Has Manual Data Entry Outsourcing become a Waste of Time and Resources?
In today’s fast-paced digital world, data entry services are a necessity for any business. Data entry is a manual operation currently found in numerous professional data entry companies as an automated process. Education, medical, legal, architecture, real estate, restaurant, fashion, and many other businesses typically require manual data entry services.
Manual data input takes time, but for particular documents, such as handwritten documents, old scripts, medical entries, and many more, it is preferable to use manual data entry services. Since professionals do the data entry task, one can expect high-quality outputs.
Drawbacks of Manual Data Entry
While Manual Data Entry may appear to be a smart option when it comes to saving money and improving cost-efficiency, it does, however, have several flaws. Before eliminating all the other alternatives, weighing the advantages and disadvantages of the Manual Data Entry process is still critical.
A High Error Rate
In 2009, an experiment showed a high rate of error when it comes to Manual Data Entry.
In the study, 215 university students became participants and received 30 datasheets, each of which had six different categories of data that they had to manage. The researchers discovered the participants made, on average, 10.23 errors when processing the data.
Simultaneously, the automatically checked entries through a software system had an average error rate of 0.38. The naked eye relies on visual cues to detect errors, and these cues can be harder to identify.
A high data entry error rate could eventually cost the business a lot of money over the long term. It is undeniable that the problem affects both small and large companies. When a business makes a clerical error, it risks overpaying contractors and can even lead to legal or financial issues arising in the future. Data entry errors have already cost businesses millions of dollars in some research articles. As a result, cost-cutting on process automation can end up being rather costly in the long run. Moreover, the technique makes no sense when it comes to the organization’s strategic and long-term development.
Slow Turnaround Time
Manual Data Input is not only erroneous, but it also requires a lot of time to finish.
Professional manual data entry from physical to electronic is estimated to take between 10,000 and 15,000 keystrokes per hour on average. The next issue would be involving sophisticated messages as it would be more difficult for them to understand before being interpreted. To do the process, one has two options. It is to either teach the staff to do Manual Data Entry or hire professionals. The process will take a long time to finish as the staff would first get trained for the first option. In the other option, one has to pay for the process to get outsourced. Even if the company comes to the aid of professionals, the risk of losing focus and making mistakes is still significant if a massive amount of data needs to get handled.
Automated document management is a much simpler solution that is already available. A competent scanner and technologies like optical character recognition and the appropriate software can significantly minimize the time it takes to process data. At the same time, as mentioned in the preceding section, companies will considerably reduce the likelihood of error.
The Team Will Hate The Process
Manual Data Entry is tiresome and monotonous, not to mention time-consuming. If one is searching for a great way to disempower the team, this is the way to go.
Professionals have the ability and capability to partake in far more productive activities than simply transferring data from paper to digital. It doesn’t make sense to burden personnel with the same jobs when automated solutions are already available.
The work they do can motivate employees to give it their all or contribute to a completely decreased motivation. Businesses must manage tasks and processes with caution. Manual data entry, for example, could lead to a lack of efficiency and perhaps a high turnover rate. These concerns are also among the most costly issues that firms must deal with today.
Quality Checks Will Be Required
One needs to learn about the 1-10-100 rule to comprehend why this is a negative thing. According to the guideline, businesses must spend one dollar to validate records and assure quality maintenance, ten dollars to repair mistakes. However, it is one hundred dollars to resolve difficulties resulting from failure to complete either of the other two steps.
Quality checks must become an inherent element of the Manual Data Entry process since one mistake in a corporate record can cost a lot of money. A company will need to hire additional employees or outsource the process to accomplish this goal. Both of these options come at an additional cost that many small businesses may not be able to afford.
All of these problems can cause a shift in focus, away from essential or strategic objectives. Manual data entry is inefficient, incorrect, and expensive. Given that such processes do not contribute much to solve the problem, it is hard to understand why they are still in use.
Innovation in Data Entry
Data entry automation aims to improve processes’ efficiency normally performed by a person typing on a keyboard. The need for data entry and processing grows dramatically as a business grows. It is critical to follow particular, basic approaches to enhance the process to ensure that the data is outstanding and accurate and that the data entry procedure is efficient.
Optical Character Recognition, or OCR, enables users to convert scans of handwritten or typed words into machine-readable text that can get altered and searched digitally. Invoices, receipts, and PDF contracts are common elements for OCR scanning, although its applications are diverse, ranging from digitizing ancient documents to license plate identification.
A typical scanner can only produce a raster image made up of colored or black-and-white dots. However, this is just a screenshot; it’s not something a machine can interpret. Therefore it’s not something users can edit for data entry or extraction. OCR involves a three-stage technique that includes the steps listed below:
- Pre-processing: Here, the program will prepare the data for processing in many ways to ensure accurate conversion.
- Conversion: Feature Extraction and Matrix Matching are two algorithms that OCR software can use to convert images. While they’re comparable, the former often gets used on documents of lesser quality.
- Post-processing: This is the final step in the conversion process, and it aims to be as accurate as possible.
The field of machine learning is still in its early stage. While it has been successfully applied and implemented in various disciplines, certain businesses remain dependent on human resources to run their primary processes.
Financial analysis and planning, for instance, remain exclusively human domains. To interpret data, it still uses the same manual methods. It will not be long before software engineers create a Machine Learning algorithm that makes data analysis easier and more accurate for people in the banking business.
Some algorithms are already used by stock traders, as previously stated. The Machine Learning algorithm looks for and analyzes data that could get used for technical and fundamental research.
Manual Data Entry VS Machine Learning
Machine learning data capture automates data entering operations by utilizing machine learning.
Machine learning, or ML, is a branch of artificial intelligence (AI) that executes actions using algorithms and statistical models. This technology enables computers to predict accurate findings without the need for a programmer to code their action strings.
Machine learning systems use data to learn and enhance their performance over time. Machine learning takes tricky patterns or graphical information and performs statistical analysis to predict output data when it comes to automated data entry. This data learning surpasses the advantages of Manual Data Input services as one of the fastest-growing technologies today. It is considered superior to outsourcing data entry in the ways listed below.
Faster and Reliable Turnaround Times
Machine Learning makes faster and reliable turnaround times with its data almost 100% automated. Moreover, Machine Learning Data Capture follows guidelines that ensure the output gets delivered on time. The systems adjust the algorithms to improve performance and automate data with 100% accuracy. Machine Learning is better than Manual Data Entry, especially when time and accuracy are considered essential factors.
Easily Scalable No Matter What the Job is
With each day, the industry becomes more competitive. Businesses must keep up with the competition by implementing best practices that result in high-quality output in the least amount of time. Moreover, they can relieve their workforce’s workload by automating data entry processes, allowing them to focus on duties that contribute to their success.
Lower Cost of Implementation and Use
Implementing an automated application can be less expensive than hiring data entry professionals. They don’t have to waste time and money seeking and training new talent. To make it function, all they have to do is execute the software.
Though it costs less, issues may still arise. Most of the outsourced staff could get located across the globe. They may also come from different cultures, which suggests that meeting halfway may be challenging. When one side refuses to learn about the other’s culture and location, poor communication and productivity can ensue.
When outsourcing, one needs to figure out what works best for the data collecting task at hand. Businesses should also consider the following:
- The size of the project
- Will the time be limited, or will it be ongoing?
- How soon is the output needed to be finished?
- Are there any other considerations, or is a simple Data Capture sufficient?
It makes sense to use technology for massive, continuous projects. In the long term, the expense of execution and the time it takes will pay for themselves. Manual data entry is frequently the best solution for one-time, small data capture operations.
Manual Data Entry and Machine Learning are both beneficial. When choosing between the two, one should consider the advantages and disadvantages that come with them. For small businesses, it is ideal for sticking with Manual Data entry, whereas, in much larger companies, it is recommended to adopt Machine Learning as time is a significant factor.