BUILDING SCALABLE ANALYTICS FRAMEWORKS FOR MULTI LOCATION ORGANIZATIONS: CHALLENGES, STANDARDIZATION  STRATEGIES, AND PERFORMANCE OPTIMIZATION

Nimisha Reddy Kolukuri

BUILDING SCALABLE ANALYTICS FRAMEWORKS FOR MULTI LOCATION ORGANIZATIONS: CHALLENGES, STANDARDIZATION STRATEGIES, AND PERFORMANCE OPTIMIZATION

Authors

Nimisha Reddy Kolukuri

Keywords:

Analytics Framework, Multi-Location Organizations, Machine Learning, Performance Measurement, Data Governance, Predictive Analytics, Scalability

Abstract

This study develops a scalable analytics framework formulti-location organizations to support consistentperformance measurement, standardized reporting,and evidence-based decision-making. The proposedapproach combines data preprocessing, analytics
maturity assessment, machine learning, clustering, andperformance consistency evaluation to identifyoperational patterns across distributed locations. The Random Forest classifier achieved 85% accuracy

References

[1] Davenport, T.H. and Harris, J.G., 2007. Competing on Analytics: The New Science of Winning. Harvard Business School Press.

[2] Khatri, V. and Brown, C.V., 2010. Designing data governance. Communications of the ACM, 53(1), pp.148-152

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Published

2024-09-20

How to Cite

Nimisha Reddy Kolukuri. (2024). BUILDING SCALABLE ANALYTICS FRAMEWORKS FOR MULTI LOCATION ORGANIZATIONS: CHALLENGES, STANDARDIZATION STRATEGIES, AND PERFORMANCE OPTIMIZATION. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 9372–9384. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/5724