People Analytics

People analytics refers to the systematic and scientific process of applying quantitative or qualitative data analysis methods to derive insights that shape and inform employee-related business decisions and performance. More specifically, people analytics can be described as the process of collecting, analyzing, and reporting people-related data for the purpose of improving decision making, achieving strategic objectives, and sustaining a competitive advantage. In other words, people analytics involves scientific organizational research. As a field, people analytics has emerged as an extension of traditional human resources (HR) metrics and reporting, such as annualized turnover rate and cost per hire. Further, as an interdisciplinary field, people analytics integrates decades of science and practice in the areas of industrial and organizational psychology, organizational behavior, human resource management, statistics, mathematics, information systems and management, data science, finance, law, and ethics. Other related terms include HR analytics, human capital analytics, workforce analytics, and talent analytics. A major emphasis of people analytics is on making more effective data-driven HR decisions to realize organizational strategic objectives and to achieve a competitive advantage. Although the concepts and practices underlying people analytics are not entirely new, recent advances in computing power and information systems have brought people analytics to a wider audience via more affordable information system and enterprise resource planning platforms, and more powerful mathematical and statistical programs. Moreover, the growing scholarly and business interest in people analytics is based, in part, on the emergence of HR as a strategic business partner in organizations and the growing emphasis placed on making data-driven decisions in HR. Finally, people analytics can be differentiated into three levels of increasing complexity and organizational value: (a) descriptive analytics describe what has already happened in the organization, (b) predictive analytics predict what will or could happen in the organization, and (c) prescriptive analytics take data-analytic findings and use them to improve decisions and to make specific actions.