Data Capture Methods and Expectations

Since the age of computers began, so did data capture. The digital capturing of data saves more time and human resources as it is more effective and more efficient. Data capturing is greatly needed and is of great help in processing forms, understanding archives, historical data, and gathering business ideas and inputs to make appropriate decisions. It also helps in understanding and shaping general human behavior.

Automatic data capture has various procedures available. These can capture, classify, and draw out vague data like images, emails, documents, websites, surveys, videos, and many more. The details of each procedure are listed and discussed below.

Manual Data Entry

Manual data entry is the process of integrating and inspecting digital information on paper into a digital arrangement. This process of keying also has different types. First is the Single Key, which is a single pass by the keyer. Most could expect only about 99-93% quality as its general quality is low.

The second is the factor of the Double Key. The Double Key is when two keyers process and differentiate the same data. A quality control person verifies and inspects the mismatches and also decides which data is the correct data. In Double Key, one can expect a high 99% quality.

These processes are deliberate and dull, and it also relies greatly on human capital making it vulnerable to human error. Project build-ups are slow while the costs and the assignment of employees are both high. From the quality, you can achieve price and turnaround time, usually only two out of three.

Unfortunately, due to human error, it is rare to achieve all three with manual data entry.

Considerations for Manual Data Capture Services

Turning digital, especially digitizing data, is essential as it involves a swift and efficient process. It also saves time as it does the work in just a matter of minutes or even seconds. One great disadvantage of having a manual data capture service is that it consumes a lot of time. It prolongs the process, and it also is vulnerable to having a long queue. Additionally, it also becomes susceptible to human errors that add more complications which is not ideal in a high-speed data processing environment.

Avoidable Errors

Manual processing of data tackled by humans is prone to errors as it is a repetitive job to do. Machines and computers stick to programs and orders and never get tired unless it breaks down, unlike humans. When one continuously engages in repetitive work, making errors is unavoidable.

Slow Rate of Data Capture

A person is nothing compared to a machine or a computer when it comes to processing speed. Additionally, the long and elaborate process of checking, browsing, going through numerous and various data, and validation to ensure data security accuracy and integrity manually for security situations can cause a system failure in manual data capture. Assume that a surveillance system allows humans to check every person encoding manually. This need leads to the slow process of inspecting all incoming and outgoing data creating traffic, leading to the system shutting down.

Inconsistency

Consistency and accuracy of data are of great importance in this field or environment. Without having a reliability plan, you can never trust the result you receive from any data. Without reliable results, you can never ensure the work you do has the value your numbers suggest, which is completely counterintuitive.

It also plays a crucial role in the success of a system. Having or implementing a manual data capture service is not ideal and advisable and will always be a disadvantage as humans tend to create more errors when subjected to repetitive work. Maintaining consistency is a great challenge for humans, as making businesses must avoid mistakes.

High Costs of Training

One great advantage of having an automated data capturing system is having a low cost of doing and making business. Having and using a manual data capture system means that more staff are needed to increase service delivery. Additionally, each person in the workforce should get trained to meet the required standard to do the work efficiently.

System Vulnerability

The utilization of a manual data capture system could make the system prone to issues and compromise its security. Leaking of classified information may happen, and sensitive data could also be removed or deleted, compromising the design.

Optical Character Recognition (OCR)

Reading data from a paper or digital. Every Digital images using software is called Optical Character Recognition or OCR. The Optical Character Recognition reads and processes digital information and transfers its findings. After the processing of data, a manual quality correction and verification follows. One can expect the resulting quality to be as high as 99%.

With a high percentage of accuracy, the information of the OCR is accessible and readable. Flatbed scanners usually and commonly exhibit high accuracy as it produces reasonable to high-quality images. Optical character recognition information processing is a swift process as large quantities of text get processed simultaneously. At present, paper-based forms get converted electronically.

This tool makes capturing information more straightforward and easily stored or sent through the mail. Though this process can be pricey and the verification and extraction of data are still highly reliant on people, it still works well with highly structured forms. OCR can differentiate alpha from numeric data when both are mixed. Additionally, OCR does not work well with handwriting. It also relies greatly on human quality control which makes it prone to human errors. OCR is also not cost-effective for most projects. It could only decrease turnaround time, and you need to implement OCR correctly to receive good quality data.

Considerations for Optical Character Reader

Just like any other system, the Optical Character Reader or OCR also has its disadvantages.

1. Printed Text

Optical Character Reader only works well and efficiently when the printed text gets provided. Handwritten text is not ideal as it needs to be learned by the computer first.

2. Optical Character Readers work Efficiently

OCR systems are expensive, although they still work efficiently. One just needs to consider the weight of the disadvantages and one’s needs.

3. Space

The images produced and made by OCRs require a huge amount of data storage space.

4. Image Quality

During the processing of the images, the quality of its outcome could be affected as the final image’s quality greatly depends on the initial image’s quality.

5. Verification and Processing

Every document must be inspected first, checked over, and manually corrected before undergoing the OCR process. Additionally, mistakes could still happen in the process as OCR is not 100% accurate. Moreover, OCR is not ideal for small amounts of text to be processed.

Machine Learning

Even though Big Data is widely known and widely promoted, Machine Learning cannot get separated. Machine learning is very promising when it comes to solving problems, and it also offers a career that pays well. Different companies greatly benefit from machine learning as it helps make predictions to come up with better solutions and decisions.

Machine Learning Process

The machine learning process happens when an OCR reads data, and you add a Machine Learning context for the data getting read. This machine learning process copies how a single human processes a captured data. Like humans, the machine receives instructions to put or turn the data into context. Machine learning can also make basic decisions, unlike the OCR. It only requires a small amount of human intervention to adjust its algorithm to increase quality and efficiency. Machine learning has a quality percentage of 99.9%. Machine learning can also work with all form structures. Additionally, it is not vulnerable to human errors, unlike manual keying and OCR. Organizations can see high quality, cost decrease, and shorter turnaround time when using machine learning outsourcing.

Errors are also significantly reduced when using automated processing compared to manual data capture, where even highly skilled personnel can still make mistakes when entering data. These errors can cause problems, take up time and money, and, worse, could corrupt data, leading to poor decision-making and misleading information. Though small errors may not cause a huge problem, an empirical study on data entry errors showed that mistakes on data entry could urge a more serious problem to arise. This study showed 28% of participants made at least an error during data entry, and some significantly impacted the inferences made from the misleading data.

However, switching to automated data capture for processing forms removes keystroke errors, distractions, and other common human errors seen and found on manual data entry. This improvement means that the data input is more accurate and reliable when coming up with well-informed decisions and conclusions. Taking measures to reduce human errors leads to a lesser, shorter time spent tracing down the datasets’ mistakes and discrepancies. Efficiency and accuracy of performance are also more assured when utilizing an automated system essential and beneficial in any organization.

Considerations for Machine Learning

1. Employment

One huge disadvantage that greatly affects the employees is the loss of jobs. Even though this is feared by many, in reality, companies that implement automated systems can open more doors for job seekers. As companies become more successful and efficient, more projects come to their aid to serve the growing number of customers.

2. Cost for Investment

A considerable amount of initial investment is involved when one plans to implement a process automation solution. This factor must be compared and contrasted to the benefits when it comes to productivity and efficiency. Moreover, the implementation of Cloud solutions can make lower costs significantly than non-automated solutions.

3. Loss of Flexibility

Workflows should get modified as work and processes could demand definite rigidity. A method of planning and consulting and its availability could help in counteracting this demand. Also, one must make an informed choice and decide on which automation product is best. Take notes on the scalability and flexibility of the development concerning the transformation that has to happen.

Conclusion

Data capture has come a long way as computers, mobile technology, cloud computing, artificial intelligence continue to advance. This option proves that the digital world can coexist with the physical and the world of business. Though this is evident, one should still consider the needs and means before coming up with solutions and acting on them.

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