Data Mining Case Study Summary

1. Summarize the concerns expressed by this data analyst. Data mining in the real world is a lot different from the way it’s described in textbooks for many reasons. First of all, data are always dirty with missing values, values way of the range of possibility, and time value that make no sense. In addition, missing values are problematic because missing values make data dirty and it’s not possible to use dirty data which means missing values decrease data. Sometimes it’s possible to do a much better day analysis by adding more data, but due to non-availability of data and limitation of the time and the budget for reprocessing data will make it hard and impossible. Another problem is overfitting, which is a huge problem because a model that …show more content…
If my boss again disagrees then I need to report it to the higher members in the organization but there are some risks for it. First of all, am going against my boss’s decision which is not good for my career. Moreover, there is chance that higher members won’t believe me even if I say my reasons for my beliefs because they believe senior data analyst more than junior analyst. Furthermore, there is a chance that higher member won’t understand my beliefs about data mining because they don’t know about data mining. Even with all these risks, I will still report it to higher member of organization because if our model is not effective then it will badly effect our organization and also data mining team will get a bad reputation. The effectiveness of the data model depends on how it made, if it made good then it will effective otherwise it will be bad. So it is important for a data analyst to make an effective data model otherwise people will start to believe that data mining is always ineffective and there is no good in doing it. Reporting the faults in data model is one of my duty and my other duty is to fix the faults that in found in the data