A KPI Automation Model for Fitness Enterprises Using Jenkins-Orchestrated Data Pipelines

Tope David Aduloju, Babawale Patrick Okare, Olanrewaju Oluwaseun Ajayi, Okeoma Onunka and Linda Azah

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023, vol. 9, issue 4, 730-744

Abstract: Key Performance Indicators (KPIs) are vital metrics that fitness enterprises rely on to monitor operational performance, member engagement, and financial outcomes. However, manual KPI calculation and fragmented reporting workflows often result in inefficiencies, errors, and delayed insights, hindering timely decision-making. This paper proposes a comprehensive automation model that leverages Jenkins to orchestrate data pipelines dedicated to KPI computation and reporting within fitness organizations. The model integrates diverse data sources, including gym management systems, wearable devices, and CRM platforms, into an automated, scalable pipeline that extracts, transforms, and aggregates data to deliver accurate, timely KPIs. Through automated scheduling, error handling, and monitoring, the model ensures pipeline reliability and operational resilience. By delivering near-real-time KPI dashboards, fitness enterprises can enhance strategic planning, improve member retention, and optimize revenue streams. The paper also discusses implementation considerations such as integration challenges, security, and scalability, providing a practical blueprint for adoption. Finally, future research directions are identified, including AI-driven KPI forecasting and real-time analytics integration, aimed at further enhancing the agility and intelligence of fitness enterprise performance monitoring. This model offers a scalable and robust solution that transforms KPI management into an efficient, automated process supporting data-driven decision-making and sustainable growth in the fitness industry.

Keywords: KPI Automation; Data Pipeline Orchestration; Jenkins; Fitness Enterprise Analytics; Data Integration; Performance Monitoring (search for similar items in EconPapers)
Date: 2023
Note: Article URL: https://ijsrcseit.com/CSEIT23564525
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