The proposed book chapter aims to integrate diversity, equity, and inclusion (DEI) metrics into recruitment analytics of the hiring process of a data science start-up company. A CRISP-DM (cross-industry standard process for data mining) methodology is used to develop a predictive model that can accurately predict whether a candidate will be hired based on the available data to identify the best features highly correlated to hiring. HR metrics focusing on DEI would be integrated into the model to help build a more diverse and inclusive team. The proposed chapter contributes to the growing research on integrating DEI metrics into recruitment analytics. It provides a practical example of achieving this in a real-world setting. Integrating DEI metrics into recruitment analytics provides insights to organizations to build more diverse and inclusive teams, leading to better decision-making, and increased productivity.
TopIntroduction
Quality recruitment is crucial for organizations because it leads to better job performance, lower turnover rates, improved workplace culture, and cost savings (Breaugh, 2008). Hiring the right people with the necessary skills, knowledge, and experience can increase employee engagement, productivity, and teamwork, ultimately contributing to the organization's success (Huselid, 1995). In today's competitive business environment, organizations increasingly leverage recruitment analytics to optimize their hiring processes and make data-driven decisions (Cascio & Boudreau, 2011).
Recruitment analytics involves collecting, analyzing, and interpreting data related to the recruitment process (Ployhart & Ryan, 1998). It enables organizations to gain insights into various aspects of their recruitment strategies, such as the efficiency of hiring channels, the effectiveness of their employer branding efforts, and the quality of candidates they attract (Sullivan, 2004). Descriptive analytics, measuring candidate fit, reducing bias, and predictive analytics are advantages of using analytics in recruitment (Hausknecht & Holwerda, 2013). Descriptive analytics can help organizations understand historical hiring patterns and identify areas for improvement (Ployhart & Ryan, 1998). On the other hand, predictive analytics can identify potential high-performing candidates based on data analysis of past successful candidates and using algorithms to identify patterns in candidate data (Boudreau & Ramstad, 2005). This data-driven approach can enhance the overall effectiveness of the recruitment process and contribute to better hiring decisions.
Recruitment metrics are the specific data points used in recruitment analytics to measure and analyze the recruitment process (Hausknecht & Holwerda, 2013). These metrics are quantitative measurements of recruitment activities, such as the number of applications received, time to fill positions, cost per hire, and applicant-to-hire ratio. Recruitment analytics uses data visualization tools and statistical methods to analyze the metrics and identify trends and patterns that can inform recruitment decisions (Ployhart & Ryan, 1998). By tracking and analyzing these metrics, organizations can make more informed decisions about their recruitment strategies and better allocate resources to maximize return on investment (Cascio & Boudreau, 2011).
The advent of the 21st century has brought a paradigm shift in how organizations approach recruitment. A vital aspect of this transformation has been the increasing recognition of Diversity, Equity, and Inclusion (DEI) as indispensable components of a successful recruitment strategy. DEI initiatives prioritize creating a diverse workforce that mirrors the rich tapestry of society, implementing equitable policies and practices that ensure all employees, irrespective of their background, have equal access to opportunities, and fostering an inclusive environment where all individuals feel valued and accepted (Herring, 2009; El-Amin, 2022). A solid commitment to DEI helps organizations adhere to ethical standards and legal requirements and drives business performance and innovation (Cox & Blake, 1991). Research has shown that diverse and inclusive teams are more innovative, productive, and better at problem-solving (Page, 2007). These teams bring a more comprehensive range of perspectives, ideas, and experiences to the table, fueling creativity and enhancing decision-making capabilities.