Machine Learning in Data Capture Outsourcing
Machine learning is embraced globally and is very evident wherever one may be. In response to our voice commands, digital assistants play music and surf the web. Websites are recommending products based on what the consumers watched, listened to, or bought. Robots are vacuuming floors; spam detectors stop undesired emails from entering inboxes. Doctors see tumors much easier through medical image analysis, and cars can now run on the road with the autopilot feature.
Expectations continue to rise as time goes by quickly. Machine learning will continue to drive more significant and more prominent productivity. It will continue to be more efficient as scientists continue to develop more algorithms, as computers become more powerful and more affordable. Big data continues to grow.
Defining Machine Learning
Machine learning concentrates on creating applications that learn from data and enhance their accuracy with time without needing to be programmed to do so, making machine learning a branch of artificial intelligence or AI.
An algorithm, in data science, is an order of statistical processing steps. In contrast, algorithms in machine learning aim to find features and patterns in vast amounts of data to make predictions and make decisions based on the new data acquired. Better algorithms result in better and more accurate predictions and decisions as more data get processed.
Machine Learning: The Applications
The start of the internet era marked the increase of the applications of machine learning.
Manufacturing serves as the backbone of healthy economies. Machine learning greatly helps modify the manufacturing sector, from optimizing resource planning to shortening the time to market.
Machine learning also helps in saving a company’s money—for instance, the UPS. ORION, or On-Road Integrated Optimization and Navigation, is utilized by UPS in managing their fleet systems. The up-to-date algorithms of ORION create optimal routes based on the data supplied by the customer for delivery drivers. The drivers could also change the routes on the spot depending on the needs and sudden changes like weather conditions or accidents. ORION’s algorithm would also optimize more routes to attend to the deliveries that need to get sent and completed. With ORION, the time and cost savings and emission reduction are exceptional.
Artificial intelligence is a big hit in the healthcare sector. Traditionally, healthcare is highly dependent on highly skilled professionals and manual interventions, but through machine learning, coming up with a better patient diagnosis, data-driven decisions preventing diseases, and faster root cause identification and detection are made possible. Major platforms, like Facebook, Google, and Qualcomm, also invest in machine learning-based healthcare research.
Machine learning plays a crucial role in digital marketing in a world that has 25 billion-plus connected devices. Ad clicks lead to predictions that would then show relevant ads. In marketing, churn analysis and identifying target customers are vital applications of machine learning.
Digital Media and Entertainment
Machine learning applications are huge in social media, digital media, and entertainment. The vital applications include user behavior analysis, social media analysis, monitoring, spam filtering, and personalized recommendation.
One great example is Netflix. Through machine learning, Netflix can create targeted programming selections. Their most triumphant algorithm, Netflix Recommendation Engine, or NRE, filters content based on every individual user profile. The algorithm filters 3,000 titles at once using 1,300 recommendation groups based on a user’s activity and preference. Additionally, 80% of the activity of Netflix viewers gets driven by the algorithm’s personalized recommendations showing how accurate the algorithm could be. Massive platforms like LinkedIn, Amazon, Instagram, YouTube, and Spotify also use recommendation engines to boost and make the business boom.
Machine learning developments, at present, is the key stakeholder of e-commerce’s modification. Personalized recommendations get seen whenever one browses through an e-commerce website. This level of personalization develops through collaborative filtering or content-based filtering. E-commerce is leading the market for the reason that large-scale user data is more available than in retailers. Fashion designing also uses machine learning. Myntra, a leading Indian E-commerce machine learning development, has deep learning systems which design various brands.
Human Resource Management
Machine learning is just getting started. Even though it is still new and young, machine learning, at present, is being utilized to manage human resources. Video analytics and bots get used by big organizations like HDFC Bank and Amazon at different phases and stages of their recruitment process. Human resource optimization also uses IBM Watson.
Chatbots are commonly used by many different organizations for customer services whenever concerns arise. Chatbots continue to change the customer service terrain to a considerable length, mainly because they are cost-effective. The advanced text to speech, speech to text systems, and automated translations continually help to prevail over language barriers.
Machine Learning in Data Capture Industry
Although data capture assumes various forms, it tends to be repetitive and mechanical at its most elementary form. Whatever the form, all data capture but have the same underlying goal: to transform an existing document or pieces of information into more accessible actionable data in digital format.
As companies across industries increasingly adopt data capture outsourcing, it may come as no surprise that more professionals and organizations who specifically handle data capture processes also increasingly emerge.
Although these external service providers are responsible for most of a company’s data capture tasks, still, some aspects are left to certain in-house positions. For one, a receptionist in a clinic might have to capture patient information data during scheduling. For another example, a boss might leave a business analyst with the task of transcribing or editing existing performance data for use with software tools.
While the data capture process may have a wide girth, there are instances when everyone involved is required to participate in data capture for a certain goal. The most common occurrence of this is the accuracy and security of data, such as resolving transcription and transposition errors.
These errors are not only costly, but they also lend true data capture harder. Furthermore, errors add more burden to the data analyst professionals’ painstaking task of assessing the integrity and accuracy of captured data and information.
However, on the bright side, transcription and transposition errors can now be prevented or overcome by adopting newer technologies such as machine learning and Robotic Process Automation (RPA). These technologies avoid or rectify errors and make life a lot easier for professionals behind the data capture processes.
Robotic Process Automation
Robotic Process Automation (RPA) is a fairly new business process automation technology that involves creating repeatable and verifiable operations necessitating minimal human input and oversight. Essentially, RPA automates mundane, programmable tasks allowing in-house employees to shift their focus and devote more time to higher-value tasks.
Unlike human experts, RPA systems do not grow tired and never get burnt out. Thus, eliminating the possibility of human errors and ensuring correct and accurate data.
Aside from freeing employees of repetitive, time-consuming tasks, RPA systems can also function for mass data capture or mass document generation, as in mass emails. Moreover, RPA can also analyze and process lists and other data, and more.
In terms of data capture processes, Robotic Process Automation and machine learning reduce the occurrence of errors, optimize performance, and heightens data security as fewer people have access to sensitive data.
Apart from the undeniable contribution in data capture processes, RPA also benefits “knowledge workers.” More specifically, RPA systems free knowledge workers, or people whose jobs largely involve handling or using information, are liberated from highly structured, routine, and monotonous tasks to focus on more engaging work.
Ultimately, Robotic Process Automation reduces, if not eliminates, data capture anomalies such as transcription and transposition errors, thereby maximizing cost savings, accuracy, and overall efficiency.
Machine Learning and OCR
Machine Learning and Optical Character Recognition (OCR) is one of today’s most adopted data capture solutions. In essence, OCR allows for a digitized scanning and recognition of written and printed text. The OCR works by scanning the printed document text, analyzing it, and translating it into character codes, thereby digitizing the whole text.
Although OCR is a viable option in transforming human-to-human communication into a digitized text, it falls short in converting more structured documents such as forms that necessitate the use of machines.
Human-to-human communication takes the form of free text and is called unstructured data. Although such unstructured data can be easily understandable and great for human-to-human communication, machines can make no sense of such data, lending them unusable. However, this problem is overcome by OCR as it converts unstructured data into machine-readable codes, thereby making them searchable and accessible for human consumption.
Even though OCR can capture and covert texts into machine-readable codes, it can only do so for unstructured data. On the other hand, forms are designed for human-machine communication, allowing machines to automatically act on the data humans provide. With that, service providers must still process OCR results with machine learning to transform machine-readable data into machine-actionable data.
OCR Use Cases
As OCR is still a relatively new technology for business process automation, most industries still employ legacy systems. However, as companies increasingly shift digital, OCR technology is projected to be a business necessity. The following use cases further support such projection:
Among the most popular use cases for OCR includes digitizing books and unstructured documents that render human-to-human communication easier. One such example is Google Translate OCR technology that allows users to read in any language.
OCR also holds promise in its use case for the banking industry. With OCR, banks can capture and digitize information found in checks, such as account information, handwritten amount, and the customer’s signature. Furthermore, OCR can also be useful in capturing sensitive information in mortgage applications and payslips.
OCR can also contribute to the ever-growing insurance industry. Particularly, OCR can automate insurance claims processing for faster transactions.
OCR allows legal firms to have their printed documents such as affidavits, judgments, filings, statements, wills, among others, digitized.
The OCR can greatly contribute to the healthcare industry. Specifically, the OCR technology can digitize data from reports containing X-rays, patient’s history, treatments or diagnostics, tests, and overall hospital records.
Through OCR, tourists and hotel guests can now check-in automatically by simply scanning their passports to a hotel’s website or mobile application.
With mobile OCR, customers can now redeem vouchers by scanning for serial codes using their mobile phones.
Undeniably, data capture outsourcing is necessary for the better management of actionable data. Even more helpful is the emergence of business automation processes, particularly machine learning, that can operate 24/7 and work significantly faster than human experts, automatically optimizing data capture outsourcing processes.
As machine learning continually develops, it goes beyond data capture and holds significant use cases across multiple industries.