Data Capture / Data Entry Quality Definition, Process, Techniques and Example

Data is among your most valuable assets as a business, so it’s no surprise that organizations spend a lot of time and money considering their options for data capture and data entry. But in the realm of data capture, quality makes all the difference. After all, a data set is of little use if you cannot trust its accuracy. In fact, you could see tremendous damage arise as a result of inaccuracies associated with poor data entry quality.

But how do you evaluate data entry quality? And what are the best practices for achieving a high degree of data capture quality?

The Importance of Data Entry Quality and Its Impact on Your Business

Data entry quality is primarily evaluated by its accuracy — how well does the captured data align with the source content? With the right technology, the alignment should be perfect or near-perfect.

In the world of data capture, accuracy is paramount. In order to be of any real use, your data must be accurate; this is true whether it is an invoice, a contract or a company handbook. If you cannot trust your data and its accuracy, then you’re going to be hesitant to actually use that data in any meaningful way. This can defeat the purpose of pursuing a data entry project in the first place.

Data-driven decision-making is rapidly becoming the norm amongst business leaders. Imagine the potential disasters that could result if they call upon flawed, inaccurately-captured data to make those decisions. When you consider this, it really underscores the importance of data capture quality and its potential impact on a business.

Data entry quality also affects the degree to which human intervention is required to achieve perfect accuracy. A low-quality OCR software platform without any machine learning capabilities may generate content that is so flawed that it takes more time to review and correct it than it would take to simply capture the data manually from the outset.

Time is also a consideration — albeit a secondary consideration — when evaluating data capture quality. Accuracy is essential, but the usefulness and value of that data may wane if it takes you days to achieve the necessary degree of accuracy. Therefore, you need a solution that can capture data accurately and at a fairly rapid rate.

How Do You Evaluate Data Capture Quality?

Data capture quality is typically evaluated according to how accurately the source information is captured and rendered in digital format. After all, accuracy really is the most important factor when it comes to data. If the data is inaccurate, all other factors matter naught.

The most effective method of evaluation involves having a human compare the data output to the original source content, looking for differences. These differences can include:

• Missing words or characters – Faint words or characters overlaying an image may be captured only partially or even omitted completely.
• A character is mistaken for another character – An “I” may be captured as “1.”
• A character is captured incorrectly – An “H” with a faint crossbar may be captured as “I I.”

In cases where you are using machine learning coupled OCR software, this comparison process is the point at which you may discover that there is a flaw in the algorithm that requires correction. That is an important discovery which would need to be addressed so that future data capture projects are not adversely impacted.

Admittedly, this is an arduous and time-consuming method for evaluating data entry quality, but it is a necessary step to ensure your data is trustworthy. Fortunately, these checks only need to be performed periodically — typically when you are working with a new source type for your data capture projects.

Evaluations are also prudent in cases where the data source is imperfect or flawed. For example, if your source material has faded type, wrinkles, tears, spots, lots of texture, low contrast or other properties that could make the content difficult to “read,” an accuracy check would be advisable.

Data entry quality can also be evaluated, in part, by the amount of time it takes to achieve a clean, accurate data set. The number and nature of any errors impacts how long it takes to correct these issues.

Best Practices for Achieving High Quality Data Capture

By implementing a few data capture best practices, you can increase your chances of seeing a clean data set in very short order. Consider these tips to improve data entry quality.

Start with the best possible source content – High-contrast typeset content works best with OCR software. For low-contrast content, image editing software — or even an old-school Xerox machine — can be used to increase the contrast. This will result in a more accurate scan.

Establish a workflow process – An established workflow is important, especially for larger data capture projects. Accidentally process a single document multiple times and you will end up with a flawed data set. You must create clearly defined steps for your data entry project, including information on each individual’s role.

Perform spot checks for accuracy – By periodically evaluating the accuracy of your data capture project, you can make adjustments that will improve the overall quality.

Use OCR with machine learning capabilities – Machine learning coupled OCR software is far faster and more accurate than its traditional OCR counterparts. It’s important that you choose the right data capture solution for your needs; otherwise, you may be setting yourself up for disappointment due to unrealistic expectations.

Data entry projects can be complex, but when performed with the right processes and the right technology, you can see tremendous benefits. At iTech, we specialize in helping clients make the most of their data. Contact the team atiTech today to discuss how we can help you leverage your data to its full potential.

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