People Analytics Getting Started Guide: From Data to Decisions
HR teams are sitting on a goldmine of data. Employee demographics, performance ratings, engagement survey results, compensation details, turnover records, time-to-fill metrics, learning completion rates, and more. The problem is not a lack of data. It is that most organizations have not built the capability to turn that data into insights that drive better decisions. People analytics bridges that gap. It transforms raw HR data into evidence that helps leaders make smarter choices about hiring, development, retention, and organizational design.
This guide is for HR professionals and people leaders who want to start using data more effectively, whether you are building a people analytics function from scratch or looking to mature an existing one.
What Is People Analytics?
People analytics is the practice of collecting, analyzing, and applying data about people and work to improve business outcomes. It goes by several names: HR analytics, workforce analytics, talent analytics, or people data. Regardless of the label, the goal is the same: replace gut-feel decisions about people with evidence-based ones.
What People Analytics Is Not
It is not surveillance. Good people analytics focuses on aggregate patterns and organizational insights, not monitoring individual employees' keystrokes or bathroom breaks.
It is not just dashboards. Dashboards are a delivery mechanism, not the analytics itself. A dashboard full of metrics that nobody acts on is decoration, not analytics.
It is not only for large companies. You do not need a dedicated data science team to practice people analytics. A 50-person company can use spreadsheet analysis of turnover patterns and engagement survey data to make better decisions.
The People Analytics Maturity Model
Organizations progress through predictable stages of analytics capability. Understanding where you are helps you plan realistic next steps.
Level 1: Operational Reporting
What it looks like: Basic HR metrics tracked in spreadsheets or HRIS reports. Headcount, turnover rate, time-to-fill, demographics.
The questions it answers: "What happened?" How many people did we hire last quarter? What is our turnover rate? What is our demographic breakdown?
Most organizations are here. If you can pull a headcount report and a turnover report from your HRIS, you are at Level 1. This is the foundation, and there is nothing wrong with starting here.
Level 2: Advanced Reporting
What it looks like: Metrics combined across multiple data sources, segmented by meaningful dimensions, and tracked over time. Dashboards that update automatically.
The questions it answers: "What happened, where, and to whom?" Which departments have the highest turnover? How does engagement vary by manager? What is the relationship between tenure and performance ratings?
The key difference from Level 1: Segmentation and trend analysis. Instead of "our turnover is 18%," you can say "turnover among high-performers in engineering has increased from 8% to 14% over the last three quarters."
Level 3: Strategic Analytics
What it looks like: Statistical analysis that identifies relationships, tests hypotheses, and quantifies impact. Often involves correlation analysis, regression, and controlled experiments.
The questions it answers: "Why is it happening?" What factors predict voluntary turnover? Does our manager training program actually improve engagement scores? What is the ROI of our employee referral program versus other sourcing channels?
Level 4: Predictive Analytics
What it looks like: Machine learning models that forecast future outcomes based on historical patterns. Flight risk models, workforce demand forecasting, succession planning algorithms.
The questions it answers: "What is likely to happen?" Which employees are at highest risk of leaving in the next 6 months? How many people will we need to hire next year given projected growth? Which internal candidates are most likely to succeed in leadership roles?
Note: Level 4 is aspirational for most organizations and requires significant data infrastructure, technical talent, and ethical governance. Do not try to jump here before building a solid foundation at Levels 1-3.
Maturity Assessment
| Indicator | Level 1 | Level 2 | Level 3 | Level 4 |
|---|---|---|---|---|
| Data sources | Single (HRIS) | Multiple, integrated | Multiple, linked | Comprehensive, real-time |
| Analysis method | Manual counts | Segmentation, trends | Statistical testing | Machine learning |
| Tools | Spreadsheets | BI dashboards | Statistical software | ML platforms |
| Team | HR generalist (part-time) | Analyst (dedicated) | Data scientist + analyst | Analytics team |
| Output | Static reports | Interactive dashboards | Research insights | Predictive models |
| Decision impact | Informational | Directional | Causal understanding | Forward-looking |
Essential Metrics to Track
Do not try to measure everything at once. Start with these foundational metrics, organized by category.
Workforce Composition
| Metric | Formula | Why It Matters |
|---|---|---|
| Headcount | Total active employees | Baseline for all other metrics |
| Headcount growth rate | (Current - Prior) / Prior x 100 | Tracks organizational growth trajectory |
| Diversity breakdown | % by gender, ethnicity, age, etc. | Monitors representation and DEI progress |
| Span of control | Average direct reports per manager | Indicates management load and organizational flatness |
| Contractor ratio | Contractors / (Employees + Contractors) | Shows workforce composition and flexibility |
Talent Acquisition
| Metric | Formula | Why It Matters |
|---|---|---|
| Time to fill | Days from job posting to offer acceptance | Measures recruiting efficiency |
| Time to productivity | Days from start date to full productivity (manager assessed) | Measures onboarding effectiveness |
| Quality of hire | Average performance rating of new hires at 12 months | Validates recruiting and selection processes |
| Source effectiveness | Hires by source + quality of hire by source | Optimizes recruiting spend |
| Offer acceptance rate | Offers accepted / Offers extended x 100 | Signals compensation competitiveness and candidate experience |
| Cost per hire | Total recruiting costs / Number of hires | Measures recruiting efficiency |
Retention and Turnover
| Metric | Formula | Why It Matters |
|---|---|---|
| Overall turnover rate | Separations / Average headcount x 100 | Baseline retention health |
| Voluntary turnover rate | Voluntary separations / Average headcount x 100 | Measures controllable attrition |
| Regrettable turnover rate | High-performer voluntary separations / Average headcount x 100 | Most critical retention metric |
| First-year turnover | Employees leaving within 12 months / New hires x 100 | Signals hiring or onboarding problems |
| Turnover by manager | Voluntary turnover segmented by manager | Identifies management effectiveness issues |
Engagement and Experience
| Metric | Formula | Why It Matters |
|---|---|---|
| Employee Net Promoter Score (eNPS) | % Promoters - % Detractors | Quick gauge of overall employee sentiment |
| Engagement score | Composite from engagement survey | Comprehensive engagement health |
| Survey response rate | Completed surveys / Eligible employees x 100 | Indicates trust in the feedback process |
| Manager effectiveness score | Average manager rating from upward feedback | Links management quality to team outcomes |
Performance and Development
| Metric | Formula | Why It Matters |
|---|---|---|
| Performance distribution | % at each rating level | Checks for calibration and rating inflation |
| Internal fill rate | Internal hires / Total hires x 100 | Measures internal mobility health |
| Promotion rate | Promotions / Average headcount x 100 | Tracks career progression opportunities |
| Learning participation | Employees completing learning / Total employees x 100 | Measures development culture |
| 9-box distribution | % in each box of potential/performance grid | Supports succession and talent review |
Data Sources and Integration
Where Your Data Lives
| Data Type | Typical Source | Key Fields |
|---|---|---|
| Employee demographics | HRIS (Workday, BambooHR, etc.) | Name, role, department, location, start date, compensation |
| Recruiting data | ATS (Greenhouse, Lever, etc.) | Applications, interviews, offers, sources, time-to-fill |
| Performance data | Performance management system (Lattice, Culture Amp, etc.) | Ratings, goals, feedback, review history |
| Engagement data | Survey platform (Qualtrics, Culture Amp, etc.) | Survey scores, comments, response rates |
| Learning data | LMS (LinkedIn Learning, Degreed, etc.) | Course completions, skills, certifications |
| Compensation data | HRIS or compensation platform | Base salary, bonus, equity, total rewards |
| Time and attendance | Time tracking system | Hours worked, absences, overtime |
Integration Strategy
The biggest technical challenge in people analytics is getting data from multiple systems into one place where it can be analyzed together. You have several options:
Manual integration (Level 1): Export CSV files from each system monthly and combine them in a spreadsheet. This works for small organizations but does not scale.
HRIS reporting (Level 2): Many modern HRIS platforms offer built-in analytics modules that pull data from integrated systems. If your HRIS connects to your ATS, performance system, and engagement platform, this may be sufficient.
Business intelligence tool (Level 2-3): Tools like Tableau, Power BI, or Looker can connect to multiple data sources and provide interactive dashboards and analysis. This requires some technical setup but offers much more flexibility.
Dedicated people analytics platform (Level 3-4): Platforms like Visier, One Model, or Crunchr are purpose-built for people analytics. They provide pre-built data models, automated integration, and advanced analytics capabilities.
Building Your First Dashboard
Start simple. A single dashboard with 5-7 key metrics that leadership actually reviews and acts on is infinitely more valuable than 50 dashboards that nobody looks at.
Recommended Starter Dashboard
Executive People Dashboard - Updated monthly, reviewed in leadership team meetings.
Section 1: Workforce Overview
- Current headcount with month-over-month change
- Headcount by department (bar chart)
- Diversity breakdown (gender, ethnicity at aggregate level)
- Open positions count
Section 2: Talent Flow
- Monthly hires and separations (trend line, 12-month view)
- Voluntary turnover rate (trend line with industry benchmark)
- Regrettable turnover rate (trend line)
- Average time to fill (trend line)
Section 3: Engagement Pulse
- Latest eNPS score with trend
- Engagement score by department (heat map)
- Top 3 survey themes requiring attention
Design principles:
- One page maximum (force prioritization)
- No metric without a benchmark or trend for context
- Include 1-2 narrative bullet points explaining what the data means and what action is recommended
- Use consistent color coding (green = at/above target, yellow = needs attention, red = requires action)
Tools and Technology
For Small Teams (1-50 Employees)
| Tool | Cost | Best For |
|---|---|---|
| Google Sheets/Excel | Free | Basic reporting and analysis |
| HRIS built-in reporting (BambooHR, Gusto) | Included with HRIS | Standard HR metrics |
| Google Looker Studio | Free | Simple dashboards with data connectors |
For Growing Teams (50-500 Employees)
| Tool | Cost | Best For |
|---|---|---|
| Power BI | $10/user/month | Interactive dashboards, Microsoft ecosystem |
| Tableau | $15-$70/user/month | Advanced visualization, multiple data sources |
| ChartHop | $8+/user/month | Org design and people analytics in one |
| Lattice Analytics | Included with Lattice | Performance and engagement insights |
For Enterprise (500+ Employees)
| Tool | Cost | Best For |
|---|---|---|
| Visier | Custom pricing | Purpose-built people analytics, benchmarking |
| One Model | Custom pricing | Data integration and advanced analytics |
| Crunchr | Custom pricing | European-focused, GDPR-compliant analytics |
| Workday Prism Analytics | Add-on to Workday | Deep Workday data integration |
Building the Business Case
To get budget and executive support for people analytics, frame it in business terms.
The Cost of Not Having Analytics
Turnover costs you cannot see: Without analytics, you do not know which departments, managers, or employee segments are driving turnover until it is too late. Each preventable departure of a mid-level employee costs $50,000-$100,000.
Hiring inefficiency: Without source effectiveness data, you may be spending recruiting budget on channels that produce low-quality hires while underinvesting in channels that produce top performers.
Engagement blind spots: Without pulse survey analytics, you learn about engagement problems when they manifest as attrition, which is six to twelve months too late.
The ROI Framework
Present the business case in three tiers:
Tier 1 (Year 1): Cost avoidance. "By identifying turnover risk factors and addressing them proactively, we can reduce voluntary turnover by X%, saving $Y in replacement costs."
Tier 2 (Year 2): Efficiency gains. "By analyzing our recruiting funnel, we can reduce time-to-fill by X days and redirect $Y in sourcing spend to higher-performing channels."
Tier 3 (Year 3): Strategic value. "By building predictive workforce planning capabilities, we can anticipate talent needs 12-18 months in advance, reducing emergency hiring and enabling proactive development."
Common Pitfalls
Starting Too Big
The most common mistake is trying to build a comprehensive analytics capability all at once. Start with one business question that matters to leadership, answer it well, and build from there.
Bad approach: "We are going to build a predictive attrition model." Good approach: "Leadership is concerned about engineering turnover. Let us analyze the data to understand what is driving it and recommend specific actions."
Ignoring Data Quality
Analytics is only as good as the data it is built on. Before building dashboards, audit your data:
- Are job titles consistent across the organization?
- Is your HRIS up to date with current employee information?
- Are performance reviews completed on time with meaningful differentiation?
- Are exit interview records complete and coded consistently?
Invest time in data hygiene before investing in analytics tools.
Measuring Without Acting
Data without action is just trivia. Every metric on your dashboard should have a clear owner and a defined threshold that triggers action. If engagement scores in a department drop below a certain level, who is responsible for investigating and responding? Define this in advance.
Neglecting Privacy and Ethics
People analytics involves sensitive personal data. Establish clear governance:
- Anonymity thresholds: Never report data for groups smaller than 5-10 employees to protect individual privacy.
- Purpose limitation: Collect and analyze data only for defined, legitimate business purposes.
- Transparency: Tell employees what data you collect, how it is analyzed, and how it is used.
- Bias auditing: Regularly check whether your analytics and any predictive models produce biased outcomes across demographic groups.
- Compliance: Ensure your practices comply with GDPR, CCPA, and other applicable data protection regulations.
Forgetting the Human Element
The most analytically sophisticated insight is worthless if it is not communicated in a way that resonates with decision-makers. People analytics professionals need to be storytellers as much as data analysts. Present findings as narratives with clear recommendations, not just charts and numbers.
A 90-Day Getting Started Plan
Days 1-30: Foundation
- Audit your data. Inventory what data you have, where it lives, and its quality.
- Identify stakeholders. Who are the leaders and managers who will use analytics insights? What decisions do they need to make?
- Pick your first question. Choose one specific business question to answer with data. Make it something leadership cares about.
- Set up basic reporting. Ensure you can pull headcount, turnover, and time-to-fill reports from your HRIS.
Days 31-60: Build
- Analyze your first question. Pull the data, clean it, analyze it, and develop actionable recommendations.
- Build your starter dashboard. Create the executive people dashboard described above.
- Present findings to leadership. Share your first analysis and dashboard. Focus on insights and recommended actions, not methodology.
- Gather feedback. What questions do leaders have? What additional data would be useful?
Days 61-90: Scale
- Refine based on feedback. Update your dashboard and analyses based on stakeholder input.
- Establish a cadence. Set a monthly or quarterly rhythm for updating and reviewing people analytics.
- Identify the next question. Based on what you learned, choose the next business question to tackle.
- Build the roadmap. Draft a 12-month plan for maturing your people analytics capability, including technology needs and skill development.
Conclusion
People analytics does not have to be complicated, and you do not need a team of data scientists to get started. What you need is clean data, a specific question worth answering, the ability to present findings clearly, and the organizational will to act on what the data reveals. Start at Level 1 with basic reporting, build your first dashboard, answer your first business question, and grow from there. The organizations that use people data effectively will make better decisions about hiring, developing, and retaining talent, and in 2026's competitive labor market, that advantage compounds quickly.
The data is already in your systems. The question is whether you are going to use it.