Why are qualitative analysis and business case studies excluded from the data analytics square?
This infographic contradicts several of the previous videos. As previously said, qualitative analytics is a combination of intuition and analysis. Finding interdependencies between past events is part of analysis, which can be accomplished by breaking a large dataset into smaller parts, implying that the dataset is utilized to perform an analysis.
As stated in the example: "You performed a detailed analysis of women clothing articles" - I assume these articles are also part of the dataset that was analyzed.
The same is applicable for business case studies. It is relatively correct that it usually illustrates a past situation or company challenge and shows us how the business overcame this issue. However, all of this is normally done on the basis of certain industry/economic/finance analyses, which involve datasets as well.
The datasets are therefore most likely divided in some way by kinds. Could you maybe explain the logic behind why some datasets are viewed as data and included in the data analytics square while others are not?
Thanks for reaching out.
Your observations are quite correct. More precisely, I am referring to the fact that business case studies are, as you say, normally done on the basis of industry/economic/finance analyses containing datasets.
I suppose you mean "datasets containing quantitative information", right?
Yes, this is true. In fact, rarely would one provide a qualitative analysis or a quantitative analysis only. They'd usually go hand in hand. Perhaps, different sections/chapters of an analysis would be only qualitative or only quantitative.
Nonetheless, the idea behind this lecture is that we would like you to differentiate between qualitative and quantitative data, as well as distinguish between analysis (work referring to the past) and analytics (work referring to making predictions about the future).
Thus, you can end up with four main types of work, if we may say:
past-related: qualitative analysis, quantitative analysis
future-related: qualitative analytics, quantitative analytics.
It is not really more complicated than that, but oftentimes in the industry people use these terms interchangeably, while there is a difference. We would like you to be aware of it as it can help you improve your work on a certain dataset by helping you to recategorize the type of research/exploration/computations you are making.
Hope this helps.