Automated metadata tagging is better than manual tagging since it is exactly that – automated. By utilizing an automated process, tags will become uniform and follow a strict set of rules, governed by the system. This is decided before the tagging begins so there isn’t a discrepancy in the process. If tags were to be done manually, teammates may see the data in different ways. One person may categorize based on the date while the other person may categorize based on performance. They may also spell tags differently which will negatively impact how data is classified.
In addition to various ways of categorization, manual tagging is also more prone to human error. As we are just people (and not computers), we do make mistakes. There is a greater chance that manual tagging will incur some sort of error when a human is the one doing the tagging.
In contrast to manual tagging, automated metadata tagging is completed based on specific guiding principles, hugely decreasing the chance of error. Also, as hard as it may be to admit, a computer is simply faster than a human. Automatic tagging is completed within seconds, as opposed to hours, days, or even weeks by a manual team.
By implementing Machine Learning, Octopai can automatically tag the provenance of the metadata sources, categorize them, and speed the way a user can analyze its data. This reduces the number of possible mistakes and saves the data team many hours, if not days of manual work.
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