What is HR analytics? Definition, metrics and examples

As technology advances and organizations become more data literate, the expectations for what it means to be “data-driven” keep rising.

Simply collecting information is no longer enough. Companies need to interpret it, connect it to strategic priorities, and use it to make better, evidence-based decisions.

HR analytics turns everyday workforce data into insight you can act on. From understanding why employees leave to forecasting future talent needs, it helps organizations move from reacting to problems to planning ahead.

HR analytics is not a new concept, but over the past decade a growing number of organizations have built dedicated capabilities to use workforce data for better decisions, higher productivity, and stronger employee retention.

In this guide, we will explore:

• What is HR analytics?
• HR analytics vs people analytics vs workforce analytics
• Why is HR analytics needed?
• Examples of HR analytics
• How does HR analytics work?
• Examples of HR analytics metrics
• Pros and cons of HR analytics
• Predictive HR analytics
• How to get started with HR analytics
• HR analytics best practices
• How AI is changing HR analytics
• Where HR analytics sits in Valamis
• FAQ about HR analytics

What is HR analytics?

HR analytics is the process of collecting and analyzing human resources (HR) data in order to improve an organization’s workforce performance. The process can also be referred to as talent analytics, people analytics, or workforce analytics.

It takes data that HR already collects and connects it to HR and organizational objectives. Doing so provides measured evidence of how HR initiatives contribute to the organization’s goals and strategies.

For example, if a software engineering firm has high employee turnover, it is not operating at a fully productive level. It takes time and investment to bring new employees up to speed.

HR analytics shows what is working well and what is not, so organizations can make improvements and plan more effectively. In the example above, knowing the cause of the firm’s high turnover gives it a clear place to start reducing it, and less turnover means more of its people working at full productivity.

Read: How to successfully implement learning analytics in your company

What is the difference between HR analytics, people analytics, and workforce analytics?

The three terms are often used interchangeably, and many organizations treat them as the same discipline. Where people do draw a line, it usually comes down to scope.

In practice, the same data feeds all three. Workforce analytics is often closely tied to talent management and workforce planning, and the term is also used for the software that reports on employee data. Rather than worrying about the label, start with the question you want to answer and the data that can answer it.

Why is HR analytics needed?

Most organizations already collect plenty of employee data, so why do they need a specialized form of analytics?

Raw data on its own does not provide much insight. It is like looking at a large spreadsheet full of numbers and words: without organization or direction, it appears meaningless. Once it is organized, compared, and analyzed, it starts to answer real questions, such as:

• What patterns can be revealed in employee turnover?
• How long does it take to hire employees?
• What investment is needed to get employees up to a fully productive speed?
• Which employees are most likely to leave within the year?
• Are learning and development initiatives having an impact on employee performance?

Having data-backed evidence means organizations can focus on the improvements that matter most and plan future initiatives with confidence.

What are some examples of HR analytics?

Here is how HR analytics can help with two common organizational problems.

1. Turnover

When employees quit, there is often no real understanding of why. There may be reports on individual cases, but no way of knowing if there is an overarching reason or trend.

Turnover is costly in lost time and productivity, so organizations need this insight to stop it becoming an ongoing problem. HR analytics can:

• Analyze past turnover data to identify trends and patterns in why employees quit.
• Look at current data on engagement and performance to understand how employees are doing now.
• Compare both types of data to understand the factors that lead to turnover.
• Support a predictive model that flags employees who match the patterns of those who left.
• Inform strategies that improve the work environment and engagement levels.
• Identify patterns of employee engagement, satisfaction, and performance.

2. Recruitment

Organizations are looking for candidates with the right skills and the right attributes to match their work culture and performance needs.

Basing a hiring decision on basic information from hundreds or thousands of resumes is limiting, and good candidates can be overlooked. One company may discover, for example, that creativity is a better indicator of success than related work experience. HR analytics can:

• Bring candidate data together quickly from multiple sources.
• Give a fuller picture of candidates by considering more variables, such as development potential.
• Identify candidates with attributes comparable to the organization’s top performers.
• Reduce the influence of one person’s opinion on which applicants are considered, as long as the data and models are checked for bias.
• Show how long it takes to hire for specific roles, so departments can plan ahead.
• Highlight past periods of over-hiring and under-hiring, so organizations can build better long-term hiring plans.

How does HR analytics work?

HR analytics is made up of four stages that feed into each other:

Each of these in more detail:

1. Collecting data

HR collects large amounts of information to analyze and evaluate key practices, including recruitment, talent management, training, and performance.

Collecting and tracking high-quality data is the first step, and the most important. The data needs to be easy to obtain and possible to bring into a reporting system. It can come from existing HR systems, learning and development systems, or other sources such as mobile devices.

The system that collects the data also needs to aggregate it, so it can be sorted and organized for analysis. Typical data includes employee profiles, performance, salary and promotion history, demographic data, onboarding, training, engagement, retention, turnover, and absenteeism.

2. Measurement

At the measurement stage, data is continuously measured and compared. These measures are often called HR metrics.

HR analytics compares collected data against historical norms and organizational standards. It cannot rely on a single snapshot, but needs a continuous feed of data over time.

The data also needs a baseline. How does an organization know what an acceptable absence rate is if it has not defined one?

The key areas monitored are:

• Organizational performance: data is compared to understand turnover, absenteeism, and recruitment outcomes.
• Operations: data is monitored to assess how effective and efficient day-to-day HR procedures and initiatives are.
• Process optimization: organizational performance and operations data are combined to identify where processes can improve.

What are some examples of HR analytics metrics?

Here are some common metrics, what they measure, and how the insight can be applied.

3. Analysis

The analysis stage reviews metric results to identify trends and patterns that may have an organizational impact. The method depends on the question you want to answer.

Descriptive analytics focuses on understanding historical data and what can be improved. Predictive analytics uses statistical models to forecast future risks or opportunities. Prescriptive analytics goes a step further and recommends what action to take.

At this stage, the metrics start to raise useful questions:

• Time to hire: the time between a job posting and the actual hire shows how efficient the hiring process is. Does it take too long to find the right candidate, and what could be affecting that?
• Turnover: turnover rates can show which departments are struggling with retention and the possible factors involved, such as dissatisfaction with the work environment or a lack of training support.
• Absenteeism: how often and how long employees are away, compared with the organization’s norm, can be an indicator of engagement. Because absence is costly, the metric helps HR investigate the reasons behind high rates.

4. Application

Once metrics are analyzed, the findings are used to make organizational decisions:

• Time to hire: if the job application itself is the barrier, organizations can make an informed decision about how to make it simpler and more accessible.
• Turnover: understanding why employees leave means decisions can be made to prevent it. If a lack of training support is a factor, initiatives to improve ongoing training can be put together.
• Absenteeism: understanding the reasons for long-term absence helps organizations improve the factors in the work environment that affect engagement.

What are the pros and cons of HR analytics?

Data that is routinely collected across the organization offers little value without aggregation and analysis, which is what makes HR analytics so useful. But while it helps move HR from the operational level to the strategic level, it is not without its challenges.

What is predictive HR analytics?

Predictive analytics analyzes historical data to forecast the future. The difference from standard HR analytics is how the data is used.

In standard HR analytics, data is collected and analyzed to report on what is working and what needs improvement. In predictive analytics, the data is used to make predictions about employees or HR initiatives, from which candidates are likely to succeed to who is at risk of leaving within a year.

How does it work?

Statistical techniques are used to build models that identify trends and likely future behavior. These can point to risks or opportunities that organizations can plan for.

Predictive HR examples

• Turnover: a model can estimate how likely employees are to leave within a given timeframe. Knowing who is at risk lets organizations step in early and avoid the cost of lost productivity and rehiring.
• Organizational performance: historical data can pinpoint reasons for poor performance, while predictive analytics can suggest which initiatives are most likely to improve it. If engagement is correlated with performance, organizations can focus on initiatives that raise engagement.

The benefits and challenges of predictive HR analytics

Benefits: predictive HR analytics lets organizations be proactive with their data. Instead of fixing past problems, they can head off future ones before they happen, saving time, money, and productivity.

Challenges: it requires a level of skill, technology, and investment that many organizations do not yet have. People are unpredictable, with different personalities, backgrounds, and experiences, so reducing them to an algorithm to predict their performance or future raises both practical and ethical questions.

Regulation is catching up with this. Under the EU AI Act, AI systems used in recruitment, or to make decisions about promotion, termination, or monitoring performance, are classed as high-risk. That brings requirements around transparency, data quality, and human oversight, so predictive models used in HR need clear governance.

How do you get started with HR analytics?

Data on its own does not mean much, and it wastes the time of managers who have to look at it. These four steps give an HR analytics project a clear direction from the start:

1. Start with a problem or question the organization needs to answer. For example, can we improve employee engagement by addressing skills gaps? A specific question tells you what data and analysis you need.
2. Decide what information decision-makers need. For the question above, that might be the relationship between training completion rates and employee survey results.
3. Set a baseline or goal. Make sure historical data or benchmarks are available to compare against, such as completion of a specific training course.
4. Decide how to analyze and report the results. Work out which levels and relationships to measure. Is employee satisfaction higher for people who have completed the skills training than for those who have not?

What are the best practices for HR analytics?

1. Focus on key business problems

Data is most valuable when it answers clear questions. Identify the issues or opportunities you want to tackle (for example, “Which training programs reduce time to productivity for new hires?”) so the analysis is actionable and aligned with organizational priorities.

2. Build analytics into everyday workflows

Rethink processes so decisions are informed by evidence as a matter of routine, not only in an annual report.

3. Build data and AI literacy

Make sure HR teams are comfortable interpreting data and understanding predictive models. Invest in data and AI literacy as part of your learning culture, so professionals can turn insights into decisions.

4. Maintain data quality and compliance

Reliable analytics starts with clean, accurate data. Audit regularly for completeness and consistency, and make sure all integrations comply with privacy regulations and internal policies.

5. Invest in the right tools

Know which tools you need to organize, process, combine, interpret, and present your data. Take advantage of integrations: connecting your HCM system with your LMS, for example, gives you a single view of learning and workforce data.

6. Maintain ethical standards and transparency

Respect privacy, reduce bias in predictive models, and make sure employees understand how their data is used. Ethical analytics builds trust and protects sensitive workforce information.

How is AI changing HR analytics?

In 2024, Gartner predicted that by 2028, about 33% of enterprise software applications would include AI agents.

Unlike generative AI, which creates content in response to prompts, agentic AI can make decisions and take goal-oriented actions with minimal supervision.

HR teams already manage large amounts of employee data, from performance metrics and engagement surveys to talent assessments and compliance records. Agentic AI can analyze this information continuously, uncover patterns, identify emerging skills gaps, and suggest targeted interventions.

For example, if a company rolls out a new software platform, AI could pinpoint employees who need upskilling, highlight teams lacking expertise, and recommend mentoring or temporary role adjustments to keep the rollout on track.

Agentic AI is still at an early stage, but it promises to take routine analysis off HR teams’ hands and give them more time for strategic work. As the EU AI Act shows, the more decisions these systems influence, the more important human oversight becomes.

Where does HR analytics sit in Valamis?

Valamis covers the learning side of HR analytics. It brings training completion, assessment results, certifications, and skills data together, so you can see progress and gaps by role, team, or location.

Through integrations with HR systems such as SAP SuccessFactors, that learning data can sit alongside the workforce data you already track, giving you a fuller picture of how development connects to retention and performance.