Thesis of Abdel-Rahmen Korichi

People Analytics and Organizational Network Analysis for Better Decision-Making and Human Resource Management

Defense date: 18/09/2023

Advisor: Hamamache Kheddouci


In today’s fast-paced and ever-changing business landscape, organizations must continuously adapt and innovate to maintain their competitive edge. People Analytics and Organizational Network Analytics have emerged as vital tools for harnessing data to support decision-making processes and enhance organizational effectiveness. People Analytics focuses on analyzing workforce data to gain a deeper understanding, manage, and optimize human capital. In contrast, Organizational Network Analytics explores communication patterns, interactions, and relationships among employees to foster a highly collaborative and efficient organization.

Numerous benefits arise from employing People Analytics and Organizational Network Analytics, such as improved employee engagement, increased productivity, and better decision-making, among others.

Attrition is a particularly costly and disruptive issue for many organizations, and using data-driven analysis to predict and mitigate it can provide significant advantages.

The primary objective of this thesis is to make a meaningful contribution to the field by developing a framework for creating analytic tools and visualizations focused on People Analytics and Organizational Network Analytics. This will be complemented by practical use cases in which we perform advanced analysis on client case studies using real data from Panalyt to demonstrate their benefits.

Additionally, this thesis will address the inherent challenges and ethical concerns associated with analyzing sensitive employee information. It is crucial to address these issues to maintain trust and ensure responsible data usage.

Another critical aspect of this research involves examining and developing attrition prediction models that utilize the historical information of employees’ journey within an organization. One case study will also showcase that metrics derived from communication metadata are effective predictors of attrition.

The research methodology will involve analyzing real client data from Panalyt’s client base, the company where I conducted my thesis, and the experimentation with these analytic tools and models will be within the context of medium to large-sized client organizations. This approach will help to better understand their applicability and effectiveness in real-world settings.

Mme Guessoum ZahiaMaître de conférenceUniversité de Reims Champagne- ArdenneRapporteur(e)
M. Hadjali AllelProfesseur(e)Université de PoitiersRapporteur(e)
Mme Bonifati AngelaProfesseur(e)LIRIS Université Lyon 1Examinateur​(trice)
M. Cherifi HocineProfesseur(e)Université de Bourgogne Franche-ComtéExaminateur​(trice)
M. Kheddouci HamamacheProfesseur(e)LIRIS Université Lyon 1Directeur(trice) de thèse