How to Avoid Data Capture Errors

Many companies may not even realize it, but data is amongst an organization’s most valuable assets. Data is at the core of virtually all business operations in some way, shape or form, whether it’s customer data and sales records, personnel files in HR or website analytics. A strong data set can even inform critical business decisions, propelling a company forward and upward.

But data is only as valuable as it is accurate. An inaccurate, corrupted or incomplete data set is virtually useless. This underscores the importance of accurate, precise data capture.

Errors in data entry hold the potential to cause tremendous and costly damage. Just consider the following scenarios.

A numeral is omitted from a company’s sales data. This data is ultimately used to determine how many items to order for the next production run. The company ends up with a massive shortage and is unable to fulfill all of its pre-orders, resulting in a major kerfuffle on social media. Hundreds of upset customers pitch a virtual fit, leading to awful PR. The company is forced to order an additional production run at a higher per-item cost since it needs to be expedited. They also pay a tidy sum to a PR firm to help clean up the mess on social media.

A business seeks to have its operational data compiled for analysis. But there are some significant data capture errors and only some of the information is compiled. This results in a faulty analysis that leads to inaccurate conclusions. As a result, the company leaders fail to identify opportunities for expansion into new markets. The organization misses out on a couple of great chances to grow.

These are a couple examples of how bad data capture and data entry errors can lead to losses and missed opportunities. While it may be clear that data capture errors can lead to anything from a mild inconvenience to outright disaster, ensuring that you get high-quality and accurate data is a bit more challenging.

How Do You Avoid Data Capture Errors?

Knowledge is power and this is true if you are trying to prevent data capture errors. By ensuring that the following measures are in place, an organization will be well on its way toward accurate data entry and a trustworthy data set that will prove to be a valuable asset. Consider these strategies to ensure accurate data entry.

Ensure that manual data entry is checked and rechecked. In the case of a project involving manual data entry, it is critical that you utilize the double key method. The double key data entry method involves using two individuals who enter the same set of data, which is then run through a computer program that cross checks the information. Differences in the two sets of data are identified and flagged. The flags are then reviewed by a quality control technician who will make necessary corrections.

Identify the source of data capture errors. You need to know the nature of the problem in order to fix it and this is certainly true in cases of data errors. Human error is always a possibility in the case of manual data entry. Identifying the source of an error in data capture software can be a bit more challenging, but equally important.

The process should always begin by identifying the data capture errors and evaluating them to determine the exact nature of the error(s). For instance, is an optical character recognition (OCR) scanner introducing errors due to a hard-to-decipher font? An adjustment to the software’s algorithm could correct the problem and in the interim, manual data entry technicians can enter any data with that problematic font. Is a data capture software program omitting decimals or commas from certain numeric fields? If so, you’ll need to have the software algorithm re-jigged or opt for another data entry solution. Is a manual data entry technician generating errors due to a sticky key on their keyboard, resulting in extra or missing characters? The fix could be as simple as a new keyboard!

Take the time to evaluate the nature of the error in a systematic and transparent way. This will let you pinpoint the source of the problem, enabling you to implement measures that will correct the issue.

Establish processes and ensure that these processes are followed properly. The data capture process must be consistent and systematic. A clear, precise and repeatable process minimizes the chances of generating errors or making omissions in a data set.

Take the time to evaluate each and every stage of the data capture process. Something as simple as where a manual data entry technician places paper documents after the data is entered can have a tremendous impact on accuracy. Let’s say a document has been entered into the system, but the tech doesn’t file the paper away and she heads off to lunch. A fan blows the paper off her desk and a coworker picks it up and returns it to her desk — atop the pile of papers that have yet to be entered into the database. The tech returns from lunch and enters the document’s information into the system for a second time, leading to a faulty data set that causes major problems down the road.

This example underscores the critical nature of creating appropriate processes and putting those processes to work. This will maximize the accuracy and quality of your data sets.

Opt for technology such as high-quality OCR scanners and machine learning-powered data capture technology. Humans do have a role in the data entry process, such as analyzing anomalies and implementing processes to minimize the chances of seeing errors. But technology is far and away more precise, more accurate and much faster than any human data entry specialist. An OCR scanner enhanced by machine learning technology is one of the best options.The machine learning component is the key here because without it, OCR is a rather “dumb” matching technology that hits a wall when it encounters a character or numeral that it can’t “read.” Machine learning is used to decipher commonly-confused or hard-to-identify characters with increasing accuracy since the algorithm is continually improved over time. Machine learning can also be used to perform contextual evaluation, identifying potential errors and recommending an appropriate fix.

This technology makes higher quality machine learning-powered OCR scanning programs quite accurate and very expedient, freeing human resources to focus on tasks that technology cannot perform effectively. The end result is an accurate, high-quality data set that can be trusted.

Outsource data capture projects to the experts. The quality will speak for itself. It is extremely difficult to achieve a trustworthy data set if your receptionist is handling data entry during her downtime in the front office. It may be tempting to handle a data entry project in-house but the consequences of an inaccurate data set are too potentially dramatic to ignore. Further, it is often impractical to purchase a high-quality machine learning OCR scanner solution for a one-time project or even smaller recurring projects.

Outsourcing to data capture experts is typically the most cost-effective solution. There is no pricey software to purchase and maintain. In-house staff can focus on other tasks that cannot be performed by technology. And perhaps most importantly, you will greatly minimize the chances of seeing data errors.

Data capture outsourcing services represent a time-efficient and cost-effective option for companies in all sectors. At iTech, we understand the importance of accurate data and work with clients to develop a data capture strategy that will bring the best possible results. Reach out to iTech today to discuss how our data capture solutions can benefit your organization.

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IDS Commander iTech2021

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