Is AI necessary for effective data clean-ups?

The process of data cleansing may seem tedious, but with the continued development of AI systems in this field, the possibilities of what it will be capable of doing with our data is limitless


Data only presents true value when reflecting complete accuracy. Humans make mistakes, which is how inaccurate data usually finds itself on computer logs. Perhaps AI programming could provide a course correction when human foibles lead to data inaccuracies. AI's ability to perform a multitude of tasks continues onward. Recently, suggestions emerged that AI could take on the task of data cleansing; the duty of correcting, updating, repairing, and improving old data. For companies reliant on current data, these inroads in AI could finally deliver the clean-up assistance they have long sought after.

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Why data cleaning is important

One small bit of inaccurate data has the ability to skew entire data resources. Data sometimes relies on other data to produce accurate information. If a business wishes to examine trends in consumer spending from one year to the next, an incorrect percentage in a previous year ruins all the analysis moving forward. Going in and "cleaning up" the data becomes critically important to restoring integrity to present figures. Can humans manually find these errors in the first place though?

A modern approach emerges

In the past, manual means and standard computer programs were the only options available for locating errors and fixing them. Discounting the value of these methods wouldn't be wise as any tool which helps locate errors deserves appreciation. However, the limitations of these methods undermine their total effectiveness. AI, due to its learning potential, could address and diminish those limitations.

A faster process

Another benefit emerges from using AI programs. Utilizing AI could speed up the overall process of pinpointing and extracting troubling data. Manually locating data errors might prove incredibly time-consuming. Time is not on any business' side when dealing with inaccuracies. Whatever problems inaccurate data causes continue unabated until it has been cleaned up which causes more issues to arise. As more issues unfold, the necessity to perform more corrective action intensifies. And there is no guarantee that a fixup task can reverse all the ensuing disasters.

While speeding up the process of data clean up won't necessarily eliminate all troubles, faster clean-up work would likely deliver better results than drawn-out, cumbersome ones. AI may potentially work faster due to its "self-learning" component and may educate itself about how to be more efficient. Theoretically, the program should perform the cleanse work quicker, which should present scores of upsides as long as it doesn't compromise the accuracy in work performance.

A reliance on patterns

Data creates patterns. A statistical analysis examines these patterns. Upon careful analysis, useful information may then be procured from the available data. Hopefully, inaccuracies and irregularities will be revealed to either the human or a computer program performing the review of this data. However, the more complex the patterns, the more difficult locating irregularities becomes. The ability to read and analyze data in association with self-learning is another way AI program shine.

Without machine learning (ML) capabilities, a program might not be able to discover anomalies in patterns. Such a program won't be capable of serving the business relying on it well. Hence, AI programs prove more attractive and valuable. As ML grows more sophisticated, its ability to work with patterns should further improve.

A challenge to data clean-up

Removing inaccurate or unreliable data serves as the first step to fixing things. The next step involves inputting the correct data to replace the inaccuracies. Can AI help with the latter? Discovering an error is not the same task as inputting accurate material. An AI program may not be sophisticated to handle both duties, so consider it premature to assume AI will entirely replace the human element in data cleaning work.

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