Will AI replace data entry jobs?

Will AI replace data entry jobs?

Data entry has been the gateway for much of legacy content created by mankind, to be digitized, making it suitable for all the applications and uses that any digital content would have. Whether it is analysis, whether it is running mathematical formulae on, or whether it is for smooth transfer from one application into another.   

In the present day, the job of data entry appears to be an anachronism, a dinosaur that ancient man, about twenty years in the past, might have been comfortable with, but that has no place in today’s internet connected world of virtual meetings, space travel, drone deliveries, driverless cars and artificial intelligence (AI).

After all, as digital systems have become widespread, as required legacy content held on manual output media, mainly paper, has gradually been digitized, should the need for data entry not have substantially reduced?

But, guess what, data entry continues to be a huge need for companies all around the globe, with new types of data entry requirements coming up, like data annotation services.

 

What do we mean by data entry?

What exactly is a data entry job?

Let us take a look at what constitutes data entry and what the types of jobs that are referred to as data entry jobs are. A perusal of the job descriptions of a few data entry job postings might throw up some of the following requirements:

  • Compiling information from various sources in preparation for data entry
  • Reviewing information to ensure uniformity prior to initiating entry of data into an application
  • Entering information into a database from manual records or typed sheets
  • Typing out a handwritten document or types sheets into a word processor
  • Running reports to vouch for accuracy of data that has been manually entered
  • Preparing a database to receive manual entries
  • Researching and obtaining information, where found deficient or inaccurate
  • Checking for accuracy of input of other operators
  • Generating reports and performing backup operations as assigned
  • Running documents through OCR software for the purpose of digitization
  • Checking for accuracy of documents digitized through an OCR process

This is only indicative but, hopefully, provides an understanding of the day-to-day expectations from data entry operators. It is often said that the job profile of a data entry clerk is so vast that it might be better to describe it by stating what it does not do than what it does.

There are additional dimensions. The work might be for the insurance industry or shipping. Sometimes the data entry may need to be done for the Sales team of the company and at another time for the HR department. Each data entry project would have its own nuances and variations.

What does make it possible is that in most cases the work is repetitive. Thus, people with little prior knowledge of or experience in data entry can be trained for work as data entry clerks. Hence, data entry projects can be executed manually with a great deal of ease.

 

The push towards automation

Though data entry projects may be easy to execute manually, man’s push for automation is relentless. And we know the major reasons for it.

Machines don’t make mistakes

Rather, they don’t make random mistakes. They can make a mistake, if trained or programmed incorrectly, but they will make the same mistake again and again, without fail, each time the same situation is encountered. Thus, even if they make a mistake, it is likely it will be detected with relative ease.

Unlike mistakes made by humans, which can be varied and difficult to detect without significant investment in control processes. Humans might also make the same mistake again, but they also keep making new mistakes.

They are easier and cheaper to maintain

Human beings need food and water at the minimum, plus housing and transportation and entertainment, and many other things. For these they need to be compensated adequately. But these may not be enough. Humans also need social and emotional bonding, a sense of purpose as well as fulfilment which even considerate employers cannot do much about.

Machines don’t have any of these requirements. They may need power to run and occasional maintenance and servicing, and nothing more. The result is that while automation may require an initial investment, in the long run it works out cheaper.

They don’t tire, or aspire

Six months into a data entry job an operator might start wondering about a raise, or better work profile. A machine will never. A human being will need sleep and a process of refreshment and rejuvenation. Machines will generally don’t. Most importantly, doing the same thing day in and day out will lead to burnout for a human, but never for a machine. Repetitive tasks is what they were created for. They don’t want promotions or pay hikes. They don’t feel bad if someone else has been assigned a better work profile.

They can perform dangerous tasks

While we know about dangerous tasks in the physical world that are increasingly being done by machines, like sweeping streets in a war-torn city for mines, or collecting radioactive waste or removing slag from molten metal, there are parallels in the digital world we perhaps know less about. Moderation of content on social media sites is one of them, where workers have to contend with the possibility of having to deal with graphic violence and abuse that can leave scars on the psyche.

oWorkers has been supporting automation initiatives of clients with their access to the most modern technologies, thanks to their partnerships with leading technology companies. The engagement allows them access to the most advanced technologies that it can deploy for client projects.

It is led, from the front, by a management team with over 20 years of hands-on experience in the business. The leadership team is supported by an Internal Quality team that acts as its eyes and ears on the shopfloor, while implementing best practices and undertaking process improvement initiatives in tandem with the delivery teams.

 

Changing profile of data entry work

With the relentless push for automation, how have data entry jobs sustained?

One of the things that has happened is that the data entry work profile has kept changing. While some of the existing data entry jobs may have been automated, paradoxically, new technologies and new processes are giving rise to entirely new requirements of data entry.

Data entry is a process through which each character that is input has a distinct and unique identification. By pressing the keys on a computer keyboard in specific sequences and combinations, we transfer manual information into digital. The computer system recognizes each key on the keyboard and is able to store the information based on it. However, the right key to be pressed is determined by the human doing the entry based on her interpretation of the character or symbol appearing on the manual media. This has largely been a manual process since character formation in handwritten, and even typewritten, information, has tended to vary. Besides, the same information could be organized differently in different places.

The data entry that we know and understand has been digitizing legacy content for many years. It was focused on content held on legacy media like paper and considered relevant for the future. It could be a document, or a set of numbers representing temperature readings maintained by the meteorological department, or municipal records of birth and death.

As in other spheres, data entry has also been the subject of automation efforts. The two most commonly known ones are Optical Character Recognition (OCR) and Artificial Intelligence (AI) based approaches.

Optical Character Recognition (OCR) is able to take in each character or symbol as an image, match it to a library of valid characters in its database, and make the entry. While there has been some success with structured data, the jury is pretty much out on its suitability for unstructured data.

Artificial Intelligence (AI) is in the fray for automation of data entry, among the many applications it seeks to make a difference in. The broad objective of AI is to get machines to think and process information like humans, so that they can handle unstructured information. While human beings, with their intelligence and creativity, can interpret raw data, a software, or an AI engine, cannot, unless trained for the purpose. This process of training is based on converting raw data into a format that can be understood by the machine. Let us call this smart data.

Raw data is converted into smart data through a process known as data annotation, or data annotation services. This conversion is through a data entry process that can be handled primarily through human intelligence. For example, if an AI model is being developed for a driverless car, it needs to be fed examples of the scenes it will encounter while driving, and connect them with actions it is expected to take. For example, it needs to be shown what a traffic light is, and what action to take based on the color of the light. This is done through data annotation services that oWorkers has been providing to clients from around the world for over 7 years. The larger the data that is fed into the model, the more intelligent it becomes. It reaches a point where, based on the information it has ingested, it is able to recognize the next scene and take action based on the training it has received. This training process is known as Machine Learning (ML) and huge data sets have to be created through the manual data entry process of data annotation services.

Wittingly or unwittingly, AI has spawned a need for increased data entry.

With experienced providers like oWorkers, you don’t have to worry about the volume of your data annotation requirement. oWorkers has been recognized as one of the three best providers of data based BPO services in the world.

 

Can AI eliminate data entry?

This question is a bit like asking, “Can data annotation services be automated?”

While one can never predict the future with any certainty, it will be useful to examine why human beings are needed for some activities.

They can process unstructured data

The depth and complexity of the human brain has not been fully grasped. Many times, there is no logic or reason, but we just know. If a big snorting bull is rushing towards us, we know we need to take evasive action even though we have never encountered the situation earlier in life. Machines, with the help of AI, may be able to process unstructured data, only to the extent of what they have been taught.

They can create

Human beings are creative. When faced with challenges they can create out of the box solutions. Machines cannot. Musicians, film-makers, writers, etc., your jobs are quite safe.

They can connect and exchange subtle signals

When you communicate with another human being, the communication happens not only at the verbal level, but in many other ways. Whether you are fidgety, or looking directly at the other person, or keep glancing at your watch, are all signals that have meaning and the quality of action taken as a result. Machines can only understand the overt.

AI needs humans to develop

And, in a strange paradox, unless humans create AI, with diligent feeding of voluminous ML, AI cannot replace humans. So, as per current technology, AI can perhaps be only as good or as bad as its human trainer and developer.

 

The last word

The reasons human intelligence is needed for some activities are the reasons data annotation services have to be handled manually. The AI model will only recognize a traffic light once it has been trained by a human. The AI model will only understand that the car needs to stop when the light is red when a human has trained it. Hence, at the primary level, when new experiences are being ingested by the model, human involvement appears to be essential. It is possible that once created, some output from data annotation services may be shared with other AI models. However, creating an AI model capable of handling primary training situations, that have not been encountered earlier, appears to be a far cry at this point.

oWorkers operates from super secure facilities with protocols for enhanced data security. It is GDPR compliant, ISO (27001:2013 & 9001:2015) certified, and able to operate either from office or from home.

It operates out of three global locations and employs a multicultural team that enables offering services in 22 of the most commonly spoken languages of the world. It receives a constant stream of walk-in talent interested in employment which provides choice of talent while keeping hiring costs low. This also provides the flexibility to ramp up at short notice, by almost a hundred people in 48 hours, to handle volume spikes.

No doubt human beings will continue to move processes into the ‘automated’ bucket. However, as we have seen in the case of AI, in some cases, even the creation of automation tools creates the need for manual data entry services.

The day when a new requirement for data entry, such as data annotation services, can be handled through an automation tool without use of human intelligence, does seem far at this point.

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