Laying the Groundwork for Predictive Workforce Planning Through Strategic Data Analytics and Talent Modeling

• Author(s): Toluwanimi Adenuga ; Amusa Tolulope Ayobami ; Francess Chinyere Okolo
• Paper ID: 1708907
• Page: 159-180
• Published Date: 10-10-2019
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 3 Issue 3 September-2019

Abstract

Laying the groundwork for predictive workforce planning through strategic data analytics and talent modeling has become essential for future-ready organizations seeking agility and resilience in talent management. This paper explores the foundational steps necessary to develop robust predictive models for workforce management by harnessing historical labor data, conducting comprehensive skill gap analyses, and applying scenario-based forecasting. These early interventions form the bedrock for anticipating future workforce requirements, managing workforce churn, and enhancing organizational readiness in a rapidly evolving labor market. Strategic workforce planning begins with collecting and structuring relevant data, including employee demographics, attrition rates, performance metrics, training histories, and external labor market indicators. By integrating these datasets using advanced analytical frameworks, organizations can identify trends, detect emerging skill gaps, and predict future talent shortages or surpluses. Scenario modeling enables decision-makers to simulate various business environments such as technological disruption, economic shifts, and policy changes and evaluate the corresponding human capital implications. This foresight empowers HR leaders to align recruitment, upskilling, and succession strategies with long-term business goals, reducing reactive hiring and minimizing operational risk. Moreover, this study discusses the role of early-stage workforce analytics tools such as competency frameworks, workforce segmentation, and statistical forecasting in laying the technological and cultural foundation for AI-enabled human capital solutions. By embedding data-driven decision-making processes into the talent lifecycle, organizations accelerate their transition toward intelligent workforce planning systems that leverage machine learning and predictive analytics. These solutions now power real-time talent dashboards, attrition prediction engines, and personalized career pathing, offering a competitive edge in attracting and retaining top talent. The integration of strategic data analytics into workforce planning fosters greater workforce agility, improves talent pipeline visibility, and supports evidence-based HR strategies. As businesses face increasingly complex labor dynamics, early investments in workforce analytics capabilities are proving invaluable in shaping resilient, future-proof talent ecosystems.

Keywords

Predictive Workforce Planning, Strategic Data Analytics, Talent Modeling, Skill Gap Analysis, Scenario-Based Forecasting, Workforce Churn, Organizational Readiness, Human Capital Analytics, AI In HR, Workforce Segmentation, Data-Driven HR, Talent Lifecycle Optimization.

Citations

IRE Journals:
Toluwanimi Adenuga , Amusa Tolulope Ayobami , Francess Chinyere Okolo "Laying the Groundwork for Predictive Workforce Planning Through Strategic Data Analytics and Talent Modeling" Iconic Research And Engineering Journals Volume 3 Issue 3 2019 Page 159-180

IEEE:
Toluwanimi Adenuga , Amusa Tolulope Ayobami , Francess Chinyere Okolo "Laying the Groundwork for Predictive Workforce Planning Through Strategic Data Analytics and Talent Modeling" Iconic Research And Engineering Journals, 3(3)