Building Real-Time Workforce Analytics Dashboards in SAP SuccessFactors Using SAP BTP and Embedded Machine Learning Models

Manoj ParasaUSA

Vol 6 No 10 (2022): Volume 6, Issue 10, October 2022 | Pages: 148-152

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 11-11-2025

doi Logo doi.org/10.47001/IRJIET/2022.610030

Abstract

This study investigates the design and deployment of real-time workforce analytics dashboards within the SAP SuccessFactors ecosystem, leveraging the capabilities of the SAP Business Technology Platform (BTP) and embedded machine learning models. The primary objective is to transform static HR reporting into predictive, interactive decision support tools. Using a design science approach, the research integrates data pipelines from SAP SuccessFactors through SAP Data Intelligence, processes this information via SAP HANA, and visualizes insights using SAP Analytics Cloud. Custom machine learning models, trained on historical HR data, predict employee attrition and engagement scores. The results show substantial improvements in the accuracy and speed of workforce analysis, enabling timely interventions in talent management and strategic planning. Stakeholder feedback highlights the dashboard’s role in proactive workforce governance. The study concludes that embedding machine learning into HR dashboards through SAP BTP enables data-driven decision-making and positions HR departments as strategic business enablers.

Keywords

SAP SuccessFactors, SAP Business Technology Platform, Workforce Analytics, Real-Time Dashboards, Embedded Machine Learning, SAP Analytics Cloud, Talent Intelligence Hub, HR Metrics, Predictive Analytics, SAP Data Intelligence, SAP HANA, Attrition Prediction, Employee Engagement, Workforce Planning, Integration Architecture, Intelligent Enterprise, Human Capital Management


Citation of this Article

Manoj Parasa, “Building Real-Time Workforce Analytics Dashboards in SAP SuccessFactors Using SAP BTP and Embedded Machine Learning Models” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 6, Issue 10, pp 148-152, October 2022. Article DOI https://doi.org/10.47001/IRJIET/2022.610030

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