Data Measures Selection and Factor Profiling: An illustration of Data-Mining Screens

Abstract

Context The selection of the data-measurement metric should be the initial decision in the current field of Data Analytics [DA]. We discussed with DA-students and -colleagues re: vetting of the Nature of the Data to be used in decision-making. Uniformly, they indicated that rarely are vetting-screening-tests used for the accrued-data to determine if the Nature of the Data is in sync with the expected veracity of the DA-inferential results. This reticence seems to create inferential-issues that may well compromise the acuity and relevance of the inferential-output of DAs.  To address these inferential-issues, we have selected a typical DA-screening problématique. First, we will vet the Nature of the Data and then address the problématique. Additionally, we will assume that the problématique requires the cooperative tri-interaction of: The Chief Operating Officer, The Financial Analysis Group & The Data Analytics Group. To illustrate these interactions, we suggest that Data Analytics Group is configured as “Internal Consultants” thus avoiding outsourcing elections. In this proposed Data Analytics Group-context, the Data Analytics Group elects to use a Factor Model [FM] as the Screening platform to “deconstruct” the Pearson Product Moment association profiles for the Data-Panels pursuant to addressing the problématique. Features: We will detail: (i) A Data-Panel Screening protocol, (ii) A Factor pedagogic illustration, (iii) The Carvalho-script re: electing the Geometric-data-context, and (iv) The demonstration of these cooperative tri-cooperative interactions using the Microsoft™, Inc. [MSFT] Data-Panel. The overall goal is to offer illustrations, the intention of which, is to assist the pedagogical needs for instructors, and to populate the panoply for researchers and practitioners with effective and efficient inferential tools. Results We found that the Geometric-context was likely for our sample of market-traded organizations. Thus, we used the ln-transformation for the Panel of Data for our sampled firms. Additionally, we used a Factor Model as the screening tool for addressing the selected the problématique. The Data Analytics Group was cast as an “InSource” as this seems to be current institutional configuration adopted by many MNCs.