# Body of Knowledge (BoK) ### The Society for People Analytics --- ## Overview of Structure This Body of Knowledge (BoK) describes the key topics comprising People Analytics. It was designed to work as a clear and usable framework — a simplified initial format that suits a wide audience: HR practitioners, people analysts, educators, and those who want to understand how data helps at work. The BoK is practical and applied: it provides a list of topics that answer questions like: what matters, why it matters, and how to use simple methods and tools in real HR work. --- ## How the BoK is Organized To make the content easier, the BoK is organized into four dimensions: 1. **The Evolution of People Analytics.** Discusses the history of People Analytics and why the field looks like it does today. 2. **Functional Areas of People Analytics.** The main People functions where analytics adds value (e.g., recruitment, learning, compensation, performance). Each area explains basic concepts and practical examples. 3. **Applications of People Analytics.** Presents different components of the people analytics work that span across common use cases and business questions that People Analytics helps solve. This links the functional areas to real actions and decisions. 4. **Tools & Technology in People Analytics.** The systems, software, and simple techniques used to collect, analyze, and share people data and insights. We describe tools in a practical way and link them to use cases. --- ## Purpose and Use - Provide a simple, consistent reference to understand People Analytics. - Help teams design People Analytics learning content. - Serve as a foundation for future development and new assets (more detailed curricula, case studies, or accreditation materials). - Act as a living document. The BoK will evolve as the People Analytics field and the environment in which it operates evolve. - If you have questions about any aspect of the BoK, please refer to the FAQ. --- ## How the BoK is Updated The BoK was developed by a sub-team of SPA's Education Committee in 2025. It is intended to be a starting point and is approved by the inaugural 2025 Education Committee and the SPA Board of Directors. Further updates to content and governance beyond 2025 will be undertaken by the new sub-team that will be elected and selected for this purpose. --- ## Table of Contents - [Dimension: The Evolution of People Analytics](#dimension-the-evolution-of-people-analytics) - [HR Pre-Analytics Era (Before 2000s)](#hr-pre-analytics-era-before-2000s) - [Early Analytics/Digital Records Era (2000–2010)](#early-analyticsdigital-records-era-20002010) - [Expansion Era (2010–2020)](#expansion-era-20102020) - [Current Era (2020–Today)](#current-era-2020today) - [Future Outlook](#future-outlook) - [Dimension: Functional Areas of People Analytics](#dimension-functional-areas-of-people-analytics) - [Talent Acquisition Analytics](#talent-acquisition-analytics) - [Learning and Development Analytics](#learning-and-development-analytics) - [Performance Management Analytics](#performance-management-analytics) - [Compensation & Benefits Analytics](#compensation--benefits-analytics) - [Employee Experience Analytics](#employee-experience-analytics) - [Workforce Planning Analytics](#workforce-planning-analytics) - [Dimension: Applications for People Analytics](#dimension-applications-for-people-analytics) - [Ethics and Professional Conduct in People Analytics](#ethics-and-professional-conduct-in-people-analytics) - [People Analytics Foundations](#people-analytics-foundations) - [Basic People Reporting](#basic-people-reporting) - [People Analytics and Change Management](#people-analytics-and-change-management) - [Dimension: People Analytics Tools & Technology](#dimension-people-analytics-tools--technology) - [HR Information Systems (HRIS) and Data Sources](#hr-information-systems-hris-and-data-sources) - [Data Analysis and Visualization Tools](#data-analysis-and-visualization-tools) - [Survey and Feedback Tools](#survey-and-feedback-tools) - [Collaboration and Communication Platforms](#collaboration-and-communication-platforms) - [Emerging Tools and Technologies](#emerging-tools-and-technologies) - [Conclusion](#conclusion) - [Resources](#resources) - [Disclaimer](#disclaimer) - [Glossary](#glossary) --- ## Dimension: The Evolution of People Analytics This dimension explains how People Analytics developed over time. It shows the key shifts in focus, tools, and practices across different Eras. Understanding this history helps professionals see how the field evolved and why it looks the way it does today. ### HR Pre-Analytics Era (Before 2000s) During this period, HR mostly focused on record-keeping. The role of data was administrative, not strategic. - Paper files and basic HR systems were used mainly for payroll and attendance. - Reports were rare and usually manual and time consuming. - The main goal was compliance with labor laws. *Example: An HR team tracked employee absences using spreadsheets or paper logs without further analysis.* ### Early Analytics/Digital Records Era (2000–2010) This was the first step toward data use in HR. Companies began to collect data digitally and prepare standard reports. - HR Information Systems (HRIS) became common. - Basic metrics such as headcount, turnover, and hiring time were tracked. - Dashboards appeared but were limited in scope. *Example: HR produced a monthly turnover report and presented it to management to show how many people left the company.* ### Expansion Era (2010–2020) People Analytics started to grow as a distinct practice. Organizations recognized its business value. - Predictive models were introduced to forecast attrition or hiring needs. - Workforce planning and talent analytics gained importance. - Specialized roles such as "People Analyst" appeared. - More advanced tools like dashboards and integrated HR systems were used. *Example: A company predicted which employees might leave within 6 months and created retention plans.* ### Current Era (2020–Today) Analytics became central to HR strategy. Data is now used for real-time decisions. - Use of artificial intelligence and machine learning with HR data. - Ethical and privacy concerns became critical. - Analytics linked directly to business strategy, diversity, and well-being. - Self-service dashboards for managers became common. *Example: A manager uses a real-time dashboard to track employee engagement scores and take immediate action.* ### Future Outlook The field continues to evolve and faces new opportunities and challenges. - Increased focus on responsible AI and fairness in algorithms. - Greater integration of workforce data with business and financial data. - Expansion of skills needed, including data science and storytelling. - Movement toward global standards and accreditation in People Analytics. *Example: Future HR teams may use wearable devices or digital traces to monitor workplace well-being in real time, while balancing strict ethical rules.* --- ## Dimension: Functional Areas of People Analytics This dimension explains where People Analytics is applied inside HR. Each functional area shows how data can support better decisions and improve employee and business outcomes. ### Talent Acquisition Analytics This section explains how data and reporting can improve recruitment and hiring. The focus is on using analytics to attract the right candidates, reduce hiring costs, and make the process more fair and effective. #### Key Knowledge - **Recruitment Metrics** — Learn the main measures used to track recruitment success. *Example: Time to fill, cost per hire, source of hire, offer acceptance rate.* - **Candidate Funnel Analysis** — Understand how to look at each stage of the hiring process. *Example: Analyzing how many applicants pass screening, interviews, and final offers.* - **Source Effectiveness** — Use data to see which hiring sources work best. *Example: Comparing LinkedIn, employee referrals, and job fairs to see which brings higher-quality candidates.* - **Fair and Inclusive Hiring** — Apply analytics to monitor diversity and fairness in hiring. *Example: Tracking gender or ethnicity diverse across candidate pipelines.* - **Improving Hiring Decisions** — Use evidence instead of intuition in selection. *Example: Measuring the link between structured interviews and employee performance after 6 months.* - **Assessments** — Used to evaluate the skills and potential performance of an individual for a particular job. --- ### Learning and Development Analytics This section focuses on how data can be used to plan, deliver, and measure learning and development (L&D) programs. The goal is to make sure training efforts are effective, relevant, and add value to both employees and the organization. #### Key Knowledge - **Training Participation and Completion** — Track who joins and finishes learning programs. *Example: Monitoring compliance training completion rates by department.* - **Learning Effectiveness** — Measure if training actually improves skills and knowledge. *Example: Using pre- and post-training assessments to check skill growth.* - **Return on Investment (ROI)** — Link training outcomes to business results. *Example: Comparing sales performance before and after sales training.* - **Upskilling and Career Development** — Use analytics to identify skills gaps and guide career growth. *Example: Mapping employee skills to future job needs.* - **Employee Feedback on Training** — Collect and analyze learner feedback. *Example: Using post-training surveys to improve course design.* - **Succession Planning** — The volume and % of ready candidates for critical roles, bench diversity, and plan independence. Expected time to promotion. --- ### Performance Management Analytics This section focuses on how data and analytics can improve the way organizations measure, manage, and support employee performance. The goal is to make performance management more fair, evidence-based, and useful for both employees and leaders. Analytics helps identify trends, reduce bias, and connect individual performance to business outcomes. #### Key Knowledge - **Core Performance Metrics** — Understand the common measures used in performance management, such as goal achievement, distribution of ratings, and percentage of employees exceeding expectations. *Example: Tracking the share of employees rated "exceeds expectations" across departments.* - **Identifying Performance Patterns** — Use analytics to spot trends and patterns in performance data. This includes recognizing high performers, underperformers, and team-level differences. *Example: Identifying a team that consistently outperforms others in customer satisfaction scores.* - **Linking Performance to Business Results** — Learn how to connect performance ratings with organizational outcomes like revenue growth, productivity, turnover or customer success. *Example: Showing that teams with higher performance scores also deliver projects on time more often.* - **Bias and Fairness in Performance Evaluation** — Understand how to detect and address possible bias in evaluations, such as differences across gender, age, or other groups. *Example: Finding that women consistently receive lower ratings in certain departments and addressing the cause.* - **Supporting Talent and Career Decisions** — Use performance data to guide promotions, succession planning, and targeted development programs. *Example: Using performance data over three years to select candidates for a leadership program.* - **Continuous Improvement through Feedback and Development** — Apply analytics to monitor ongoing feedback and not just annual reviews. This supports more timely performance discussions and employee growth. *Example: Analyzing check-in data to see if regular feedback improves employee engagement.* --- ### Compensation & Benefits Analytics This section focuses on using data to design and manage fair, competitive, and cost-effective compensation and benefits programs. The aim is to ensure employees are rewarded appropriately while keeping pay practices aligned with business goals and legal standards. Analytics helps HR identify gaps, improve pay equity, and show the return on investment of benefits programs. #### Key Knowledge - **Compensation Metrics** — Understand key measures such as pay equity, compa-ratio, and salary range penetration. These help identify how pay is distributed across employees and whether it is fair. *Example: Comparing average salaries of male and female employees in the same job level.* - **Market Benchmarking** — Learn how to use external market data to compare internal pay levels with industry standards. This ensures competitiveness in attracting and retaining talent. *Example: Using salary survey data to adjust ranges for software engineers.* - **Benefits Utilization and Cost Analysis** — Track how employees use benefits such as health insurance, retirement plans, or wellness programs, and evaluate their cost versus value. *Example: Analyzing which departments have the highest usage of mental health benefits.* - **Pay Fairness and Compliance** — Recognize how analytics can support compliance with equal pay laws and internal fairness. This includes identifying pay gaps and ensuring policies meet legal requirements. *Example: Detecting a potential gender pay gap and preparing corrective adjustments.* - **Budgeting and Cost Control** — Use data to monitor and control compensation and benefits costs, making sure spending aligns with organizational budgets. *Example: Tracking year-over-year increases in benefits costs and benefits consumption rates and proposing cost-sharing options.* - **Communicating Compensation Insights** — Develop clear reports and visualizations to explain compensation and benefits trends to leadership and employees. *Example: Presenting a dashboard that shows salary distribution by department along with benefits participation rates.* --- ### Employee Experience Analytics This section focuses on understanding how employees feel about their work, their workplace, and their organization. The goal is to use data to improve engagement, satisfaction, and overall employee well-being. Analytics in this area helps organizations identify issues, measure the impact of initiatives, and create a better working environment that supports performance and retention. #### Key Knowledge - **Employee Engagement Metrics** — Learn how to measure engagement levels using surveys, pulse checks, and participation rates. Engagement data shows how connected employees feel to their work and company, and how ready they are to go the extra mile. *Example: Using an annual engagement survey to track changes in commitment levels across departments.* - **Sentiment and Feedback Analysis** — Collect and analyze open feedback from employees, such as survey comments, focus groups, or sentiment analysis from digital tools. *Example: Using text analysis to identify recurring concerns about workload in employee comments.* - **Employee Net Promoter Score (eNPS)** — Understand how to calculate and interpret eNPS, which measures employees' likelihood to recommend their organization as a place to work. *Example: Tracking eNPS trends over time to assess the impact of leadership communication.* - **Well-being and Work-Life Balance Indicators** — Learn how to monitor data related to employee health, stress, and work-life balance. This helps identify risks of burnout. *Example: Comparing absenteeism rates with survey data about stress levels.* - **Retention and Experience Link** — Analyze the connection between employee experience scores and turnover data to highlight the cost of poor experiences. *Example: Finding that teams with low engagement scores also have the highest resignation rates.* - **Using Analytics to Drive Action** — Translate survey and experience data into concrete action plans and improvement initiatives. *Example: Launching a mentoring program after identifying low satisfaction among early-career employees.* --- ### Workforce Planning Analytics This section explains how data can be used to plan, manage, and prepare the workforce for current and future needs. The goal is to make sure the organization has the right number of people, with the right skills, in the right roles, at the right time. Analytics helps leaders see gaps, risks, and opportunities in their workforce strategy. #### Key Knowledge - **Headcount and Workforce Metrics** — Understand the basics of tracking headcount, full-time equivalents (FTEs), and workforce distribution. These numbers form the foundation of workforce planning. *Example: Reviewing headcount by department to check if staffing aligns with business priorities.* - **Forecasting Future Needs** — Learn how to use historical data, business plans, and workforce trends to project future hiring, turnover, and workforce requirements. *Example: Estimating future hiring needs by accounting for expected growth and anticipated employee turnover.* - **Skills Gap Analysis** — Identify current skills in the workforce and compare them with the skills required in the future. This helps guide recruitment, training, or redeployment. *Example: Discovering a shortage of data analytics skills and planning targeted training programs.* - **Succession and Talent Pipeline Planning** — Analyze readiness of employees to fill critical roles and build pipelines for leadership positions. *Example: Mapping key roles in finance and identifying who could step in if a leader leaves.* - **Workforce Risk Analysis** — Use data to identify workforce risks such as high or rising turnover, skill loss, aging workforce, or over-reliance on specific roles or individuals. *Example: Identifying departments with persistently high turnover rates that may affect business continuity.* - **Using External Labor Market Data** — Compare internal workforce data with external labor market trends to make better planning decisions. *Example: Using government labor reports to predict skill shortages in healthcare professionals.* --- ## Dimension: Applications for People Analytics This dimension explains the key applications of People Analytics that help HR professionals put theory into practice. These areas cover the foundation skills, ethical responsibilities, basic reporting, and the role of analytics in supporting organizational change. Together, they form the starting point for applying people data in real organizational contexts. ### Ethics and Professional Conduct in People Analytics This section highlights the importance of handling people data in a responsible, fair, and transparent way. Ethics is not only about laws but also about building trust with employees and ensuring that analytics supports rather than harms people. #### Key Knowledge - **Respect for Privacy** — Always protect employee personal information and only use it for the intended purpose. *Example: Hiding personal identifiers like names and addresses in reports when not needed.* - **Informed Consent** — Employees should know what data is collected, how it is used, and who can access it. *Example: Telling employees in advance when survey responses will be analyzed at the team level.* - **Fairness and Avoiding Bias** — Analytics should not reinforce discrimination or unfair treatment. *Example: Checking that an AI screening tool does not reject candidates based on gender or ethnicity.* - **Transparency in Methods** — Be clear about how analytics models are built and avoid "black box" decisions. *Example: Explaining the factors considered in a promotion prediction model.* - **Accountability and Governance** — Define roles and responsibilities for data handling, and make sure someone is accountable for ethical standards. *Example: A People Analytics team having a data governance policy reviewed regularly by HR leadership.* - **Professional Conduct** — Analysts and HR professionals must act with honesty, integrity, and respect when using people data. *Example: Not sharing sensitive salary analysis with unauthorized colleagues.* --- ### People Analytics Foundations This section introduces the basic ideas, terms, and skills needed to start working with People Analytics. It is about understanding what People Analytics is, why it matters, and how it supports better decisions in HR and business. #### Key Knowledge - **Definition and Purpose** — People Analytics is the practice of using worker-related data, combined with human judgment, to support work-related decisions and improve organizational outcomes. *Example: Using turnover patterns to determine whether to adjust retention strategies.* - **Core Concepts and Terms** — Understand similarities and differences between data, metrics, KPIs, dashboards, and insights. *Example: A "metric" is the turnover rate, while a "KPI" is the target turnover rate the company wants to stay below.* - **Link to Business Strategy** — People Analytics should always connect HR actions to business outcomes. *Example: Showing how improving employee engagement scores can increase customer satisfaction.* - **Types of HR Data** — Learn what kind of data HR systems capture and how it can be used. *Example: Demographic data, performance ratings, learning history, and absence records.* - **Analytics Process Basics** — Get familiar with the steps of the analytics cycle: ask a question, collect data, clean data, analyze, and share insights. *Example: Asking "Why are high performers leaving?", analyzing exit interviews and survey results, and presenting findings to leadership.* - **Data Infrastructure and Governance** — To ensure accuracy in People Analytics work, core data infrastructure and governance elements must be established clearly. For example, a reliable integrated data environment combining HRIS, ATS, LMS, and survey platforms. A Single Source of data to ensure consistent reporting and analysis. --- ### Basic People Reporting This section focuses on how HR professionals can create and share simple reports that show what is happening in the workforce. The goal is to provide clear, accurate information that supports daily decisions in HR and management. #### Key Knowledge - **Types of People Data** — Understand the common data stored in HR systems. *Example: Demographics, job titles, hire dates, promotions, absence records, and turnover data.* - **Standard HR Reports** — Learn the most common reports HR teams prepare. *Example: Monthly headcount reports, attrition reports, diversity breakdowns, or training completion reports.* - **Report-Building Basics** — Know the steps to create a simple HR report. *Example: Exporting employee data from an HRIS, cleaning it in Excel, and summarizing by department.* - **Data Quality Awareness** — Be able to spot errors or issues in the data before reporting. *Example: Duplicate employee IDs, missing hire dates, or inconsistent job titles.* --- ### People Analytics and Change Management This section shows how analytics can support organizational change initiatives. Data helps leaders understand how employees react to change, measure adoption, and identify areas of resistance. Using analytics ensures that change is managed effectively and sustainably. #### Key Knowledge - **Understanding Change in People Analytics Adoption** — Adopting People Analytics as a form of organizational change, expecting resistance and using change management to build acceptance and new habits. *Example: Interview HRBPs to understand concerns or anxiety about using data, then address these through clear communication and training.* - **Measuring Change Readiness** — Use surveys and assessments to understand how ready employees are for upcoming changes. *Example: A readiness survey before a new HR system launch shows that 70% of employees feel unprepared.* - **Tracking Adoption Rates** — Monitor how quickly and effectively employees adapt to new processes, systems, or policies. *Example: Measuring the percentage of employees actively using a new performance management tool.* - **Identifying Resistance Points** — Analyze feedback, participation, and usage data to find where resistance is strongest. *Example: Discovering that one department has low usage of a new digital tool and needs extra support.* - **Evaluating Communication Effectiveness** — Use analytics to test if communication about change is clear and reaching the right people. *Example: Tracking email open rates and attendance at town hall meetings during a reorganization.* - **Measuring Impact of Change on Performance** — Evaluate whether the change is improving productivity, satisfaction, or other business outcomes. *Example: Tracking employee engagement scores before and after a restructuring.* - **Navigating Organizational Politics** — Identifying power structures and power players and applying political acumen to achieve People Analytics priorities, including funding. *Example: Identifying key stakeholders and performing a stakeholder analysis.* --- ## Dimension: People Analytics Tools & Technology Tools and technology are the enablers of People Analytics. They help HR professionals collect, analyze, and present data in ways that support decision-making. This dimension introduces the basic categories of tools and the ways technology supports analytics practice, without requiring deep technical expertise. ### HR Information Systems (HRIS) and Data Sources HR and workforce data systems form the foundational data layer for People Analytics. These systems store, manage, and integrate workforce data needed for reporting and analysis. This includes Core HRIS / HCM systems, ATS, LMS, Engagement surveys, etc. #### Key Practices - Get familiar with common HRIS systems. - Understand what types of data are stored: employee demographics, job details, compensation, training records, performance scores, and absence/leave. - Learn the basics of data extraction: how to generate reports, export CSV or Excel files, and move data into analysis tools. - Develop skills in checking data quality: spotting errors like duplicate employee IDs, missing hire dates, or incorrect job codes. - Recognize that HRIS data is often combined with external data (labor market benchmarks, surveys, or economic reports) to add context. **Examples:** - *Exporting headcount data from HRIS to monitor growth by department.* - *Using HR Software to pull absence and turnover data for a quarterly HR report.* - *Adding external salary survey data to internal compensation records for benchmarking.* --- ### Data Analysis and Visualization Tools Once data are available, professionals need tools to analyze patterns and present results clearly. These tools help translate raw numbers into insights that managers can act on. #### Key Practices - Start with Excel or Google Sheets for sorting, filtering, pivot tables, and simple charts and move forward with some advanced tools if work requires. - Use enterprise-grade analysis and business intelligence (BI) tools (e.g., Tableau, Power BI, R, Python, etc.) to analyze and present results. - Apply visualization best practices: choosing the right chart (bar, line, scatter), keeping reports simple, avoiding clutter. - Learn how to automate regular reports and refresh dashboards with new data. - Ensure that visuals tell a clear story that business leaders can understand quickly. **Examples:** - *Building a dashboard to show monthly attrition by gender, department, and location.* - *Using Tableau to track hiring pipeline stages and spot bottlenecks in the process.* - *Creating Excel pivot tables to compare training completion rates across teams.* --- ### Survey and Feedback Tools Surveys and feedback platforms give direct insights into employee sentiment, engagement, and experience. These tools add a "voice of the employee" layer to HR data. #### Key Practices - Use employee listening software to administer surveys and analyze results. - Understand different survey types: pulse surveys (short and frequent), lifecycle surveys (e.g., onboarding, exit), annual engagement surveys, and 360-degree feedback. - Ensure surveys are anonymous where appropriate and follow data privacy guidelines. - Learn how to analyze survey results: calculating averages, identifying trends, running cross-tab analysis (e.g., comparing satisfaction by department). - Present survey findings in a way that highlights actionable areas for leaders. **Examples:** - *Running a pulse survey every month to track employee morale during a major change initiative.* - *Using employee listening software to analyze drivers of engagement such as recognition, workload, or career opportunities.* - *Conducting a 360-degree feedback survey for leadership development.* --- ### Collaboration and Communication Platforms Sharing analytics is as important as producing them. Collaboration platforms ensure insights reach decision-makers in the right way and at the right time. #### Key Practices - Distribute dashboards and reports through MS Teams, Slack, SharePoint, or Google Workspace. - Enablement and Engagement: these platforms support information sharing, coordination, and feedback across the organization. - Embed analytics in everyday tools managers already use, reducing barriers to adoption. - Use storytelling techniques when presenting data: begin with the key insight, then show the supporting numbers. - Encourage two-way communication: let managers and employees ask questions or suggest new analyses. - Keep sensitive data secure while sharing by controlling access permissions. **Examples:** - *Uploading monthly workforce reports to a shared Teams channel for HR leadership.* - *Presenting a turnover analysis in a leadership meeting via Google Slides linked to live data.* - *Sharing an interactive dashboard with managers to monitor diversity goals.* --- ### Emerging Tools and Technologies The field of People Analytics is evolving quickly, with new technologies adding power and complexity. While not all organizations adopt them immediately, professionals should stay informed. #### Key Practices - Learn how AI-powered features in HR systems (e.g., LinkedIn Recruiter, Workday AI) can support matching candidates or predicting turnover. - Stay updated on real-time analytics enabled by mobile and cloud platforms. - Be cautious about risks: privacy, bias, transparency, and ethical concerns. - Recognize that emerging tools should complement existing systems, not replace them too quickly. **Examples:** - *Using AI to analyze open-ended survey comments and detect themes like workload stress.* - *Testing wearable devices to track employee well-being (e.g., stress or activity levels) while ensuring data consent and protection.* --- ## Conclusion The development of this Body of Knowledge (BoK) is an important step in shaping the practice of People Analytics. It provides a clear structure to understand how data can be used responsibly and effectively in Human Resources. This BoK is not meant to be a static document. It is a foundation that will continue to grow and adapt as the field of People Analytics evolves. New technologies, changing workforce dynamics, and emerging ethical questions will require regular updates and revisions. The aim is to: - Give learners a simple and practical entry point into People Analytics. - Support HR professionals in building confidence with data. - Promote ethical and professional standards in the use of employee information. - Encourage the development of a community that shares knowledge, tools, and best practices. The BoK serves as a basis for other parties to develop learning materials, case studies, and training modules. These resources will help transform the framework into practical content that can be studied, tested, and applied in real workplaces. Ultimately, the success of this BoK depends on ongoing feedback and engagement from the People Analytics community. By working together, we can ensure it remains a useful and trusted guide for professionals at all stages of their journey — whether they are entering the field, deepening their expertise or leading advanced initiatives. *Generative AI has been used in some of the work of this document, such as rephrasing and editing for better understanding.* --- ## Resources 1. INFORMS Analytics Body of Knowledge (ABOK). INFORMS, 2021. Available at: https://info.informs.org/abok 2. Coursera: People Analytics (Wharton School, University of Pennsylvania). Online Course, 2024. Available at: https://www.coursera.org/learn/wharton-people-analytics 3. AIHR – People Analytics Certificate Program. Academy to Innovate HR (AIHR), 2023. Available at: https://www.aihr.com/courses/people-analytics-certificate/ 4. Rutgers University – People Analytics Syllabus. School of Management and Labor Relations, Fall 2024. Available at: https://smlr.rutgers.edu/sites/default/files/Images/37-533-354-01_People_Analytics_Syllabus_Fall_2024_Elanwer.pdf 5. MIT Professional Education – People Analytics: Transforming Management with Behavioral Data. 2024. Available at: https://professional.mit.edu/course-catalog/people-analytics-transforming-management-behavioral-data 6. NYU SPS – Certificate in People Analytics. New York University, School of Professional Studies, 2024. Available at: https://www.sps.nyu.edu/certificates/people-analytics.html 7. UCSC Extension – People Analytics: Delivering Measurable Business Impact. University of California, Santa Cruz, 2024. Available at: https://www.ucsc-extension.edu/courses/people-analyticsdeliveringmeasurable-business-impact/ 8. Carlson School of Management – Graduate Topics in HRIR: People Analytics. University of Minnesota, 2021. --- ## Disclaimer This Body of Knowledge (BoK) was developed using a mix of openly available resources, professional experience, and secondary references. Some of the sources listed in the reference section require paid access or enrollment, and therefore the authors were not able to fully review all of their contents. Instead, we relied on published summaries, course descriptions, and available documents to guide the structure and topics. This BoK is not meant to replace the original materials, but to offer a practical and accessible framework for anyone interested in People Analytics. It can be used as a reference to support learning and practice, while readers are encouraged to consult the official sources for deeper study and advanced understanding. --- ## Glossary 1. **Data:** Facts, numbers, or information collected from different sources. Example: employee age, job title, or salary. 2. **Metric:** A single measure that shows a piece of information. Example: turnover rate. 3. **Analytics:** The process of using data, statistics, and models to find insights and support decisions. 4. **People Analytics:** The practice of analytics by using data and analysis to make better decisions about people in the workplace. 5. **Attrition Rate:** The percentage of employees leaving an organization during a specific period. 6. **Benchmarking:** Comparing HR metrics against industry or market standards. 7. **Compa-Ratio:** A metric comparing an employee's salary to the midpoint of a pay range. 8. **Dashboard:** A dynamic visual tool (charts, graphs) used to monitor HR metrics and KPIs. 9. **Data Governance:** Policies and procedures ensuring accuracy, security, and responsible use of data. 10. **Engagement Survey:** A questionnaire measuring employee satisfaction, motivation, and commitment. 11. **Headcount:** The total number of FTEs in an organization or department. 12. **HRIS (Human Resource Information System):** A system used to store and manage HR data such as employee records, payroll, and benefits. 13. **KPI (Key Performance Indicator):** A measurable value showing how effectively objectives are being met. 14. **Learning Management System (LMS):** A platform to deliver, track, and report training programs. 15. **Pulse Survey:** A short, frequent employee survey to capture real-time feedback. 16. **Regression Analysis:** A statistical method used to identify relationships between variables (e.g., training hours and performance). 17. **Pay Equity:** The fairness of pay between employees doing similar work, regardless of gender, age, or other factors. 18. **Succession Planning:** Preparing future leaders by identifying and developing employees for critical roles. 19. **Turnover Rate:** The percentage of employees who leave the organization within a given timeframe. 20. **Workforce Planning:** Using data to predict future hiring, skills, and workforce needs. 21. **Employee Engagement:** The level of commitment and motivation employees feel towards their work and organization. 22. **eNPS (Employee Net Promoter Score):** A simple survey measure that shows how likely employees are to recommend their workplace to others. 23. **Workforce Planning:** The process of analyzing and planning future staffing needs to ensure the right people are in the right roles. 24. **Predictive Analytics:** Using past data to forecast future outcomes. Example: predicting which employees are at risk of leaving. 25. **Ethics in People Analytics:** The responsible and fair use of employee data, ensuring privacy, transparency, and avoiding bias.