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Business Data Analytics and Predictive Modeling
Subject area: Management and Commerce · Area of research: Business Analytics and Predictive Modeling
Abstract
This study examines the role of business data analytics and predictive modelling in improving organizational decision-making and performance. Business data analytics integrates statistical techniques, data management, machine learning and data visualization to identify patterns, trends and relationships within organizational data. Predictive modelling further enables organizations to forecast future outcomes related to customer behaviour, sales, employee attrition, financial risk and operational performance. The study considers key factors such as data quality, analytics capability, employee analytical skills, technological infrastructure and management support as determinants of predictive analytics capability. A quantitative research approach is proposed using structured questionnaire data collected from employees and managers involved in data-driven business activities. Statistical techniques such as descriptive statistics, reliability analysis, exploratory factor analysis, correlation, multiple regression and mediation analysis can be employed to examine the proposed relationships. In addition, machine-learning techniques such as logistic regression, decision trees, random forest and gradient boosting can be used to evaluate predictive performance. The study is expected to contribute to the growing literature on business analytics by integrating organizational capabilities with predictive modelling and decision-making effectiveness. The findings may provide useful implications for organizations seeking to strengthen their analytical capabilities and develop evidence-based strategies in an increasingly data-driven business environment.
Keywords
Business Data Analytics, Predictive Modelling, Machine Learning, Data-Driven Decision-Making, Business Intelligence, Organizational Performance, Predictive Analytics.
References
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[4] Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big Data, 1(1), 51–59. SAGE
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How to cite this paper
@article{1723442,
author = {Dr. V. P. Amuthanayaki, Dr. A. Y. Kettiramalingam},
title = {Business Data Analytics and Predictive Modeling},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {3},
pages = {3988-3994},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1723442.pdf},
abstract = {This study examines the role of business data analytics and predictive modelling in improving organizational decision-making and performance. Business data analytics integrates statistical techniques, data management, machine learning and data visualization to identify patterns, trends and relationships within organizational data. Predictive modelling further enables organizations to forecast future outcomes related to customer behaviour, sales, employee attrition, financial risk and operational performance. The study considers key factors such as data quality, analytics capability, employee analytical skills, technological infrastructure and management support as determinants of predictive analytics capability. A quantitative research approach is proposed using structured questionnaire data collected from employees and managers involved in data-driven business activities. Statistical techniques such as descriptive statistics, reliability analysis, exploratory factor analysis, correlation, multiple regression and mediation analysis can be employed to examine the proposed relationships. In addition, machine-learning techniques such as logistic regression, decision trees, random forest and gradient boosting can be used to evaluate predictive performance. The study is expected to contribute to the growing literature on business analytics by integrating organizational capabilities with predictive modelling and decision-making effectiveness. The findings may provide useful implications for organizations seeking to strengthen their analytical capabilities and develop evidence-based strategies in an increasingly data-driven business environment.},
keywords = {Business Data Analytics, Predictive Modelling, Machine Learning, Data-Driven Decision-Making, Business Intelligence, Organizational Performance, Predictive Analytics.},
month = {September},
}