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Building a People Analytics Strategy: A Roadmap for Getting Started

As companies work to navigate changing market conditions and increasing competition for talent, people data and analytics are playing a growing role in how organizations develop strategy and make decisions. By leveraging employee data and analytics, HR and business leaders can gain valuable insights into topics like engagement, performance, retention and the overall employee experience. However, establishing a successful people analytics function takes careful planning and execution of the right strategic steps.

Today we will explore creating an effective people analytics roadmap, then dive deeper into each component and provide real-world examples.

Developing the Foundation

The first step in any people analytics initiative is to lay the proper groundwork. This involves clarifying objectives, assessing existing capabilities, and gaining organizational support.

  • Setting Objectives: It's important to start by defining clear goals and priorities for the people analytics program. Are you looking to improve engagement, reduce turnover, inform hiring decisions or some combination? Establishing a focused set of 2-3 objectives helps orient efforts and ensure resources are allocated appropriately. For example, a software company found recruiting and retaining engineers was a major challenge impacting growth. Their people analytics program prioritized understanding factors influencing engineer satisfaction and likelihood to stay or leave.

  • Assessing Capabilities: Take inventory of existing HR systems, data collection practices, analytic skills and support from leadership. This helps identify gaps and determines requirements for people, process and technology investments. A large retailer evaluated their systems and discovered engagement survey results were spread across multiple databases with inconsistent formats, making analysis difficult. They streamlined collection and storage to enable easier exploration of trends.

  • Gaining Buy-In: Clear communication regarding objectives, benefits and resource needs helps gain support across HR, business and executive leadership. Their input and sponsorship is vital for success long-term. For example, an engineering-focused company outlined how people analytics could help their top priority of engineering staffing. This resonated with leadership and helped secure funding to hire additional data scientists.

Obtaining and Preparing Data

The next step involves collecting, integrating and organizing the necessary data sources to enable insightful analysis.

  • Identifying Data Sources: Combine structured and unstructured data from sources like HR systems, survey results, performance reviews, attrition records, calendars, emails and more. Consider both employee and business metrics. As an example, an insurance provider merged employee records with customer satisfaction and sales data. This uncovered connections between staff engagement and client retention.

  • Cleaning and Linking Data: Often data comes from disparate systems and needs cleaning, standardizing, de-duplicating and linking to create a single, usable dataset. Invest in data governance processes to ensure quality. A software company might combine records from multiple HR databases containing redundant or conflicting data into a single, consistent employee master file before analysis began.

  • Addressing Privacy and Compliance: Develop policies and safeguards to ensure proper handling of sensitive employee information according to applicable regulations like GDPR. Consider anonymizing or aggregating data where possible. To protect individual privacy, an insurance company could remove identifying fields and aggregated results when linking individual survey responses to business outcomes.

Performing Analysis

Once the foundational work is complete, analysis can begin. The goal is to test hypotheses, discover insights and measure impacts.

  • Exploratory Analysis: Start with exploratory techniques like visualizations to uncover patterns, outliers, relationships and form new hypotheses without preconceived notions. Iterate and refine as understanding improves. Visualizing responses to an onboarding survey question about manager support, a software company might notice a spike in negative responses for employees hired in a specific quarter. This led them to examine onboarding processes during that time.

  • Testing Causal Relationships: Move beyond correlational analysis to test impact and attribute causation using techniques like regression modeling and control groups. This provides stronger evidence for decision-making. Through A/B testing different onboarding program structures, the insurance company found the condition offering more 1:1 manager time led to a 15% increase in 1-year retention rates versus the control, demonstrating a clear impact.

  • Benchmarking Performance: Compare metrics like engagement, retention and productivity to external industry benchmarks and over time periods. This identifies areas of strength or underperformance requiring investigation. The retailer benchmarked store manager performance metrics against competitors in their region. Stores in the bottom quartile received coaching to identify and address local issues driving relatively low scores.

  • Communicating Insights: Finalize the analysis phase by communicating meaningful, actionable insights and recommendations to stakeholders. Support decision-making with compelling narratives and visualizations. During quarterly business reviews, the engineering company presented findings linking higher compensation to project completion rates above roadmap targets. This supported a proposal to adjust salary bands for experienced engineers.

  • Implementing Actions: The ultimate goal is applying analytics insights to make tangible improvements. Stakeholder input and pilot tests help refine interventions before wide deployment. Monitor resulting business and people impacts. Following an audit revealing lack of diversity in technical roles discouraged some groups, the software firm piloted targeted recruiting, internships, and mentoring programs. Six months later, diversity metrics increased by 10% and employee surveys showed growing inclusion.

  • Continuous Improvement: Sustain the people analytics function through ongoing data collection, continual skill development and iterative refinement based on learnings. Capture additional use cases and gain endorsement as the value becomes apparent. Two years after launch, the insurance provider expanded their program by adding location data to model geographic differences in attrition drivers. This informed localized retention efforts proving even more impactful. Gradually, people analytics became engrained in regular decision-making.


Establishing an effective people analytics program requires strategic planning, cross-functional collaboration and persistence over time. By following a roadmap approach to lay the groundwork, obtain and prepare the right data sources, conduct insightful analysis and communicate actionable results, organizations can gain valuable objective insights to strengthen both their people and business strategies. Continual skill development and refinement further compounds these benefits to help companies outperform through data-driven people decisions.


Jonathan H. Westover, PhD is Chief Academic & Learning Officer (HCI Academy); Chair/Professor, Organizational Leadership (UVU); OD Consultant (Human Capital Innovations). Read Jonathan Westover's executive profile here.



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