Let’s face it. There are too many professional books one can read in a lifespan. This list of People Analytics and HR-Tech books is not exceptional. I won’t be able to complete reading all of it, though I have these books on my Kindle, ready for browsing. I find here inspiration, blended with practical guidance and validation for my practices, mixed with new ideas and innovative tools, but most of all, an “open door” to a professional community, which I’m happy and honored to be a part of. So here is my People Analytics and HR-Tech reading list on Kindle (no paper books, as I like the trees), ordered chronologically from newest to oldest. Click titles for Kindle versions (Work in progress. First published: May 14th, 2018. Last update: March 30th, 2024. Total Kindle books mentioned: 79). If you are interested in additional free resources for data science and R programming, check the comment section of this post. And be careful! These books can change your career!

People Analytics – Build the Value Chain
Open Book (work in progress) by Littal Shemer Haim

The book is based on the Littalics.com blog posts from 2017 to 2022. It allows you to peek into the author’s thoughts and experience transitioning from an applied researcher to a People Analytics consultant and educator to HR. Enjoy Littal’s unique voice and take the time to contemplate, absorb ideas, and, hopefully, overcome barriers. You will find sixteen lessons in this book, organized into four milestones that, from the author’s experience, build the People Analytics value chain.

Strategic Workforce Planning: Best Practices and Emerging Directions

Marc Sokol, Beverly Tarulli (2024). “An overview of strategic workforce planning, covering best practices across organization types, geographies, and methodologies, and addressing new directions in the field. Contributors share case examples and experience-based insights, spanning the evolution of the field, best practices for analytics and consulting, maturity models, and when to organize around planning for future tasks vs planning for future roles. The Book also discusses changes in the workforce and workplace due to global disruptions like the COVID-19 pandemic and the impact of quickly evolving technologies, and re-examines the impact of strategic workforce planning on individual organizations and beyond.”

Data-Driven HR: How to Use AI, Analytics, and Data to Drive Performance *

Bernard Marr (2023). “AI is now an integral part of being data-driven. With this updated edition of Data-Driven HR, practitioners can unlock business potential and success through data and analytics. Covering topics such as recruitment, employee engagement, performance management, well-being, and training, HR practitioners can benefit from knowing how to be data-driven through data and AI. HR teams will learn to identify business goals, scrutinize useful data sources, and gain rich and diverse insights from their vast amounts of data. The book offers guidance on managing challenges that come with data and AI and how to responsibly and transparently use data to improve decision-making. It also includes predictive analytics and how to place warning systems into databases for any potential workforce issues.”
* The previous edition

The Fundamentals of People Analytics: With Applications in R *

Craig Starbuck (2023). “The book offers key concepts spanning the entire analytics lifecycle, along with step-by-step instructions for their applications to real-world problems, using ubiquitous and freely available open-source software, enabling to gain a deeper, data-informed understanding of organizational phenomena impacting the bottom line.”
* No Kindle version available, but free access to a PDF file.

Talent Assessment: Embracing Innovation and Mitigating Risk in the Digital Age

Tracy Kantrowitz, Douglas Reynolds, and John Scott (2023). “The intersection of new technologies, globalization, and market shifts among assessment providers has created dramatic opportunities for the field along with some significant challenges. Artificial intelligence (AI) and other technological advances have altered what is possible in talent acquisition. The book focuses on trends and innovations in talent assessment, framing practical solutions for managing the disruption in assessment while incorporating new insights and technologies into organizational assessment programs. It covers advances in the foundational science of assessment, technology-related innovations, updates to regulations, principles, and standards, and  assessment for development.”

Introduction to People Analytics: A Practical Guide to Data-driven HR *

Nadeem Khan and Dave Millner (2023). “How can HR practitioners with little or no experience in analytics feel confident in their ability to find, analyze, and use workforce data to make better business decisions? This book provides expert guidance on the key aspects of analytics, enabling all HR professionals to handle employee and organizational data. It features new material on applying data to respond to external disruption and how to develop a people analytics journey. With updated case studies and thought leadership examples from leading companies, this book demonstrates how people analytics can be leveraged to improve culture and employee engagement, increase performance, and reduce costs.”
* The previous edition

Data & Analytics for Instructional Designers

Megan Torrance (2023). “With the advance of new learning technologies and data specifications, instructional designers have access to more and richer data sources than ever before. With that comes the question of what to do with the data. The book delves into the foundational concepts that will enable instructional designers and L&D professionals to use data in their roles. It defines key data and analytics terms, data specifications, learning metrics, and statistical concepts. It then lays out a framework for using learning data to plan how to gather data and build scale and maturity in your data operations.”

Data, Methods, and Theory in the Organizational Sciences: A New Synthesis (SIOP)

Kevin R. Murphy (2022) “This edition of the SIOP series explores the organizational sciences’ long-term evolution and changing relationships between data, methods, and theory. In the last 50 years, theory has dominated research and scholarship in these fields. Yet, the emergence of big data and the increasing use of archival data sets and meta-analytic methods to test empirical hypotheses has upset this order. This volume examines the evolving relationship between data, methods, and theory. It suggests new ways of thinking about the role of each in the development and presentation of research in organizations. This is an essential resource for researchers, professionals, and educators looking to rethink their current approaches to research and who are interested in creating more useful and more interpretable research in the organizational sciences.”

Network Psychometrics with R: A Guide for Behavioral and Social Scientists

Adela-Maria Isvoranu, Sacha Epskamp, Lourens Waldorp, and Denny Borsboom (2022). “A systematic, innovative introduction to the field of network analysis that provides a comprehensive overview of and guide to both the theoretical foundations of network psychometrics as well as modeling techniques developed from this perspective. The book features an introduction to the statistical programming language R that guides readers on how to analyze network structures and their stability using R.”

Inclusalytics: How Diversity, Equity, and Inclusion Leaders Use Data to Drive Their Work

Victoria Mattingly, Sertrice Grice, Allison Goldstein (2022) ” Whether due to pressure from internal and external stakeholders, competition for top talent, or a moral imperative, businesses increasingly acknowledge that DEI work needs to be done and done right. The book shares how modern businesses are using data to unlock DEI success. Employing a combination of case studies, practical examples, and step-by-step instructions, it guides DEI practitioners through developing a robust, data-centric approach to DEI. It provides instruction on gathering, analyzing, and reporting data to understand better the diversity, equity, and inclusion issues employees are experiencing and gain those workers’ trust. It demonstrates how companies can use the data to prioritize the issues they uncover, identify solutions, and evaluate whether the solutions they’ve chosen to spend time and money to implement are making a difference.”

Talent Tectonics: Navigating Global Workforce Shifts, Building Resilient Organizations and Reimagining the Employee Experience

Steven Hunt (2022) ” Shifting demographics combined with the digitalization of all aspects of life are transforming the nature of work. This forces companies to rethink how they design jobs and recruit, develop, and engage employees. This book explains how technology is changing the purpose of work and why creating effective employee experiences is critical to building organizations that can thrive in a world of accelerating change and growing skill shortages. The book explores how business strategy, organizational psychology, and work technology interact to create nimble companies.”

Talent Intelligence: Use Business and People Data to Drive Organizational Performance

Toby Culshaw (2022). “Improved access to people and business data has created huge opportunities for the HR function. However, simply having access to this data is not enough. HR professionals need to know how to analyze the data, what questions to ask, and where and how the insights from the data can add the most value. The book offers a practical guide that explains everything HR professionals need to know to achieve this. It also explains how and why talent intelligence differs from workforce planning, sourcing research, and standard predictive HR analytics. It also shows how to assess where talent intelligence can have the biggest impact and how to demonstrate the results to all stakeholders”.

Organizational Planning and Analysis: Building the Capability to Secure Business Performance

Rupert Morrison (2022). “The book offers a data-driven approach to workforce planning. It allows HR professionals, OD practitioners, and business leaders to monitor an organization’s activities and analyze business data to adjust plans to ensure the business succeeds regularly. This book covers building this function, the difference between strategic and operational workforce planning and managing demand and supply, matching people to new or changing roles, and developing robust succession planning. It also covers how to work with HR operations, including recruitment, L&D, reward, and performance management, and how human capital analytics allow a business to improve the return on investment for each employee”.

Humanizing Human Capital: Invest in Your People for Optimal Business Returns

Solange Charas, Stela Lupushor (2022). “The key to thriving through disruption is understanding and practicing human capital strategies that will drive enterprise performance and value-creation. The authors shift decision-making about people from a gut sense to an evidence-based approach. The book reveals a step-by-step method to apply analytics approaches to human capital while anticipating inevitable changes in the workforce landscape. This will enable human capital professionals to generate positive outcomes for all stakeholders and allow management to make decisions that work for the entire enterprise. This book compares “traditional” with the “future-forward” approach to human capital management, case studies, real-world situations, and 20 business principles”.

Handbook of Graphs and Networks in People Analytics: With Examples in R and Python

Keith McNulty (2022). “The book covers the theory and practical implementation of graph methods in R and Python for the analysis of people and organizational networks. Starting with an overview of the origins of graph theory and its current applications in the social sciences, the book proceeds to give in-depth technical instruction on how to construct and store graphs from data, how to visualize those graphs compellingly, and how to convert common data structures into graph-friendly form”.

Artificial Intelligence for HR: Use AI to Support and Develop a Successful Workforce *

Ben Eubanks (2022) “Artificial intelligence is changing the world of work. This book discusses how HR professionals can understand the variety of opportunities AI has created for the HR function and how best to implement these in their organization. From using natural language processing to ensure job adverts are free from bias and gendered language to implementing chatbots to enhance the employee experience, artificial intelligence can add value throughout the work of HR professionals. The book demonstrates leveraging this potential and using AI to improve efficiency and develop a talented and productive workforce. Outlining the current technology landscape and the latest AI developments, this book ensures that HR professionals fully understand what AI is and what it means for HR.”
* The previous edition

Humans at Work: The Art and Practice of Creating the Hybrid Workplace

Anna Tavis, Stela Lupushor (2022). “Technology, data analytics, and artificial intelligence already impact how people work and engage with organizations. A dispersed workforce, greater transparency, social change, generational shift, and value chain disruptions are driving new behaviors and expectations from the workplace. Together, these trends are shaping a new era of distributed and digitally enabled networks of workers where the work comes to workers instead of the workers going to work. The authors advocate for adopting human-centric practices as a critical and necessary part of adapting work and workplaces to the future of work and provide examples of how innovative companies are building workplace infrastructure and reshaping norms, serving new markets, and adopting new technologies”.

The Future of Recruitment: Using the New Science of Talent Analytics to Get Your Hiring Right

Franziska Leutner, Reece Akhtar, Tomas Chamorro-Premuzic (2022). “The book helps professionals, researchers, employers, and everybody interested in the world of work to understand the past, present, and future of recruitment. The authors describe the modern technologies and ideas that are changing recruitment, many driven by artificial intelligence. Evaluating ethical issues and highlighting how technology might be used to make recruitment and progression at work meritocratic, the book reflects on longstanding issues in recruitment and its role in building today’s unequal world of work”.

Introduction to HR Technologies: Understand How to Use Technology to Improve Performance and Processes

Stacey Harris (2021). “Technology can have huge benefits for the HR function: saving time by streamlining processes, boosting engagement by enabling analysis of people data or improving employee development by allowing staff to access the content they need on different platforms, wherever and whenever they need it. However, with more apps, software, and platforms than ever before, the volume and variety of available technologies can be overwhelming. The book offers a clear, accessible, and jargon-free guide for HR professionals to understand how to use technologies to add tangible business value. It covers all the core areas of HR including recruitment, performance management, learning and development (L&D), and reward. There is also discussion of artificial intelligence (AI), machine learning and the Internet of Things (IoT) and what they mean for HR”.

Introducing HR Analytics with Machine Learning: Empowering Practitioners, Psychologists, and Organizations

Christopher M. Rosett and Austin Hagerty (2021). “For today’s organizational psychologists to successfully work with their partners, they must go beyond behavioral science into the realms of computing and business acumen. Similarly, today’s data scientists must appreciate the unique aspects of behavioral data and the circumstances surrounding HR data and HR systems. Finally, traditional HR professionals must become familiar with research methods, statistics, and data systems to collaborate with these new specialized partners and teams. The book introduces these areas and guides on building the connectivity across domains required to establish well-rounded skills for individuals and best practices for organizations when applying advanced analytics to workforce data. It will also introduce machine learning and where it fits within the larger HR Analytics framework”.

Handbook of Regression Modeling in People Analytics: With Examples in R and Python

Keith McNulty (2021). “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 modeling. It is primarily focused on examples related to the analysis of people and talent, and includes a step-by-step guide and easily reproducible examples and code so that the methods can be put into practice immediately”.

The Science of Dream Teams: How Talent Optimization Can Drive Engagement, Productivity, and Happiness

Mike Zani (2021). “The book details a data-driven approach to talent optimization that makes hiring, motivating, and managing people more efficient and effective. It employs sophisticated assessments, tools, and software that enable leaders to: measure the traits and characteristics that predict success in a role or fit on a team, build finely tuned project teams and well-balanced leadership teams, boost employee productivity, engagement, retention—and happiness, unlock the hidden potential of individual workers and your organization as a whole”.

Excellence in People Analytics: How to Use Workforce Data to Create Business Value

Jonathan Ferrar, David Green (2021). “Effectively and ethically leveraging people data to deliver real business value is what sets the best HR leaders and teams apart. The book provides practical guidance on how to create sustainable business value with people analytics and develop a data-driven culture in HR. It allows HR professionals and business executives to translate their data into tangible actions to improve business performance while navigating the rapidly evolving world of work. Following a nine dimensions model, the book demonstrates how to use people data to increase profits, improve staff retention and workplace productivity as well as develop individual employee experience”.

HR Analytics Essentials You Always Wanted To Know

Michael Walsh (2021). “The book covers the following topics: Define what HR Analytics can do for an organization, Determine the best HR analytics role, Assess the readiness of organizations for undergoing a study using HR analytics, Apply HR Analytics in various HR disciplines, including recruiting and staffing, labor negotiations, incentives, and training, Use Excel to efficiently manage data for your HR analytics. The book walks readers through the many benefits of using analytics to make better people decisions. As it shows, HR Analytics is both an art and a science that can help organizations make informed decisions that benefit all stakeholders, including employees”.

Agile Workforce Planning: How to Align People with Organizational Strategy for Improved Performance

Adam Gibson (2021). “As business priorities change and focus shifts to address arising issues, HR professionals need to be able to reorganize talent swiftly and plan for future needs to enable the business to succeed. The book covers how to use agile workforce planning to forecast organizational demand for people, resources, and skills, and how to analyze and fill the gap between supply and demand. This is a practical guide for HR and organization development practitioners needing to align their staff, skills, and resources with evolving company goals. It also covers how to identify the skills needed in the workforce, where these skills are already available and when they’re missing, how to decide whether to buy, borrow or build them. It explains how to collect data to calculate and predict staff churn as well as how to use qualitative and quantitative demand modeling to forecast for future needs and provides strategies to address these including lateral internal recruitment”.

Digitalised Talent Management: Navigating the Human-Technology Interface

Sharna Wiblen (Editor) (2021). “The book focuses on the use of information technologies in talent management. It affords theoretically, methodologically, and empirically informed insights that are especially salient given the need for executives and organizations to balance the role of humans and technology while ensuring competitiveness in an interconnected and increasingly digital world. It contributes to the industry conversations about the role of technological innovations in enabling organizations to transition towards digital ways of organizing talent and the implications for different stakeholders. The book adds value by assembling subject matter experts currently siloed within traditional research domains while highlighting the complexity of managing talent”.

People Operations: Automate HR, Design a Great Employee Experience and Unleash Your Workforce

Jay Fulcher, Tracy Cote, Kevin Marasco (2021) “In this book, you’ll receive an end-to-end guide to Digitizing legacy HR functions, Using robots for the busywork you hate, Employing software to design and improve your employee experience, Assembling and empowering your “people team”, and Utilizing the included plans and templates to guide each stage of your business transformation.”

HR Analytics: Quantifying the Intangible: Linking People, processes, and analytics

Anshul Saxena (2021) “The book introduces the emerging discipline of ‘Human Resource Analytics’. The target audience is the students who have taken up bachelor’s and master’s degree programs in the field of management or enthusiasts who have even a wee-bit understanding of HR and are interested in diving deeper into the subject. The readers will develop insights into the topics like primary metrics, KPIs, and processes involved in different HR subdomains like recruitment and employee engagement.”

The ROI of Human Capital: Measuring the Economic Value of Employee Performance *

Jac Fitz-enz (2020) “The lifeblood of any business enterprise is its people. Yet it wasn’t until the publication of this groundbreaking book that there was a reliable way to quantify the contributions of people to corporate profit. Updated with new metrics, the book shows executives and HR professionals how to gauge human costs and productivity at three critical levels: organizational (contributions to corporate goals), functional (impact on process improvement), and human resources management (value added by five basic HR department activities). The book also includes topics such as corporate outsourcing, developments in behavioral science, and advances in trending and forecasting that have dramatically changed the way organizations measure the bottom line effect of employee performance.”
* The previous edition

Employee Surveys and Sensing: Challenges and Opportunities

William H. Macey, Alexis A. Fink (2020) “The design and execution of employee survey has evolved over the past decade. Technological advances and new interest in talent analytics have combined to create an exciting space with a good deal of innovation along methodological lines. Providing a solid grounding in the basic issues of content development, interpreting results, and driving action, the book addresses cutting-edge topics in the area of survey analytics, including applications of computational linguistics and artificial intelligence. Significant emphasis is given to ethical issues for ensuring the protection of data and the privacy of survey respondents”.

Solving the Productivity Puzzle: How to Engage, Motivate and Develop Employees to Improve Individual and Business Performance

Tim Ringo (2020). “The book is a practical guide for all people management professionals to address the challenge of stagnating people productivity. It covers how to embed learning and development activities to ensure that employees feel equipped with the skills they need to meet their goals, motivate a workforce made up of six generations with competing priorities, develop an effective workforce planning strategy to make sure the right people are in the right place at the right time, with the right motivation in the organization to build a company culture that allows people to thrive. It also includes expert guidance on how to implement change to opportunity in the workforce, track and measure productivity, and leverage new technologies to support employees.”

Digital HR Strategy: Achieving Sustainable Transformation in the Digital Age

Soumyasanto Sen (2020). “A crucial reading for HR practitioners and leaders about organization adaptation to changing and increasingly competitive environment. The book features case studies and covers the importance of cultural change and creating a human-centric employee experience, leveraging value propositions, and harnessing data insights and analytics to improve performance. It also explores frameworks, strategies, and opportunities for wellbeing initiatives, upskilling and reskilling workforces to respond to and establish a culture of collaboration and innovation”.

Measurement Demystified: Creating Your L&D Measurement, Analytics, and Reporting Strategy

David L. Vance, Peggy Parskey (2020) “The book breaks new ground with a framework to simplify the discussion of measurement, analytics and reporting as it relates to L&D and talent development practitioners. it helps practitioners select and use the right measures for the right reasons; select, create, and use the right types of reports; and create a comprehensive measurement and reporting strategy.”

Evidence-Based Recruiting: How to Build a Company of Star Performers Through Systematic and Repeatable Hiring Practices

Atta Tarki (2020) “Defending your business from the competition through regulations, differentiated technologies, brand recognition, and other methods is no longer a sustainable competitive advantage. This book guides you through the process of designing and implementing a data-driven hiring strategy that will secure your business for the foreseeable future. Revealing how today’s top innovators, it shows how you can do the same by implementing systematic and repeatable processes that lead to better, more consistent hiring outcomes. You’ll learn how to: envision an evidence-based approach to hiring, distinguish useful data from the data you don’t need, use the best technologies to achieve your recruitment goals, build an effective talent-acquisition team, improve on-the-job success predictions, design well-defined and objective measures to improve hiring outcomes, avoid the most common hiring pitfalls.”

Learning Analytics: Learning Analytics: Using Talent Data to Improve Business Outcomes

Cristina Hall, John R Mattox, Peggy Parskey (2020). ” Effective evaluation and measurement of learning and development initiatives are critical to maximize the impact of training, identify gaps for improvement and ensure that efforts are aligned to the business needs. The book outlines how analytical approaches can respond to these challenges, the types and benefits of technological solutions, and how to ask the right questions of organizational data in order to build a learning organization that boosts performance and competitive advantage.”

Fundamentals of HR Analytics: A Manual on Becoming HR Analytical

Fermin Diez (2019). “The future of the Human Resource Management profession is inextricably linked to understanding data analytics. The book provides practical, hands-on approaches to connect data to HR policies and practices to help influence overall business performance. It makes understanding and engaging with data analytics possible for professionals at all levels. Leveraging key statistical and financial concepts, including ROI and people productivity, and commonly available tools, it explores key skills and tasks in an accessible and illuminating way, including data-analytic thinking, data management, data collection, clean-up, and warehousing, building descriptive and predictive models, applying HR analytics skills and tools to workforce planning, recruitment, training, and turnover analysis”.

Investing in People: Financial Impact of Human Resource Initiatives

John W. Boudreau, Wayne F. Cascio, Alexis A. Fink (2019). “The future of work, talent, and employment are changing at an unprecedented pace, and organizational decisions about investment in people are under increasing scrutiny. Leaders’ decisions about human resources are crucial in an uncertain and interconnected world, yet decisions about people remain among the least systematic and evidence-based compared to resources such as money and technology. The book draws upon state-of-the-art practice and research across disciplines, including psychology, economics, accounting, and finance to provide HR professionals and leaders with proven guidelines for evaluating key HR initiatives. It is based on a comprehensive framework that clarifies and supports strategic linkages between investments in human capital and important outcomes that senior leaders most care about, such as talent acquisition, engagement, learning, customer service, and higher financial returns”.

Predictive HR Analytics: Mastering the HR Metric *

Martin Edwards and Kirsten Edwards (2019). “Organizational people-related data is an invaluable source of information from which to identify patterns to make effective business decisions. HR practitioners often lack statistical and analytical know-how. The book provides a clear, accessible framework for understanding People Analytics. It includes examples such as employee engagement, performance, and turnover. It shows how to use the results to develop effective evidence-based HR strategies. This 2nd edition has been updated to include machine learning, biased algorithms, data protection, GDPR considerations, and a new example using survival analyses. A new appendix shows main R coding and online resources consisting of SPSS and Excel data sets”.
* The previous edition

People Analytics & Text Mining with R

Mong Shen Ng (2019). “This book teaches you R (open-source programming language), People Analytics, Text Mining and Sentiment Analysis. It is written for people with no knowledge of R, with step-by-step print-screen instructions. The book covers the full People Analytics scope (Benefits, Compensation, Culture, Diversity & Inclusion, Engagement, Leadership, Learning & Development, Personality Traits, Performance Management, Recruitment, Sales Incentives) with numerous real-world examples, and shows how R can help”.

People Analytics for Dummies

Mike West (2019). “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”.

Human Capital Management Standards: A Complete Guide

Wilson Wong, Valerie Anderson, Heather Bond (2019) “A comprehensive guide to the BSI and ISO frameworks for people management, providing internationally agreed definitions and best practice guidance. The book covers everything from organizational governance, workforce planning, diversity & inclusion to learning and development. It explores the critical areas of people management throughout the employment life cycle, from initial hire to the time people move on from the organization. There is also coverage of additional business standards, such as those related to occupational health and safety, and the implications of implementing standards in a globalized and interconnected organizational context. The book also includes essential coverage of the standards assessment process and tips and advice on achieving successful accreditation.”

Human Resource Management: People, Data, and Analytics

Talya Bauer, Berrin Erdogan, David E. Caughlin, and Donald M. Truxillo (2018). “The authors introduce the fundamentals of talent management with integrated coverage of data analytics and how they can be used to inform and support decisions about people in an organization. Features tied to SHRM competencies and data exercises give readers hands-on opportunities to practice the analytical and decision-making skills they need to excel in today’s job market. Engaging examples illustrate key HRM concepts and theories, which brings many traditional HRM topics concepts to life”.

People Analytics. Data and Text Analytics for Human Resources

Eduardo Valencia (2018). “We have more information at our disposal about tastes, opinions, and certain behaviors that could be very useful in making decisions about performance, salaries, turnover rate, leaves of absence, etc. Accordingly, People Analytics processes that information through analysis tools that allow you to look at talent management decisions in a new way. People Analytics is based on data analysis as opposed to traditional methods of relationships or personal experiences. People Analytics offers a unique opportunity for human capital professionals to position themselves as strategic senior management partners, using analytically proven techniques to hire and retain employees who drive higher value in companies”.

The Practical Guide to HR Analytics: Using Data to Inform, Transform, and Empower HR Decisions

Rachael Johnson-Murray, Lindsay McFarlane, Valerie Streets, Shonna Waters (2018). “The need for HR professionals to understand and apply data analytics is greater than ever. Today’s successful HR professionals must ask insightful questions, understand key terms, and intelligently apply data, but may lack a clear understanding of the many forms, types, applications, interpretations, and capabilities of HR analytics. HR Analytics provides a practical approach to using data to solve real HR challenges in organizations and demystifies analytics with clear guidelines and recommendations for making the business case, starting an HR analytics function, avoiding common pitfalls, presenting data through visualization and storytelling, and much more”.

Digital HR: A Guide to Technology-Enabled Human Resources

Deborah Waddill (2018). “In times when HR leaders implement innovative technologies, such as Watson, AI, predictive modeling, and real-time analytics are being, this book can help. It offers the practical insights, real-world case studies, tips and tools, recommendations, and additional resources, to guide HR practitioners through each of the major technologies and to address vital strategic and implementation issues, that eventually enhance, enable, revitalize, and empower Human Resources”.

Human Resource Excellence: An Assessment of Strategies and Trends

Edward E. Lawler and John W. Boudreau (2018). “the book delivers new findings of what makes HR successful and how it can add value to today’s organizations. Tracing changes in a global sample of firms, it provides an international benchmark against which to measure a company’s HR practice. HR is most powerful when it plays a strategic role, makes use of information technology, and has tangible metrics and analytics. The authors offer insights about HR’s changing role in strategy, big data, social and knowledge networks, and the gig economy”.

Adaptive Space: How GM and Other Companies are Positively Disrupting Themselves and Transforming into Agile Organizations

Michael Arena (2018). “In this book, Michael Arena, an organizational networks expert who helped enable GM’s legendary turnaround, explains how to transform a company through the concept of adaptive space: enabling creativity, innovation, and novel ideas to flow freely among teams, across departments, and throughout the company. Using GM case study and other highly adaptive organizations, Arena provides a model to follow.”

Evidence-Based Management: How to Use Evidence to Make Better Organizational Decisions

Eric Barends, Denise M. Rousseau (2018) “The book has evolved with the goal of improving the quality of decision-making by using critically evaluated evidence from multiple sources – organizational data, professional expertise, stakeholder values and scientific literature. It sets out and explains the specific skills needed to gather, understand and use evidence to make better-informed organizational decisions.”

Strategic Workforce Planning: Developing Optimized Talent Strategies for Future Growth

Ross Sparkman (2018). “A playbook for workforce planning from beginning to end. It enables HR professionals to answer core business questions including how do I analyze future hiring demand? How do I assess what skills will be required in the future? How should I prioritize investments like training and development? How do I assess the supply of talent around the world? How do I identify the business drivers that impact workforce demand? It also covers the impact of artificial intelligence (AI), automation and machine learning on the global workforce and how to deal with these implications.”

Strategic Workforce Planning: Guidance & Back-Up Plans

Tracey Smith (2018). “The book aims to proactively plan to put the right people, in the right place, at the right time at the right cost in order to mitigate workforce risk. Written by the former head of global strategic workforce planning for FedEx Express, this basic introduction guides the reader through the planning framework and presents practical tools and approaches for successful implementation. It also discusses some of the major challenges of implementation facing leaders today”.

Doing HR Analytics – A Practitioner’s Handbook With R Examples

Lyndon Sundmark (2017). “The book seeks to cut through the jargon and hype that currently accompanies the current state of this field. It provides foundational definitions and concepts, a suggested ‘known’ analytics framework, and practical illustrative examples of how to use analytics to address HR issues. It gets you to think informationally about ‘all’ of HR and offers examples that ‘scratch the surface’ of what is possible.”

The Power of People: How Successful Organizations Use Workforce Analytics to Improve Business Performance

Nigel Guenole, Jonathan Ferrar and Sheri Feinzig (2017). “Drawing on incisive case studies and vignettes, three experts help you bring purpose and clarity to any workforce analytics project, with robust research design and analysis to get reliable insights. They reveal where to start, where to find stakeholder support, and how to earn “quick wins” to build upon.”

The Data-Driven Leader: A Powerful Approach to Delivering Measurable Business Impact through People Analytics

Jenny Dearborn and David Swanson (2017). “How to transform the HR function and overall organizational effectiveness by using data to make decisions grounded in facts vs. opinions, identify causes behind your company’s thorniest problems and move toward a winning, future-focused business strategy. Realistic and actionable, this book tells the story of a successful sales executive who, after leading an analytics-driven turnaround (in Data-Driven, this book’s predecessor), faces a new turnaround challenge as chief human resources officer.”

Competing on Analytics: Updated, with a New Introduction: The New Science of Winning

Thomas Davenport and Jeanne Harris (2017). “The roadmap for becoming an analytical competitor, this book shows how to create new strategies for organizations based on sophisticated analytics, introduces a five-stage model of analytical competition, and describes typical behaviors, capabilities, and challenges of each stage. It equally emphasizes human and technological resources, and contains examples from different industries and business functions”.

The Talent Delusion: Why Data, Not Intuition, Is the Key to Unlocking Human Potential

Tomas Chamorro-Premuzic (2017). “Bridging the gap between the psychological science of talent and common real-world talent practices, this book aims to educate HR practitioners and leaders on how to measure, predict and manage talent. It provides a data-driven solution to the common problems around employee selection, development, and engagement; how to define and evaluate talent; how to detect and inhibit toxic employee behaviors; and how to identify and harness leadership potential.”

Next Generation Performance Management: The Triumph of Science Over Myth and Superstition

Alan L. Colquitt (2017). “Business leaders and HR professionals focus on practices instead of the fundamental purpose of performance management. Standard economic and psychological theories for motivation and behavior fail to account for the increasingly complex nature of organizations and their environments today. This book confronts the traditional dogma, paradigms, and practices and holds them up to the bright light of scientific scrutiny. It encourages HR professionals and business leaders to abandon them and it offers up a more appropriate alternative paradigms to guide them and practical solutions that are better supported by scientific research.”

Quantifiably Better: Delivering Human Resource (HR) Analytics from Start to Finish

Steve VanWieren (2017) “The book provides a path to follow in search of answers to questions about turnover rate, leadership issues, engagement, and company culture. It offers help if you are just getting started with your HR analytics initiative or if you are looking for ways to expand your existing HR analytics practice.”

People Analytics in the Era of Big Data: Changing the Way You Attract, Acquire, Develop, and Retain Talent

Jean Paul Isson and Jesse S. Harriott (2016). “This book presents a practical framework for real-world talent analytics, backed by groundbreaking examples of workforce analytics in action across the U.S., Canada, Europe, Asia, and Australia. Leverage predictive analytics throughout the hiring process, Utilize analytics techniques for more effective workforce management, Learn how people analytics benefits organizations of all sizes in various industries, Integrate analytics into HR practices seamlessly and thoroughly.”

Winning on HR Analytics: Leveraging Data for Competitive Advantage

Ramesh Soundararajan and Kuldeep Singh (2016). “HR analytics is pivotal in identifying, measuring, and articulating the objectives and outcomes of different programs. Consider predicting which high performers were at risk of leaving six months before they walked out the door or triage incoming resumes overnight to predict employee success and tenure before you hire. It is possible with sophisticated technology and analytics, as demonstrated by companies such as Google, Walmart, and American Express.”

HR Analytics and Innovations in Workforce Planning

Tony Miller (2016). “Process approach to interviewing provides greater structure and has the potential for significantly reducing bias. The book demonstrates how to deter and filter poor performers and benefit from structured, prewritten score-able questions. The evidence shows that many HR functions have failed to understand the long-term financial consequences of poor performance or realize that talented people do much more work than other employees. This book gives a detailed guide to the nine-step process with work examples.”

Strategic Analytics: Advancing Strategy Execution and Organizational Effectiveness

Alec Levenson (2015). “Business people who devise the strategies and the human resources people who get employees to implement them use completely different analytics. Business analytics can determine if operational priorities aren’t being achieved but can’t explain why. HR analytics reveal potentially effective policy and process improvements but can’t identify which would have the greatest strategic impact. This book shows how to use an integrated approach to bring these two pieces together.”

Optimize Your Greatest Asset – Your People: How to Apply Analytics to Big Data to Improve Your Human Capital Investments

Gene Pease (2015). “The author offers a framework for optimizing human capital investments through predictive analysis. You’ll learn how to transition from anecdotes and surveys to more advanced measurement techniques and combine the data from multiple systems into a unified plan of action that improves business results. The book includes practical examples and case studies to show how these techniques are applied in real-world settings, and executives and thought leaders weigh in on how advanced analytics are informing better business decisions every day.”

Work Rules!: Insights from Inside Google That Will Transform How You Live and Lead

Laszlo Bock (2015). “A compelling manifesto with the potential to change how we work and live, Laszlo Bock, Ex. Google’s Senior Vice President of People Operations offers both a philosophy of the new world of work and a blueprint for attracting the most spectacular talent and ensuring the brightest and best prosper.”

Making Human Capital Analytics Work: Measuring the ROI of Human Capital Processes and Outcomes

Jack Phillips and Patricia Pulliam Phillips (2015). “The tools and techniques you need to use analytics to show top decision-makers the value of HR in your organization. Focusing on three types of analytics, descriptive, predictive, and prescriptive, the book shows how you can apply analytics by Developing relationships between variables, Predicting the success of HR programs, Determining the cost of intangibles that are otherwise difficult to value, Showing the business value of particular HR programs, Calculating and forecasting the ROI of various HR projects and programs.”

Predictive Analytics for Human Resources

Jac Fitz-Enz and John Mattox (2014). “The book explains the basics of every business: the vision, the brand, and the culture, and shows how predictive analytics supports them. The authors put the focus on the fundamentals of predictability and include a framework of logical questions to help set up an analytic program or project, then follow up by offering a clear explanation of statistical applications.”

Predicting Success: Evidence-Based Strategies to Hire the Right People and Build the Best Team

David Lahey (2014). “A practical guide for using analytics to finding the perfect member for a team and avoiding bad culture fits, mismatched skillsets, entitled workers, and other hiring missteps that drain the team of productivity and morale. The book provides guidance toward implementing tools to build the best teams and achieve best outcomes.”

Employee Surveys That Work: Improving Design, Use, and Organizational Impact

Alec Levenson (2014). “Poorly designed employee surveys frustrate participants, analysts, and executives and can end up doing more harm than good. The book offers sensible, practical ways to make surveys more useful and accurate and provides specific advice for ensuring that the purpose and desired outcomes of surveys are clear, the questions are designed to provide the most relevant and accurate data, and the results are actionable. It also covers the best benchmarking practices, the benefits of multivariate modeling for analyzing results, the linking of survey data with performance data, the best ways to measure employee engagement, and the pros and cons of respondent anonymity.”

Developing Human Capital: Using Analytics to Plan and Optimize Your Learning and Development Investments

Gene Pease, Barbara Beresford, and Lew Walker (2014). “To make the most of your workers, nothing beats quantitative performance and investment measurement. To keep your business moving forward, effective management of human capital is crucial. It generates plenty of data, and deep analysis of this data helps you provide feedback and make adjustments to capitalize on the combined knowledge, skills, and creativity of your workers. The book provides a guidebook for collecting, organizing, and analyzing the data surrounding human capital so you can make the most of your employees’ potential.”

People Analytics: How Social Sensing Technology Will Transform Business and What It Tells Us about the Future of Work

Ben Waber (2013). “Sensors and analytics can give you an unprecedented understanding of how your people work and collaborate, and actionable insights for building a more effective, productive, and positive organization. The book covers cutting-edge case studies, e.g., changing the way call center employees spent their breaks to increase performance by 25% while significantly reducing stress, tweaking the balance of in-person and electronic communication to enhance the value of both and many more.”

The Talent Equation: Big Data Lessons for Navigating the Skills Gap and Building a Competitive Workforce

Matt Ferguson, Lorin M. Hitt, Prasanna Tambe (2013) “At every stage of the employee life cycle, a data-driven approach to HR can help companies make smarter decisions about their most important asset: their people. Companies face looming skill shortages, retention concerns, and questions regarding the most effective composition of their workforce. The book offers hiring professionals and business leaders a roadmap to attract and retain top talent. It covers a study of more than 2,700 employers and 33 million resumes to find the relationship between market performance, education attainment, and employee tenure. It explores the ROI of increased education levels and retention rates, the benefits of continuous recruitment and talent pipelines technology market, how workforce analytics tools are changing talent acquisition, and the importance of reducing long-term unemployment through training and reskilling.”

Applying Advanced Analytics to HR Management Decisions: Methods for Selection, Developing Incentives and Improving Collaboration

James C. Sesil (2013) “The author identifies widespread flaws in today’s HR decision-making processes and reveals how advanced analytics can help organizations make far more robust decisions about employee selection, performance management, strategy alignment, collaboration, and more. In this book, he shows how to integrate Business Intelligence, ERP, Strategy Maps, Talent Management Suites, and advanced analytics — and use them together to make far better decisions. The reader will learn how to measure and improve the value of HCM decision-making in workforce/succession planning, talent acquisition, career development, corporate learning, and beyond”.

Human Capital Analytics: How to Harness the Potential of Your Organization’s Greatest Asset

Gene Pease, Boyce Byerly, Jac Fitz-enz (2012) “Human capital analytics is the application of sophisticated data mining and business analytics techniques to human resources data. The book provides an in-depth look at the science of human capital analytics, giving practical examples from case studies of companies applying analytics to their people decisions and providing a framework for using predictive analytics to optimize human capital investments. The challenge of human resources analytics is to identify what data should be captured and how to use the data to model and predict capabilities so the organization gets an optimal return on investment on its human capital. The goal is to provide an organization with insights for effectively managing employees so that business goals can be reached quickly and efficiently”.

Positioned: Strategic Workforce Planning That Gets the Right Person in the Right Job

Dan Ward, Rob Tripp (2012) “The book captures the best workforce planning practices from leading organizations such as Boeing, HP, the US Intelligence Community, and others in the private and public sectors to help businesses address the constant challenge of having the right people available when needed in order to maximize creativity, efficiency, and productivity. By examining the evolution of workforce analytics and the roles of human resources professionals, and by incorporating input on best practices from expert people strategists, the authors provide insight into how organizations can adjust to turnover seamlessly and do so in a way that produces even better results.”

Calculating Success: How the New Workplace Analytics Will Revitalize Your Organization

Carl Hoffmann, Eric Lesser, Tim Ringo (2011). “Most companies today have access to reams of data: marketing, supply chain, finance, as well as critical data about their workforce. Yet research shows that leaders at these companies lack the confidence to effectively integrate and use this data to compete effectively in the marketplace. While executives know the results they need to achieve, they often fail to analyze the impact of their decisions on the firm, its processes, and its people. The book shows how to can take an analytical approach to both anticipate and address the changes required by a company’s competitive environment and drive superior performance: Analyze the work that needs to be done and know how to structure it; Provide the right types of people to do it; Motivate them to engage in that work; Explore innovative ways to get the work done.”

Transformative HR: How Great Companies Use Evidence-Based Change for Sustainable Advantage

John W. Boudreau and Ravin Jesuthasan (2011). “Proven HR strategies that can have a real impact on organizational success. This book demonstrates how some of the world’s most admired and prominent organizations are redefining HR leadership by using evidence-based change to inform human capital decisions that optimize efficiency, effectiveness and strategic impact. The authors present the five foundational principles to the new HR decision science: Logic-driven analytics, segmentation, risk leverage, synergy and integration and optimization.”

Diagnosing and Changing Organizational Culture: Based on the Competing Values Framework

Kim S. Cameron and Robert E. Quinn (2011). “This book provides a means of understanding and changing organizational culture in order to make organizations more effective. It provides validated instruments for diagnosing organizational culture and management competency; a theoretical framework for understanding organizational culture; and a systematic strategy and methodology for changing organizational culture and personal behavior.”

The New HR Analytics: Predicting the EconomicValue of Your Company’s Human Capital Investments

Jac FITZ-ENZ (2010). “This book reveals how human resources professionals can apply expense-based knowledge to make the most strategic staffing decisions for their companies, and how to measure and evaluate past and current returns and apply the information to make predictions about the future value of human capital investments: Evaluate and prioritize the skills needed to sustain performance; build an agile workforce through flexible Capability Planning; determine how the organization can stimulate and reward behaviors that matter; apply a proven succession planning strategy that leverages employee engagement and drives top-line revenue growth; recognize risks and formulate responses that avoid surprises.”

Retooling HR: Using Proven Business Tools to Make Better Decisions About Talent

John W. Boudreau (2010) “The book shows how HR leaders can adapt powerful analytical tools already used by other functions to the unique challenges of talent management. Drawing on his research and examples from companies including Google, Disney, IBM, and Microsoft, the author explains six proven business tools leaders already use. And he shows how HR can apply these tools to talent management”.

The Workforce Scorecard: Managing Human Capital To Execute Strategy

Mark A. Huselid, Brian E. Becker, and Richard W. Beatty (2005). “In a marketplace fueled by intangible assets, anything less than optimal workforce success can threaten a firm’s survival. Yet, in most organizations, employee performance is both poorly managed and underutilized. To maximize the power of their workforce, organizations must meet three challenges: view their workforce in terms of contribution rather than cost; replace benchmarking metrics with measures that differentiate levels of strategic impact; and make line managers and HR professionals jointly responsible for executing workforce initiatives. The authors show how to create a Workforce Scorecard that identifies and measures the behaviors, competencies, mindset, and culture required for workforce success and reveals how each dimension impacts the bottom line.”

Diagnosing and Changing Organizational Culture: Based on the Competing Values Framework

Brian E. Becker, David Ulrich, and Mark A. Huselid (2001). “The book introduces a measurement system that showcases how HR impacts business performance and describes a seven-step process for embedding HR systems within the firm’s overall strategy and measuring its activities in terms that line managers and CEOs will find compelling. Analyzing how each element of the HR system can be designed to enhance firm performance and maximize the overall quality of human capital, the book heralds the emergence of HR as a strategic powerhouse in today’s organizations.”

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What new roles do HR leaders have in the 4th industrial revolution? HR departments practice People Analytics to help business leaders to improve performance and growth through insights from people data. But what’s beyond People Analytics? How should HR leaders be prepared for the fourth industrial revolution?

AI changes everything. We have new responsibilities.

We’ve discussed a lot the two trends that contributed to the emergence of People Analytics a few years ago: data democratization – managers demand people’s data to run the business, and data consumerization – employees require to use data for growth, well-being, and positive experience at work, just as they do in other aspects of their lives.

But today, AI can make everybody better in many fields. I use AI to make myself more productive, e.g., I use speech-to-text and text-to-speech to cover content more quickly. Moreover, I collect data about myself in many aspects of my life.

However, I think I’m still among the few who read privacy policies, and I consider them when I choose apps. Data can make us heroes, but it might also destroy us if misused or abused. We leave data traces everywhere: when we drive our cars, watch TV, buy products, consume web content or interact with people on social media. And, of course, we leave data traces with every breath we take at work when we move across offices, write e-mails, manage calendars, learn, conduct our work, or even when we don’t show up to work.

Who owns these traces of data? The regulation now defines it in many parts of the world, but it lags compared to technology. So it is our responsibility, not only as managers or consultants but also as people, parents, and citizens, to understand the rapid changes and make informed decisions. I mean, not only by insights derived from data but mainly informed choices about the usage of data-based apps, which are every app. 

New learning path. New employer rating.

Privacy and ethics are not new in the organizational research field. For instance, we discussed the smallest group we can analyze in surveys more than two decades ago. However, we now have so many new data sources for the workforce, from sensors, smartphones, and desktop apps. As I mentioned, people are not aware of the digital footprints they leave. Therefore, this data might be turned against their interests.

I think it’s time for people to learn how to protect themselves, and this learning path should also be a new responsibility of organizations. We discuss Ethics in People Analytics and HR tech, but we must keep in mind that this is a crucial topic in educating our employees. To do so, learning leaders must already understand this domain thoroughly. Unfortunately, this is not the case in many organizations.

Furthermore, there’s much talk in the HR sector about employee experience. I believe that soon enough, employees will start exercising their rights to data privacy. We’ll see employer ratings based on data transparency and data usage aligned with employee interests – growth and well-being, which is what we mean when we talk about using employee data for good.

New skills. HR people are not there yet.

As AI sweeps HR tech and is introduced to many HR practices in every stage of the employee lifecycle, someone in the organization will have to pick the right solutions for the proper needs. I forecast demand for two new skills in the HR role: Procurement and Ethics. However, if HR people keep procrastinating their up-skilling in analytics, the consequences might be that Procurement and Ethics roles will be filled by someone else in the organization.

You can’t evaluate AI solutions without understanding the basics of practical machine learning and predictive analytics. You don’t have to be a data scientist for that. It’s like driving a car – you don’t need to be a mechanical engineer to buy or drive your vehicle, but you need to know how to hold the wheel and obey traffic rules, so you don’t kill anybody. Therefore, I call HR professionals to start their journey into the data world. And start it today.

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Educating and mentoring HR professionals to embrace the practices of People Analytics is a challenge. There are barriers, and it takes time and effort to overcome them. However, one issue remained unsolved for years: The lack of open HR data to practice on. Although there are many inspiring case studies of People Analytics, obviously, organizations don’t share their people data for the sake of learning. Simulation-based data may be an alternative, though usually it is oversimplified and lacks real or interesting patterns to explore.

 

A Practice with Open Data

In my recent teaching initiatives, e.g., the People Analytics session in Lahav Executive Education at the University of Tel Aviv, I wanted to demonstrate HR managers that their academic background, professional experience, and their common sense, is enough for exploring organizational occurrences and effects based on data. HR managers don’t have to become data scientists in order to conduct People Analytics projects. But they do need to communicate with Data Scientists, bring them business questions to study, and request research outputs. For that reason, I constantly search for open HR data and use it in learning sessions. Fortunately, I could present a case study of Gender Equality, that theoretically and methodological was based on a real project, but the analytics part was conducted on open data that was offered by other organizations.

For the Analysts and Data Science enthusiasts among my readers, it is worth mentioning that although it is not the first time I demonstrate People Analytics practices based on open data, this time my objective is a bit different. I did not use practical Machine Learning in this case study. The analysis process was based on research methodology and Statistics that a Bachelor of Social Science, i.e., someone with a B.A. degree, should understand and can comfortably communicate. Nevertheless, I used R for my analysis, because I believe that HR people who may not have learned or used R and manage to receive analytics from an inner supplier or an outsource service, should have a grasp on how a desktop of a Data Scientist looks like, and what in the functionality of R Studio makes it so popular.

My source and inspiration for the dataset was Montgomery County Maryland’s employee salaries in 2017. The open data included annual salary information such as gross pay and overtime pay for all active, permanent employees, and some demographics. The reason for opening this dataset to the public is the Digital Government Strategy of Montgomery County Maryland which aims to serve residents, employees, and other partners better. In this case, it serves the purpose of education, in an open-source community of People Analytics students, professionals, and enthusiasts. However, the dataset used is anonymized and randomized.

 

Gender Pay Gap

Pay transparency is among Global Talent Trends in 2019, according to LinkedIn. But “Transparency isn’t the goal. The goal is paying everyone fairly”, as Anil Dash, CEO at Glitch was wisely quoted in the report. Transparency forces Organizations to make sure they keep the compensation balanced across genders and other groups’ characteristics. Although people share salaries on sites like Glassdoor and LinkedIn, only 27% of companies are transparent about pay. The first step to establishing pay transparency, as recommended in LinkedIn’s report, is to conduct an internal audit, and explore how the company’s pay compares to competitors and whether it has a major pay gap across gender, race, and those in similar roles. If significant inequities are found, a detailed plan to fix them is recommended.

A pay gap audit or exploration may be a People Analyst’s task. However, in the People Analytics project, descriptive statistics is not enough. We need to go deeper into understanding the reasons for our findings and the directions for a solution. In the following analysis, I included some diagnostics and Inferential Statistics, to understand the reasons for the patterns in pay data. I assumed that as any American public organization, Montgomery County Maryland is subjected to some kind of strict regulation regarding equal pay. But only going beyond the basic descriptive statistics enabled me to find some interesting patterns. So, without further ado, let’s explore the findings.

Gender Pay in Montgomery County Maryland

“Telling a story with data” is almost a cliché in our field. Nevertheless, there is no substitute for the exploration of data visually, before moving on to test the hypothesis. There are plenty of visual tools out there. The great thing about R, however, apart from its price (free!), is the flexibility it enables in creating the story and reproduce it again and again as the data is updated. In the following description of my analysis, I did not explain every term in statistics, since I assume the readers learned them on their undergraduate studies. But “no one remembers”, right? So, the links in every statistical term may walk you through a “memory refreshment experience”, if you choose to follow them. 

I started my exploration, as shown in Figure 1, with the pay distributions. I intended to present, in a single slide, both common and separated gender pay distributions. I also wanted to explore both indications for center and dispersion, without losing information about outliers. So, I placed a boxplot near a histogram with a density plot and ordered the genders vertically, one on the top of the other, so the comparison would be easy for the bare eye.

If you look closely in Figure 1, you’ll notice a little difference between men and women, both in the deviation of histograms from the shared distribution, i.e., that normal approximation curve, and the center of the boxplot, which represent the median. Running t-test resulted in a p-value below 0.05, which means that on average, the pay differences between men and women are statistically significant. This significant result is impacted by a large number of cases in the dataset (about 9400 employees). The average yearly pay gap is about 4.5k US$. (I repeated the visualization and t-tests for all pay variables I had in my dataset, but for the purpose of simplicity, let’s remain with only one variable).

 

Figure 1: Gender Pay Distributions

Obviously, the average pay gap is not the whole story. Additional variables should be added, to deeply understand the source of the gap. Adding background variables, e.g., full vs. part-time job and tenure may change the story. For the analysis presented in Figure 2, I had to create new variables based on the raw data. I mention it because it is important to take into consideration that, usually, the data you download from your systems won’t be ready for analysis. A significant part of the Data Scientist time will be invested in cleaning, mounting, and preparing the data for the analysis.

Exploring gender pay averages across tenure ranges reveals that while both genders are promoted while gaining tenure, men are promoted with higher rates, as the different slope indicates. Running ANOVA reveals that the interaction between the gender and tenure variables is significant, meaning that the different slopes are not a random occurrence. Such interaction was not found between gender and full/part-time. However, we do witness full-time employees promoted at a higher rate, in comparison to part-time employees, as slops indicate. This interaction, between full/part-time and tenure, is also significant.

 

Figure 2: Gender effect, Tenure effect, Full/part-time effect

 

But who holds most of the part-time jobs? Apparently, the proportion of part-time employees in Montgomery County Maryland is significantly higher among women (18%), in comparison to men (3%). In other words, the accumulative gap between men and women throughout their careers, as they gain tenure, may stem from their assignment in full and part-time jobs. In a Linear regression model that explains the annual salary by gender, assignment, and tenure, the gender is not a significant predictor, as opposed to the other variables: tenure and assignment. Together these variables explain 37% of the variance of annual pay, which is a fair result, but still, other factors impact it too. Positions and occupations may be among those factors.

Indeed, a critical reader may raise a question about the male’s and female’s occupation. The dataset includes some occupations with both genders and other occupations with only men or women. I repeated the whole analysis after screening out those male and female occupations, and I got similar results. Yes, analysis within each occupation is also needed. However, there are 390 occupations in this dataset, so I prefer to leave this task to People Analysts in Montgomery County Maryland. (For dynamic charts of this case study, by departments for example, please visit my GitHub)

 

Additional thoughts

The gender pay gap analysis in this article is straightforward. Most HR managers with a B.A. education can handle it, with a little help from a data scientist on some occasions. I encourage HR practitioners who start their journey in People Analytics to practice this analysis. The data is available, and the insights may be vital. According to Gartner’s Digital Employee Experience Survey in 2018, #1 in the top ten memorable experiences that affect employee experience is “Being discriminated against at work”.  No doubt that transparency and closing the pay gap is crucial for employee engagement and indirectly to employer branding.

My last note may be the most important. Women still don’t get their fair share, according to an analysis by Visier. Data from this People Analytics platform reveals that the gender pay gap widened in 2017 rather than becoming smaller: In 2016, women made 81 cents to the dollar a man-made, but in 2017, women made 78 cents to the dollar, according to Visier data. Organizations still have a long way to go to close the gender pay gap, so why don’t you start by analyzing the situation in your organization?

(To explore the R code used in this article, check my GitHub).

Related Course

The People Analytics Journey

An overview of future role of HR leaders in improving business performance by informed decisions about people based on data. People Analytics transforming HR; The Role of People Analytics Leader; Case Studies and Simulations; Emerging trends of HR tech.

The Syllabus

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If you randomly select an HR leader and ask about her progress in the journey into data-driven HR, the chances that she would tell you that she is already on the track, according to a recent survey, are about 70%. However, if you dig deeper into your conversation, you might find out that you and your interlocutor mean completely different things when pronouncing the words “People Analytics”. What your partner sees as progress might not be at all a progress in your eye. No wonder that this might be the case, when the definitions of this field are vague, and we are still struggling to form its practices.

So how can we describe the status of People Analytics, better than a single number in survey results? In one of my public talks about People Analytics this year, I challenged my self to describe the state of our practice in five sentences only. My qualitative effort worked, I guess, at least according to the audience response. Looking back at my list, I realize now that each point I made implies a myth about People Analytics. HR leaders should be aware of the following five misconceptions, or otherwise, continue to let these false ideas about People Analytics inhibit their advancement.

Myth #1: People Analytics is an established practice within HR management

No. Although we witness higher adoption rates every year, there are barriers, and overcoming takes time. Among companies that presented case studies at a recent conference in Europe, many mentioned a time span of one or two years until gaining a return on investment from People Analytics activities. I was fortunate to hear dozens of lectures about People Analytics this year. Those case studies presented in conferences are just a handful. Conferences curators will not pick organizations that have not reached significant milestones. 

However, other organizations, those who are still struggling, shuffling, freezing, or just learning from their mistakes, are not less interesting, nor less important. If you experience difficulties in your People Analytics journey, you are certainly not alone. In a recent study published by Visier, obstacles of organizations include the lack of connection between analytics and business results, basing analytics HR system data only, insufficient “data-driven” skillset among HR people, data quality issues, unstandardized metrics and over-dependence on IT for analytics.

Myth #2: People Analytics is professional research about HR practices

No. People Analytics is a multidisciplinary profession, that aims to support business decisions related to people on data. HR leaders are not using People Analytics to measure the efficiency of HR practices, but rather to understand the impact of their practices on the business results. HR people manage a variety of processes throughout the employee’s life cycle: planning, recruitment, learning, evaluation, recognition, reward, mobility, promotion, safety, welfare, and more. These processes create aggregated workforce capabilities: engagement, culture, efficiency, leadership, innovation, and so forth. Those capabilities enable the organization to achieve its business goals: productivity, quality, and customer satisfaction, which, in turn, result in business outcomes, e.g., revenue growth and stakeholders return. 

People Analytics means that HR focus on the use of people data, derived from their processes, to impact the business. In this context, it is worth to mention, again, that the HR dashboards are not People Analytics. Both People Analytics and HR dashboards deal with Performance. However, each practice has a different approach. Dashboards enable us to present different HR KPIs, but can’t answer the question: Why? For that purpose, we need People Analytics, which enable us to understand the factors that drive those KPIs presented on our dashboards.

Myth #3: Traditional research is outdated in the era of People Analytics

No. People Analytics practices combine new data sources and technologies with good old practices. Mentioning tradition, let me share the puzzlement I’ve experienced in a People Analytics conference, this year in London. I chose in advance to participate in those sessions that seemed most innovative. However, I discovered that some speakers relied on quite traditional research methods, that actually, I’ve been practiced my self in organizations for years. How does it fit in with new technologies, and with plenty of new sources of people data? 

The bridge between traditional research methods and innovation are two trends. The first is data integration. We no longer settle for analytics based on HR data sources, but rather combine many types of data about people, both from HR and business units. The second is our new perspective in the future. We refer to people data at different stages in the employee life cycle: candidates, employees, and former workers, and we focus our analytics efforts at forecasting outcomes related to business questions.

Myth #4: People Analytics is great only if you are C-suite

No. People Analytics is about different objectives and questions, of old and new stakeholders: Executives, HR, managers, and people. Yes, the people! Despite the emerging trends, new technologies, and data sources, leaders in organizations still ask the same old and basic question: Who? In the past, this question was quite general: Who are the people with the skills, work habits, knowledge, experience, and personal qualities that drive the organization to meet its goals? Today they still ask “who?”, but more specifically, and with a focus on business metrics: who create the best new products, make the most revenue, find the greatest efficiencies, build great workplaces, adapt to changing business conditions, delight customers, attract others to join the organization? In other words, we put the question “who?” with things that are outside the traditional territory of HR. Traditional research, e.g., employee engagement or training effectiveness, which was already out there, for decades, is now connected directly to the business. 

However, HR and business leaders are not the only ones who raise questions. Today employees expect to receive personalized service in the organization, just like they do in any other context of their lives. We all live through our smartphones, and there’s no reason why employees should expect it to be different at work, and regarding significant questions about career and wellbeing. The People Analytics function should address these needs.

Myth #5: People Analytics means having a data scientist in the HR department

Not necessarily. Although it would be nice to have such a professional in every HR department, we do witness a shift from a research perspective and data science projects to analytics products. Part of the progress in People Analytics is the implementation of HR-tech solutions that enable real-time analysis instead of research cycles. Organizations implement analytics solutions throughout the entire employee’s life cycle. My HR-tech classification includes Workforce Planning and Mobility, Sourcing, Selecting and Hiring, Onboarding and Culture Fit, Employee Experience and Sentiment Measures, Employee Wellness, Health, and Safety, Employee Growth, Learning & Development, Goals Tracking, Performance Review, Productivity, Organizational Design, Networks, Teams, and Collaboration. But of course, there are so many other ways to capture the HR-tech ecosystem. 

People Analysts have a lot to offer in the processes of HR-tech implementation. They help using technology to amplify, not overtake, the influential role in humanity in organizations. They can do so, mainly due to their ability to embrace two new responsibilities: procurement and ethics.

I truly believe that awareness of these five misconceptions contributes to faster progress in People Analytics. However, there may be more false ideas about this new practice. Did you tackle more Myths? Please share it in a comment.

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Workforce data are the molecules of People Analytics. No predictive model, diagnostic analysis, or visualization can possibly be created without proper and relevant data. Anyone who appreciates the advantages of data-driven HR should stress quality in HR data. However, when I start a conversation about data with HR leaders, many of them spontaneously respond with a sigh. They know the naked truth: HR data is a mess! Nevertheless, there is so much that HR leaders can do to cope with this challenge, starting today. Let’s start with the following six suggestions, which hopefully will inspire us to face this painful issue.

 

1. Understand the advantage of workforce data access

Workforce data is everywhere in the organization: HRIS, ATS, CRM, LMS, etc. Business leaders need insights, which derived from that data, to improve business performance. A huge variety of technological solutions are available today, which enable HR people and other non-technical professionals to create insights from the data. The missing link is a desire to access the data, and to use it in actionable ways that reveal new opportunities for the company. The ability to access the data and use it properly will empower HR people to have ownership and responsibility of workforce data, and encourage them to maintain data quality in order to support informed decisions in the organization. Data democratization is a demand for many business domains. There is no reason it skips over HR. Therefore, HR leaders should consider the right tools and training to keep their team’s progress on this journey.

 

2. Understand the complexity of workforce data

Workforce data may be scattered on many platforms, both in the HR department and in different lines of business. It comes in many formats. Parts of it are structured, while other parts are unstructured, e.g., text fields from employee reviews. Sometimes, the data is not recorded digitally, due to certain difficulties or priorities. In other times, when the data did get recorded, old records are deleted or replaced, due to database structure constraints. Different users may have different needs, which a shared platform does not support, therefore some of them may keep supplement records, e.g., in Excel sheets. Furthermore, when new needs emerge, relevant data may be recorded elsewhere, in different systems. Hence, one of the most challenging issues is the different unique identifiers in different data sources, which sometimes makes it impossible to automatically combine data by matching field. Understanding the complexity of workforce data is the first step to deal with it. HR Leaders must start to get to know workforce data as much as they understand HR processes.

 

3. Prepare to improve workforce data

The struggle toward data integrity is worthwhile. It yields high-quality data that enable meaningful analytics. HR practitioners should configure their systems in a way that prevents or reduce errors. For example, they may want to eliminate mandatory requirements for fields that are not always available at the time of data entry, consolidate fields with duplicate information, and remove fields with no immediate purpose. When analytic questions are on HR people’s minds, higher the chances that they configure their system in a way that contributes to improved data quality. However, some of them still need guidance in system configuration and data entry processes.

 

4. Prepare to integrate data from different sources

Throwing all the data into a data lake and hoping for an amazing insight to emerge is a nice fantasy that is about to fade away. Instead, you must pick an important business problem to solve, identify and gather relevant data into the data lake, which will include HR structured data, HR unstructured data, and a variety of data from different lines of business. This involves huge challenges: First, you don’t want to disrupt anything in your business processes. Secondly, assuming you found the data, you must deal with duplications, versions, incomplete data, and issues of unique identifiers. And finally, you must do it fast enough, to face managers’ demands, in accordance with organizational and business challenges. You may find out that IT is not available to help with your initiatives, and worse, IT may lack the HR context to understand the data. Therefore, HR leaders should start reassessing their platforms and exploring the ability to integrate them with other solutions, e.g., their ATS and LMS. They must also be aware of other tools that may be needed: blending data tools (e.g., Alteryx), advanced statistics tools (e.g., R programming), and visualization tools (e.g., Tableau).

 

5. Prepare to build stakeholders’ trust

Data scientists and People Analysts usually have a hypothesis about the subject in question. In other words, before they dive into analysis, they acknowledge their expectations about the results. They must start their exploration with a question in mind, otherwise, they would not know where to start in the infinity of analytic directions. However, this is not always the case with other stakeholders – employees and managers. They may be surprised, shocked, confused, or embarrassed when exposed to the findings. Therefore, it is important to know in advance something about their expectations, attitudes, and beliefs. Whether the analysis supports or disproves stakeholders’ expectations, the analyst should dig deeper into the data, to provide supporting details. An analyst who anticipates potential questions and concerns can be better prepared with answers and contributes to stakeholders’ trust.

6. Remember the cause: Serving the organization’s goals

For HR to take a strategic role in management, it needs to broaden the scope of its analytics agenda to business questions. By blending people’s data with business data, HR can provide insights beyond HR metrics and may answer questions such as: How good is the workforce in executing the business strategy? It can start to analyze the connections between employee behavior and productivity, predict business outcomes by competencies, and measure the impact of various training programs.

I believe that any HR leader experiences these six angles in the ride to data-driven HR, but the mixture and volume depend on the phase in the journey. Any other suggestions? Please share it in a comment.

References:

Bernard Marr, “What Is Data Democratization? A Super Simple Explanation And The Key Pros And Cons“, forbes.com
Vangie Beal, “Structured data“, webopedia.com
Stacy Chapman, “Silos in Talent Data – Sigh“, linkedin.com
Alyssa Ruff, “How Smart HR System Design Leads to High-Quality Data“, analyticsinhr.com
“Data lake“, en.wikipedia.org
Roger Nolan, “Digital Transformation: Finding Your Data is Half the Battle“, business2community.com
Eric Knudsen, “3 Rules for Building Stakeholder Trust in Your HR Data“, visier.com
Rupert Morrison, “Five mindsets HR needs to get right to deliver business impact“, http://blog.orgvue.com

Related Course

The People Analytics Journey

An overview of future role of HR leaders in improving business performance by informed decisions about people based on data. People Analytics transforming HR; The Role of People Analytics Leader; Case Studies and Simulations; Emerging trends of HR tech.

The Syllabus

The post Workforce data is a mess! What can you do about it? appeared first on Littal Shemer Haim.

When I talk to a typical prospect, i.e., an HR leader who is interested to start the journey to data-driven HR, our conversation always starts with an important distinction: Are we talking about “People Analytics” or “HR Analytics”. These two terms are confounding, but they are certainly not synonyms. Understanding the differences between the two terms is the key to the successful discussion and plan of our joint mission.

 

People Analytics vs. HR Analytics

People Analytics refers to the exploration of employee data patterns, and communication of significant results to business leaders, in order to support decisions related to people in the organization and improve business performance. HR Analytics, which sometimes is presented as a dashboard, is not aimed to improve business performance directly, but rather to serve the efficiency of HR functions.

The distinction between “People Analytics” and “HR Analytics” is clearly explained by Guenole, Ferrar and Feinzig, in their book “The Power of People: How Successful Organizations Use Workforce Analytics to Improve Business Performance”. The authors define People Analytics as “the approach of measuring behaviors in organizations and knowing how to knit them together to improve business performance. The approach is similar to that taken with customer behavior, but this one concerns employee behaviors”. Their definition of HR Analytics is “the functioning of the HR team itself—for example, analyzing HR key performance indicators (KPIs) such as time to hire. Such analytics are about holding the HR team accountable”.

 

Why do you need both?

Practically, both People Analytics and dashboards of HR Analytics deal with Performance. However, each practice has a different approach: Dashboards enable us to present different KPIs, and to answer questions such as: Did we reach our goals? How far are we from achieving our goals? However, by using dashboards, we actually can’t answer the question: Why? For that purpose, we need People Analytics, which enables us to understand the factors that drive those KPIs that we presented on our dashboards. We can do so with different levels of Analytics: Descriptive, Diagnostic, Predictive, and Prescriptive. (See illustration: Gartner analytics ascendancy model). In other words, the company needs both dashboards and People Analytics practices because it must be aware of its KPIs, and it also needs to understand how to improve those exact KPIs.

Let’s take an example, to demonstrate how dashboards and People Analytics are complementary. Many organizations deal with the challenge of employee retention. Suppose that we have a dashboard that contains a yearly employee attrition rate. A presentation of this KPI separates different kinds of voluntary and involuntary turnover, includes some comparisons between employee sectors, displays metrics trends, and even points to outliers. A People Analytics solution for the employee attrition challenge may be a predictive model that enables one to point to certain characteristics of employees that are prone to leave or stay in the organization. Such insight may lead to different approaches toward different employees, which will eventually result in better outcomes in the long run.

 

HR leaders prefer Dashboards first

When my prospects understand the distinction between dashboards and People Analytics they usually express enormous curiosity about the second. For example, many of them are fascinated when I describe how to predict employee attrition. However, when we get to the more practical ground, in order to start a project, we go back to discuss dashboards. Although HR leaders are very interested to get insights from People Analytics, their immediate need is usually a tool that integrates data from multiple sources and displays them uniformly and clearly, in order to monitor and control their operations. After all, dashboards have become an important part of other business departments, and HR should not be different in that sense.

 

An effective HR Dashboard

HR dashboards are just like any other BI dashboards. When they are well designed they can tell a whole story at a glance. They connect data and analysis that are most needed to specific business questions, i.e., KPIs, in a simple and clear way. Their layout and data visualizations enable the users to access the data they need to get answers from and get exactly those answers — correctly and completely.

However, HR dashboards are distinctive. They are not created only for HR leaders, but rather for business leaders in the organization. Business leaders and HR leaders should cooperate to define the right KPIs, and monitor the right data, aligned with the company strategy and goals. Effective HR dashboards provide a concise and clear display of those workforce KPIs, which are relevant for the business performance, and assist in decision making. They rely on meaningful data, which can be linked to future actions. This challenges HR to fit a dashboard to each line of business. HR must understand the unique workforce needs of every unit, and then determine what metrics to present. For example, if a dashboard shows that top performers in one sector are found via LinkedIn, and at job fairs in another sector, recruitment can be planned, executed, and measured accordingly.

 

In my future articles, I’ll review the best practice of building and publishing HR dashboards. But to conclude this discussion, let’s remember, that since business questions always evolve, HR dashboards are, and will always be, developing tools. It is important to make sure, once in a while, that the HR dashboard still provides actionable information.

Related Course

The People Analytics Journey

An overview of future role of HR leaders in improving business performance by informed decisions about people based on data. People Analytics transforming HR; The Role of People Analytics Leader; Case Studies and Simulations; Emerging trends of HR tech.

The Syllabus

The post HR Dashboards are not People Analytics – but you need both! appeared first on Littal Shemer Haim.

(A version of this article was published in TLNT magazine)

Think about data that you share at work, in the most personal sense. You share with your employer, and sometimes with potential employers, so many aspects of your life: details about your professional path, your personal status, health care, social-economics, legal and geographical background. You also agree to share information about what you do in different times and places, who you meet, what information you consume, and so on. Moreover, you leave your digital footprints on the web, social networks, and different apps, where data reveals to employers a lot about you. Did you ever consider how data might affect you at work? How does your employer actually use the data about you, and how technology enables it? What is allowed to do with your data, and what is considered crossing a red line in terms of ethics and regulations?

AI (Artificial Intelligence) and ML (Machine learning) are two buzzwords that dominate the HR tech world today. We don’t know yet if there is a bubble in this field or rather a huge influence on management practices. Nevertheless, the common opinion among professionals is that managers will make better decisions, more informed decisions, related to the workforce, by using predictive algorithms that, for example, fit candidates in jobs or let employers know who is at “flight risk.” There are tons of discussions about this subject, but mainly from the organization’s point of view. What I’d like to do now, for a change, is to take the employee perspective.

 

Should employees worry?

If you tried to land a job lately, perhaps you had a video interview (e.g., by HireVue), or you were asked to play some mobile games (e.g., by Knack). These technologies, which probably offer you a nice experience as a candidate, actually enable organizations to predict your performance in certain roles, basically by pre-exploring reactions of high and low performers in those exact roles. As a candidate, you’ll probably consent to participate in those practices, even though you don’t know exactly what data these machines collect about you and what is the secret predictive model they use backstage.

I’m not saying that predictive models are bad. On the contrary, I believe that in general, a machine that fits the right person to the right job and does so better than a human whose perceptions may be biased is actually positive, not only for organizations but also for employees, since they may have a better chance to thrive in the right roles. However, anyone who has some general knowledge about ML can point to the confusion matrix and demonstrate that algorithms are not perfect, or more precisely, how much imperfect they are.

Why are predictive algorithms not perfect? There are many technical and statistical reasons, but the one that concerns me, in this context, is the possibility that human biases affect seemingly unbiased machines. The promise of ML and AI was that the more information we feed these sophisticated computer algorithms, the better they perform. Unfortunately, when the input data reflects the history of an unequal workplace, we are, in effect, asking a robot to learn our own biases. Garbage in, garbage out, right?

Such unfortunate effects can easily occur in the workplace. For instance, if an analyst explores people who were promoted in the organization for the last decade and decides to use their data to predict high performance, it might result in a model that exclude minorities from predictions about high performance, since maybe minorities were rarely promoted in the past due to social biases or discrimination. This example may be extreme, but it can underline many other subtle possible occurrences.

 

Who will defend employees?

Defense (and self-defense) starts with awareness. Indeed, the awareness of data protection and privacy is increasing, and influencing society in general, particularly in regulation. Employee rights are broadened these days in the context of workforce data, although not evenly in each corner of the world. In the EU, a new privacy regulation, the General Data Protection Regulation (GDPR), was published lately (and will be enforceable starting May 25th, 2018). It has serious implications for any employer who processes its employees’ and potential employees’ data, whether it is data regarding work environment or internet behavior. Among many issues, the GDPR offers employees additional rights to reinforce control over their personal data, e.g., extended access and rights to be informed about data usage, data transferring, and period of storage. The new regulation is currently covered by legal experts, and anyone who analyzes employee data will soon start to consult legal departments regarding activities that did not require consultation in the past. In Europe, a new organizational stakeholder emerges – a Data Protection Officer (DPO) – and will be involved in analytics projects.

However, in my opinion, compliance with the GDPR is only a starting point. It will surely force awareness of the HR analytics team to privacy issues. But although it aims to protect privacy, I believe it will also influence employees’ behavior, and HR analytics practitioners will have to respond: When people start exercising their rights and request access to their data, People Analysts will be ready in advance to give them comprehensive information about their data usage. When employees start asking to correct or erase their data, employers will request more transparency and security from HR software providers. Organizations will ensure that they process only the personal data that is necessary for the specific purpose they wish to accomplish, and therefore, they’ll need long-term planning and more serious considerations. This will move the field of People Analytics forward. The implications for employees and candidates: Transparency! But not only…

 

Beyond transparency

I believe that eventually, even if it will take a few years, the People Analyst role will include more components of procurement. Analysts will make less programming on their own and be experts in HR tech and analytics solutions. They will learn, for the sake of regulations and ethics, to ask vendors hard questions and be more critical about model accuracy and data privacy, and therefore, they’ll contribute not only to a culture of a data-driven organization but also to a safe work environment regarding employee data. Employees and candidates, for their part, will judge employers, in addition to Employee Experience perceptions, by employer ethics in data management, and when feeling secure, they’ll be more receptive and enthusiastic to participate and cooperate with AI and ML to influence their career path.

 

References:
Kevin Markham, “Simple guide to confusion matrix terminology,” dataschool.io
Stephen Buranyi, “Rise of the racist robots – how AI is learning all our worst impulses,” theguardian.com
Arnold Birkhoff, “9 Ways the GDPR Will Impact HR Data & Analytics“, analyticsinhr.com

Related Course

The People Analytics Journey

An overview of future role of HR leaders in improving business performance by informed decisions about people based on data. People Analytics transforming HR; The Role of People Analytics Leader; Case Studies and Simulations; Emerging trends of HR tech.

The Syllabus

The post Employees in the big data era: Will you let robots determine your future at work? appeared first on Littal Shemer Haim.