Maximize performance with better data Developing a successful workforce requires more than a gut check. Data can help guide your decisions on everything from where to seat a team to optimizing production processes to engaging with your employees in ways that ring true to them. People analytics is the study of your number one business asset—your people—and this book shows you how to collect data, analyze that data, and then apply your findings to create a happier and more engaged workforce. Start a people analytics project Work with qualitative data Collect data via communications Find the right tools and approach for analyzing data If your organization is ready to better understand why high performers leave, why one department has more personnel issues than another, and why employees violate, People Analytics For Dummies makes it easier.

How can HR practitioners with little or no experience of analytics feel confident in their ability to find, analyse and use workforce data to make better business decisions? This book has the answers. An understanding of people analytics is a crucial skill for all HR professionals. This new edition provides expert guidance on the key aspects of analytics, enabling all HR professionals to feel confident in their ability to handle employee and organizational data. It features new material on applying data to respond to external disruption such as COVID-19 as well as how to develop a people analytics journey. There is also advice on recruiting people analytics specialists and embedding new data-driven operating models within HR. This book is essential reading for all HR professionals to develop understanding of how and where HR analytics can make a tangible difference to organizations. With updated case studies and thought leadership examples from companies including NHS, AstraZeneca and Swarovski, this book demonstrates how people analytics can be leveraged to improve culture and employee engagement, increase performance and reduce costs.

Human Resources (HR) departments often have significant data sets related to employees and positions within their organizations, but optimizing use of this data can present challenges. As the business world rapidly transforms due to technological advancements, experts within the HR domain must learn to effectively use data to improve workforce performance and assist with strategic decisions. A comprehensive understanding of HR analytics and its multiple levels, ranging from descriptive to perspective, can emphasize how the data can support, track, and monitor employee performance, culture, turnover rate, and absenteeism. HR Analytics in an Era of Rapid Automation is a valuable guide for academics, researchers, and practitioners interested in the latest developments in HR analytics. It covers relevant theories and conceptual models based on quantitative and qualitative findings and emphasizes the importance of utilizing HR analytics for sustainable decision making. With a focus on recruitment analytics, talent acquisition, employee performance analytics, and more, this book provides practical solutions to the challenges facing HR professionals in the rapidly changing business world. By highlighting the value of people and HR analytics for business success, this book offers several solutions for the analysis of challenges facing HR professionals today.

The new edition of Raymond Stone's Human Resource Management is an AHRI endorsed title that has evolved into a modern, relevant and practical resource for first-year HRM students. This concise 15-chapter textbook gives your students the best chance of transitioning successfully into their future profession by giving them relatable professional insights and encouragement to exercise their skills in authentic workplace scenarios.

Discover how to develop and implement an HR analytics system that benefits employees, as well as your organization The potential of HR analytics is a major discussion among scholars, practitioners, thought leaders, and technology vendors, with companies like Amazon, Apple, Google, and Meta digging deeply into HR research and analytics practices that extend beyond simple metrics, scorecards, and reporting. Additionally, ethical questions have begun to arise about the potential abuses of HR analytics with respect to technological advancements and the “datafication” of personal—and often trivial—characteristics, preferences, and behaviors that have little relevance to job performance. As a former chief human resources officer, head of Global HR Research and Analytics at a Fortune 100, and thought leader on this subject, Salvatore Falletta has witnessed first-hand the emergence of “creepy analytics” as a hot-button issue. In this one-of-a-kind guide, Falletta delivers a proven step-by-step process for establishing HR analytics capabilities that serve employees and organizations alike. You’ll learn how to rethink and redefine HR analytics, determine stakeholder requirements, gather and transform data, communicate intelligence results, and establish an ethical ecosystem to ensure HR analytics remains a force for good.

This book highlights the state-of-the-art research on data usage, security, and privacy in the scenarios of the Internet of Things (IoT), along with related applications using Machine Learning and Big Data technologies to design and make efficient Internet-compatible IoT systems. ICT and Data Sciences brings together IoT and Machine Learning and provides the careful integration of both, along with many examples and case studies. It illustrates the merging of two technologies while presenting basic to high-level concepts covering different fields and domains such as the Hospitality and Tourism industry, Smart Clothing, Cyber Crime, Programming, Communications, Business Intelligence, all in the context of the Internet of Things. The book is written for researchers and practitioners, working in Information Communication Technology and Computer Science.

Confidently analyse your organization's HR data using R and R Studio to gain insights that improve people strategy and business decision-making. Effective use of HR data has the power to transform a business. However, this is only possible if HR practitioners have the knowledge, skills and confidence to analyse the data and to draw evidence-based insights from it. This book is the practical guide that HR professionals need. Through worked examples, this book shows readers how to carry out and interpret analyses of HR data in areas such as recruitment, performance, employee engagement and diversity. People professionals are then shown how to use the results to develop robust people strategies and to support more effective evidence-based decision-making. Using R in HR Analytics provides a thorough grounding in the differences between descriptive reporting and predictive analytics as well as the methods and measures used to identify the validity of results. There is also expert guidance on the role of artificial intelligence, machine learning and large language modelling on HR analytics. Written for HR professionals at any level, there is essential coverage of data privacy and the ethical considerations of using people data. Online resources include sample datasets to allow readers to practice analysing HR data.

Despite the recent rapid growth in machine learning and predictive analytics, many of the statistical questions that are faced by researchers and practitioners still involve explaining why something is happening. Regression analysis is the best ‘swiss army knife’ we have for answering these kinds of questions. This book is a learning resource on inferential statistics and regression analysis. It teaches how to do a wide range of statistical analyses in both R and in Python, ranging from simple hypothesis testing to advanced multivariate modelling. Although it is primarily focused on examples related to the analysis of people and talent, the methods easily transfer to any discipline. The book hits a ‘sweet spot’ where there is just enough mathematical theory to support a strong understanding of the methods, but with a step-by-step guide and easily reproducible examples and code, so that the methods can be put into practice immediately. This makes the book accessible to a wide readership, from public and private sector analysts and practitioners to students and researchers. Key Features: 16 accompanying datasets across a wide range of contexts (e.g. academic, corporate, sports, marketing) Clear step-by-step instructions on executing the analyses Clear guidance on how to interpret results Primary instruction in R but added sections for Python coders Discussion exercises and data exercises for each of the main chapters Final chapter of practice material and datasets ideal for class homework or project work.

This is the essential guide for HR practitioners who want to gain the statistical and analytical knowledge to fully harness the potential of HR metrics and organizational people-related data. The ability to use and analyse data has become an invaluable skill for HR professionals to not only identify trends and patterns, but also make well-informed business decisions. The third edition of Predictive HR Analytics provides a clear, accessible framework for understanding people data, working with people analytics and advanced statistical techniques. Readers will be taken step-by-step through worked examples, showing them how to carry out analyses and interpret HR data in areas such as employee engagement, performance and turnover. Learn how to make effective business decision with this updated edition that includes the latest materials on biased algorithms and data protection, supported by online resources consisting of R and Excel data sets.

This state-of-the-art Handbook provides an overview of the role of big data analytics in various areas of business and commerce, including accounting, finance, marketing, human resources, operations management, fashion retailing, information systems, and social media. It provides innovative ways of overcoming the challenges of big data research and proposes new directions for further research using descriptive, diagnostic, predictive, and prescriptive analytics.