Diversity Analytics: Solving Subjective Business Problems with Analytics & Data Science

Gopal Krishna K Iyer, Kumrashan Indranil Iyer

Abstract: Diversity and inclusion remain top priorities for modern organizations, yet they are often addressed through fragmented and subjective approaches. This paper presents diversity analytics as a data-driven methodology to quantify and resolve the inherently subjective dimensions of workplace diversity. Using foundational principles from people analytics, statistical modeling, and organizational behavior, the paper demonstrates how organizations apply descriptive, predictive, and prescriptive analytics to address challenges such as bias in hiring, pay inequity, and inclusion gaps. It emphasizes the importance of aligning ethical considerations with algorithmic practices and highlights real-world case studies where data science drives measurable impact. By reframing diversity as a solvable business problem, this work illustrates how analytics transforms a historically intangible area into one of strategic advantage and accountability.

Keywords: Diversity Analytics, People Analytics, Workforce Inclusion, Bias Detection, Predictive Modeling, HR Analytics, Organizational Diversity, Data-Driven Decision Making, Workplace Equity