Types of Workforce Data: Turning Insights into Performance

Types of workforce data: Turning people insights into performance

Workforce data is the foundation of modern people analytics. It’s the qualitative and quantitative information that helps HR professionals, managers and business leaders understand their teams—how they work, what drives them, and where improvements can be made.

When used well, workforce data fuels data-driven decision making. It enables HR teams to forecast future workforce trends, optimise hiring processes, strengthen employee engagement, and align talent strategies with business goals.

Key takeaways

• Workforce data offers a 360° view of employee performance, engagement, and wellbeing.
• Data-driven insights support better workforce planning, retention, and compliance.
• Combining quantitative and qualitative data leads to smarter, more human HR decisions.
• Reliable systems and data hygiene are essential for trust, accuracy, and impact.

What is workforce data?

Workforce data refers to any information related to your people—from attendance and payroll to engagement scores, training records, and performance metrics.

This data helps HR departments and leaders measure the health of their organisation, identify skills gaps, and develop strategies to improve productivity, retention, and overall business performance.

Collecting and managing employee data responsibly also supports compliance, accurate payroll data, and effective workforce planning.

Types of workforce data

There are many categories of workforce data. Each type offers valuable insights into different aspects of your organisation and, when combined, provides a complete picture of your workforce health.

Quick reference: Types of workforce data

Data type	Key metrics & KPIs	Primary use case
Attendance & Time Data	Absenteeism rate, overtime %, average shift length	Identify scheduling issues, improve workforce planning
Payroll & Compensation Data	Payroll accuracy rate, total labour cost, pay equity ratio	Ensure compliance, forecast costs, build pay transparency
Leave & Absence Data	Leave utilisation rate, unplanned absence frequency	Maintain coverage, prevent burnout, plan resources
Turnover & Retention Data	Turnover rate, retention rate, cost per hire	Understand attrition causes, improve employee retention
Performance Data	Goal attainment %, productivity ratio, performance rating	Track employee performance, guide development plans
Engagement & Sentiment Data	Engagement score, satisfaction index, NPS	Measure morale, boost engagement and satisfaction
Learning & Development Data	Training completion rate, skills gap closure rate	Identify skill gaps, enhance future workforce readiness
Compliance & Governance Data	Audit success rate, data breach incidents	Maintain compliance, strengthen data protection

#1: Attendance and time data

Attendance and time data software reveals patterns in labour utilisation, punctuality, and scheduling efficiency. It includes data points such as:

• Clock-in and clock-out times
• Overtime hours
• Break durations
• Absenteeism rate.

Example KPIs: Absence rate, overtime percentage, average shift length.

Use case: By analysing attendance data, HR teams can identify trends that affect productivity or wellbeing. For example, repeated overtime may signal burnout risk, while absence trends can highlight seasonal issues or engagement challenges.

#2: Payroll and compensation data 

Payroll and compensation data ensure employees are paid accurately and on time, but they also offer financial insight into workforce costs and equity.

Typical data points:

• Wages and salaries
• Bonuses and incentives
• Deductions and benefits
• Pay equity ratios.

Example KPIs: Payroll accuracy rate, total labour cost, cost per hire.

Use case: Finance and HR departments can leverage this data for budgeting and forecasting. Accurate payroll data also strengthens trust and supports retention by ensuring transparency and compliance.

#3: Leave and absence data 

Leave and absence data provide a real-time picture of staffing availability and workforce balance.

Data points include:

• Annual and personal leave
• Parental or carers leave
• Unpaid leave
• Unplanned absences.

Example KPIs: Leave utilisation rate, unplanned absence frequency, average leave balance.

Use case: Analysing leave trends helps managers plan for coverage, reduce burnout, and maintain operational continuity. It also supports fair and transparent leave management practices.

#4: Turnover and retention data

Turnover and retention data tracks how long employees stay and why they leave. It’s a key indicator of organisational health and employee satisfaction.

Data points include:

• Attrition rate (voluntary vs involuntary)
• Average tenure
• Exit interview insights
• Retention rate by department.

Example KPIs: Employee turnover rate, retention rate, cost of turnover.

Use case: By combining quantitative metrics with qualitative data from surveys or interviews, HR professionals can pinpoint retention risks and improve talent acquisition and onboarding strategies.

#5: Performance data

Performance data tracks how individuals and teams contribute to business objectives.

Data points include:

• Key performance indicators (KPIs)
• Goal achievement rates
• Productivity metrics
• Peer and manager feedback.

Example KPIs: Goal attainment %, performance rating distribution, productivity ratio.

Use case: Performance management analytics enables HR teams to recognise high performers, provide targeted coaching, and align development plans with strategic talent management objectives.

#6: Engagement and sentiment data (qualitative)

Engagement and sentiment data capture how employees feel about their work and the organisation. This qualitative data offers context that pure metrics can’t provide.

Sources include:

• Employee engagement surveys
• Pulse checks
• Exit interviews
• Anonymous feedback tools.

Example KPIs: Engagement score, satisfaction index, Net Promoter Score (NPS).

Use case: Tracking engagement scores helps HR teams assess morale, identify cultural challenges, and design initiatives that improve employee satisfaction and retention.

#7: Learning and development data

Learning and development (L&D) data highlights skills growth and training effectiveness.

Data points include:

• Training records
• Course completion rates
• Skills assessments
• Development plans.

Example KPIs: Training completion rate, time-to-competency, skills gap closure rate.

Use case: Analysing training data allows HR teams to identify skill gaps and create targeted programs supporting employee growth and future workforce needs.

#8: Compliance and governance data

Compliance data ensures legal and ethical management of your workforce.

Data points include:

• Work eligibility records
• Certifications and licences
• Data retention policies
• Audit logs.

Example KPIs: Compliance completion rate, audit success rate, data breach incidents.

Use case: Regularly reviewing compliance data helps organisations maintain trust, meet regulatory standards, and reinforce sound data security practices.

How to collect and analyse workforce data

Collecting workforce data starts with the right systems and processes. A connected HR tech ecosystem ensures accuracy, visibility, and real-time insight. Common data sources include:

• HRIS (Human Resource Information Systems) for core HR data and payroll
• Applicant tracking systems (ATS) for recruitment analytics
• Performance management systems for KPIs and reviews
• Engagement and survey tools for qualitative data
• Learning management systems (LMS) for training data.

Best practices for data collection and analysis: 

• Automate data capture where possible to reduce manual entry errors
• Validate and clean data regularly to maintain accuracy and data hygiene
• Establish clear access control and data retention policies
• Protect employee privacy with secure data storage and encryption
• Use workforce analytics software to interpret data and generate actionable insights.

How to apply workforce data

Once collected, workforce data becomes a strategic asset. By interpreting data through workforce analytics, HR teams can uncover trends and make data-driven decisions that improve business outcomes.

Practical applications include: 

• Forecasting future workforce trends to guide recruitment and training
• Improving employee engagement and retention through targeted initiatives
• Enhancing performance management and development planning
• Supporting diversity, equity and inclusion reporting
• Aligning workforce planning with broader business strategies.

Example: Using predictive analytics on historical data can help forecast turnover risk, enabling proactive retention actions before key talent leaves.

Unlock the power of workforce data with OneAdvanced

Managing employee data doesn’t have to be complex. With OneAdvanced’s workforce management solutions (including Time & Attendance), HR teams can centralise data collection, analyse performance metrics, and uncover valuable insights to drive stronger business performance and a more engaged workforce.