate business value using state-of-the-art technologies. It doesn’t matter how intelligent machines are, one thing that we are sure of is they cannot absorb and interpret information like humans do use their senses. Training these machine learning and deep learning algorithms also needs the data to be in digitized form. AI-based solutions, therefore, have become the need of the hour to properly process and comprehend the data.

Automated Data Capture Solutions to Redefine Business Development

Integrating smart data transformation solutions across workflows enables businesses to be agile and nimble as they combine human intelligence with AI, ML, and RPA. The raw and unstructured public or private data can be converted to normalized and schema-compliant XML that facilitates strategic decision-making.

When we talk about the automated data capture process, it implies that machines are used to capture and transform the data into meaningful insights. These automated data collection report says that about 15% of the world’s population lives with some form of disability, out of which 2-4% experience significant difficulties in functioning. This estimate for disability is following an upward trajectory due to rapid spread of chronic diseases, aging population, and improvements in methods used to measure disability. The different types of disabilities are:and data capture solutions add value to business processes. Choosing the right solution varies according to the type of business and its strategic goals.

Here are 6 automated data capture methods:

 automated data collection

Optical Character Recognition – One of the established and proven technologies, Optical character recognition (OCR) has disrupted the traditional document management approach and remains as relevant as ever.

It is the perfect solution for digitizing loads of electronic and paper documents. This technology is widely applied across areas such as healthcare, logistics, banking, finance, governing, and so on. Another exciting feature of multipurpose OCR systems is that they can significantly optimize data capturing costs as well as automate manual and repetitive tasks. Though human reviewing is required for output—especially while working with financial reports and legal documents, the OCR solution is a prerequisite to ascertain effective document management.

Intelligent Character Recognition – Intelligent character recognition (ICR) has the potential to solve more sophisticated challenges. In this, machines are taught to process hand-written, raw documents. The levels of accuracy vary from 50% to 70% depending on various factors including cursive handwriting, block letters, fonts, styles, etc. However, there’s room for improvement and this rate can be upgraded by further training the machine learning algorithms on rich datasets.

Optical Mark Recognition – Optical mark recognition (OMR) is one of the ways to manage documents generally used to support and expedite human-marked data captured. For instance, MCQ tests, results of polls, consumer feedback, surveys, etc. The algorithm detects the location after scanning the documents and recognizes hand-written marks multiple times faster than humans. This tech-based approach helps in completing routine tasks in a timely and resource-efficient manner; thus, fostering business workflow automation.

QR Code Recognition – Have you seen how Amazon Go changed the traditional way of shopping—visitors are instructed to download the app, and the purchases are made in the check-out free store by scanning the QR code given at the entrance? This is one of the data capture methods to power the scan-and-go system.

Walmart leverages QR codes to facilitate self-servicing. The in-store registers enable the customers to scan QR codes via a mobile app in large shopping facilities.

Take another case in point, Starbucks uses QR codes to let coffee lovers learn about their favorite beverages and promote coffee roasts. Apart from the in-store locations, the QR codes are also placed on the fliers inviting visitors to scan them to get coffee products information such as expert opinions.

With data being encrypted in QR codes, automated data capture can be conducted using different systems and industry leaders have already turned this technology into a novel solution for capturing payments and merchandise data.

Intelligent Document Recognition – A lot of unstructured documents are generated in complex business processes in the finance and logistics industry. Intelligent Document Recognition facilitates data extraction from any part of the document, including tags and meta descriptions. It can also interpret tables, patterns, and content in both electronic and paper formats, identifying the start and end of documents, and sorting them according to the categories accordingly. Finally, the extracted data is prepared for storing in business apps or a database.

Such solutions are most sought after as they help mitigate risks and optimize costs to uncover new business perspectives and unearth maximum valuable data.

Voice Recognition – Voice assistants like Alexa, Siri, and Cortana have inbuilt natural language processing (NLP) technology. Simply say “Hey Siri” or “Alexa” to awaken the system, give the command, and boom—Siri/Alexa is ready with your answer.

Numerous deep learning algorithms powered by leading-edge NLP approaches require huge amounts of labeled data to digitize human speech, identify voice patterns, process big data, and so on to react like humans.

Apart from the well-known areas of technology implementation like support services, interpreting, security, marketing, etc., NLP can also be embedded in electronic data capture systems. These solutions help cumulate patient data or clinical trial data that can be entered via voice or manually.

Final Words

Among the advantages of smart data transformation are expedited ways to facilitate research, uncover perspectives, retrieve insights, and predict risks, improved customer experience, and so on. Investing in data transformation services is an efficient and financially savvy way to collect and process data with little to no human involvement. Though automated data collection and capture solutions aren’t perfect, they are continually improving and getting to the next level.

Irrespective of the industries or verticals dealing in, you must adopt an efficient way of gathering and processing big data. Partnering with a reliable service provider helps you become owners of top-tier solutions that meet niche-specific requirements—this is the key to success in the present-day highly competitive market scenarios.

Learn how you can benefit from Smart Data Transformation

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