3 Ways Error-Detection Software Ensures Data Integrity
by Ryan Szporer | June 17, 2019
There are many ways to ensure data integrity. As automation becomes increasingly prevalent in the 21st century, software, especially on the back-end, is as important as ever. Not only does it serve as the driving force behind technology in all of its incarnations, it can also act as a fail-safe. As the term implies, “error-detection software” is one viable way to catch and stop errors that risk corrupting data in their tracks. This is how:
1. Enhances Security
While data security is different than data integrity, the two go hand in hand. Like data quality, data security is a single facet of data integrity (but not vice versa). Nevertheless, without the proper degree of security, data can become compromised due to breaches, among other threats. In other words, for data to have integrity, it must first be secure.
As a result, error-detection software can be considered a key component of any complement of tools designed and implemented to enhance the security of data. Errors are simply outliers or anomalies, which are defined as observations that lie outside of norms. Error-detection software can build baselines of systems, their users, and the data they create, leading to the easy detection of behavioral deviations, whether there is malicious intent or not.
2. Reduces Human Error
There’s an inherent risk whenever you rely on human resources. There are some things a machine will likely never be able to do as well, but analyzing data is not one of them. It’s similar to the situation with manual proofreading, where, the longer the process is, the less likely errors are to get caught. Fatigue sets in eventually and the effectiveness of proofreaders declines over time.
In much the same way, the automated analysis of unstructured data saves time, thereby improving the overall efficiency of the process. Employees wouldn’t be replaced, either. There would still be a need to oversee the analysis. The right error-detection software would all the while keep all relevant parties apprised of how the data behaves. As described in Point 1 above, that’s critical.
3. Prevents Issues from Recurring
It isn’t just the errors software might catch, but the ones in the future that would otherwise slip through the cracks. Consider digital proofreading software as an example. A form of error-detection software, GlobalVision features an audit trail for compliance with FDA 21 CFR Part 11.
So, the platform doesn’t just go over the document pixel by pixel or character by character to detect graphics and text differences (among other types). The application tracks parameter changes and log-ins, so data becomes “attributable” (which is one of the five principles of data integrity). The others are “Legible,” “Contemporaneous,” “Original,” and “Accurate” (spelling ALCOA).
The end result? Detected differences between master and sample files from the printer can be tied to individual departments and testers. The exact origin of any error can be easily discovered and addressed. Similar errors can be prevented in the future. In that way, the number of potential mistakes gets dwindled down. Proper company quality standards get corrected and set moving forward.
As another example, a Corrective And Preventive Action (CAPA) system prevents the recurrence of product and quality problems. In manufacturing, it can become a vicious cycle of sorts. If high-quality products aren’t routinely manufactured, there is pressure to falsify data so that it passes. That leads to a lack of data integrity. So, it can be argued, a lack of data integrity is a sign of a lack of quality.
In contrast, verifying all possible data sources for the root cause of errors keeps the chances of them recurring low. From a data integrity perspective, that means fewer lapses. Product quality and customer satisfaction, whatever the industry in question, can only improve as a result.
The difference between data integrity and security is clear. As far as information technology is concerned, you can’t have one without the other, as the integrity of data relies in large part on it being kept secure throughout its entire lifecycle.
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