KDD is the process of discovering utilizable cognizance from an accumulation of data. In this case there was no preconceived hypothesis thus the result gather from the data can not answer specific questions for the Organization.

Whether in Data Analytics we look for the specific answers by analysing the data.IN Data anlytics there may be a hypothesis for testing.

There is need of a various tools to extract correct data analytics, like data visualisation tool, Python or R to perform robust data analytics.

KDD is type of exploratory data analysis, a sub type of data analysis.

The most common form of data analytics historically would have been confirmatory data analysis (CDA) where you start with a hypothesis, design a study to falsify it, collect data in that study then analyse it to determine it it supports or does not support the hypothesis. The drawback of this approach is identifying useful hypotheses to test can be difficult, often requiring high levels of technical insight combined with creativity.

KDD :

1) starts with no assumptions, just data.

2) does not lead to conclusions

3) it generates hypotheses. Due to the open ended nature of KDD it can generate any number of hypothesis so multiple hypothesis testing means the alpha required also becomes more stringent as more options are explored.

4) A significant analysis in KDD cannot be used for decision making without some form of robust validation.

KDD is open to the many same risks as CDA but in a greatly magnified way as the data is often less rigorously collected and curated than in a more focused dataset built for CDA. Data biases, high noise, missing data, format standardisation and many more imperfections can give false positives. This means the alpha level for acceptance should be set to be tough on potential false positives.

Thanks James Renwick Beattie for your insight-full answer. Thanks