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What is meant by artificial intelligence when it is applied to data cleansing and transformation tools?
Wiki User
∙ 8y ago
Data cleansing has been an important part of data management and this is developing rapidly. Data cleansing in big data is considered to be a certain challenge due to the increasing volume and variety of data. As real-life data is so large, therefore the importance of data quality management in business is highlighted. So, data cleansing is the process of correcting corrupt or inaccurate data.
Why there is a need for AI data cleaning? Nowadays, every large organization has tons of data that need to get processed. Manually this task gets tough as it would need a lot of time. Here, Artificial Intelligence makes it easier to analyze all the information, to learn and make the changes as per the estimates. In the past, there were only two options to clean the data which is by manual and by standard computer programs. But these methods are outdated now as there are plenty of limitations that undermine their effectiveness. AI, on the other hand, is able to diminish those limitations.
How does AI help to clean data? Data cleaning is much required and it’s not the same as deleting some heavy files from your computer. In most cases, it’s a hectic process that includes several steps. There must be a complete analysis of the data that will show which errors should be omitted out. The analytic programs are experts at picking up the metadata about the resources. When the errors are removed, then automatically the clean data will be able to replace the old data. This guarantees that applications have the refreshed data.
Data cleaning with the help of AI There are plenty of options that can be used to clean data. The manual way will take plenty of time which means that it would be a time-consuming activity plus it would be a waste of resources. According to a study, at least 90% of the time goes into it. This is not the case in AI, it gets easy with AI and you will get clean data, no more hours spent on coding, etc.
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