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What is Data Capture? – Methods and Expectations

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The invention of computer technology has prompted the development of ever-improving data capture and collection systems, as companies, organizations and others try to make sense of the information that’s flowing through their systems and devices. In fact, the right data capture methods and collection systems are critical for success in a super competitive business world. It’s a place where data is critical for documenting information, interpreting historical data and making smart business decisions for the future. 


What is Data Capture and Collection? — Systems and Methods

What is data capture and what do data collection systems look like in today’s digital world? Data capture methods can include:

  • Manually transferring data from paper forms, spreadsheets and documents and converting into a digital format;
  • Using optical character recognition (OCR) technology to scan data from paper or digital formats; and
  • Using machine learning (ML) technology that “reads” data from paper or digital formats, and then adds context or makes corrections to improve data accuracy. 

Accurate data capture and collection creates a solid foundation, allowing companies to generate data sets that shape the business world, our economy and human behavior in general. Let’s take a look at these data capture methods and how they compare in terms of accuracy, cost and timeframe. 

Manual Data Entry as a Data Capture Method

Manual data entry is the most primitive option for data capture since it entails one or more individuals reviewing information in paper format and then manually entering that data into a digital format. 

Manual data entry is tedious, time-consuming and it requires human collateral to get the job done. The quality and accuracy rate will vary depending upon an individual’s skill level and whether you use a single key or double key approach:

  • Single Key Data Entry: This process uses a single individual who manually keys in data in a single pass, with no real cross-checks or verification mechanisms in place. Generally, you can expect a 93% to 99% accuracy rate. 
  • Double Key Data Entry: This process uses two keyers who enter the same data set, which is then compared. Any differences in the two data sets are then highlighted and analyzed by a quality control technician who determines which data set is correct. Double key data entry can be up to 99% accurate. 

Manual data entry is time-consuming. That’s a problem if your project is waiting on a digitized data set. Additionally, human error is always going to be a factor — even with the most experienced data entry specialists. Employee turn-over and costs are typically high for manual data entry work. It can be very challenging to achieve high quality, low cost and fast turnaround time with this form of manual data capture. 

Optical Character Recognition (OCR) as a Digital Data Capture Method

Optical character recognition (OCR) technology involves software that “reads” data from paper document scans or digital images. This data is then rendered in an editable, digital document, which is manually reviewed by a quality control specialist. Some degree of quality verification and correction is necessary because OCR software can omit or misinterpret characters, but overall, this is a fairly accurate data capture method with an accuracy rate of up to 99%.

OCR data capture technology can be expensive, particularly if your data set does not play well with the OCR software that’s in use. Optical character recognition technology works for highly-structured data sets, but it can struggle with semi-structured or unstructured data. For example, poor contrast in images, handwriting or more decorative fonts can be difficult to process; human reviewers may need to perform extensive verification and correction. When humans are introduced into the process, there is a possibility of human error. 

This data capture method can work for some applications, reducing turnaround time and bringing a good quality digital data set at the end of the day. For others, the amount of human verification and correction that’s required makes OCR-based data capture impractical and costly. 

OCR With Machine Learning as a Digital Data Capture Method

By pairing machine learning (ML) technology with OCR software, you can dramatically reduce or even eliminate the need for human intervention in the data capture process. 

Machine learning technology is designed to mimic the human’s role in the data entry process by evaluating the data and making (or just suggesting) edits based on context. A ML-powered OCR software platform can make decisions that simple OCR scanners cannot. Even better, the machine learning algorithm gradually improves over time, making automatic adjustments to increase accuracy and quality. This technology works for all data structure types too. 

It’s not uncommon to see accuracy levels of 99.9% or better for OCR software with machine learning capabilities. Less human intervention is required as time passes, thereby removing human error from the equation. Businesses and organizations that outsource their project to a company offering machine learning-powered OCR data capture services can expect to see high quality, rapid turnaround time and a lower cost when compared to the aforementioned data capture methods. 

Choosing the Best Data Capture System for Your Needs

The best data capture system will vary depending on your budget, timeframe, the type of data you’re processing and your accuracy requirements. Most will find that manual data entry is going to be too costly and time-consuming, with questionable accuracy. But this option can work well for smaller projects with a generous timeframe and projects that don’t demand extreme accuracy. 

OCR data capture methods are a good option for highly-structured data sets with high-contrast, commonly-used fonts, since this brings the greatest accuracy and requires the least amount of human intervention (this subsequently drives down some of the cost and timeframe). But if you have lots of handwriting, decorative fonts or a semi-structured / unstructured data set, OCR data capture will lack accuracy. This necessitates more human intervention which increases cost and turnaround time. 

Machine learning OCR technology is the most accurate of all the data capture methods and the timeframe and cost can be quite reasonable if the project is outsourced. Human intervention is minimal and it decreases over time. Companies that require continual data capture solutions can even invest in their own customized machine learning algorithm.

iTech specializes in providing smart data capture automation and outsourced data entry services. If you have questions surrounding iTech’s services or would like a demonstration of our automation solutions, just fill out the form below, or email us at info@iTechData.AI.

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