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The Power of People - How Successful Organizations Use Workforce Analytics To Improve Business Performance
FT Press Analytics
In a sentence
A practical, expert-informed guide to establishing, operating, and leading a workforce analytics function that uses people data to improve business performance.
The Power of People is the definitive practitioner's handbook for transforming HR from an intuition-driven, administrative function into an evidence-based, analytically powered driver of business value. Drawing on interviews with dozens of the world's leading workforce analytics practitioners, academics, and executives, the authors lay out a clear, repeatable approach to doing workforce analytics: framing business questions, building hypotheses, gathering and analyzing data, revealing insights, making recommendations, communicating through storytelling, and implementing and evaluating change. Through proprietary frameworks (the Eight Step Model for Purposeful Analytics, the Seven Forces of Demand, the Complexity-Impact Matrix, and the Six Skills for Success) and five detailed real-world case studies, the book shows how organizations can harness the latent power of their workforce data to predict outcomes, reduce attrition, grow sales, improve well-being, and increase profitability. If you want to build a workforce analytics capability that earns credibility and produces measurable impact, this book is your roadmap.
The four lenses
- Science
- Statistics
- Systems
- Strategy
Tags
The model
A causal-path model derived from the book asserting that design levers (analytics methodology, leadership, team skills, technology/data foundation, operating model, stakeholder engagement, storytelling) and contextual conditions (demand for analytics, analytical culture, resistance) shape psychological and behavioral states (analytical mindset, sponsor commitment, recommendation adoption) that produce outcomes (workforce outcomes such as retention/engagement and ultimately business performance).
Analytics Methodology Rigordesign lever
The degree to which a workforce analytics project follows a disciplined, purposeful methodology including framing business questions, building hypotheses, gathering data, conducting analyses, revealing insights, and determining recommendations.
Analytics Leadership Capabilitydesign lever
The capability of the workforce analytics leader, including business acumen, political astuteness, capacity to think, willingness to develop others, ability to inspire, and drive to achieve, as well as appropriate reporting access to the CHRO.
Team Skill Breadth (Six Skills for Success)design lever
The extent to which the analytics team has access to the six skill domains: business acumen, consulting, human resources, work psychology, data science, and communication, configured appropriately to project demands.
Data Quality and Governancedesign lever
The relevance, completeness, accuracy, currency, and governance of workforce data available for analysis, including data definitions, stewardship, and ethical/legal handling.
Technology Enablementdesign lever
The fit and capability of the technology stack (HRIS, data warehouse, reporting, statistical/machine learning, visualization, cognitive) to support the analytics vision and mission, including share/subscribe/own decisions.
Operating Model Maturitydesign lever
The degree to which the function has clear strategy alignment, governance, decision-making processes, defined roles and responsibilities, disciplined project management, and accountability mechanisms.
Stakeholder Engagementbehavioral pattern
The breadth and quality of relationships and two-way communication the analytics team maintains with served, depended-upon, and impacted stakeholders, including listening, translating, and helping them succeed.
Project Sponsor Commitmentpsychological state
The degree to which an influential, well-connected sponsor actively supports, resources, advocates for, and holds others accountable across the lifecycle of an analytics project.
Demand for Workforce Analytics (Seven Forces)contextual condition
The contextual pull for analytics arising from competitive edge, top-down requests, regulatory requirements, operational efficiency, cost pressure, humanistic concerns, and HR-for-HR needs.
Analytical Culture in HRpsychological state
The extent to which HR professionals and the broader organization embrace an analytical mindset, distributed along a spectrum from analytically savvy to willing to resistant, supported by leadership and translators.
Resistance to Workforce Analyticscontextual condition
Stakeholder skepticism, financial frugality, and HR hesitancy that impede the adoption and impact of workforce analytics.
Storytelling and Visualization Qualitybehavioral pattern
The effectiveness with which analytics insights are communicated through fact-based narrative and clear visualization tailored to audience to drive understanding and action.
Recommendation Adoption and Implementationbehavioral pattern
The degree to which insights are translated into accepted recommendations, decisions, and implemented organizational change.
Workforce Outcomesoutcome metric
Improvements in people-related outcomes such as retention/attrition, employee engagement, well-being, candidate quality, and time-to-productivity resulting from implemented analytics recommendations.
Business Performanceoutcome metric
Ultimate organizational results such as profitability, sales growth, cost reduction, and value to constituents that workforce analytics aims to improve.
How they connect
- demand for analytics → influences analytics methodology rigor
- analytics leadership capability → influences team skill breadth
- analytics leadership capability → predicts stakeholder engagement
- team skill breadth → predicts analytics methodology rigor
- data quality and governance → influences analytics methodology rigor
- technology enablement → influences analytics methodology rigor
- operating model maturity → influences analytics methodology rigor
- analytics methodology rigor → predicts recommendation adoption
- storytelling and visualization → predicts recommendation adoption
- stakeholder engagement → mediates recommendation adoption
- sponsor commitment → moderates recommendation adoption
- analytical culture → influences recommendation adoption
- resistance to analytics − moderates recommendation adoption
- recommendation adoption → predicts workforce outcomes
- workforce outcomes → predicts business performance
- analytical culture − influences resistance to analytics
The process
This playbook outlines a comprehensive, end-to-end methodology for establishing and maturing a workforce analytics function. It begins with foundational steps: creating a strategic operating model and implementing robust data governance. With this groundwork in place, the playbook guides practitioners on how to build credibility by selecting and executing an initial 'quick win' project, emphasizing the critical role of continuous stakeholder engagement throughout the process. The core of the playbook is a systematic project execution framework that covers the entire analytics lifecycle, from framing business questions and managing data quality to conducting analyses and communicating insights effectively. The methodology stresses the importance of storytelling and data visualization to translate complex findings into actionable recommendations. Finally, the playbook addresses long-term success by focusing on evaluating project impact and systematically cultivating an organization-wide analytics culture, ensuring the function not only delivers value but also grows in capability and influence over time.
Establishing a Workforce Analytics Operating Model
To create a structured framework that defines how the workforce analytics team operates, aligns its activities with organizational strategy, and builds the necessary capabilities for success.
When to use: When initiating a workforce analytics function or when the existing function requires a strategic overhaul to improve effectiveness and alignment.
Step 1Define and periodically confirm the vision and mission of the analytics team to align with business strategy.
Entry: Organizational commitment to establishing a workforce analytics function.
Exit: A documented and agreed-upon vision and mission statement.
In: Organization's overall strategy, Stakeholder feedback · Out: Analytics team vision and mission statement
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Step 2Assess required skills, budget, and resource availability to determine the sourcing strategy.
Entry: Vision and mission are defined.
Exit: A decision on the primary sourcing model for analytics capabilities.
- Choose between in-house, in-source, or outsource strategies, or a hybrid model.
In: Budget constraints, Assessment of internal skills, Analysis of local labor market · Out: Sourcing strategy plan
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Step 3Establish a governance framework and optimize the team's reporting structure.
Entry: Sourcing strategy is determined.
Exit: A documented governance framework and organizational chart placement.
- Decide on the optimal reporting structure for the analytics team.
In: Organizational structure, Data privacy policies · Out: Governance framework, Defined reporting structure
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Step 4Structure the team with clear roles and responsibilities, and adopt a consulting approach for project management.
Entry: Governance and reporting structure are in place.
Exit: Defined team roles and a standardized project management process.
In: Team member skill sets · Out: RACI matrix for team roles, Project management framework
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Step 5Build a business case for the function and define metrics to measure its effectiveness.
Entry: Team structure and processes are defined.
Exit: A compelling business case and a set of performance metrics.
In: Projected costs and benefits · Out: Workforce analytics business case, Key performance indicators (KPIs)
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Data Governance Implementation
To establish clear policies, roles, and standards for managing workforce data, ensuring its quality, consistency, and security.
When to use: When establishing a new analytics function or when data quality and consistency issues are hindering analysis.
Step 1Define which data elements will be measured and stored.
Entry: Organizational commitment to data governance.
Exit: A list of prioritized data elements for governance.
- Choosing which data elements to prioritize for governance.
In: Business requirements, Existing data systems inventory · Out: List of governed data elements
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Step 2Create a business glossary or data dictionary to standardize definitions.
Entry: Data elements for governance are identified.
Exit: A published and accessible data dictionary.
In: List of governed data elements, Input from subject matter experts · Out: Business glossary/data dictionary
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Step 3Determine roles and responsibilities for data integrity and maintenance.
Entry: Data dictionary is created.
Exit: Defined and assigned data stewardship roles.
- Determining appropriate owners for different data domains.
In: Organizational structure chart · Out: Matrix of data governance roles and responsibilities
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Step 4Foster a culture of accountability for data quality.
Entry: Governance roles are assigned.
Exit: Ongoing communication and reinforcement of data accountability.
In: Communication plan · Out: Increased organizational awareness of data quality
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Step 5Regularly review and update governance policies.
Entry: Initial governance framework is implemented.
Exit: A schedule for periodic review of governance policies.
In: Feedback on governance effectiveness, Changes in business strategy · Out: Updated data governance policies
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Selecting an Initial 'Quick Win' Project
To identify and execute an initial workforce analytics project that delivers substantial business impact with relatively low complexity, thereby establishing the credibility and value of the analytics function.
When to use: When launching a new analytics function or aiming to demonstrate value to skeptical stakeholders.
Step 1Pause and assess current capabilities, including data, technology, and team skills.
Entry: Mandate to establish an analytics function or deliver an initial project.
Exit: A documented assessment of current analytics capabilities.
In: Inventory of available data sources, List of available analytical tools, Team skill matrix · Out: Capabilities assessment report
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Step 2Listen to stakeholders and prospective sponsors to understand their needs and pain points.
Entry: Capabilities assessment is complete.
Exit: A list of potential project ideas aligned with business needs.
In: Stakeholder feedback, List of organizational KPIs · Out: List of potential analytics projects
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Step 3Use a Complexity-Impact Matrix to evaluate potential projects.
Entry: A list of potential projects has been generated.
Exit: A visualized mapping of projects by complexity and impact.
In: List of potential analytics projects · Out: Completed Complexity-Impact Matrix
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Step 4Select a project that is low-to-medium complexity and moderate-to-high impact.
Entry: Projects have been plotted on the Complexity-Impact Matrix.
Exit: A single 'quick win' project is selected and approved.
- Choosing the final project from the 'quick win' quadrant of the matrix.
In: Completed Complexity-Impact Matrix · Out: Selected 'quick win' project charter
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Step 5Plan for potential hurdles and challenges during project implementation.
Entry: A project has been selected.
Exit: A project plan that includes risk mitigation strategies.
In: Selected project charter · Out: Project plan with risk assessment
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Stakeholder Engagement and Communication
To identify, engage, and collaborate with stakeholders to ensure workforce analytics projects are relevant, supported, and successfully adopted.
When to use: Throughout the entire lifecycle of any analytics initiative.
Step 1Identify and categorize stakeholders based on their roles and interests.
Entry: An analytics project or initiative is being considered.
Exit: A categorized list of all relevant stakeholders.
In: Project concept, Organizational chart · Out: Stakeholder map
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Step 2Identify potential sponsors and engage them early to secure commitment and support.
Entry: Stakeholders are identified.
Exit: One or more committed project sponsors.
- Deciding which stakeholders to approach for sponsorship.
In: Stakeholder map · Out: List of confirmed project sponsors
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Step 3Meet with key stakeholders to understand their needs and involve them in framing business questions.
Entry: Sponsors are secured.
Exit: A set of well-defined, stakeholder-validated business questions.
In: Stakeholder availability · Out: List of business questions for the project
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Step 4Establish regular, tailored communication channels to keep stakeholders informed.
Entry: Project is underway.
Exit: An ongoing communication rhythm is established.
In: Project plan, Communication tools · Out: Stakeholder communication plan
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Step 5Validate analytical findings with stakeholders before final implementation.
Entry: Initial analysis is complete.
Exit: Stakeholder buy-in on analytical findings and recommendations.
In: Preliminary analytical findings · Out: Validated findings, Refined recommendations
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Executing a Workforce Analytics Project
To provide a systematic, end-to-end framework for conducting workforce analytics projects that ensures business relevance, analytical rigor, and actionable outcomes.
When to use: When undertaking a new workforce analytics project.
Step 1Frame Business Questions.
Entry: A project has been initiated and sponsored.
Exit: A set of agreed-upon business questions.
In: Stakeholder needs · Out: Defined business questions
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Step 2Build Hypotheses.
Entry: Business questions are framed.
Exit: A list of testable hypotheses.
In: Business questions, Domain expertise · Out: Project hypotheses
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Step 3Gather Data.
Entry: Hypotheses are formulated.
Exit: A clean, analysis-ready dataset.
- Deciding which data sources to use.
- Determining how to handle data quality issues.
In: Data source specifications · Out: Analysis dataset
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Step 4Conduct Analyses.
Entry: Data has been gathered and prepared.
Exit: Results of the statistical analysis.
- Selecting the most appropriate analytical methods.
In: Analysis dataset, Project hypotheses · Out: Analytical results
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Step 5Reveal Insights.
Entry: Analysis is complete.
Exit: A set of key insights derived from the results.
In: Analytical results · Out: Actionable insights
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Step 6Determine Recommendations.
Entry: Insights have been revealed.
Exit: A list of concrete recommendations for action.
In: Actionable insights, Business context · Out: Business recommendations
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Step 7Get Your Point Across.
Entry: Recommendations are determined.
Exit: Stakeholders understand and accept the recommendations.
In: Insights and recommendations · Out: Final presentation or report
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Step 8Implement and Evaluate.
Entry: Recommendations have been accepted.
Exit: The impact of the implemented solution is measured and documented.
In: Accepted recommendations · Out: Business change, Impact evaluation report
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Data Management and Quality Assurance
To ensure that data used for workforce analytics is accurate, complete, and timely by systematically assessing its quality and addressing any identified issues.
When to use: During Step 3 ('Gather Data') of an analytics project, whenever a new dataset is being prepared for analysis.
Step 1Familiarize yourself with the data and engage subject matter experts (SMEs).
Entry: A raw dataset has been identified for use in a project.
Exit: A clear contextual understanding of the dataset.
In: Raw dataset, Access to SMEs · Out: Documented data context and definitions
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Step 2Assess data quality using automated profiling tools and manual checks.
Entry: Contextual understanding of the data is established.
Exit: A data quality assessment report identifying issues.
- Deciding if the data quality is sufficient to proceed with analysis.
In: Raw dataset, Data profiling tools · Out: Data quality report
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Step 3Address missing data by determining its nature and selecting a handling strategy.
Entry: Data quality assessment has identified missing values.
Exit: A dataset with missing values handled appropriately.
- Choosing to exclude cases vs. imputing or using proxies for missing values.
In: Data quality report · Out: Cleaned dataset
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Step 4Handle outdated data by managing data refreshes.
Entry: Data quality assessment has identified outdated information.
Exit: A dataset that is sufficiently current for the analysis.
- Deciding whether to wait for a data refresh or find an alternative way to update the data.
In: Data refresh schedules · Out: Updated dataset
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Step 5Address non-existent data by creating new collection methods or using proxies.
Entry: Required data for analysis is found to be non-existent.
Exit: A strategy to acquire or approximate the necessary data.
In: Project data requirements · Out: New data collection plan or approximated data variables
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Communicating Analytical Insights
To effectively communicate complex analytical findings and recommendations to stakeholders by structuring them as clear, engaging, and persuasive narratives supported by data visualization.
When to use: During Step 7 ('Get Your Point Across') of an analytics project, when findings need to be shared with stakeholders.
Step 1Identify the key messages and tailor them to the audience.
Entry: Analysis is complete and key insights have been identified.
Exit: A clear understanding of the core message and the target audience.
In: Analytical insights, Audience analysis · Out: List of key messages
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Step 2Structure the key messages using a storytelling framework.
Entry: Key messages are identified.
Exit: A drafted story outline.
In: List of key messages · Out: Narrative structure
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Step 3Select and create appropriate data visualizations to support the narrative.
Entry: Narrative structure is defined.
Exit: A set of visualizations that support the key messages.
- Choosing the type of visualization best suited for the data and message.
In: Key data points, Visualization tools · Out: Data visualizations
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Step 4Draft the full story, integrating the narrative and visualizations.
Entry: Narrative and visualizations are created.
Exit: A first draft of the communication materials.
In: Narrative structure, Data visualizations · Out: Draft presentation or report
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Step 5Test the story with colleagues and gather feedback.
Entry: A draft presentation is ready.
Exit: Constructive feedback from colleagues.
In: Draft presentation or report · Out: Feedback on the communication
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Step 6Refine the narrative and visualizations based on feedback.
Entry: Feedback has been gathered.
Exit: A final, polished presentation or report.
- Deciding which feedback to incorporate.
In: Feedback on the communication · Out: Final presentation or report
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Project Impact Evaluation
To systematically assess the impact and effectiveness of implemented analytics recommendations to measure value, justify investment, and improve future projects.
When to use: During Step 8 ('Implement and Evaluate') of an analytics project, after a sufficient amount of time has passed for the implemented changes to have an effect.
Step 1Establish success metrics before project implementation.
Entry: Project recommendations are approved for implementation.
Exit: A list of agreed-upon success metrics.
- Deciding which metrics best capture the project's intended impact.
In: Project recommendations, Business objectives · Out: Evaluation plan with success metrics
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Step 2Collect data on project outcomes post-implementation.
Entry: Recommendations have been implemented for a specified period.
Exit: A dataset containing post-implementation performance data.
In: Evaluation plan · Out: Project outcome data
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Step 3Analyze the effectiveness of the recommendations.
Entry: Outcome data has been collected.
Exit: A quantitative analysis of the project's impact.
In: Project outcome data, Baseline data · Out: Impact analysis report
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Step 4Gather qualitative feedback from stakeholders on the project's implementation and outcomes.
Entry: Impact analysis is complete.
Exit: A summary of stakeholder feedback.
In: Stakeholder list · Out: Stakeholder feedback summary
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Step 5Review and document lessons learned for future reference.
Entry: All impact data and feedback have been collected.
Exit: A documented 'lessons learned' report.
In: Impact analysis report, Stakeholder feedback summary · Out: Lessons learned document
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Cultivating an Analytics Culture and Capability
To build a sustainable, data-driven culture within HR and the broader organization by developing skills, fostering collaboration, and aligning analytics work with strategic business needs.
When to use: As an ongoing process for maturing the workforce analytics function and increasing its impact across the organization.
Step 1Secure leadership support and build supportive coalitions among influential stakeholders.
Entry: The analytics function is established and delivering initial projects.
Exit: A network of influential supporters for workforce analytics.
In: Organizational influence map, Leadership priorities · Out: List of analytics champions
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Step 2Assess and segment HR professionals' analytical skills.
Entry: A plan to build HR capability is in place.
Exit: A segmentation of the HR population by analytical comfort level.
In: HR team roster · Out: HR analytical skill segmentation
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Step 3Deliver customized training and enablement activities for each segment.
Entry: HR professionals have been segmented.
Exit: A running program of tailored analytics training.
In: HR analytical skill segmentation, Training resources · Out: Customized training plans
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Step 4Promote lifelong learning and 'extreme collaboration' across functions.
Entry: A baseline of analytical capability is established.
Exit: Ongoing cross-functional analytics projects and a continuous learning plan.
In: List of key stakeholders in other functions · Out: Cross-functional project charters
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Step 5Maintain an 'Outside-In Focus' by aligning analytics with pressing business challenges.
Entry: The analytics function has a portfolio of potential projects.
Exit: The project roadmap is aligned with current business priorities.
- Deciding which business challenges to prioritize.
In: List of current business challenges · Out: Prioritized analytics project roadmap
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Step 6Share success stories and case studies to demonstrate value and inspire adoption.
Entry: Successful projects have been completed and evaluated.
Exit: A repository of success stories and a regular communication cadence.
In: Project impact evaluation reports · Out: Internal case studies and communications
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A candidate measure
The Power of People - How Successful Organizations Use Workforce Analytics To Improve Business Performance — derived measurement candidates
Analytics Methodology Rigor
eight-step adherence audit score; percent of projects with sponsor-signed business question; percent of projects evaluated post-implementation
self-report suitability: medium
Analytics Leadership Capability
360-degree competency ratings; reporting-line proximity to CHRO; sponsorship secured per project
self-report suitability: medium
Team Skill Breadth (Six Skills for Success)
skills inventory coverage percentage; gap analysis results; ratio of specialists to project demand
self-report suitability: medium
Data Quality and Governance
percent missing values; outlier counts; data dictionary completeness; governance audit score
self-report suitability: low
Technology Enablement
technology capability checklist score; required vs available capability gap
self-report suitability: low
Operating Model Maturity
maturity rubric score; presence of governance framework; percent projects with business cases
self-report suitability: medium
Stakeholder Engagement
stakeholder engagement survey scores; communications plan completion rate; stakeholder attitude shift over time
self-report suitability: high
Project Sponsor Commitment
sponsorship behavior checklist; sponsor availability frequency; resources secured
self-report suitability: high
Demand for Workforce Analytics (Seven Forces)
count and emphasis of active forces; number of project requests by force
self-report suitability: medium
Analytical Culture in HR
distribution across savvy/willing/resistant categories; capability scan scores; attitude survey index
self-report suitability: high
Resistance to Workforce Analytics
resistance type/source assessment; count of blocked projects; skepticism survey items
self-report suitability: medium
Storytelling and Visualization Quality
audience comprehension test results; artifact quality rubric score; principle adherence (educate/enlighten/convince)
self-report suitability: medium
Recommendation Adoption and Implementation
percent recommendations accepted; percent implemented; time-to-implementation
self-report suitability: medium
Workforce Outcomes
voluntary attrition rate; engagement index; well-being incident rate; time-to-productivity
self-report suitability: medium
Business Performance
profitability percentage; revenue/sales growth; cost avoidance; project ROI
self-report suitability: none
The story
The reader An HR or business leader (or aspiring workforce analytics leader) who wants to improve business performance by using workforce data and analytics.
External problem
People decisions are made on intuition rather than evidence, and HR struggles to demonstrate measurable contribution to business outcomes.
Internal problem
The reader feels uncertain about where to start, lacks confidence in data and analytical methods, and fears being seen as non-strategic or non-analytical.
Philosophical problem
In a data-rich world, it is simply wrong to manage the organization's largest investment—its people—without rigorous, evidence-based analysis.
The plan
- Understand why workforce analytics matters and clarify what the function is called and where it reports.
- Set your direction by finding sponsors, identifying demand, and creating a vision and mission.
- Apply the Eight Step Model to run purposeful analytics projects with strong research design.
- Get a quick win, then build capability in data, technology, team skills, partners, and an operating model.
- Establish an analytics culture through enablement, overcoming resistance, and storytelling.
- Implement, evaluate, and continually communicate impact to drive lasting change.
Success
- HR makes evidence-based people decisions that demonstrably improve business performance.
- The analytics function earns credibility, secures sponsorship, and grows in scope and impact.
- The organization reduces attrition, grows sales, improves well-being, and increases profitability through people analytics.
- An analytical mindset becomes embedded across HR and the wider business.
At stake
- People decisions remain based on gut feel, leading to costly mistakes and missed opportunities.
- HR continues to be perceived as administrative and non-strategic, losing relevance.
- Competitors who embrace workforce analytics gain a sustained advantage.
- Analytics projects stall, lose sponsorship, and fail to drive any organizational change.
Chapter by chapter
ch08p01Engage with Stakeholders (part 1/2)
This chapter discusses the critical role of stakeholder engagement in workforce analytics, emphasizing how involving the right stakeholders can enhance project success and drive actionable insights.
- Stakeholder engagement is not just beneficial; it is essential for the success of workforce analytics projects.
- Strong project sponsors can provide essential resources and remove barriers, which is critical for project viability.
- Clear communication creates alignment between analytics initiatives and stakeholder expectations—this is fundamental to earning their trust and input.
- Engaging stakeholders early in the process helps ensure that analytics objectives align with business needs and strategic goals.
ch08p02Engage with Stakeholders (part 2/2)
Effective engagement with stakeholders is crucial in analytics projects; without it, even the best analyses can fail to drive actionable outcomes.
- Engagement with stakeholders is not just a formality; it is essential to the success of analytics projects.
- Effective storytelling and clear visualizations are vital for ensuring that insights lead to organizational buy-in and action.
- A strong partnership with the right sponsors can propel analytics projects forward and enhance their impact.
- Anticipating common project pitfalls can significantly increase the likelihood of successful analytics implementations.
ch11Get a Quick Win
In establishing workforce analytics within an organization, choosing a first project that delivers high impact with manageable complexity is crucial for credibility and future success.
ch16p01Establish an Operating Model (part 1/2)
This chapter argues that establishing an effective operating model for workforce analytics hinges on understanding and managing data imperfections while promoting a culture of continuous learning and improvement within organizations.
- High data quality is crucial, but the aspiration for complete perfection can lead to paralysis—progress should be valued over inaction.
- The phrase 'garbage in, garbage out' highlights the importance of assessing data quality before analysis, revealing the need for foundational data practices.
- An iterative approach to analytics encourages adaptability and fosters resilience in the face of data challenges.
- Collaborating with subject matter experts enhances the understanding of data relevance and context, driving more informed analyses.
ch16p02Establish an Operating Model (part 2/2)
This chapter explores the strategic options for staffing a workforce analytics team, outlining in-house, in-source, and outsource frameworks while assessing their respective advantages and disadvantages.
ch17Establish an Operating Model
This chapter elucidates the critical need for a structured operating model in workforce analytics, asserting the importance of aligning analytics practices with organizational strategy for enhanced decision-making and business performance.
- A well-defined operating model for workforce analytics allows organizations to focus on strategic business decisions rather than reactive problem-solving.
- Governance structures and ethical guidelines are essential for ensuring responsible use of HR data, maintaining trust within the organization.
- Regularly validating the vision and mission of the analytics team ensures continued alignment with evolving organizational goals and stakeholder expectations.
- The integration of diverse perspectives in decision-making processes leads to superior outcomes and enhances team accountability.
ch20Communicate with Storytelling and Visualization
This chapter argues that effective communication relies on the use of storytelling and visualization, engaging audiences in a manner that traditional methods cannot.
- Stories have the unique ability to engage audiences at an emotional level, making them more memorable and impactful than traditional information alone.
- The effectiveness of visual communication increases significantly when it supports rather than detracts from the narrative being presented.
- Professionals must view communication as an art that intertwines storytelling and visualization to foster genuine audience connection.
- A well-structured narrative can inspire action, turning passive listeners into engaged stakeholders.
ch21p01The Road Ahead (part 1/2)
The chapter foresees the future of workforce analytics, detailing anticipated technological advancements, emerging data sources, and the evolving structure of analytics functions within organizations.
- Workforce analytics represents a seismic shift in how organizations approach human capital management, providing opportunities for strategic insights that were previously unavailable.
- The integration of emerging data sources and technologies will redefine HR practices, making them faster, more efficient, and more responsive to employee needs.
- While the risks associated with new data sources are significant, the potential benefits of doing so can position organizations as leaders in talent management.
- An evolving workforce analytics function must include close alignment with other business areas, enhancing the overall strategic capabilities of HR.
ch21p02The Road Ahead (part 2/2)
As workforce analytics continue to evolve, the integration of diverse data sources and collaboration across functions will be pivotal for organizations to effectively utilize people data and drive business performance.
- Workforce analytics is rapidly evolving, and professionals must adapt to emerging technologies and methodologies to remain effective.
- Collaboration across business functions is essential for maximizing the impact of workforce analytics on organizational performance.
- Rigidly following established analytics maturity models may impede an organization’s ability to respond effectively to current business challenges.
- Continuous learning is a fundamental requirement for workforce analytics professionals in navigating the complexities of modern data landscapes.
Questions this book answers
- Why is workforce analytics increasingly essential for organizations?
- What should the workforce analytics function be called and where should it report?
- How do you run a workforce analytics project so it has purpose and delivers business impact?
- How do you build the team, data foundation, technology, and operating model needed for success?
- How do you overcome resistance and create an analytical culture within HR?
Glossary
- Analytics Methodology Rigor
- The extent to which workforce analytics projects are executed with disciplined adherence to a purposeful, end-to-end methodology that ensures clarity of purpose, sound research design, and actionable recommendations.
- Analytics Leadership Capability
- The competence of the workforce analytics leader to manage, challenge, integrate, and represent the team while exercising business acumen and key leadership attributes.
- Team Skill Breadth (Six Skills for Success)
- The degree to which the analytics team possesses or can access the six core skill domains required for end-to-end workforce analytics success.
- Data Quality and Governance
- The fitness-for-purpose of workforce data and the strength of governance practices ensuring relevance, accuracy, currency, and ethical/legal handling.
- Technology Enablement
- The adequacy and fit of the workforce analytics technology stack to support the function's vision, mission, and project requirements.
- Operating Model Maturity
- The degree to which the function has aligned strategy, governance, decision processes, clear roles, disciplined project management, and accountability.
- Stakeholder Engagement
- The breadth and quality of the analytics team's relationships and communication with stakeholders it serves, depends upon, and impacts.
- Project Sponsor Commitment
- The active interest, resourcing, advocacy, and accountability provided by an influential sponsor across a project's lifecycle.
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