Data Mining Techniques for Enhanced Business Decision-Making

This discussion board post explores the application of data mining techniques in business strategy, particularly in manufacturing. The initial post discusses using forecasting and decision trees to optimize factory output by strategically positioning operators. A response suggests incorporating exploratory techniques like cluster analysis for preliminary insights into operator abilities. The discussion highlights the importance of data mining in managerial decision-making and references various data mining techniques and their relevance in different scenarios. The post also touches upon utilizing data to identify environmental factors and optimize resource allocation in project engineering, emphasizing the broad applicability of data mining across diverse industries.

Running head: DATA MINING ASSIGNMENT PART 2_KEILCRONAUER
DATA MINING ASSIGNMENT PART 2__KEILCRONAUER
Name of Student
Name of University
Author Note

1DATA MINING ASSIGNMENT PART 2_GREGORYEZEANI
Hi Keil,
Your idea regarding how the techniques of forecasting and decision trees to be able to gather
supplementary insight as guidance regarding business decisions you have to make as manager is
a great example. Data mining is indeed an exceptional tool aiding those is managerial positions
such as yours across domains to get better mastery over their tasks and shaping business
outcomes (Ertek et al., 2017). I am glad to know that you have been enlightened regarding the
wonders of data science and how useful data mining can be for those in your position. I would
like to add a suggestion to your analysis idea however. I understand that you wish to be able to
quantify the performances or rather the output of the operators in the factory you are employed
and determine their ideal positioning so as to maximize output. You might look into some
exploratory techniques, cluster analysis for example as a way to group your operators as per their
abilities aside from the techniques that you are considering as suggested in Sarstedt &Mooi
(2014). It could aid to give you preliminary idea regarding the possibilities. Following this you
could use the other inferential techniques of forecasting and decision trees(Witten et al., 2016).
The preliminary analysis might aid you in framing of the model for this part (Zhao, 2015). I also
noted that you missed out on mentioning whether and why you find some other technique of data
mining as inappropriate. Nonetheless, your post was a pleasure to read.
Regards,

2DATA MINING ASSIGNMENT PART 2_GREGORYEZEANI
References
Ahmed, K. J., Ahmed, M. K., & McKay, S. (2015). A brief review of alternative uses of data
mining: Education, engineering, & others.
Ali, S. M., &Tuteja, M. R. (2014). Data Mining Techniques.
Ertek, G., Tunc, M. M., Zhang, A. N., Tanrikulu, O., & Asian, S. (2017, December). Data
Mining of Project Management Data: An Analysis of Applied Research Studies.
In Proceedings of the 2017 International Conference on Information Technology(pp. 35-
41). ACM.
Sarstedt, M., &Mooi, E. (2014). Cluster analysis. In A concise guide to market research (pp.
273-324). Springer, Berlin, Heidelberg.
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine
learning tools and techniques. Morgan Kaufmann.
Zhao, Y. (2015). Data mining techniques