4 Ways To Automate Data Entry Processes

4 Ways To Automate Data Entry Processes

It is a strange conundrum that data entry, a process that seeks to digitize information in a manner that it could henceforth be accessed with ease, digitally, should be a manual system. We have not been able to devise a method through which the end goal of digitization and automation can be met by automation at the first step itself.

Then again, maybe it is not so strange. The reason for the existence of data entry in the human world, as an activity for which money gets exchanged for one side to do, and the other side to get done, data entry and related work, is perhaps that better ways do not exist. If there were better ways, human beings would be deploying them.

As all things manual, manual data entry has limitations. Data entry automation is an ongoing effort on part of humans to find more efficient and less error-prone processes for data entry.

Benefits of data entry automation

Automation is a journey. It is a change of status quo. An effort at automation means that currently that particular process is not automated. There is an intent of automating the process, partially or fully, in an effort that some value is released for the business that seeks to automate.

Data entry is no different. If there are ongoing efforts to achieve automation, it means that there is some value that will be released if it were to be achieved. It means that there could be limitations with the way it is being handled at present and that if the same were to be changed it could result in some benefits.

As a leading provider of data services, oWorkers works with partners on varying processes and tools for data entry, creating new knowledge and processes that will benefit everyone involved. We have been identified as one of the top three data service BPO companies in the world on more than one occasion.

Let us make an effort to understand the benefits of automating the data entry process.

Reduction in errors

For human beings every action is deliberate. It is a result of a force that motivates us to do something while being guided by our thoughts, emotions, moods and all the other elements that constitute a human being.

As a result of the complexity of their being, they will come across as different creatures from time to time. Motivated and alert at some times, sullen and lethargic at others. Happy and involved at some times, sad and aloof at others. As a result, their performance also varies. Human beings are prone to making errors. They are not machines who, given a constant input, will always produce the same output. Leo Messi might shine in one game of football but be totally off color in the next. A call center executive might wow a customer on one call but manage to completely put off the caller in the next one.

Recognizing this aspect, organizations built checks and balances to ensure that errors are caught and corrected. They have various methods of doing so, the most common ones being to either get two different people to do the same work and pass it if it is identical and catch it if not, or to have a quality checking system that does either a full audit of the data entry or based on samples. Both these methods entail cost of the organization.

Data entry automation is expected to result in a reduction of the errors being made in human data entry. Not just reduction, but elimination of errors in the parts that can be handled through automation.

Many of our clients from the US and Western Europe testify to the quality they receive from oWorkers, which is at least as good as their pre-outsourcing quality, while enabling them to shave almost 80% off the processing cost.

Quicker processing

Human beings will work at human speed. Sequentially. If a good typist can type fifty words in a minute, most human typists will be typing at similar rates, with some exceptional ones going to maybe sixty and a few poor performers struggling at around forty, with all others in between. This is the rate at which they are able to type, regardless of the situation and requirement. Attempts to type faster could result in a greater number of errors. If the volume of work goes high, more typists will need to be engaged to finish it in a given time or the work will take longer to finish compared to a smaller volume of work. Human beings also need time for rest and rejuvenation and sleep. All these activities reduce the time available to do productive work.

Machines, or computer programs, have no such constraints. They can work day and night. Their ability to type is much faster than humans. The constraint they face is that they are not as smart as humans. However, if the data entry can be done based on defined rules and the software can be programmed on how to do it, a much greater volume can be processed in a much shorter period of time.

Our leadership team, with over 20 years of hands-on experience in the industry, are constantly pushing all project teams to deliver optimum outputs.

Employee can move up the value chain

Being the thinking, sensing beings that they are, human beings have a preference for activities that provide a challenge, mentally or physically. Data entry often provides neither. That is why many types of data entry jobs are considered as monotonous, dull and repetitive and are often placed closer to the bottom end of the organizational hierarchy. Doing them day in and day out is a source of mental fatigue and burnout for humans. It could also be the cause of various other conditions like carpal tunnel syndrome, eye strain and neck-related issues. This, in turn, becomes a cause for further errors.

Data entry automation can deliver human beings from the drudgery of certain types of data entry work and enable them to move up the value chain where they can be more mentally challenged by the work they need to do. With the plain data entry work done by machines, humans can start essaying roles that require thought and some amount of decision-making.

With a model of employment, as opposed to freelancing and contracting adopted by some competitors, oWorkers is constantly exploring avenues for enriching the jobs of our colleagues.

Lower costs and greater value

Development of automation is an investment. It takes time and money. Often, during a research and development cycle, it is not certain how long it will take for the investment being made to be recovered. Or, indeed, if it will be recovered at all.

However, once developed, it will take a much lower cost to run as compared to a human, who needs to be paid all the time so that he can be fit for the job that he needs to do. Thus, each day or hour that automated data entry is done, it will be saving money as compared to the manpower-driven model. Further, by accumulating these savings over a period of time, a point will come when it will exceed the amount of money spent in building the solution. That would be the day when payback would have been achieved.

oWorkers understands the need for manual data entry and supports many clients in the same. As a preferred employer in the communities we work with, oWorkers attracts a constant flow of talent interested in working for us. This enables us to hire throughout the year while keeping our costs low. The benefits of low cost are shared with our clients. For all manual data entry requirements, oWorkers is a great cost-effective option.

Data entry automation

Now that we understand that there are benefits that will result if we are able to automate the process, let us explore how it could be done.

Make no mistake; this is not a low hanging fruit. The low hanging fruits are gone. Data entry is a mature industry. Any advancement today has to be based on deep thought, intelligent effort, and investment of resources and money.

There are some emerging technologies that hold promise of revolutionizing data entry processes.

Optical Character Recognition (OCR)

OCR is the process of digitally converting information in a format that is not machine readable, such as a typed, printed or handwritten document, and transferring it to a format that can be understood by machines. This is the objective of most manual data entry too. This technology has been in use since the nineties and has developed over time to become more accurate.

An early popular use of OCR technology was in banking where clearing cheques, which already had the bank’s sort code pre-printed on them, with amounts encoded by the presenting bank, would be sorted by the reader-sorter device of the clearing house and debits and credits between banks resolved, instead of manually sorting each cheque and adding up values. However, that early use was based on the characters to be read being formed and placed in a defined manner. Data entry requires characters in any shape and form, placed anyhow, to be read and understood, which is where the focus is now.

OCR has played a big role in digitizing historical information, like old newspapers, into formats that can be easily stored, searched and accessed.

With its high standards of data and information security, compliance with GDPR regulations and ISO (27001:2013 & 9001:2015) certification, we work with leading as well as developing technologies such as OCR on behalf of our clients.

Natural Language Processing (NLP)

The discipline of NLP operates at the intersection of linguistics, computer science and artificial intelligence. Human beings are a part of the mix as well, since it is their language that is being deciphered and decoded. The objective of NLP is to understand and make sense of the human language, usually the spoken word or words. Once that is achieved, other, related objectives could emerge, talking back to humans being one of them. Many of us have experienced Alexa and Siri. Their responses to our verbal inputs are the result of NLP engines working in the background. NLP is expected to be one of the stepping stones to data entry automation.

What makes this challenging is the complexity of human beings and their language. While it is reasonably easy for humans to learn and master a language, it is not the same for a computer. There are nuances that are complex. “Really!” could be an enthusiastic confirmation of a person’s query, “Did Usain Bolt really run the 100 meter sprint in 9.58 seconds” or it could be a sarcastic “Really?” in response to the person saying “I ran the 100 meter sprint in 9.58 seconds today.”

With services in 22 languages across our three main sites, language processing has been an area of strength as well as interest for oWorkers. We are taking it to the next level with participation in NLP projects with clients and technology providers.

Robotic Process Automation (RPA)

RPA is a form of automation through which I machine, a software program really, learns to mimic human actions and is able to carry them out in future transactions of a similar nature. Left to themselves, these ‘bots’ are able to perform the assigned tasks much faster and with greater accuracy. From the data entry perspective, a human being might be able to read a few words on a page at a time and transfer them to a digital medium, in a few seconds. An RPA ‘bot’ on the other hand, might do it one character at a time, but it will be done in a flash so that it would have finished several pages before the human finished with the first set of words. And flawlessly, based on the instructions it had been given. The human, despite instructions, could make mistakes.

The human will only have an advantage where the input content deviates from the defined rule and some thinking is required. Here the ‘bot’ will fall short and either make a mistake or wait for guidance from its human master. Thankfully!

oWorkers has worked out flexible tie-ups with technology companies that allows us to access their cutting-edge technologies to be deployed for client work. Through our network of IT partners, we access the latest technologies like RPA.

Artificial Intelligence (AI) for data entry automation

AI is the buzzword in technology. At least one of the buzzwords.

AI is the name given to a technology that seeks to enable a machine or a software program embedded in the machine, behave like a human and handle inputs accordingly. Formatted text has been intelligible to machines for a long time, as long as software programs have been in existence, as this was the method programs were created that allowed computers to process. However, unformatted text, and other formats in which information exists, like unformatted text, a doctor’s handwritten prescription for example, or images, or audio, or video, has been beyond the scope of machines.

With the help of AI, and its sister discipline Machine Learning (ML), machines are learning to understand and process unformatted information as well. Not only that, once the initial training has been done, many algorithms even permit the program to keep learning from the additional information that they keep receiving once they have been ‘deployed’ and keep adjusting their output accordingly.

oWorkers has been actively engaged with clients as they have built their AI models. Data annotation, tagging and labelling are areas where we have worked with a wide set of clients from around the world.

Conclusion

While the new technologies make headway in data entry automation, there are some steps that we can take even today that will facilitate the process. These will probably be useful even when we deploy one of the evolving technologies.  These can be achieved through a simple ‘eyeballing’ of the information which does not take time. Once input incorrectly, it will take a lot more effort and time and money to retrace steps and redo. These are:

Standardize the process – this will create consistency in form filling and manual operators will know what to expect

Ensure data consistency – Already filled information, if eyeballed, can reveal inconsistencies, if they exist, at a glance, and save much rework later

Periodic review to eliminate redundancy – To ensure redundant information is not being taken, like DOB as well as Age, and removing fields no longer relevant

Create logical checks in input forms – Checking for DOB format or no. of characters in the mobile number field

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