Analysis requires sufficient data

A widely recognized collection for machine learning tasks.
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shaownhasan
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Analysis requires sufficient data

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For example, when performing quantitative analysis, if the amount of data is small, the numbers may be biased or it may be impossible to eliminate events that occur by chance. If improvement measures are taken based on such inaccurate data, there is a possibility that the results will be worse rather than better. However, qualitative analysis involves carefully analyzing the raw voices of each individual user, so even if you are only able to collect one piece of data, it can still be used as data worth analyzing.


Of course, you need to carefully consider whether malaysia telegram phone number list the raw voices are really accurate, and the more data you have, the better, but there is no doubt that the inability to reproduce quantitative analysis is an advantage. Identifying issues that cannot be understood through numerical data Another benefit of qualitative analysis is that it has the potential to uncover issues that are not reflected in the numbers. As mentioned above, even if something appears to be working well in quantitative analysis, there are cases where users are actually very dissatisfied.


If you do not conduct qualitative analysis, you may not notice hidden issues and may end up causing users to leave without making the right improvements. When conducting qualitative analysis, it is a good idea to collect data on areas that cannot be found in quantitative analysis and that support the quantitative analysis. Points to note when conducting qualitative analysis Qualitative analysis has strong advantages, but that doesn't mean you should just blindly carry it out. From here on, we'll explain some points to keep in mind when carrying out qualitative analysis.
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