Case Study On Data Mining And Business Analytics

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Running head: Data Mining & Business Analytics Mid Term

MIS 5375 580: Data Mining & Business Analytics Mid Term Exam
Mukesh Reddy Dhanagari
Texas A&M International University

Author Note
Mukesh Reddy Dhanagari is a student of Texas A&M International University from the department of Information Systems.
This document is in correspondence with the course MIS 5375 580 for the purpose of midterm examination only.
Any concerns regarding can be addressed to
Running head: Data Mining & Business Analytics Mid Term
1. Explain what data mining and business analytics consist of and how they differ and resemble each other.
Data mining is a process of extracting data which cannot be identified using simple
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"Next Generation Business Analytics". Lightship Partners LLC. Retrieved 2009-06-20). Business analytics can be characterized as the expansive utilization of information furthermore, quantitative analysis for decision making inside organizations. It incorporates reporting, yet tries to more prominent levels of mathematical problems. It incorporates analytics, obviously, yet includes gathering them to meet business requirements. Business analytics enables individuals in the organizations to make better choices, enhance processes and produce results. (The New World of “Business Analytics”, Thomas H. Davenport March 2010)
Data Minging and Business Analytics terms may be closely related to each other as they both deal a lot in common with the business data but they both differ in the context of business. Data mining is a process of extracting data for the large data bases, whereas business analytics refers to the usage of extracted knowledge to understand and plan business strategically.
2. What role does theory play in SEM? What role does theory play in evaluating the results of a structural equation or PLS
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In this article the WARP PLS is the tool used to process the data collected. The article states that the relationship between variables describing the natural and behavioral attributes is not common even though U curve and S curve relationship are common. SEM Software tools do not estimate the association taking nonlinear relationships between latent variables. In SEM method the path analysis are conducted with variety of latent variables(LVs), which cannot just measured directly. The data which is related to five latent variables are collected and they are indicated as ECU – extent of electronic communication used by the teams, ECUvar – refers to the variety of different electronic communication, Proc - refers to the degree to which each team employed established project management techniques, Effi – efficiency of the each team and Effe - is the effectiveness of each team. This study is much concerned about the nonlinear relationships among latent variables and adjusts the values of path coefficients accordingly. WARP PLS is a software which can handle this kind of data analysis perfectly. The algorithm used in here in this study is the PLS Regression. It is estimating the P values for the path coefficients automatically instead of giving the T values or standard errors. This algorithm is also said to estimate

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