You probably won’t read these lines if you’ve already used GenAI. Instead, you’ll ask an app to summarize it and craft the summary in a written work. As AI tools become familiar, reading and writing may diminish, and human attention to content may shrink. Strategic People Analytics with AI will impact you even more.

Therefore, in a recent keynote to CHROs and HR leaders, I decided to skip the traditional speaking engagement and immediately offered key takeaways at the opening. During the remaining time, I told four stories about the impact of GenAI on HR and people analytics in my entirely human fashion. Hopefully, these stories that I cover here briefly in ascending complexity will give you a clue about what to do next to tame the data beast, i.e., the unstructured data representing your organization’s knowledge.

Distinguish between AI impacts on HR

You can’t avoid stumbling over the word combination AI for HR these days. However, some contents mix automation and augmentation while neglecting the purpose. Next time you encounter associations of AI and HR, ask about the context: Is AI a means, a method, or an objective? 

Let me explain this distinction. AI is a means in HR operations that automates repetitive tasks and enables data democratization and consumerization. AI is a method in strategic people analytics that enables the improvement of decision-making by providing actionable insights from significant patterns of employee data. Yet we all head to the future, and AI is a purpose in organizational readiness regarding new skills and roles.

Learn how the means and methods support the objective in your role, and leverage AI in innovative HR-Tech and People Analytics to support the value your organization will create in the age of AI.

Measure and develop data and AI Literacy

Your role in HR touches on these three distinguished impacts of AI. Therefore, data and AI literacy are core skills. Eventually, all HR professionals will “speak data” but with a “different accent” in the AI era because soft skills will become technological skills. Organizational interventions and processes will become new textual data sources that HR practitioners will manage to gain AI-powered insights.

However, the HR sector still lags in data and AI literacy and must develop competencies, such as knowing what types of data exist, what tools and methods are suitable, interpreting outputs and data visualizations, critically evaluating insights from data analysis and AI derivatives, identifying misleading data and AI usage, including biases, and communicating data-driven and AI-powered information.

Your motivation to develop and measure data and AI literacy derives from the abovementioned objective: organizational readiness to create value with AI. You need to know how to connect data on employee behaviors and attitudes to business performance, highlight opportunities and risks, and turn organizational conversations into applied research, which data experts or machines will execute. Learn how to assess data and AI literacy using valid tools backed by academic research.

Become a manager of AI in your future role

Will AI replace you at work? The World Economic Forum researchers analyzed Jobs of Tomorrow, covering over 19,000 tasks across nearly 870 professions to assess risks. Their findings reveal that routine tasks have a high potential for automation, and tasks requiring abstract thinking and problem-solving have a high potential for augmentation. Tasks with lower risk are those requiring personal interaction and collaboration.

I examined the findings from the People Analytics perspective by screening jobs that were similar in tasks. I discovered that jobs with high automation potential are analysts and statistical assistants. Jobs with high augmentation potential relate to research and content editing. Jobs with low automation or augmentation potential relate to education and consulting. New roles relate to AI ethics and governance. I advise you to use this research to understand AI’s impact on your job and start upskilling to integrate AI into your workflows.

Don’t panic if your job includes a large portion of automation and augmentation. An essential skill for working with AI is managerial, even if you don’t manage others. Delegating tasks, communicating, setting expectations, analyzing results, and providing feedback – In the age of AI, everyone will be a manager. Therefore, get familiar with frameworks and AI tools that will enable you to bring this inner manager to life.

Leverage AI to access knowledge as unstructured data

To be able to manage AI tools in my workflow, I try to embrace the idea of designing a dialogue with AI through task analysis (who is suitable for what?), interaction protocols (who alerts whom?), and feedback loops (who measures and evaluates what?).

I am overwhelmed with content in my workflow, which is not exceptional. Like many professionals in trendy and evolving fields, I cover books, podcasts, videos, social media, blogs, journals, webinars, and other resources. To systematically and healthily manage this overload, I created a systematic approach to curate, learn, create, and present content. Various AI tools I integrated into my workflow now leverage my knowledge management. However, the secret sauce of my success includes only two ingredients: every piece of knowledge is unstructured data, and all pieces of knowledge are a network.

In knowledge management, humans and AI tools are interdependent, and they both need data, particularly unstructured data. To understand this interdependency, I advise you to be familiar with some terms: LLMs and RAGs on the machine side, ontology and knowledge graphs on the human side. We can develop a competitive advantage by connecting humans to machines and stream data correctly.

Simply put, while LLMs can predict the suitable words in sentences based on massive content, RAGs support them by bringing relevant information from a specific knowledge base in response to a user’s query. Ontologies and knowledge graphs represent the human in the loop. Respectively, they are like the architectural blueprint of a domain and the blocks that are organized based on that plan.

To guarantee valid and controlled conversations with my knowledge using AI, the main goal now is building the first ontology for the domain of people analytics. I complete this mission these days based on the resources I collected in two decades of professional experience. You will do that too someday in your organization, creating an excellent opportunity to gain a competitive advantage.

The name of the game now is to organize your knowledge as pieces of unstructured data and combine them as a network, so AI solutions will enable you to continue organizing, storing, retrieving, and conversing with your knowledge. By doing so, you’ll enter the future of People Analytics.

Most of the knowledge your organization holds about its people is unstructured. After decades of technology-enabled automation of data analysis, i.e., structured data analysis, we are finally at the rise of a revolution. For the first time, anything is data. Therefore, the knowledge economy as we know it will change, and people analytics will demonstrate such transformation, offering the organization a strategic foundation for informed decisions based on unstructured data.

The post Taming The Data Beast: Strategic People Analytics with AI appeared first on Littal Shemer Haim.

(Updated: March 11th 2024) I’m not a time traveler, alas. However, the recent advancement in LLMs (Large Language Models), the constant exploration of how AI apps fit my workflows, and the ubiquitous conversations about the future of work make me feel that I have a decent supplement. So, in this article, I share some personal experiences, contemplations, and resources to crack some of the mysteries about LLMs, AI-Copilot, skills, and their impact on future roles.

Hopefully, my insights into how AI reshapes my current work will encourage you to adapt. Whether you are a leader in an organization or an individual player in the industry, this article emphasizes the need to develop new skills and behaviors for effective collaboration with AI. However, I skipped the technical details and shared my journey with a smile.

Current job titles say (almost) nothing about the future roles

I hate to use a cliche, but AI transforms our work. As such, job titles that once implied a specific set of responsibilities may no longer hold as AI takes over certain aspects of that role or introduces new tasks that were not previously part of the job.

For instance, take my domain of expertise. A Workforce Analytics title no longer fits because work is not limited to employees (there are outsourced roles and freelancers, too). The title People Analytics is not a good fit either because work is not limited to people (there are robots and algorithms, too). So, what would be an excellent title to redefine a job that analyzes the association between work processes and business results?

In a recent interview, business psychologist Max Blumberg discussed a transformation of HR Management into Resource Management and emphasized the need to shift from focusing on people, processes, and policies to a more business-driven approach. Following the notion of such transformation, I asked one of the AI tools I currently use in my workflow (Perplexity.ai if you persist on knowing), “What is the definition of work in the context of business strategy?” Interestingly, the answer I got did NOT include any of the words people, employees, or any of the derivative HR titles. Here’s the informed answer I received: 

“…work refers to the coordinated efforts and actions of different departments within a business to achieve the strategic goals and objectives of the organization. It involves aligning decision-making, resource allocation, and implementation to support the overall direction of the company. This collaborative effort is essential for the successful execution of the business strategy and the realization of the company’s long-term vision and objectives.”

So, beyond showing off my AI literacy here, i.e., my ability to interact effectively with AI to solve this puzzlement, it seems that in the era of AI, at least based on an answer of AI, work is not necessarily related to skills, abilities, and aptitudes that a specific individual in a particular role possesses.

Current collaboration with AI (sometimes) leads to infinite loops

Eventually, LLMs will impact many roles; I believe mine is no exception. But how? Encouraged by previous AI outputs, I asked Gemini, an AI tool developed by Google, about LLMs and people analytics. Specifically, I asked, “I’m an expert in people analytics, and I explore how the future of this domain is shaped by LLMs. What do you think I should read today to keep up with the advancement in the field?”

Bard generously offered a long list of resources, including some articles, research papers, podcasts, and events. However, my professional universe was almost collapsing in on itself when I discovered an article by Littalics (That’s me!) in the fifth resource, alongside publications by Visier, LinkedIn, and other leading resources. Was that a joking flattery, or does this app have a severe blind spot?

Fortunately, I came to my senses quickly and continued asking my artificial interlocutor: “There are many resources to this topic. Can you tell me why you picked the above to your list?” Its immediate answer listed several criteria: Relevance, Credibility, Variety, Timeliness, Depth, and Accessibility. Impressive!

However, as the conversation with Bard continued, a deeper examination of its answers provoked questions about meeting these criteria and this artificial collaboration in general. Will LLMs change how I curate knowledge, offer advice, and educate professionals in the field? Which of my tasks will be augmented by AI? To figure out, I did not settle with AI this time but turned to methodological research done by humans (with just a little help from AI).

Potential for automation and augmentation by language dominance

A white paper published by the World Economic Forum discusses the potential direct, near-term impacts of LLMs on jobs. It analyzes individual tasks across occupations, assessing the potential exposure of each task to LLM adoption, classifying them as tasks with a high potential for automation, high potential for augmentation, low potential for either automation or augmentation and unaffected (non-language tasks). It also provides an overview of new roles emerging due to adopting LLMs.

To assess the impact of LLMs on jobs, researchers analyzed over 19,000 individual tasks across 867 occupations. The analysis reveals that tasks with the highest potential for automation by LLMs tend to be routine and repetitive. In contrast, those with the highest potential for augmentation require abstract reasoning and problem-solving skills. Tasks with lower potential for exposure require a high degree of personal interaction and collaboration.

Whatever your role or industry, you can find clues for future work in this research. I explore the results from my angle as an expert in People Analytics. I specifically looked for jobs that are similar task-wise to mine. I found that the jobs ranking highest for potential automation are analysts and statistical assistants. Jobs with the highest potential for task augmentation are statisticians and editors. Jobs with lower potential for automation or augmentation are educational, guidance, and advisers. Lastly, new roles reported are AI ethics and governance specialists.

Implications for organizations: Leverage management skills

My chats with AI might be artificial, but my career questions are sincere. My future work will require collaborating with AI, enhancing productivity and efficiency across various tasks. According to business innovation expert Tom Davenport, those who refuse to adapt will find themselves at a disadvantage. Introducing new skills and behaviors is necessary to enter this copiloting era. But there is no single blueprint to change the mixture of skills.

Therefore, organizations should encourage employees to try and utilize AI systems to boost their productivity while formulating policies and guidelines for safe and ethical use. As AI substantially alters a job, the organization should consider redesigning its tasks to maintain and avoid impacting the workforce.

To facilitate this transition, departments that deliver strategic advancement and innovation must reimagine work processes and invest in developing skills that enable AI to become more integrated into the workplace. As part of this evolution, strategic planners must understand the difference between human skills that AI will replace and those it will augment. Some skills, however, will not be impacted.

When the copilot is in every employee’s pocket, says Kathleen Hogan, Chief People Officer at Microsoft, both human and technical skills will be utilized. Many human capabilities are essential to the collaboration between human workers and AI, such as analytical thinking, creative problem-solving, and the ability to detect bias. However, the most prominent factors for non-managerial employees to be effective copilots with AI are management skills, such as task delegation, clear communication, setting expectations, analyzing outcomes, and providing feedback.

Managerial skills will also enable us to fundamentally rethink how we value and utilize human skills in collaboration with AI. A fundamental approach in this process is “Design for Dialogue.” This concept emphasizes viewing AI as an assistive agent that enhances human capabilities over time rather than just an automation tool. By Incorporating its elements – task analysis, interaction protocols, and feedback loops – we can create a compelling and adaptable workflow that leverages the strengths of both humans and AI. (This paragraph is the outcome of my “interaction protocol” with my “second brain,” which I created using Mem.ai to redesign my learning and content creation workflows)

Implications for anyone: Explore AI to do more and differently

Managerial skills will be necessary beyond effective collaboration between AI and us humans. These skills will be the bridge that connects our past, present, and future of work, allowing us to harness AI and reshape our digital environment, just like how the internet revolutionized business operations in the 90s. Therefore, they are crucial for everyone.

I was fortunate to start my career in the ’90s when the internet was introduced to the business environment. As Ryan Roslansky, CEO of LinkedIn, nicely describes in his article, Talent Management in the Age of AI, three decades after it was first introduced, no one talks about the internet. But we all talk about what we do with the internet, such as e-commerce, content creation, remote work, and more.

Eventually, we won’t talk about AI either. But we will discuss how AI is changing work and life. As a multidisciplinary professional, I don’t intend to wait another three decades. I’m already exploring how AI enables me to do more and differently in every task. I explore various AI apps in my workflows, and as an orchestra conductor, I make sure they all play nicely together. Such exploration is essential to business leaders, organizational development experts, and any player in any industry, whether employee, contingent worker, or solopreneur.

The post Beyond Buzzwords: LLMs, AI Copilot, Skills and Future Roles appeared first on Littal Shemer Haim.

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.”

The post People Analytics and HR-Tech Reading List appeared first on Littal Shemer Haim.

AI applications rapidly grow, and humans respond in various ways at all levels, from individuals to organizations to states. In this article, I address the issue common to all these levels: The Law. I recently stumbled into a list of legislation trackers covering countries’ efforts worldwide to respond to AI growth in laws, regulations, standards, and guidelines. Awareness of legislative policies is insightful to broadly understanding the development of the AI landscape in diverse regions. However, I discuss AI in the domain of workforce data and explore aspects that workforce data strategists need to focus on. Therefore, in this article, I answer questions about workforce AI regulation.

Should workforce data strategists track AI regulation?

Experts advise tracking AI regulations to ensure compliance and avoid costly fines for non-compliance. Furthermore, whether the organization intentionally or inadvertently uses AI tools, leaders should reassess practices, policies, and training to keep pace with new regulations. This advice is crucial because while most executives consider increasing their investments in AI, less than half established policies on responsible use.

Rules for AI in the workplace are already here. For example, New York City has passed a bias audit law for automated employment decision tools. It makes it illegal for employers to use AI for hiring and promotion decisions without meeting specific requirements, including a published bias audit and notifying candidates and employees about the use of AI. As other states also advance AI regulations, you can’t neglect to understand the ongoing legal development.

Is tracking AI regulation enough for workforce data strategists?

Understanding the new and coming laws is insufficient to ensure a legal and responsible use of AI. Exploring how technologies are subject to the law in your regions and working with vendors is essential. Furthermore, partnering with legal departments on compliance challenges may be deficient in some organizations, so bias-audit firms are already popping up like mushrooms after a rain. Unsurprisingly, traffic in the HR compliance software category also increases as AI regulation expands.

Tracking AI regulation in the workforce domain should, therefore, include not merely the awareness of the legislation in relevant regions but also developing the appropriate protocols and behaviors likely to satisfy the legislation as it goes into effect by leveraging new collaboration with tech vendors and consultants. However, such protocols are not a substitute for carefully deciding which talent decisions should be informed by AI and determining how the organization mitigates ethical risks of AI beyond the current legislation. Until regulations govern the use of AI in the workplace, it is essential to follow public guides and even vendor perspectives to understand the responsible use of AI in workforce decisions.

How are AI regulations for workforce decisions unique?

AI regulations for workforce decisions address bias, discrimination, and malpractice of employers. Although the war for talent sometimes implies that people may have the upper hand, organizations have significantly higher power in critical moments along the employee lifecycle, and AI increases the gaps in power relations. Therefore, AI regulation focuses explicitly on preventing potential harm in workforce decisions, particularly when they contradict values and benefits for all.

Some examples of AI regulation, even if not applicable in your region, can give you some idea of how it focuses on bias and discrimination. As mentioned, according to the law NYC passed, employers are responsible for any discriminatory impact of AI when using it in hiring, promotions, or firing. In Maryland, the law requires employers who intend to use facial recognition in job interviews to provide adequate notice and obtain written consent from applicants. In Illinois, the law imposes transparency, consent, and data destruction duties on organizations using AI to evaluate job applicants.

Laws about AI in HR-Tech that have already passed shed light on what might be next in the effort to protect employees and candidates from potential bias and discrimination in AI tools. These laws may not affect your activity. Still, dozens of proposed state laws about AI regulation have focused on AI involvement in HR business functions this year. Tracking legislation in progress is helpful to explore how activities in your region will soon be affected.

Are there consequences of neglecting to track AI regulations?

Unawareness of AI regulation for workforce decisions can lead to non-compliance. It can have severe consequences, including legal and financial penalties, such as lawsuits from employees who feel that AI tools have violated their rights. Non-compliance with AI regulations can also damage reputation and lead to a loss of trust from employees and customers.

But trust issues may occur even if your region’s AI regulation has not matured. The news editions bring us stories about the negative consequences of seeming malpractice for workforce decisions using AI. For example, a recent lawsuit alleges that Workday, a vendor of applicant screening software, engaged in illegal age, disability, and race discrimination by selling its customers the company’s applicant-screening tools, which use biased AI algorithms.

Bias occurred long before, even a decade ago. When Amazon developed its recruiting platform, the AI algorithms were trained to evaluate candidates by resume data of people hired in previous years. But most of those resumes were submitted by men, meaning the tool was automatically biased against women. Amazon took the necessary actions to put away the biased AI; however, we gained an insightful story to be learned in every introductory course for people analytics.

The lesson learned from these stories is the importance of transparency. AI tools can be difficult to understand or interpret, making identifying and correcting discriminatory practices challenging. Organizations must ensure their AI tools are explainable so employees and candidates can understand how decisions are made.

In conclusion, ethical AI in workforce decisions is not a destination but a long journey. Challenges and complexities mark the path to regulating AI tools in workforce decisions. But every step taken, every ethical framework built, and every thoughtful decision made by workforce data strategists will contribute to shaping a more equitable and responsible future. We should be on that path; tracking regulation is only one perspective.

The post Workforce Data Strategists, Heads-Up: AI Regulation is Here appeared first on Littal Shemer Haim.

In the last couple of months, HR executives and experts have witnessed a transformative revolution, with the relentless advance of AI casting a profound impact on traditional talent management practices. Most HR leaders believe adopting and implementing AI solutions is necessary to prevent lagging in organizational success. However, in conversations with clients and prospective trainees in People Analytics, I keep receiving questions that express their puzzlement. In their exploration of AI, HR professionals seem to confound HR operations, People Analytics, and organizational readiness for AI. In other words, the objective, the methods, and the means are mixed and mismatched.

I believe that the HR sector profoundly influences business success in the era of AI. However, the first step is to distinguish between AI impacts on HR to be able to address each successfully. In this article, I described three distinguished effects of AI on HR and explained how they are interrelated. To conclude, I elaborated on the implications for the People Analytics practices in the organization and contemplated how it eventually changed The People Analytics Journey.

The Means: HR Operations

HR operations encompass the core management and administration functions of HR processes. From the technological perspective, it includes platforms and solutions that ensure the smooth functioning of the HR department, e.g., HRIS, ATS, C&B, L&D, etc. The emergence of AI changes the HR tech landscape, offering new advantages.

The apparent advantage of AI is that it automates repetitive and time-consuming tasks, freeing HR professionals to focus on higher-level work. However, a more prominent advantage is the visions of data democratization and data consumerization that take shape. When AI is implemented into HR tech, managers can easily access records and information about their people to run the business, and employees can use their data for growth, well-being, and positive experiences at work.

While HR Operations alone are insufficient for strategic People Analytics, they are necessary. HR data strategy can be described as a funnel through which you pour business questions and extract actionable insights. HR processes impact the business, hence the questions, but also define the data collected. Therefore, the evolution of HR tech goes hand in hand with People Analytics.

The Methods: People Analytics

People Analytics as professional practices and organizational function is constantly evolving, as is its dual definition. People Analytics can refer to both the corporate function within which data collection, analysis, and translation occur and the practices that draw on employee data to inform decision-making processes throughout the organization. As the progress of algorithms and data sources hits the domain of employee, organization, and work, new workforce-related recommendations are generated.

The People Analytics domain leverages AI to generate recommendations that help the organization anticipate and address workforce challenges. It serves various stakeholders, from management to team leaders to employees, to turn data points from different processes into insightful action plans to execute the talent strategy. It helps managers at all levels to make better decisions about their work and how they can support their teams. It also enables them to evaluate the impact of interventions or actions on business results.

Machine Learning (ML) and Large Language Models (LLMs) enable practitioners and vendors of People Analytics platforms to do so much more to support informed decisions related to people. However, two fundamentals remain in the new frontiers of AI: First, we must ensure that the organization picks the proper algorithms and data for achieving both competitive advantage and thriving employees. Secondly, we must continue our conversation with organizational sponsors to leverage the new techniques to new business challenges. The emergence of AI changes the competitive environment, and hence, the importance of People Analytics expands and takes new directions. 

The Objective: Organizational Readiness for AI

Leaders in organizations have always asked who are the people that can execute the business strategy. The emergence of AI forces them to keep asking this question to remain competitive: Who can create new products, find new efficiencies, adapt to a changing business environment, delight customers, build a great workplace, and attract others to join the organization? So, the traditional question remains, but we need to find new answers in the era of AI. 

To understand the skills of the future and lead the organization towards a new mixture of skills, roles, and responsibilities, we must adapt new tools and techniques that will enable us to monitor skill demands, e.g., critical thinking and data literacy, develop these skills at scale, and create a culture that values and rewards these skills. The skills demand in the AI era changes the HR department’s focus and pushes it to more operations automation and more valuable insights in its People Analytics services.

Implications for People Analytics Leaders

As a People Analytics expert, there are several things you can do to support your HR department to be ready for AI in talent decision-making. Start by analyzing the employee lifecycle and identify areas of talent decision-making that can benefit from AI, e.g., candidate screening, job matching, career path planning, and learning—research to identify gaps and evaluate vendors of AI tools that can support decisions.

Keep educating yourself. Assess the skills and knowledge of your team to determine if they have the necessary expertise. Ensure you are not lag, but keep up with training opportunities to help you acquire the skills needed to work with AI tools and algorithms. An essential part of your learning path is related to AI ethics to ensure the new AI tools are free from bias and other risks.

Involve HR professionals in this journey to ensure they are comfortable using AI tools. Partners with other departments, e.g., Finance, Procurement, IT, and Legal, to develop a plan for implementing AI tools in the HR department, including timelines, budgets, and key performance indicators (KPIs) to measure success.

Changing The People Analytics Journey

I created my lectures and courses in People Analytics to enable HR leaders to improve business performance by making informed decisions about people based on data. I decided to cover real-world use cases of analytics to help HR leaders become familiar with data science terms and concepts, understand the competencies needed, and develop a proper analytical mindset.

For a few years, I’ve followed a conceptual loop describing how the People Analytics domain developed. It starts with the fundamental changes, elaborates on the role of the People Analytics leader, then demonstrates case studies and simulations, and ends up with emerging trends and ethics. In the face of how AI changes work, organizations, HR practices, and People Analytics, the content of my syllabus continues to evolve. However, the loop structure remains more relevant and practical than ever and is excellent for supporting your organization on this journey.

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There is no turning back: LLMs changed People Analytics. Large language models (LLMs) altered how we work and create value. The spectacular growth of ChatGPT users in a few days was a clear sign that these models are fundamentally changing how people process unstructured data. But how can we, the People Analytics professionals, leverage these deep learning and natural language processing techniques to offer a better business case to our expertise? How can we extend our value proposition to help our organization gain better business results and improve its competitive advantage?

LLMs in People Analytics is a vast and developing topic. Therefore, I cut it into five pieces, so we can easily digest it and get ongoing updates. Each of the five parts explores a different aspect of implementing LLMs in People Analytics practices: Introduction, Advantages, Challenges, Announcements, and Career. Let’s start with a review of these five topics and then dive deeper into each one in a dedicated article. 

Since the following topics ongoing updated, I invite you to come back and check them from time to time. To make it even easier, subscribe for updates and receive a notification straight to your e-mail inbox. But if you prefer a conversation, you can ask my chatbot about the latest updates. I also cover LLMs in People Analytics in new lectures and mentoring activities.

Introduction

The first part of this series is an introduction to LLMs in People Analytics. While there are tons of resources on this emerging technology, the perspective of the People Analytics domain is relatively scarce. However, we are reasonably dissatisfied with a general description of how LLMs work. So instead, we want to explore how they impact People Analytics practices and tools. After reading Introduction: LLMs Impact on People Analytics, you’ll be able to explain LLMs generally and start a conversation in your organization about the change to come.

Advantages

This series’s second part covers the advantage of implementing LLMs in People Analytics practices and tools. Although LLMs disrupt the People Analytics profession and industry, the fundamental objectives remain the same. We are here to explore, infer, and communicate data patterns, to support business decisions related to people. Implementing LLMs can enhance our ability to offer data democratization and consumerization. After reading Advantages of LLMs in People Analytics, you’ll be able to discuss the business case for extending your tool kit to include LLMs and review the added value for various stakeholders from better access to information and insights.

Challenges

The third part of this series discusses the challenges and risks of LLMs for the People Analytics domain. The challenges can be categorized into two main types: The first encompasses ethical concerns, and the second includes all kinds of output limitations. This part is crucial because we, first and foremost, don’t want to harm people and any of our stakeholders with our analytics activities and products. However, we must also understand how to overcome berries in LLMs implementation. After reading Challenges of Implementing LLMs in People Analytics, you’ll have a clearer view of the challenges and risks. Such a view is essential for implementing LLMs responsibly.

Announcements

The fourth part of this series focuses on People Analytics vendors. We already have some fascinating news from some players in the industry regarding LLMs, and we can certainly expect many more to come as the technology develops. In addition, the creative and innovative way vendors leverage LLMs will enhance their offerings and influence how People Analytics departments operate. After reading LLM News from People Analytics Vendors, you’ll find it easier to continue navigating the rough seas of HR tech and introducing opportunities to your organization to expand further and automate your deliverables.

Career

The last part of this series is about you. The emergence of LLMs will require new considerations for your skillset and career path. LLMs are unlikely to replace human analysts who better understand people’s nuances and complexities and better capture the context and subtleties of communication and cultural factors. Nevertheless, a practical approach to the change is to prepare for a hybrid model that combines the strengths of LLMs and human analysts. After reading People Analytics Leaders Leverage LLMs, you’ll have some ideas on adjusting your career aspirations and learning path and discuss an alternative mixture of skills in your team.

I’m excited about this ongoing exploration and curation of LLMs in People Analytics. I hope it enables us a smooth entering to the new era. I invite you to discover more about the Introduction, Advantages, Challenges, Announcements, and Career considerations, return later to each part of this series for updates, and also learn more about LLMs in People Analytics in my lectures and mentoring.

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Are HR professionals capable of deriving meaningful information and actionable insights from their organization’s data about its people? Are the data literacy assessment tools valid to measure such capabilities among HR? Should we strive for a new and unique definition of data literacy to enhance such skills among HR professionals?

The discussion about data literacy is relatively new in the context of the HR function in organizations. It is crucial, though, as the methods and solutions of People Analytics gain more traction. In this article, I review definitions, measures, and assessment tools based on literature and current practices, which I interpret following my experience educating and training HR professionals in People Analytics. Hopefully, my point of view will support the HR sector in its transformation to become a better client of data. Furthermore, I believe HR professionals can impact their data literacy development, so I conclude this article with some recommendations.

Do HR professionals speak data?

A few years ago, Gartner analysts coined the term speak data, and related it to learning any second language. Unfortunately, the digitization of businesses and the emergence of advanced analytics and machine learning did not make most professionals speak data fluently. As a result, the lack of a common language for interpreting organizational data is a fundamental communication challenge, Gartner analysts stated.

Since then, many organizations have prioritized data fluency and initiated competency development in data literacy to overcome deficiencies in managing, analyzing, and applying data in various business contexts. New conversations around data and analytics focused on aspects of business questions, integration of multiple data sources, and analytical approaches.

The HR department is a late bloomer, at least concerning data literacy. According to Wharton professor Matthew Bidwell, data literacy has become a core skill for HR. Analytics may sometimes feel overwhelming for reasonable HR professionals, but sophisticated tools and techniques are unnecessary, as simple analytics can bring significant value. However, despite the expectations of most HR executives, data-driven practices are standard in less than half of global companies represented in a recent survey.

Beyond the gap between expectations and practices, another research reveals that, on average, HR leaders lag far behind other professionals in their ability to use data to guide business decisions. Sadly, business leaders still tend not to trust HR to use data to anticipate and help them fill their talent needs. Moreover, a third of HR professionals admit significant people analytics and data skills gaps. Therefore, data literacy must be developed within the business context to overcome this.

What is data literacy in the business context?

Data literacy is the ability to read, understand, create, and communicate data as information. However, I find this definition insufficient in the discussion about HR practices because it doesn’t refer to what you should and shouldn’t do with data in the business context and HR role.

Data is crucial to business success, so there is no surprise in organizations’ demand for some degree of data literacy from all their employees. But beyond the ability to derive meaningful information from data, a good definition of data literacy, in my opinion, must consider the complexity of data science as an emerging field in various business verticals, particularly for the HR department.

Data literacy should not be a label reserved for data scientists, and HR people indeed not supposed to become data scientists. However, a good definition must consider the multidisciplinary context of data science, encompassing three general domains: the business domain of expertise, statistical modeling, and hacking skills. Non-data specialists who leverage data must know how to do it right within the business purpose’s boundaries while being critical about the datasets, analytics methods, and tools.

Therefore, data literacy for HR professionals should include the following abilities: knowing what data is appropriate to use for a particular purpose, interpreting data visualizations, thinking critically about information yielded by data analysis, understanding analytics methods and their appropriateness, recognizing misinterpretations and misleading use of data, and communicating information derived from data analysis. 

The overall objective of data literacy among HR professionals is the ability to use talent data and analytics to contribute to business outcomes. By collecting data on employee behavior and attitudes and tying it to business performance, HR can highlight opportunities and risks for the business. Following their conversations with business leaders, HR professionals should be able to transform business concerns into research objectives that data experts or solutions will later conduct.  

Am I data literate?

I’m happy that you ask. But, unfortunately, the odds are not in your favor. According to the Data Literacy Project, 76% of business decision-makers aren’t confident in their ability to read, work with, analyze, and argue with data. This initiative, founded by analytics vendors and thought leaders, offers a short test to discover your data persona. Then, based on your persona, which may be a data avoider, newcomer, apprentice, or guru, the project offers resources to take you to the next level in handling data.

However, your data persona is a descriptive classification that may neglect to measure your skillset in detail. Data To The People offers a competency framework for data literacy: “Databilities” is a research-backed approach that enables organizations to measure data literacy across their workforce and compare it within professions, industries, and regions. This framework defines six levels of progression for reading, writing, and data comprehension. While the lowest competency levels focus on memory, more advanced levels focus on understanding, and the upper levels focus on applying skills.

Based on this framework and a survey among 5,000 employed individuals in northern America, Australia, India, and the UK, a global data literacy benchmark provides an updated view. According to the survey results in 2022, only 6% of the respondent can help their peers with data concepts and culture, reading, writing, and comprehending data. Furthermore, 44% of the survey participants need help with data concepts and culture, and 52% need help with data governance.

Therefore, the researchers concluded that the focus should be on creating opportunities for employees with higher data literacy levels to inspire others to continue developing their skills actively. Moreover, there is a need to provide more significant support to those in the middle levels of the data literacy competency level and more encouragement to those at the bottom to engage with data literacy concepts and seek guidance from their peers.

Suppose you’d like to take a deeper review of measuring data literacy or build your assessment tool. In that case, it is worth mentioning that parts of the “Databilities” framework were created based on the data literacy competencies initially defined by Ridsdale et al. in their report about strategies and best practices for data literacy education. The authors have synthesized a set of skills comprising various levels of data literacy, which they presented in a data literacy competencies matrix. They organized it by core aspects of their data literacy definition, which include data collection, management, evaluation, and application.

The competency framework of Ridsdale et al. is compared to many other assessment tools in a report by the national statistical office of Canada, which reviews the data literacy definition and measures from the point of view of the public service. Notably, the report provides an overview of existing approaches to measuring data literacy, from self-assessment tools to objective measures and from individual to organizational assessments.

Why are there barriers to data literacy in HR?

Did you take any of the abovementioned assessments? Did you find gaps? I’m sure you did, as I occasionally hear the frustration of many data professionals who serve or operate in the People Analytics functions. When People Analytics leaders share their experience dealing with their inner clients in the HR department, they usually describe barriers that hinder HR professionals’ development of data literacy skills.

Some HR professionals may not be fully aware of the importance of data literacy in their role and how it can contribute to evidence-based decision-making. Other HR professionals who have gained such awareness and highly prioritize data literacy in their skills development sometimes get limited access to data and analysis tools. They may also get limited training opportunities, whether formal training programs or professional development opportunities focusing on data literacy skills.

Unfortunately, there is still fear, intimidation, or lack of confidence in using data among HR professionals. It may stem from a lack of experience or training opportunities in data analysis and time constraints, which may limit their ability to invest in developing data literacy skills by themselves.

Organizational immaturity in data may also be a factor in the gap in data literacy. Immature organizations lack a culture that prioritizes data-driven decision-making or may not foster a data-driven mindset among HR professionals. Immaturity can also manifest in concerns about data quality and integrity issues, which can cause HR professionals to hesitate to utilize and interpret the data.

Sometimes I hear accusations that HR professionals are not analytical enough. I perceive HR as responsible for upskilling in this area, but as I stated in some public talks, the current state of HR is more complicated. First, academic programs still neglect People Analytics as a mandatory domain. Secondly, when they graduate, analytical expectations in HR practitioners’ roles are not high, so seeds of skills and interest might decline over time. Third, the entire HR sector relies on consultants and contractors, but unfortunately, many are also not updated, and their interest in changing the state of affairs is unclear. Fourth, tech vendors sometimes prefer to bypass HR, which also makes HR lag. Finally, HR is under-budgeted. It is most prominent when it comes to professional development. Relying on marketing content sponsored by technology vendors is insufficient for upskilling and reskilling.

Overcoming the barriers to data literacy in HR requires organizational support in providing access to data, technology, and training opportunities, fostering a data-driven culture, and addressing data quality and integrity concerns. However, HR professionals must also proactively develop their data literacy skills through self-directed learning and seeking relevant resources and opportunities.

How to become data literate more effectively?

Your curiosity and critical thinking are essential in building your data literacy skills. Like many HR professionals, you probably consider yourself a person with good intuitions about people. However, only a willingness to combine your intuitive judgments with insights from data will enable you to start your journey as a data client in HR. Ask yourself how data analysis can contribute to better judgment and decision-making. Moreover, always use the question “Why?” to explore every phenomenon or experience you encounter further. This mindset will prepare you for the long process of developing your data literacy skills.

Developing your data literacy skills, like learning a foreign language, is not a checklist. It takes an ongoing effort to understand and assimilate it. A systematic approach to building your data literacy skills includes a few principles. Experts recommend distinguishing between data literacy and technical literacy, starting with a baseline of your skills, using a common language, building a culture of learning that rewards curiosity, enabling different learning channels, defining success, and ensuring leaders are also involved.

Remember that while case studies are inspiring and convenient to learn about what others are doing, the only way to understand data is by working with your organizational data. So start to look for opportunities to get involved with analytics or research projects. I promise you that when you get involved, vague concepts about data, e.g., data quality, will become much clearer. Soon enough, you’ll discover that you have become a more sophisticated data client and user and a part of the development of data culture.

What should you do next?

Data literacy is crucial at every stage in an organization’s journey to data-driven HR. From my experience, this journey follows a roadmap of four main chapters, each including four subtopics. To get started, we must charge ourselves with the basics: definitions, job requirements, barriers, and some myths worth dismissing. Then the journey begins with a quick win based on the ability to prioritize business questions. You also develop your orientation in the data world, handle data integrity, and pick some tech tools. Next, you demonstrate capability, apply methodologies, produce processes and automation, and build the team within HR and relationships with your partners outside HR. Eventually, you create a data-driven culture, influence various stakeholders, impact communication, and identify more opportunities for influence while emphasizing ethics in the field.

Data literacy manifests itself differently in each step of this journey. However, it does not stand on its own. The purpose is to support business decisions related to people. Therefore, the path to improved decisions through data requires decision literacy first, then data literacy. In other words, you must be contextually aware of the business’s problems and understand the objectives of the decision. Gaps between reality and business objectives create decision opportunities, e.g., strategy changes or new resource allocation. The most valuable data will connect to a business’s critical problems and gaps. Business leaders will inherently find interest in data and analytics tools within the context of current business problems and gaps. Data and analytics professionals will contribute the most when they understand the data and tools available to address these problems and gaps.

So when I teach my introductory course, The People Analytics Journey, I consider the objective to guide HR leaders to impact business performance by making informed decisions about people based on data. The course covers real-world use cases of analytics. It enables HR leaders to be familiar with data science terms and concepts and understand how to leverage them for decision-making. It contributes to developing a proper analytical mindset and data literacy based on human resource decision context.

The data science entrepreneur Clive Humby coined the famous phrase “data is the new oil.” But, to be used, the oil needs to be refined by people. Likewise, data also need to go through people to be used in business. To gain their seat at the table, HR professionals don’t need to become data scientists, but in the 4^th industrial revolution, they need to be comfortable working with data. More than ever, data literacy is a crucial skill for success. It becomes essential in driving business value, as it is included formally in most data and analytics strategies and in change management programs.

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Data scientist roles are still not very common in HR departments. A successful establishment of such a function requires balancing the analytics maturity of the business and HR leaders with the data scientist’s skills. Therefore, it is essential and fascinating to explore how data science and HR needs are knitted.

I was fortunate to interview Elizabeth Esarove, a data scientist in a senior advanced analytics role in the HR department at AT&T. I contemplated many things yet to be learned and developed and found some clues for data scientists’ development between the lines.

A career path from HR roles to data science

Littal: Tell us about your background, current role, and professional journey from an HR professional to a data professional.

Liz: I was hired into an HR role due to my analytics experience in the banking industry. At that time, HR departments were beginning to understand the value of using data to measure and improve HR functions. My role was on the new data analysis team; I was the third person to join the group. That was about 20 years ago. Since then, I’ve had roles within HR that involved data analysis for Talent Acquisition, Talent Management, and Training, where I learned about each area of HR and improved my ability to provide valuable insights.

When self-service reports became important for HR, I moved into a project management role where I served as a liaison between Information Technology and HR to work on projects developing reports for HR teams. This role helped improve my ability to communicate effectively in business terms with HR leaders and technical terms with Information Technology. In addition, the experience helps in my role as a data scientist today as I explain the results of my analysis to executives.

In 2013, I read news articles about companies that used predictive models to reduce employee attrition. I could envision other potential applications for predictive models to address workforce and business issues. I was also ready to take on a new challenge in my career. So, I decided to get a master’s degree in Data Science to develop my skills in predictive analytics. One of my electives was a text analytics course which is an asset in my current role because it’s necessary to analyze comments received in our employee surveys. I started applying my new skills the year before I graduated and have been working on predictive modeling and text analytics projects for the past six years. I enjoy applying my data science skills to HR projects!

The role, skills, and tools of a data scientist in HR

Littal: How does your data scientist role differ from other roles in the team, such as data engineers or data analysts?

Liz: As a data scientist, I focus on statistical analysis, predictive models, or text analysis to provide insights for business decisions. Other roles on our analytics team include data engineers who build and maintain our data architecture and security and monitor data integrity to ensure accuracy in the products created by our data analysts and data scientists. In addition, our data analysts design, build, and manage reports that can be automated; some of them also serve as consultants with HR professionals to help with data storytelling and clarify business needs for our projects.

Littal: Are specific skills or knowledge domains needed to be a data scientist in the HR department as opposed to other departments?

Liz: I believe that understanding ethical and legal standards regarding the use of employee data is incredibly beneficial to a data scientist working in HR. Our People Analytics team works closely with the Legal team to comply with laws regarding the use of employee data. We also have a Privacy team that reviews the use of employee data in reports and predictive models; they make recommendations based on our company standards regarding the protection of and appropriate use of employee data.

Littal: Describe the tools that you use. Are there any R libraries or other open-source tools that are a must? Which is your favorite and why?

Liz: R and Python are open-source tools used by data scientists on our team. I prefer R with RStudio integrated development environment. I have over a dozen R libraries for different projects; my favorite is dplyr – it’s the one I rely on most for data wrangling and probably appears in my R code for every project. For time series forecasting, I use the feasts and fable packages. For text analysis, I use tm and topicmodels packages. The ggplot2 package is excellent for creating beautiful data visualizations.

The contribution of a data scientist to the HR department

Littal: How do business questions related to people are raised? Who initiates them? How do they become a project or a product?

Liz: Our HR Business Partners usually learn about questions from their clients in the Business Units. The HRBP contacts a People Analytics consultant on our team to discuss the project. The consultant then determines which team members should be included in the project and sets up a meeting with the appropriate stakeholders to start defining the project scope and the expectations regarding the project’s final product. The project team continues developing the solution/product and meeting with the client regularly to keep them informed of progress and, if necessary, make changes until the project is complete. If it’s a data science project, the data scientist will be involved in presenting the final product to the business unit leader and HRBP.

Some projects are initiated within our People Analytics team. For example, if we see common themes for requests from multiple business units or persistent requests, we may build a product to satisfy that business needs regularly.

Littal: What are some Ethics considerations in your day-to-day work? Specifically, how do you consider explainability when creating predictive models? Can you share some examples?

Liz: I’ll share a hypothetical example to help explain the ethical considerations. Let’s say a company develops a predictive model determining which employees are likely to leave. If they used a “black box” model that doesn’t allow one to explain the impact of the variables in that model, the result is a model that predicts which employees are likely to leave but doesn’t tell anyone why they are more likely to leave. With this type of model, it would be hard to recommend actions that the company can take to retain valuable employees.

Also, consider how predictive models are used. If misused, a model like this could negatively impact employees and the company. For example, what would happen if a supervisor had access to that information and decided not to assign a team member to a new project because the predictive model indicated that the employee was likely to leave? Perhaps assigning them to a new project will give them a new challenge and eliminate their reason for leaving.

Now consider a situation where a company used an explainable model and was able to understand that employees who have been in the same role with no job growth for years are more likely to leave. Now, the company has some information that can be used to reduce attrition! Perhaps the solution is to help employees learn about available career paths, company training, or other growth opportunities. Again, using explainable models and using them well can be very powerful for HR.

Littal: What are, in your opinion, the fundamentals in data science that should be a part of every HR professional training so that they can be better inner clients of data professionals?

Liz: I think HR professionals should have an introduction to data science with some examples to help them understand what’s possible and how data science can help answer business questions and solve problems. The introduction should include a basic understanding of the different types of predictive models available with HR-related examples of how they can be used. In addition, if the company uses surveys that include employee comments, understanding what’s possible with text analytics can help. If HR professionals have that understanding, they are more likely to recognize a situation where data science can be used, and they can tell their clients, “We can help you with that.” Also, if they understand the process involved in developing data science solutions, it could help set expectations regarding timelines and deliverables early in the conversation with business unit leaders.

Littal: Thank you, Liz, for your insightful perspective. I look forward to finding out how implementing it will lead to more advanced analytics practices within HR departments.

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This article is not about R, Bookdown, or People Analytics. Actually, I did not articulate this catchy title. Instead, it was suggested by ChatGPT, a large language model trained by OpenAI, which apparently liked to be called Assistant.

The chat context was my late attempt to publish a draft of my R Bookdown “Littalics in wondeRland” on Github. I tackled some technical challenges that took me a few hours to solve, thanks to the valuable help of people in the local R community and some online resources. At the same time, I realized that the web exploded with mind-blowing first experiences with ChatGPT. So, I wondered, could I save myself the trouble by AI?

So I tried. I asked the machine questions about my current struggle and could not resist expanding the discussion to a broader context. Beyond information, I asked for advice, opinion, feedback, compliment, and prediction. I also brought my chutzpa to the conversation. It took me a few minutes to decide that this chat was publicable, worth the experience, and amusing. So here are the questions and answers, with human editing, though machine wording quoted as is.

By the way, I specifically asked if I could quote the responses and received a positive answer: “Yes, you may quote my responses in this chat … provided that you give appropriate credit to me and OpenAI. When quoting my responses, it is important to use quotation marks to indicate that the words are being quoted and to provide a proper citation. (Assistant, 2022)” Obviously, I followed the quoting instructions meticulously.

Information

How ChatGPT discussed R Bookdown? My first question was simple and informative: How to publish my R Bookdown on Github? Boring, I know. But Assistant correctly specified all the steps, instructions, and code on how to create a GitHub account, create a new repository, clone it to a local machine, design and build R Bookdown, push changes to GitHub, and publish on GitHub Pages. Moreover, it offered a retrospective summary in a few sentences of all I learned in a couple of hours. Nice!

Advice

Can the bot offer more than instructions? What about advice? I asked: How should I promote my R Bookdown after publishing it on Github? Assistant provided several ideas and dedicated a paragraph to each: share a link on social media and relevant forums, blog about it, submit it to specific R-related directories, present it at conferences or meetups, and publish on platforms like Amazon or Leanpub. A comprehensive and clear answer – Well done!

Opinion

Pieces of advice are pretty informative. But what about opinions on the way ChatGPT discussed R Bookdown? I asked: What are the pros and cons of publishing my R Bookdown drafts on Github? Assistant offered a balanced answer, including three pros (collaboration, version control, and free hosting) and three cons (limited customization, limited features, and limited privacy). Again, it dedicated a paragraph to each item. What a diligent Assistant!

Personal

Eventually, I decided to get more personal with the machine, with chutzpah. I asked: Who is Littalics? “I’m sorry, but I am not able to provide information about specific individuals,” said Assistant, “as I do not have access to any personal information or browsing capability. Littalics is a username or pseudonym that may be used by someone on a website or platform.” Correct, but somewhat disappointing.

Feedback

I tried: Can you offer me feedback on my R Bookdown draft that I published in the link…? “I’m sorry, but I am not able to browse the web and, therefore, cannot access the link you provided,” Assistant apologized. But it offered some general tips for improving my R Bookdown draft: clear and concise language, appropriate formatting, relevant examples and code snippets, appropriate figures and images, testing the code, editing, proofreading, and considering the audience. Indeed a thorough point of view, articulated in a paragraph for each item, was generated within seconds!

Compliment

Can the machine offer a compliment when you deserve it? I asked for feedback on the name of my R Bookdown – Littalics in wondeRland. “Overall, Littalics in wondeRland is a fairly catchy and descriptive name for an R Bookdown,” Assistant replied, “although it might be helpful to include a subheading to provide more context about the content of your book.” Then, it generously offered suggestions for improving the name: make it descriptive, short and sweet, catchy, with common words, and a subheading. Yes, subheading – Will do!

Predictions

I wondered if there would be a demand for R Bookdown publication in the domain of People Analytics. Assistant predicted that “There is likely to be some demand…, as R is a popular programming language for data analysis and visualization. People Analytics is a growing field,” it explained, “that involves using data and analytics to understand and improve the performance and well-being of employees. As such, there may be a demand for resources that help professionals in this field learn how to use R to analyze and visualize data related to people and organizations.” I agree, but I’ll write the book anyway because it serves additional purposes, as I mentioned in the introduction.

Before saying goodbye, and concluding exploring how ChatGPT discussed R Bookdown, I asked the bot for its opinion about our conversation. “As an artificial intelligence, I do not have personal opinions or feelings,” it replied. ” My primary function is to provide accurate and helpful information in response to user questions and requests.” So it may not be a beginning of a long friendship. Still, I’m confident it is worthwhile to consult Assistant for content generation, in addition to AI tools already in my workflow (Grammarly, ottr.AI, play.ht, and more). But as content become a commodity, I believe my human voice is here to stay. Fortunately, paraphrasing Picasso, chatbots are useless – they can only give you answers.

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In my previous article, Finding Hidden Patterns In Gender Pay Gap Data, I demonstrated that if you go beyond reporting to exploring, a single additional variable you add to your analysis reveals new insight. In this article, I continue the exploration to find more hidden patterns. The following analysis is based on my workshop at the People Analytics World in April 2022. (Also, read my list of public speaking past engagements).

In the workshop, I simulated working with a data scientist. Before diving deeper into the data, here’s a quick review of the previously shared analysis (find the code on my GitHub profile). The simulation was based on open data that I anonymized and randomized. It included details about occupation, seniority, gender, mode of employment, and salary. The output presented women’s and men’s annual wages, and we noticed a slight difference between men and women, in general, and in a subset of gender-diverse roles. Then, we explored the interactions of variables by adding only one additional variable, tenure, and found that while both genders start at a similar earning level and are promoted while gaining tenure, men are promoted at higher rates.

Tenure as a continuous variable instead of categorical

Let’s take a more advanced point of view. When adding a single variable to the gender pay gap analysis, an alternative is using tenure as a continuous variable instead of creating tenure groups. When I previously added tenure to the gender pay gap analysis, I created tenure categories and ran ANOVA. But sometimes, continuous variables work better than categorical variables. How different are the results in this case? Will I get different results if I run linear regression instead of ANOVA?

As you recall from learning statistics fundamentals, Linear Regression is an algorithm or statistical procedure to draw an optimized straight line between two or more variables. It helps us better understand the relationship between the variables and make predictions. When visualizing the continuous option, the insight is pretty the same. As years of tenure go by, the gap between genders gets larger.

Swapping between ANOVA and linear regression

What’s beyond visualization? Can we swap between ANOVA and Linear regression? Actually, yes! Let me explain why. In both options, we have a dependent variable, the annual salary, and data reveals that it is affected by tenure and gender together. To be more formal, we can say that the effect of one explanatory variable (the tenure) on the expected response (the annual salary) changes depending on the value of another explanatory variable (the gender). Therefore, these variables interact. If you run Regression or ANOVA, you’ll get the same results about interaction, but it will be different in the language of statistics.

Statistical analysis software, or open-source code, allows you to run ANOVAs and linear regressions in different procedures. In the ANOVA model, the predictors are often called factors, but we can call them predictors like the regression case. ANOVA assumes that all the predictors are categorical, therefore, have a limited number of values, which the software creates. Then, their effect is added to the outcome’s general or grand mean. This grand mean is essentially the same as the intercept in the regression model, to which all predictors are added.

You don’t have to dig deeper into the statistics formalities. All you need to know is there are differences in the workflow of conducting regressions and ANOVAs, including how we code the predictors into our data. This continuous or categorical coding changes the output we present but not the basic phenomenon we explore. So feel free to use whatever approach you prefer.

Mode of employment as an additional categorical variable

I want to proceed with tenure as a continuous variable and add another categorical variable: full-time versus part-time jobs. I visualized it by transforming data points related to part-time jobs into more prominent dots. The chart reveals that the compensation growth rate with tenure is lower for part-time employees. Moreover, it is clear from the chart that most of those part-time dots belong to women. And indeed, analyzing part-time jobs by gender reveals that 18% of women vs. 3% of men work part-time.

What would happen to the linear relationship between compensation and tenure among genders if we split it based on the type of assignment – full-time vs. part-time? The lines of genders who work full-time got somehow closer. So, the hidden pattern we thought we discovered looks now somewhat different.

We can support these findings with multiple regression, a statistical technique that enables us to analyze the relationship between a single dependent variable and several independent variables. Alternatively, we can use three-way ANOVA, in which three separate variables (gender, tenure, and assignment) affect the outcome (salary). You can follow the code if you’d like to receive both types of analysis outputs. The bottom line is that gender is not the only predictor interacting with tenure. It is also interacting with assignments, full-time and part-time. As they gain tenure, the accumulative gap between men and women throughout their careers may stem from their full- and part-time positions. An intervention to close the pay gap should take into account these insights.

Additional insights on the Gender Pay Gap based on your actual data

The dataset that I used in the workshop is quite limited. However, assuming you integrate compensation data and additional datasets covering performance reviews, promotions, and internal mobility, you would be able to explore, across different roles, how women are compensated and promoted in comparison to men. You can also study biases in how women are evaluated compared to men. For example, are they perceived differently regarding performance, self-management, relationships, and potential leadership?

Furthermore, what is the correlation between yearly reviews and promotions for the two genders? Such analysis in actual organizations may reveal differences between genders in such perceptions. For example, do men and women who received similar reviews get a similar promotion? Such a question can undoubtedly shed more light on what’s happening during work tenure, explaining the growing compensation gap between genders. They can build a story and a business case for intervention.

But as you torture your data for additional insight, here’s an important warning: Sometimes, a statistical relationship that you explored within the entire workforce in your organization could be reversed or be different within subgroups, e.g., gender, tenure, or assignment type groups. It is known as Simpson’s Paradox. So, my recommendation when you lead the conversation with your data scientist is always to raise hypotheses to explore the statistical relationship among subgroups.

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