library / libc54f703ad568a94a
Data-Driven HR
Bernard Marr · 2018
In a sentence
A practical guide showing HR professionals how to harness big data, analytics, AI, and connected technologies to transform every core HR function and add strategic value to their organizations.
Human resources has long been one of the most data-rich yet insight-poor functions in any organization, spending its time on administrative tasks while relying on gut instinct for people decisions. In Data-Driven HR, Bernard Marr shows how the explosion of data, the Internet of Things, machine learning, and AI are turning HR into an intelligent, strategic discipline that drives performance across the entire business. Packed with real-world examples from Google, Xerox, IBM, UPS, Marriott, and many others, the book walks readers through building a robust data strategy, sourcing and analysing HR-relevant data, and applying analytics to recruitment, employee engagement, safety and wellness, learning and development, and performance management—all while navigating privacy, ethics, and transparency. Written in a friendly, non-technical style for HR professionals who never intend to become data scientists, it is a hands-on manual for adding measurable value and preparing for the future of work.
The four lenses
- Science
- Statistics
- Systems
- Strategy
The model
A causal model in which strategic design levers (data strategy, data sourcing, analytics capability, governance, and automation) drive psychological and behavioral states in employees and HR teams (engagement, wellbeing, trust) and behavioral patterns (data-driven decision making), which in turn produce outcomes such as recruitment quality, retention, safety, learning effectiveness, and organizational performance.
HR Data Strategy Alignmentdesign lever
The extent to which an HR team has a clear, focused data strategy that maps across the four layers of data and links directly to wider organizational objectives, defining which questions to answer and which data to collect.
HR-Relevant Data Sourcing Breadthdesign lever
The degree to which HR captures and combines relevant internal and external, structured and unstructured data types (activity, conversation, photo/video, sensor data) needed to answer strategic people-related questions.
HR Analytics Capabilitydesign lever
The organization's ability to turn data into actionable insights using techniques such as text, sentiment, image, video, voice, and predictive analytics, including capability, competency, capacity, churn, culture, recruitment channel, leadership, and performance analytics.
Data Governance and Transparency Qualitycontextual condition
The quality of privacy compliance, consent, ethical transparency, data minimization, anonymization, and security practices governing employee-related data across its lifecycle.
HR Automation and AI Adoptiondesign lever
The extent to which HR automates administrative and repetitive tasks using AI, machine learning, chatbots, and intelligent assistants, freeing HR to focus on strategic, higher-value activities.
Employee Trust and Buy-inpsychological state
The degree to which employees trust how their data are being used and accept data-driven HR initiatives, shaped by transparency, communication, and the value they receive in return.
Data-Driven Decision Makingbehavioral pattern
The behavioral pattern of HR teams and leaders basing people-related decisions on data and evidence rather than gut instinct or convention, including democratized real-time access to insights.
Employee Engagement and Satisfactionpsychological state
The extent to which employees feel happy, satisfied, engaged, and committed to the organization, measured through pulse surveys, sentiment analysis, and continuous feedback.
Employee Wellbeing and Safetypsychological state
The physical safety, health, and wellness of employees, improved by sensor and wearable technology, real-time monitoring, and data-driven wellness programmes.
Recruitment and Talent Qualityoutcome metric
The effectiveness of identifying, attracting, and hiring well-suited candidates through employer branding, optimized recruitment channels, and data-driven candidate assessment.
Employee Retentionoutcome metric
The organization's ability to retain valuable employees and reduce regrettable turnover, improved through churn analytics and predictive identification of flight risks.
Learning and Development Effectivenessoutcome metric
The degree to which learning programmes close skills gaps and improve employee capability through personalized, adaptive, data-driven learning and clear links between training and performance.
Employee Performanceoutcome metric
The measured performance and productivity of employees, improved by intelligent, continuous measurement and feedback that supports rather than surveils people.
Organizational Performanceoutcome metric
The overall success of the organization in achieving strategic goals, revenue, profit, and competitive advantage, to which intelligent HR contributes by optimizing people-related outcomes.
How they connect
- hr data strategy → influences data sourcing breadth
- hr data strategy → influences analytics capability
- data sourcing breadth → predicts analytics capability
- analytics capability → predicts data driven decision making
- data driven decision making → predicts recruitment quality
- data driven decision making → predicts employee performance
- analytics capability → predicts employee engagement
- analytics capability → predicts employee wellbeing safety
- analytics capability → predicts learning effectiveness
- automation adoption → influences data driven decision making
- automation adoption → predicts recruitment quality
- employee engagement → predicts employee performance
- employee engagement → predicts employee retention
- employee wellbeing safety → predicts employee performance
- learning effectiveness → predicts employee performance
- employee performance → predicts organizational performance
- employee retention → predicts organizational performance
- recruitment quality → predicts organizational performance
- data governance quality → predicts employee trust buyin
- data governance quality → moderates data driven decision making
- employee trust buyin → predicts employee engagement
- employee trust buyin → moderates employee performance
The process
The book's overall operating playbook is a systematic transformation of the Human Resources function from a traditional administrative and support role into a strategic, data-driven partner to the business. The methodology begins with a foundational phase where HR leaders create a clear data strategy that is explicitly linked to overarching organizational objectives. This involves identifying key business questions, determining the data needed to answer them, and planning the required analytics and infrastructure. In parallel, a robust data governance framework is established to ensure all activities are legally compliant, ethical, and secure, building employee trust through transparency. Once this strategic and governance foundation is in place, the playbook moves into an operational phase. HR applies its new data-driven capabilities to systematically optimize its core functions. This involves using analytics to enhance recruitment by targeting the right channels and candidates, to improve employee engagement through continuous feedback and predictive retention models, and to bolster employee safety and wellness with real-time monitoring. Furthermore, data is used to personalize learning and development to close skill gaps effectively and to modernize performance management by replacing outdated annual reviews with continuous, objective, and forward-looking feedback. Ultimately, this playbook guides HR to stop making decisions based on gut feeling and start using evidence to improve processes, boost employee performance and satisfaction, and demonstrate its value by directly contributing to the company's bottom line. The end goal is a more intelligent, efficient, and people-focused HR function that is integral to the organization's success.
Develop an HR Data Strategy
To create a clear, actionable plan for using data and analytics in HR that is directly linked to the organization's strategic objectives, ensuring that all data initiatives add tangible value.
When to use: This is the first process to execute when beginning the transformation to a data-driven HR function.
Step 1Create an HR 'Plan on a Page' to align with organizational goals.
Entry: The organization's strategic plan is available and understood.
Exit: A clear, one-page HR strategy is documented and shared.
In: Organization's strategic plan · Out: HR 'Plan on a Page'
Step 2Identify the key questions and problems to solve.
Entry: The HR 'Plan on a Page' is complete.
Exit: A prioritized list of strategic questions is created.
In: HR 'Plan on a Page' · Out: List of strategic questions
Step 3Determine the data needed to answer the key questions.
Entry: Strategic questions are prioritized.
Exit: A comprehensive list of data requirements is documented.
In: List of strategic questions · Out: Data requirements list
Step 4Select the appropriate analysis methods.
Entry: Data requirements are defined.
Exit: An analytics plan is created.
In: Data requirements list · Out: Analytics plan
Step 5Plan how to report and present insights.
Entry: Analytics plan is complete.
Exit: A communication and reporting plan is established.
In: Analytics plan · Out: Communication plan
Step 6Assess infrastructure implications.
Entry: Data, analytics, and communication plans are complete.
Exit: A list of technology and infrastructure requirements is finalized.
In: Data requirements list, Analytics plan, Communication plan · Out: Infrastructure requirements
Step 7Create a detailed action plan and make the business case.
Entry: All previous strategic planning steps are complete.
Exit: The HR data strategy is approved, funded, and ready for implementation.
In: Completed strategy document · Out: Approved action plan, Secured buy-in and resources
Establish HR Data Governance
To manage HR data as a valuable asset while ensuring compliance with legal, ethical, and security standards, thereby building employee trust and avoiding liability.
When to use: This process should be implemented in parallel with the data strategy and before collecting new types of employee data.
Step 1Inventory all people-related data.
Entry: A decision has been made to use employee data for analytics.
Exit: A complete data inventory or map is created.
Out: HR data inventory
Step 2Ensure legal compliance and obtain employee consent.
Entry: Data inventory is complete.
Exit: All data processing activities are legally compliant and consent is documented.
In: HR data inventory · Out: Consent records, Compliance documentation
Step 3Establish ethical guidelines and ensure transparency.
Entry: Legal compliance has been confirmed.
Exit: A clear, accessible data usage policy is communicated to all employees.
Out: Employee data privacy and usage policy
Step 4Implement robust data security and protection measures.
Entry: Data inventory is complete.
Exit: A data security plan is implemented and active.
In: HR data inventory · Out: Data security plan
Step 5Practice data minimization and anonymization.
Entry: Data requirements have been defined in the data strategy.
Exit: Data collection and storage policies reflect the principle of data minimization.
In: Data requirements list · Out: Data minimization policy
Optimize Recruitment Using Data
To move beyond gut-feel hiring and use data and analytics to attract, identify, and assess the most suitable candidates, leading to better hires, lower turnover, and improved performance.
When to use: When the organization wants to improve the quality of hires, reduce recruitment costs, and streamline the hiring process.
Step 1Measure and boost the employer brand.
Entry: A data strategy and governance framework are in place.
Exit: A clear understanding of the current employer brand and an action plan to improve it.
In: Social media data, Employee survey data, Glassdoor reviews · Out: Employer brand health report
Step 2Analyze recruitment channel effectiveness.
Entry: Data on historical hires and their performance is available.
Exit: Recruitment budget is allocated to the highest-performing channels.
In: Hiring source data, Employee performance data · Out: Recruitment channel ROI analysis
Step 3Use analytics and AI to identify and assess candidates.
Entry: A profile of desired candidate characteristics is defined.
Exit: A data-screened shortlist of high-potential candidates is provided to hiring managers.
In: Candidate applications, Performance data of current employees · Out: Shortlist of qualified candidates
Improve Employee Engagement Using Data
To continuously measure and improve employee satisfaction, loyalty, and retention by using data to understand what truly motivates employees and to predict and prevent disengagement.
When to use: As an ongoing process to maintain a healthy and productive work environment.
Step 1Measure employee satisfaction continuously.
Entry: A data strategy and governance framework are in place.
Exit: A real-time dashboard of employee sentiment is available to relevant managers.
In: Pulse survey responses, Internal social media posts, Anonymized communication data · Out: Employee satisfaction and sentiment analysis report
Step 2Use predictive analytics to reduce employee churn.
Entry: Historical employee data is available for analysis.
Exit: Managers are alerted to high-risk employees and provided with intervention suggestions.
In: Employee tenure data, Promotion history, Compensation data, Performance reviews · Out: Employee churn risk scores
Step 3Optimize compensation and benefits packages.
Entry: Access to internal payroll/benefits data and external market data.
Exit: Compensation and benefits packages are competitive and aligned with employee preferences.
In: Salary benchmark data (e.g., Glassdoor), Internal benefits usage data, Employee feedback · Out: Updated compensation and benefits strategy
Enhance Employee Safety and Wellness with Data
To create a safer and healthier work environment by using data from sensors and wearables to proactively identify risks, prevent accidents, and promote employee wellbeing.
When to use: To reduce workplace accidents, lower healthcare costs, and improve employee health and productivity.
Step 1Improve workplace safety with real-time monitoring.
Entry: A clear business case for improving safety in a specific area has been identified.
Exit: A system for real-time safety alerts is operational.
In: Sensor data (temperature, gas levels), Wearable data (heart rate, fatigue levels) · Out: Real-time safety alerts
Step 2Implement data-driven employee wellness programs.
Entry: A corporate wellness strategy is in place.
Exit: A wellness program with measurable outcomes is launched.
In: Aggregated fitness tracker data, Program participation rates · Out: Wellness program impact report
Step 3Analyze data to support employee mental and physical health.
Entry: A wellness program is active and collecting data.
Exit: At-risk employees are offered personalized support and coaching.
In: Biometric data, Sentiment analysis data, Absence data · Out: Individual health risk alerts (for employee/coach)
Implement Data-Driven Learning and Development (L&D)
To transform corporate training from a one-size-fits-all approach to a personalized, effective, and measurable system that closes critical skill gaps and supports employee growth.
When to use: When the organization needs to upskill its workforce, improve training ROI, and align development with strategic goals.
Step 1Identify and close learning gaps using analytics.
Entry: A data strategy and governance framework are in place.
Exit: A clear map of organizational skill gaps is created.
In: Performance review data, Capability assessments · Out: Skill gap analysis report
Step 2Deliver personalized and adaptive learning experiences.
Entry: Skill gaps and learning needs are identified.
Exit: Employees have access to a personalized online learning platform.
In: Skill gap analysis report · Out: Personalized learning paths for employees
Step 3Measure learning effectiveness and its impact on performance.
Entry: Employees are actively using the learning platform.
Exit: A report demonstrating the link between training and performance improvements is generated.
In: Course completion data, Learner engagement metrics, Post-training performance data · Out: L&D ROI report
Modernize Performance Management with Data
To replace outdated, subjective annual reviews with a continuous, data-driven, and forward-looking process that accurately measures performance, reduces bias, and genuinely helps employees grow.
When to use: When the current performance management system is seen as ineffective, time-consuming, or demotivating.
Step 1Transition from annual reviews to continuous feedback.
Entry: Leadership has approved the overhaul of the performance management system.
Exit: A new cadence of regular, future-focused performance conversations is established.
Out: New performance management policy
Step 2Measure performance objectively using multiple data sources.
Entry: The new performance management policy is in place.
Exit: Managers have access to dashboards with objective performance data for their team members.
In: Productivity tool data, Project completion data, Peer feedback data · Out: Performance data dashboards
Step 3Use analytics and AI to reduce bias and support growth.
Entry: Objective performance data is being collected.
Exit: Performance reviews are based on objective data and are free from common biases.
In: Performance data dashboards · Out: Bias-reduced performance assessments, Personalized development plans
The story
The reader An HR professional or leader who wants their function to be relevant, strategic, and to add measurable value to their organization and its people.
External problem
HR teams are data-rich but insight-poor, spending time on administration and relying on gut instinct rather than evidence for people decisions.
Internal problem
They feel overwhelmed by the data explosion, anxious about automation and AI threatening their jobs, and unsure where to start.
Philosophical problem
People are a company's most valuable asset, so it is simply wrong for HR to guess about them when data can reveal what really drives performance and wellbeing.
The plan
- Build a data strategy linked to organizational objectives using a 'plan on a page' and six key questions.
- Identify and source the HR-relevant data you actually need across activity, conversation, photo/video, and sensor data.
- Turn data into insights using the right analytics techniques while ensuring privacy, transparency, and good governance.
- Apply data-driven approaches to recruitment, engagement, safety and wellness, learning, and performance management.
- Embrace automation and AI to free HR for strategic, people-focused work and prepare for the future.
Success
- HR becomes a strategic, data-driven function that adds measurable value.
- Better, evidence-based people decisions improve recruitment, retention, safety, learning, and performance.
- Employees are happier, safer, more engaged, and more productive.
- HR professionals spend more time on high-value, uniquely human work.
At stake
- HR remains stuck in administrative, gut-instinct mode and is seen as unfit for purpose.
- The organization struggles to attract and retain the right talent and falls behind competitors.
- Poorly communicated or invasive data use erodes trust, damages morale, and harms the employer brand.
- HR misses the opportunity of the fourth industrial revolution and risks irrelevance.
Chapter by chapter
ch01What is data-driven HR?
Data-driven HR transforms traditional human resources practices by leveraging data and analytics to enhance decision-making, operational efficiency, and employee engagement, redefining the strategic role of HR in organizations.
- Data-driven HR fundamentally shifts the role of human resources from administrative tasks to strategic insights based on quantifiable metrics.
- Organizations that harness data effectively can gain a significant competitive edge via enhanced recruitment, employee engagement, and operational efficiency.
- Intelligent HR teams should prioritize data utilization to align their strategies directly with business objectives, ensuring maximum impact on organizational performance.
- The successful implementation of data-driven principles necessitates a reorganization of HR into specialized teams that focus on analytics and employee support.
ch02The evolution of intelligent (and super-intelligent) HR
This chapter explores the transformation of HR from being data-rich yet insight-poor to becoming data-driven through advanced technology, paving the way for 'super-intelligent HR' capable of automation and enhanced decision-making.
- The explosion of data presents both challenges and opportunities for HR professionals to enhance organizational performance.
- The role of HR must evolve to become more data-driven, ensuring decisions are rooted in sound analytics rather than intuition.
- Embracing technologies such as AI and machine learning is essential for automating routine tasks and improving strategic decision-making.
- The distinction between intelligent and super-intelligent HR lies in the capacity to integrate automation into both mundane and critical HR functions.
ch03Data-driven strategy: making a business case for more intelligent HR
The chapter argues that a well-structured, clear data strategy linked to broader organizational objectives is critical for maximizing the potential of data-driven HR practices.
ch04Capitalizing on the data explosion: identifying key sources of HR-relevant data
This chapter explores how HR professionals can harness various forms of data—internal and external, structured and unstructured—to derive actionable insights that benefit organizational management and employee performance.
- Organizations must embrace the data explosion; everything from smartphones to smart equipment is generating insights that HR can leverage.
- Understanding the interplay between structured and unstructured data is crucial for effective human resource strategies.
- Companies that analyze activity and conversation data gain a clearer picture of employee engagement and performance, empowering better management decisions.
- The combined use of internal and external data sources creates a holistic view that improves hiring outcomes and organizational effectiveness.
ch05Data-driven HR tools: turning data into insights with HR analytics
This chapter explores how HR teams can leverage various data analytics techniques to transform data into valuable insights that address strategic goals and enhance organizational performance.
- Data alone does not guarantee insight; the value lies in how effectively data can be translated into actionable knowledge.
- Combining different analytics techniques enhances the richness of the insights gained, supporting a more comprehensive view of HR issues.
- Predictive analytics can dramatically improve workforce management by foreseeing turnover trends and potential employee disengagement.
- Corporate culture analytics reveals the authenticity of organizational values, guiding necessary interventions to align culture with desired outcomes.
ch06Potential pitfalls: looking at data privacy, transparency and security
This chapter examines the complexities of data privacy, ethical transparency, and security within HR, emphasizing the critical need for robust data governance to prevent potential liabilities associated with mismanagement of employee data.
ch07Data-driven recruitment
The chapter argues that leveraging big data in recruitment is essential for HR teams to attract and retain talent effectively, highlighting automation's role in this evolving landscape.
ch08Data-driven Employee Engagement
This chapter examines the critical role of data-driven strategies in enhancing employee engagement, satisfaction, loyalty, and retention, presenting a case for integrating advanced analytics into workforce management.
- Disengaged employees significantly compromise organizational productivity, costing global economies billions.
- AI and data analytics provide actionable insights to enhance employee engagement.
- Transitioning from annual to continuous feedback mechanisms creates a more agile response to employee needs.
- Implementing sentiment analysis uncovers deeper insights into employee satisfaction beyond conventional methods.
ch09Data-driven Employee Safety and Wellness
This chapter explores how data-driven technologies, particularly through the Internet of Things (IoT), enhance workplace safety and employee wellness, while also outlining the challenges and ethical concerns associated with monitoring health data.
- Over half a million UK workers are injured annually in workplace accidents, underscoring a critical need for better safety practices.
- Technology such as IoT and wearable devices is revolutionizing workplace safety and enhancing employee wellness.
- Data-driven monitoring allows organizations to detect unsafe practices in real-time, fostering a proactive safety environment.
- Integration of connected technology can also lead to improved productivity and reduced costs for organizations.
ch10Data-driven Learning and Development
This chapter explores the transformative impact of data-driven technologies on learning and development (LD) within both educational institutions and corporate settings, emphasizing the identification of learning gaps and the integration of cutting-edge tools.
- With 40% of employees leaving due to poor training, having an effective LD strategy is critical to retention and engagement.
- Data analytics can provide insights into learning gaps that traditional methods may overlook, paving the way for more tailored education solutions.
- AI is pivotal in creating adaptive learning environments that facilitate personalized employee training and ultimately increase retention.
- The rise of MOOCs and corporate learning platforms illustrates the potential for scalable training that meets diverse learner needs.
ch11Data-driven Performance Management
In a landscape where traditional performance reviews falter, data-driven approaches promise enhanced employee performance and engagement, but require a delicate balance to avoid the pitfalls of surveillance.
ch12The Future of Data-driven HR
This chapter discusses the evolving landscape of HR in the context of rapid technological advancements, emphasizing the need for HR teams to adapt by embracing data analytics while defining their unique human contributions within organizations.
- The future of HR will increasingly rely on data-driven strategies as technology continues to evolve.
- HR professionals must define their unique human roles to ensure they remain relevant amidst rising automation.
- A bifurcated HR team focusing on analytics and employee support can better meet the challenges of the future.
- Skills in data analysis and comfort with AI technologies are becoming essential for HR success.
Questions this book answers
- What is data-driven or intelligent HR and why does it matter now?
- How can HR teams use data to make better decisions, improve operations, and understand employees?
- Which data sources and analytics techniques are most valuable for HR?
- How should HR build a data strategy linked to business objectives?
- How can data transform recruitment, engagement, safety, learning, and performance management?
Glossary
- HR Data Strategy Alignment
- The presence and quality of a clear, focused HR data strategy that maps across the four layers of data and is directly linked to wider organizational objectives.
- HR-Relevant Data Sourcing Breadth
- The extent to which HR captures and combines the relevant internal/external and structured/unstructured data types needed to answer strategic people questions.
- HR Analytics Capability
- The organization's ability to convert data into actionable insights using a range of analytics techniques and HR-specific analytics.
- Data Governance and Transparency Quality
- The quality of privacy, consent, ethical transparency, minimization, anonymization, and security practices governing employee data.
- HR Automation and AI Adoption
- The extent to which HR uses AI, machine learning, chatbots, and intelligent assistants to automate administrative and repetitive tasks.
- Employee Trust and Buy-in
- The degree to which employees trust how their data are used and accept data-driven HR initiatives.
- Data-Driven Decision Making
- The behavioral pattern of HR and leaders basing people decisions on data and evidence rather than gut instinct.
- Employee Engagement and Satisfaction
- The extent to which employees feel happy, satisfied, engaged, and committed to the organization.
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Tools these methods power