iTech Data Services

Which is the best option for you?

01Mar
Read Time: 4 minutes

Tasks involving data entry are the most tedious, repetitive, and time-consuming jobs in any office setting. For this reason, many companies explore other options so their workforce can focus on value-adding tasks. In other words, it aims to produce accurate data in a short period without exhausting its employees. They are often between two choices, such as traditional data entry outsourcing and machine learning data capture. Data entry outsourcing acquires work from people outside of the workforce to ease their burden.

Organizations that are focused on data include logistics companies, financial services, healthcare, legal, and real estate, depending on the traditional data entry method for documentation. Alternatively, machine learning data capture offers the same advantage but with the use of artificial intelligence instead. It provides a competitive edge given that the algorithms can tremendously reduce manual errors in data entry reports.

Moreover, our drastic transition towards the digital age led to the adaptation of more cutting-edge technologies. We can now leverage machine learning to make data entry more comfortable, more manageable, and productive. The deployment of intelligent systems and solutions makes businesses more productive and reduces high operational costs.

Traditional Data Entry Outsourcing

Manually typing texts is a centuries-old practice of documenting data for safekeeping and duplication. Performing the task is so simple, requiring little effort to implement. However, this task’s repetitive nature is one reason why such positions have high turnover rates. This conventional method is still applied to this date, although many organizations found a way to avoid compromising their productivity in the process. Companies coordinate with outsourcing organizations to deploy data entry specialists that are up for the task.

Data entry outsourcing allows companies to complete tedious tasks without sacrificing the productivity of their workforce. The average typing speed of a data entry specialist is around 50 to 80 words per minute. This typing speed means that the outsourced employees can type a large volume of data in a short period. While outsourcing for this role seems like the best decision, there are still several disadvantages that organizations must consider beforehand:

Data vulnerability: There is always a risk involved when you let confidential data be accessed by a third-party. When you outsource even the simplistic data entry tasks within the company, this still means that these employees can gain access to your business processes such as HR, recruitment, accounts and finance, payroll processing, and claims processing. Businesses must coordinate with outsourcings companies that adhere to strict security policies and measures that prevent leakage of sensitive information.

Divided Attention: Like any other third-party provider, outsourced data entry specialists deal with multiple clients with different needs. Although they possess great expertise in the field, there can be a conflict if you are looking for someone with complete focus on your tasks. Looking for outsourced vendors that set quality standards can be the solution for this predicament. It is integral that companies know the other party’s priorities before making any transactions with them.

Work With Inconsistent Quality: One of the most common dilemmas that most organizations encounter with outsourcing is inconsistent output. As previously mentioned, these employees often handle multiple clients, which can be the reason for reduced quality work, delays in deliverables, authorizing inappropriate responsibilities, and more. For this reason, companies must hire outsourced employees that stick to the imposed schedule. Give them a specific timeframe and the quality metric to understand what they need to do to accomplish quality output.

Cultural Differences: More often than not, your outsourced employees are on the other side of the world. You may have different cultures, which can indicate that there can be difficulty meeting halfway. When either party refuses to study one another’s culture and location, this leads to poor communication and plummeting productivity. Therefore, businesses need to explore outsourcing companies that have a reputation in the industry working for people in the same culture as yours. It can also help if companies arrange a trial period to see if the outsourced employee is a good fit for them in the long run.

In comparison to automated data entry, here are different ways how outsourcing data entry specialist might not be a good idea:

● Since outsourced employees rely on manual intervention, human error is always a possibility.

● Outsourced employees handle multiple clients at a given time, which means that the turnaround times for specific tasks are subject to change.

● Outsourcing a data entry specialist is more expensive than implementing an automated program that captures data.

Overall, manual data entry is efficient for handling a few documents and can afford designated employees for it. Otherwise, machine learning data capture might be the better choice.

Machine Learning Data Capture

Machine learning data capture leverages machine learning to automate data entry processes. Machine learning (ML) is the extension of artificial intelligence (AI) that takes advantage of algorithms and statistical models to execute actions. This technology allows programs to predict accurate outcomes without having a programmer code its strings of action.

Simply put, machine learning systems utilize data to learn and improve its performance from experience. When it comes to data entry, machine learning receives hard-to-spot patterns or graphical details and uses statistical analysis to predict output data. As one of the fastest-growing technologies today, this outweighs the benefits of traditional data entry services. It is far superior to outsourcing data entry due to the following ways:

● With data almost 100% automated, it creates faster and reliable turnaround times.

Machine learning data capture follows particular rules which guarantee delivery of output on time. The systems adjust the algorithm to perform better and automate data with 100% accuracy.

● Easily scalable no matter what the job is.

The industry is only getting more competitive each passing day. Businesses must stay on par with competitors by following the best practices that produce quality output at the shortest turnaround time. Automation of data entry tasks allows companies to ease their workforce’s burden and focus on tasks that contribute to business growth.

● Lower cost of implementation and use.

In comparison to outsourcing data entry specialists, implementing an automated program can cost less. They don’t need to spend time and money looking for talent and training them. All they need is to run the program to make it work.

Overall, leveraging the latest technologies such as machine learning to capture data in real-time can help businesses adapt to the changing customer behaviors and needs. Automating data entry tasks save time and money, encouraging organizations to allocate their budget to value-adding tasks. These are projects that require human intellect and intervention and ultimately bring growth to the company.


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