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People Analytics in the Era of Big Data
Jean Paul Isson, Jesse S. Harriott · 2016
In a sentence
A practical framework for applying advanced analytics and Big Data across every stage of the talent life cycle to attract, acquire, develop, and retain a high-value workforce.
People Analytics in the Era of Big Data argues that human capital is the last great competitive differentiator and that the same predictive and advanced analytics techniques that transformed marketing and finance can be applied to talent management. Drawing on the authors' decades of analytics leadership and interviews with dozens of leading organizations (Google, Microsoft, CISCO, SAS, Bloomberg, Pfizer, Xerox, and more), the book provides a Seven Pillars framework and the IMPACT Cycle methodology to move HR from gut-feel decision making to fact-based, forward-looking, business-aligned People Analytics. It shows leaders how to plan their workforce, source and acquire the right talent, onboard and engage employees, manage performance, calculate employee lifetime value, retain top performers, and promote wellness—all while creating measurable business value from talent data.
The four lenses
- Science
- Statistics
- Systems
- Strategy
Tags
The model
A causal framework in which People Analytics capability and design levers applied across the talent life cycle (planning, sourcing, acquisition, onboarding, engagement, performance, retention, wellness) drive psychological and behavioral states (engagement, satisfaction, productivity, loyalty) that in turn drive business outcomes (quality of hire, retention, employee lifetime value, business performance).
People Analytics Capabilitydesign lever
The organizational capacity to integrate disparate talent data sources and apply advanced/predictive analytics to forward-looking talent questions, including the right people, processes, technology, executive sponsorship, and alignment with business goals.
Workforce Planning Analyticsdesign lever
The use of analytics to forecast the right number of employees with the right skills at the right place, time, and cost, balancing talent supply and demand to meet current and future business goals.
Talent Sourcing Analyticsdesign lever
The application of analytics to identify, locate, and engage candidates across channels (referrals, job boards, social media, Big Data), optimizing source-of-hire performance and recruitment spend.
Talent Acquisition / Hiring Analyticsdesign lever
The use of predictive analytics to score and select candidates, optimize the interview process, and predict who will be loyal and perform well, reducing bad hires and acquisition cost.
Onboarding and Culture Fitdesign lever
The structured process of introducing, training, mentoring, and integrating new hires to organizational values and culture during the first months to secure loyalty, time-to-productivity, and engagement.
Employee Engagementpsychological state
The degree to which employees are committed, enthusiastic, motivated, and willing to give discretionary effort toward organizational goals, expressed through attitude, behavior, and outcomes.
Employee Satisfaction and Wellbeingpsychological state
The degree to which employees feel happy, supported, healthy, and content at work, encompassing morale, sense of welcome, support, connection, and competence during and beyond onboarding.
Employee Wellness, Health, and Safety Programsdesign lever
Employer-sponsored programs focused on proactively improving the well-being, health, and physical safety of employees and families through preventive care, lifestyle change, and safe environments.
Employee Productivity and Performancebehavioral pattern
The contribution an employee makes to organizational revenue and output, including ramp-to-productivity, fully ramped performance level, and ongoing performance against business-relevant objectives.
Employee Loyalty and Retentionbehavioral pattern
The tendency of employees, especially high performers, to remain with the organization rather than voluntarily leave, reflecting commitment, low flight risk, and reduced regretted turnover.
Quality of Hireoutcome metric
A measure of how well new hires perform, fit, and remain with the organization, reflecting the effectiveness of sourcing, selection, and onboarding decisions.
Employee Lifetime Value (ELTV)outcome metric
A risk-weighted prediction of the net profit attributed to an employee through their tenure in a role, combining cost, performance, and attrition curves.
Business Performanceoutcome metric
Organizational financial and operational outcomes—revenue, operating income, customer satisfaction, cost savings, growth—that result from optimized talent management.
Labor Market Conditionscontextual condition
External contextual conditions including talent scarcity, skills gaps, candidate-driven markets, globalization, and digital/social media disruption that shape the difficulty of acquiring and retaining talent.
How they connect
- people analytics capability → predicts workforce planning analytics
- people analytics capability → predicts sourcing analytics
- people analytics capability → predicts acquisition analytics
- sourcing analytics → influences quality of hire
- acquisition analytics → predicts quality of hire
- acquisition analytics → influences employee loyalty retention
- onboarding culture fit → predicts employee engagement
- onboarding culture fit → influences employee loyalty retention
- onboarding culture fit → influences employee productivity
- employee engagement → predicts employee productivity
- employee engagement → predicts employee loyalty retention
- wellness health safety → predicts employee satisfaction
- wellness health safety → influences employee productivity
- wellness health safety → influences employee loyalty retention
- employee satisfaction → influences business performance
- employee productivity → predicts employee lifetime value
- employee loyalty retention → predicts employee lifetime value
- employee lifetime value → predicts business performance
- quality of hire → influences business performance
- employee loyalty retention → influences business performance
- workforce planning analytics → influences business performance
- labor market conditions → moderates sourcing analytics
- labor market conditions − moderates employee loyalty retention
The process
The book's operating playbook is a systematic, data-driven approach to talent management that transforms Human Resources from a reactive support function into a strategic business partner. The core of this playbook is the application of a standardized analytical process, the IMPACT Cycle, across seven critical stages of the employee lifecycle, termed the 'Seven Pillars of People Analytics Success'. This methodology involves starting with a key business question, mastering the relevant data, deriving meaningful insights, recommending actions, communicating those insights, and tracking the outcomes. By consistently applying this cycle to areas like workforce planning, talent acquisition, and employee retention, organizations can move beyond intuition and historical reporting to predictive, evidence-based decision-making. The playbook begins with establishing a foundational capability through a People Analytics Center of Excellence, which brings together the right people, processes, and technology. Once established, the organization can deploy the IMPACT cycle to optimize each of the seven pillars. For instance, in talent acquisition, analytics can predict which candidates will be successful long-term, reducing costly bad hires. For retention, specific, advanced processes like calculating Employee Lifetime Value (ELTV) and building predictive attrition models allow the business to quantify the value of its talent and proactively intervene to protect its most valuable assets. Ultimately, this playbook enables an organization to create a virtuous cycle where data from one part of the talent lifecycle informs decisions in another, leading to a more engaged, productive, and stable workforce. It replaces guesswork with quantifiable insights, allowing leaders to strategically manage their human capital to drive measurable business performance and gain a significant competitive advantage.
Building a People Analytics Center of Excellence
To establish the foundational people, processes, and technology required to support a sustainable and high-impact People Analytics function within the organization.
When to use: When an organization is beginning its People Analytics journey or formalizing its existing analytical efforts into a strategic function.
Step 1Secure executive sponsorship and align with business goals.
Entry: A recognized need to become more data-driven in talent management.
Exit: Clear sponsorship from a senior leader and a documented charter linking analytics efforts to business outcomes.
In: Organization's strategic business plan · Out: People Analytics charter, Executive sponsorship commitment
Step 2Assemble a multidisciplinary team.
Entry: Executive sponsorship is secured.
Exit: Core team members with the necessary skill sets are hired or assigned.
In: Hiring budget, Role descriptions · Out: Assembled People Analytics team
Step 3Establish the standard analytical process.
Entry: The core team is in place.
Exit: A standard operating procedure for analytics projects is documented and adopted by the team.
In: Methodology frameworks (e.g., IMPACT Cycle) · Out: Documented analytical process
Step 4Select and integrate technology and tools.
Entry: The team and process are defined.
Exit: A functional technology stack is in place to support the team's work.
In: Technology budget, System requirements · Out: Integrated technology platform for People Analytics
Step 5Establish data governance.
Entry: Technology platform is being implemented.
Exit: A data governance policy and committee are established.
In: Existing data dictionaries, Data security policies · Out: Data governance framework
Applying the IMPACT Cycle for People Analytics
To provide a structured, repeatable process for conducting any People Analytics project, ensuring that it starts with a relevant business question and results in measurable action and outcomes.
When to use: When a business leader or HR partner has a strategic talent-related question that requires a data-driven answer.
Step 1Identify the business question.
Entry: A business stakeholder has a need for a data-driven talent insight.
Exit: A clear, specific, and answerable business question is documented and agreed upon.
In: Business priorities, Stakeholder interviews · Out: Defined business question
Step 2Master the data.
Entry: The business question is defined.
Exit: A clean, integrated dataset is prepared for analysis, and initial exploratory analysis is complete.
In: Raw data from various systems (HRIS, ATS, performance management, etc.), External data sources · Out: Integrated analytical dataset, Data visualizations (charts, graphs)
Step 3Provide the meaning.
Entry: Data analysis is complete.
Exit: A clear, concise interpretation of the findings is articulated.
In: Analytical results, Data visualizations · Out: Narrative explaining the insights
Step 4Act on the findings and recommendations.
Entry: The meaning of the findings is understood.
Exit: A set of specific, actionable recommendations is created.
In: Interpreted insights · Out: Actionable recommendations, Business case for action
Step 5Communicate the insights.
Entry: Recommendations have been formulated.
Exit: Key stakeholders have received and understood the analytical insights and recommendations.
In: Final analysis report, Recommendations · Out: Executive presentation, Analytical report/memo
Step 6Track the outcomes.
Entry: Actions have been taken based on the recommendations.
Exit: The business impact of the analytics project is measured and documented.
In: Business performance data (post-action) · Out: Impact assessment report, New business questions for the next cycle
Calculating Employee Lifetime Value (ELTV) and Cost Modeling
To quantify the net profit contribution of an average employee in a specific role over their entire tenure, accounting for costs, performance, and attrition risk.
When to use: When making strategic decisions about investment in hiring, training, or retention for a specific employee population.
Step 1Calculate the Employee Cost Curve.
Entry: A specific employee role has been selected for analysis.
Exit: A time-series dataset representing the average daily cost of an employee in the role is created.
In: Recruiting cost data, Training budgets, Compensation and benefits data · Out: Employee Cost Curve
Step 2Calculate the Employee Performance Curve.
Entry: The cost curve is complete.
Exit: A time-series dataset representing the average daily performance value of an employee is created.
In: Sales data, Productivity metrics, Manager estimates of ramp-up time · Out: Employee Performance Curve
Step 3Calculate the Cumulative Net Value Curve.
Entry: Cost and Performance curves are complete.
Exit: The cumulative breakeven point (when an employee has paid back their initial investment) is identified.
In: Employee Cost Curve, Employee Performance Curve · Out: Cumulative Net Value Curve, Breakeven point (in months)
Step 4Calculate the Attrition (Survival) Curve.
Entry: Historical employee tenure data is available.
Exit: A survival curve and a corresponding daily probability of termination (hazard curve) are generated.
In: Historical employee start and end dates for the role · Out: Survival Curve, Hazard Curve
Step 5Calculate the risk-weighted Employee Lifetime Value (ELTV).
Entry: Cumulative Net Value Curve and Hazard Curve are complete.
Exit: A single, risk-adjusted ELTV dollar amount for an average new hire in the role is calculated.
In: Cumulative Net Value Curve, Hazard Curve · Out: Employee Lifetime Value (ELTV)
Implementing a Proactive Talent Retention Model
To use predictive analytics to proactively identify employees at risk of leaving, understand the reasons for their flight risk, and deploy targeted interventions to retain valuable talent.
When to use: When an organization is experiencing costly or disruptive levels of voluntary employee turnover, especially among high-performers.
Step 1Gather and integrate data from disparate sources.
Entry: A clear business case for reducing turnover has been established.
Exit: A comprehensive, integrated dataset is prepared for modeling.
In: Historical HRIS data, Performance review data, External labor market data, Publicly available social media data · Out: Analytical dataset for retention modeling
Step 2Build a predictive attrition model.
Entry: The analytical dataset is ready.
Exit: A validated predictive model that identifies the key drivers of attrition is created.
In: Analytical dataset · Out: Predictive attrition model, List of key attrition drivers
Step 3Generate attrition scores for the current workforce.
Entry: The predictive model is validated.
Exit: Every employee in the target population has an assigned attrition score.
In: Current employee data, Predictive attrition model · Out: List of employees with attrition scores
Step 4Segment employees for prioritization.
Entry: Attrition scores are generated.
Exit: A prioritized list or visual grid of employees for retention focus is created.
In: Attrition scores, Employee value/performance data · Out: Talent Retention Grid
Step 5Develop and deploy targeted retention initiatives.
Entry: Employees are segmented and prioritized.
Exit: Targeted retention plans are executed for high-priority employees.
In: Talent Retention Grid, Key attrition drivers from the model · Out: Executed retention interventions
A candidate measure
People Analytics in the Era of Big Data — derived measurement candidates
People Analytics Capability
analytics maturity level; number of integrated data sources; presence of center of excellence
self-report suitability: medium
Workforce Planning Analytics
forecast accuracy; projected vs actual head count; skills gap identification
self-report suitability: low
Talent Sourcing Analytics
source-of-hire attribution; views/applies per posting; conversion rate
self-report suitability: low
Talent Acquisition / Hiring Analytics
predictive selection scores; interview-to-hire ratio; predictive validity coefficients
self-report suitability: low
Onboarding and Culture Fit
30/60/90-day survey scores; knowledge assessment scores; 360 feedback
self-report suitability: high
Employee Engagement
engagement survey scores; after-hours work; e-mail/meeting participation
self-report suitability: high
Employee Satisfaction and Wellbeing
satisfaction survey scores; Net Promoter Score; morale ratings
self-report suitability: high
Employee Wellness, Health, and Safety Programs
participation rate; absenteeism; injury rate; ROI per dollar
self-report suitability: medium
Employee Productivity and Performance
sales per period; call resolution rate; units produced; billable hours
self-report suitability: low
Employee Loyalty and Retention
voluntary turnover rate; survival probability; attrition risk score
self-report suitability: medium
Quality of Hire
performance ratings; 90-day/6-month retention; manager satisfaction
self-report suitability: low
Employee Lifetime Value (ELTV)
risk-weighted lifetime value; breakeven points; cumulative net value
self-report suitability: none
Business Performance
revenue per employee; operating income; customer satisfaction; cost savings
self-report suitability: none
Labor Market Conditions
unemployment rate; job openings; supply/demand ratio; GDP/labor data
self-report suitability: none
The story
The reader An HR leader, hiring manager, or business executive who wants to attract, develop, and retain top talent and become a strategic, data-driven partner.
External problem
The organization struggles to source, hire, engage, and retain the right talent in a competitive labor market while drowning in disconnected data and tools.
Internal problem
Leaders feel overwhelmed, confused, and uncertain—relying on gut feel and unable to prove the value of talent decisions.
Philosophical problem
Talent is the most valuable asset and last competitive differentiator, so making people decisions by intuition alone—when data exists—is just wrong.
The plan
- Migrate from business analytics to People Analytics by reframing customer techniques for talent.
- Adopt the Seven Pillars framework across the talent life cycle.
- Apply the IMPACT Cycle to turn data into actionable insight.
- Start small with quick wins, secure executive sponsorship, and align with business goals.
- Build the people, processes, and technology of a People Analytics center of excellence.
Success
- HR becomes a strategic business partner driving measurable ROI.
- Lower turnover, better quality of hire, higher engagement, and reduced talent costs.
- A workforce that is engaged, productive, loyal, and a competitive advantage.
At stake
- Losing top talent to competitors and the high cost of bad hires.
- Falling behind data-driven competitors and being relegated to the laggards.
- Slow organizational decline from gut-feel talent management.
Chapter by chapter
ch01The People Analytics Age
Organizations are locked in a relentless struggle to secure top talent, yet most cling to outdated methods for measurement and understanding; this chapter argues for the transformative power of People Analytics as a vital tool in navigating this complex landscape.
- People Analytics is no longer optional; it is essential for organizations addressing talent acquisition and retention challenges.
- Companies investing in analytics can fundamentally change their approach to managing human capital, leading to higher productivity and lower turnover.
- Understanding the rapidly evolving nature of work and employee expectations is critical for any successful People Analytics strategy.
- Organizations must integrate predictive analytics into their HR processes to visualize and respond to potential employee disengagement and retention risks.
ch02How to Migrate from Business Analytics to People Analytics
This chapter outlines how organizations can transition from traditional business analytics to People Analytics, emphasizing the necessary skills, data integration, and strategic application required to enhance human capital management.
ch03The Seven Pillars of People Analytics Success
In an era where data-driven decision-making is paramount, this chapter presents seven essential pillars for successful People Analytics, urging organizations to adopt a structured approach to optimize talent management and drive business performance.
- Organizations must adopt a structured framework to excel in People Analytics, focusing on seven critical pillars that represent the stages of talent management.
- The need for a shift from intuition-based hiring practices toward analytics-driven strategies is imperative for maintaining a competitive edge.
- Disconnected data systems create significant barriers to effective talent insights, necessitating a concerted effort to integrate analytics capabilities.
- Predictive analytics serves as a powerful tool to anticipate workforce needs and shape strategic talent decisions.
ch04Workforce Planning Analytics
To thrive in a competitive talent marketplace, organizations must leverage workforce planning analytics to accurately anticipate their future talent needs, ensuring the alignment of human resources with strategic business goals.
ch05Talent Sourcing Analytics
In an era marked by technological upheaval, organizations must adapt their talent sourcing strategies to locate the right candidates amidst evolving market conditions and the advent of Big Data.
ch06Talent Acquisition Analytics
In a dynamic job market, relying on traditional hiring methods poses significant risks; this chapter introduces talent acquisition analytics as a solution to enhance hiring efficacy and reduce turnover costs.
- Predictive analytics are essential in transforming your talent acquisition strategy from guesswork to precision.
- Resumes should not be the sole focus; behavioral assessments can provide deeper insights into candidate success potential.
- Organizations leveraging analytics in hiring can reduce turnover rates significantly and enhance overall performance.
- Companies facing talent shortages must adapt quickly, embracing data to inform their hiring decisions, thus outpacing competitors.
ch07Onboarding and Culture Fit
This chapter underscores the importance of a robust onboarding process that aligns new employees with an organization's culture, ultimately enhancing engagement and retention over time.
- Effective onboarding is crucial for new employee retention and engagement, requiring a structured approach that lasts beyond the initial weeks.
- An engaging first impression significantly impacts a new hire's perception of the organization and affects their long-term commitment.
- The four unique needs of new hires—feeling welcomed, supported, connected, and competent—should be specifically addressed during onboarding.
- Utilizing the OPEN analytical framework enables organizations to measure and enhance their onboarding processes accurately.
ch08Talent Engagement Analytics
This chapter argues that understanding and measuring employee engagement through analytics is essential for driving productivity, enhancing business outcomes, and reducing turnover.
ch09Analytical Performance Management
This chapter argues that to effectively manage employee performance, organizations must transition from traditional performance evaluations to data-driven, continuous performance management practices that align individual and organizational goals.
- Effective performance management requires a shift from traditional reviews to continuous assessment using analytics.
- Aligning employee performance metrics with business outcomes is essential for driving engagement and organizational success.
- Transparency in performance evaluations fosters accountability and motivates employees to improve continuously.
- Predictive analytics can significantly enhance promotion processes, ensuring the right candidates are recognized for advancement.
ch10Employee Lifetime Value and Cost Modeling
This chapter argues for rethinking employee metrics from mere costs to essential assets that drive company performance, introducing a robust framework for calculating Employee Lifetime Value (ELTV) alongside costs and attrition metrics.
- Employees are often seen as costs rather than assets, yet treating them as productive units can unlock significant organizational value.
- A cohesive framework of cost, performance, and attrition metrics serves as a critical baseline for making informed predictive analytics about workforce dynamics.
- Developing an accurate Employee Lifetime Value (ELTV) requires understanding that employee contributions evolve over time, necessitating continuous analysis.
- The integration of survival analytics and cost-performance metrics can transform how businesses perceive and manage their human capital.
ch11Using Retention Analytics to Protect Your Most Valuable Asset
In the face of escalating competition for talent, this chapter advocates for leveraging retention analytics to proactively manage attrition and safeguard organizational stability by addressing employee needs effectively.
- Employee retention should be viewed as a measurable outcome grounded in actionable practices rather than a fuzzy notion.
- The introduction of advanced analytics into HR serves to make talent retention initiatives both strategic and results-driven.
- Quantifying the cost of attrition reveals significant potential savings and underscores the importance of addressing turnover proactively.
- Organizations must move beyond traditional methods and embrace tailored strategies that align with individual employee needs.
ch12Employee Wellness, Health, and Safety to Drive Business Performance and Loyalty
This chapter asserts that integrating employee wellness, health, and safety into organizational strategy not only enhances business performance but fosters loyalty among employees, transforming perceptions of wellness programs from auxiliary perks to essential business imperatives.
- Integrating employee wellness into organizational strategy is not merely beneficial; it is essential for the long-term success and competitiveness of businesses in today’s marketplace.
- Effective wellness programs correlate strongly with improved employee morale, resulting in higher productivity and reduced turnover rates.
- Successful wellness outcomes require a commitment from leadership, a keen understanding of employee health needs, and strategic communication.
- Companies that invest in comprehensive wellness initiatives often see significant ROI, with documented returns of up to 6 to 1 as wellness leads to lower absenteeism and healthcare costs.
ch13Big Data and People Analytics
This chapter argues that the successful utilization of Big Data in organizations depends not merely on its collection but on the strategic application of People Analytics to enhance talent management and drive business outcomes.
- The successful application of Big Data hinges on the value derived from actionable insights, moving beyond mere data accumulation.
- Organizations must see People Analytics as an integral part of their talent management strategy to remain competitive in the evolving marketplace.
- The Seven Pillars of People Analytics Success framework serves as a critical guideline for organizations aiming to leverage data analytics effectively.
- Data-driven recruitment not only enhances candidate quality but also significantly impacts employee retention and satisfaction.
Questions this book answers
- How can organizations apply advanced business analytics to talent management across the whole employee life cycle?
- How do you migrate from traditional HR reporting to predictive, business-aligned People Analytics?
- Which workforce decisions (sourcing, hiring, engagement, retention, promotion) can be optimized with data?
- How do you measure the value of employees and the ROI of talent programs?
- What organizational conditions are required for People Analytics to succeed?
Glossary
- People Analytics Capability
- The organizational capacity to integrate disparate talent data and apply advanced/predictive analytics to forward-looking talent questions.
- Workforce Planning Analytics
- Analytics that forecast the right number of employees with the right skills at the right place, time, and cost.
- Talent Sourcing Analytics
- The application of analytics to identify, locate, and engage candidates across channels and optimize source-of-hire and spend.
- Talent Acquisition / Hiring Analytics
- Predictive analytics used to score and select candidates and optimize the interview process.
- Onboarding and Culture Fit
- The structured integration of new hires to organizational values and culture to secure loyalty, productivity, and engagement.
- Employee Engagement
- The degree of commitment, enthusiasm, and willingness to give discretionary effort toward organizational goals.
- Employee Satisfaction and Wellbeing
- The degree to which employees feel happy, supported, healthy, and content at work.
- Employee Wellness, Health, and Safety Programs
- Employer-sponsored programs improving employee well-being, health, and physical safety.
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