What Is HR Analytics and How Can It Help Your Business

Human resources (HR) analytics uses data to measure and improve HR strategies. Often called people analytics or workforce analytics, it focuses on metrics such as hiring trends, retention rates, and employee engagement to show how HR functions impact overall business performance.

"We're in an age of AI and people trying to figure out what that means in the context of the workflows they have. HR technology deals with AI not as a simple process, but as a people process," says John Phillips, VP and GM at Findem.

This perspective positions HR analytics and AI as transformative forces reshaping HR functions to be more people-centric.

What Is HR Analytics?

Human resources analytics involves collecting and interpreting key metrics to understand how HR activities influence business outcomes. Companies track specific data points to identify patterns, predict challenges, and make more objective decisions about their workforce.

While there's no shortage of HR data available, HR analytics turns that information into insights that are actually helpful. Your team can go from building strategies around gut feelings or assumptions to using analytics to uncover actionable strategies that move business forward. As AI becomes more embedded in HR systems, leveraging it can speed up data analysis and uncover deeper insights, driving more strategic decisions.

Types of HR Analytics

Using standard metrics such as turnover percentages or employee performance ratings, HR departments can identify opportunities for improvement. However, different approaches can provide varying levels of insight. HR data analytics can take several forms, including:

• Descriptive Analytics: Explores historical HR data to show you what’s already happened (e.g., turnover, absenteeism, time-to-hire metrics).
• Diagnostic Analytics: Digs deeper into the “why” behind trends. If you're seeing high turnover in a particular department, this helps you identify the root cause.
• Predictive Analytics: Looks at the bigger picture by combining current and historical data to forecast what might happen next. For example, you could uncover trends like which employees might be at risk of leaving.
• Prescriptive Analytics: Takes the assessment a step further by recommending specific actions based on predictive insights. This helps HR teams choose the best actions to achieve goals.

How Is AI Used in HR Analytics?

AI is leveling the playing field for small and mid-sized businesses by opening the door to workforce insights that were previously only available to larger companies. Adoption is growing, with more than two-thirds of small businesses in 2025 using AI regularly, up from less than half in 2024, according to the QuickBooks Small Business Insights survey.

Instead of spending hours sifting through spreadsheets, AI-powered human resources analytics can examine your data, giving you near-instant insights to spot patterns, flag issues, and identify employees with potential you might have overlooked. As Brenton Dalgliesh, Project Manager of Service at Paychex, explains: “I've seen fairly remarkable insights pulled from seemingly straightforward data time and time again. The lines between objectivity and subjectivity are often blurred when we're interpreting our own data; meanwhile, when we hold up our data in front of the AI mirror, so to speak, we often see that those lines are only blurred to us.”

When used effectively, the transformation is easy to see. For instance, AI tools for HR can help cut recruitment costs and time-to-hire by pointing you toward the best candidates faster. It can predict which staff might be considering leaving, giving you time to intervene. But most importantly, AI can connect the dots between different data points — showing you how engagement scores support performance metrics, or which factors drive productivity.

HR Analytics vs. People Analytics vs. Workforce Analytics

Although the terms may be used interchangeably, people analytics, HR analytics, and workforce analytics can involve studying different data types to support separate objectives. Some of the analytics within these categories may overlap, but understanding which company process you are targeting for review can help define your ultimate goals.

• HR Analytics: Centers around the human resources function, examining metrics such as time to hire, cost to hire, and compensation insights to determine if strategic hiring goals are met efficiently. Common uses include measuring onboarding success, tracking DEI progress, and identifying under-engaged employees.
• People Analytics: Looks beyond HR to include all people-related data, from employees to customers. Often used to focus on uniquely human concerns, such as work-life balance and customer loyalty, to determine how high-level decisions may impact these concerns.
• Workforce Analytics: The broadest of the three, this encompasses the workforce as a whole and analyzes data from contractors, full-time and part-time employees, or even AI and robotics.

Together, these three categories of analytics are used to study company performance and the ability to achieve strategic goals. They can be inter-reliant — often working best when combined.

Suppose you change an HR policy after reviewing trends in employee turnover. In that case, one future impact may be measured using people analytics through a review of an employee survey about work-life balance. Workforce analytics may also help determine whether an increase or reduction in AI use would affect employee satisfaction and possibly reduce employee attrition.

How Your Business Can Use HR Analytics

Chances are your organization already has most of the data you need for HR analytics. Many companies use HR technology to collect and maintain demographics, compensation, and performance details for compliance and reporting purposes. You can aggregate and analyze this data to identify trends, uncover potential issues, and make data-backed decisions that strengthen your workforce strategy.

You can apply HR and data analytics across nearly every stage of the employee life cycle. Here are a few common examples.

Recruiting

To understand what's working and what's not, HR teams can turn to recruitment analytics. It tracks where your best candidates are coming from and which profiles tend to succeed more often over the long term, allowing you to refine your recruitment strategies. It can also reveal gaps that slow your hiring process, like delays in candidate communication or long review times at the hiring manager level.

Hiring

With human resources analytics, you can identify hiring trends and quantify the costs of related activities. As you examine onboarding timelines, pay ranges, and offer acceptance rates, you’ll identify bottlenecks and opportunities to stay more competitive in a shifting labor market. Additionally, if costs rise unexpectedly and a budget is blown, you may need to dive further into the data to determine the cause.

Turnover

Turnover is another area that benefits from analysis. Metrics such as tenure, overtime hours, or unused vacation time can uncover patterns that negatively impact employees' work-life balance, leading to disengagement or burnout. This could also signal employee problems with managers or management styles.

Benchmarking

HR data can also benchmark your company’s performance against competitors or industry standards. Calculating compensation levels, turnover rates, and engagement metrics helps HR teams find areas where your organization might need to catch up. Armed with data, you can create goals and strategies to improve the performance of your HR function and your company as a whole.

Time and Attendance

Time and attendance analytics help organizations manage scheduling, compliance, and productivity. If you have staff working remotely from client sites or need to keep a close eye on overtime, the right HR analytics software can reveal patterns such as frequent absenteeism, overtime spikes, or scheduling inefficiencies that may impact morale or labor costs.

Employee Benefits and More

HR data analytics can simplify benefits management and help employees access the services they value most. Consider the reporting that is needed during open enrollment — an analytics-driven reporting system lets you see participation rates, pending decisions, or underleveraged benefits. This way, companies can adjust offerings to improve utilization and employee satisfaction while also gathering information needed for compliance reporting.

Supporting Company Growth

As businesses expand, HR analytics can highlight how the makeup of your workforce has changed. Comparing hiring and compensation data with growth trends allows HR teams to align staffing with business goals and financial restraints.

Benefits of HR Analytics

Grounding your HR processes in data gives leaders the confidence to make clear, consistent, and unbiased decisions. While HR is generally regarded as less quantitatively focused than other company areas, applying analytical processes provides structure and objectivity.

HR analytics builds a stronger decision-making framework. It uncovers insights, addresses challenges, and reduces reliance on guesswork.

Key benefits of HR analytics include:

• Replacing Guesswork With Clarity: HR analytics quantifies employment decisions instead of relying on subjective opinions. Adding AI can further enhance this by quickly identifying patterns.
• Identifying Opportunities for Efficiency: Reviewing past data can highlight what works and uncover inefficiencies. For example, using HR analytics in recruiting can speed up hiring and lower costs.
• Assisting With Strategic Planning: Understanding correlations helps evaluate proposed changes before implementation, predicting effects on satisfaction and work-life balance.
• Build Fairer Practices: Data-driven decisions help to reduce bias in hiring and promotions. AI tools can help eliminate outdated biases like overemphasizing degrees.

"The more data we gather, the more inclusive and impactful decisions organizations can make. The future of HR analytics lies in creating human connections that are deeper and more effective," concludes Phillips.

Challenges With Using HR Analytics

Implementing an analytical approach to HR can significantly improve decision-making, but it also can require a significant investment in time and resources. An awareness of potential challenges can help avoid missteps and ease this transition toward more data-driven processes.

Potential challenges of HR analytics include:

• Protecting Employee Privacy: HR analytics uses sensitive data, so organizations must comply with privacy laws and internal policies to reduce legal risks.
• Ensuring Data Quality and Consistency: Incomplete or inconsistent data across systems requires validation and integration — an all-in-one HR platform can help.
• Balancing AI and Automation: AI needs human oversight to monitor ethics, bias, and compliance. Regular audits help decisions stay inclusive or aligned with company values.
• Interpreting Results Effectively: Valuable insights mean little if misunderstood. Train HR staff in data literacy or partner with experts for clear, actionable analysis.

“It's human nature to take the path of least resistance and trust the advice AI offers,” says Dalgliesh. “However, we can never forget that the outputs are only as good as the model they're built upon. We owe our fellow humans the dignity of our diligence to ensure the models aren't overlooking them simply because they didn't fit an algorithmic mold."

Best Practices and Strategies for Implementing HR Analytics Tools

HR analytics can transform how small businesses make decisions, but implementing HR analytics tools can feel overwhelming. Because success depends on having the right foundation, here are some best practices to get started:

• Audit Your HR Data First: Before investing in new HR analytics tools, audit your current HR data to review how you collect and store information to spot gaps or inconsistencies.
• Set Clear Goals: Identify what you want to achieve, whether you’re looking for lower turnover, faster hiring, or better engagement. Pick one or two clear goals and decide how you’ll measure progress.
• Start Small, Then Expand: Start with straightforward HR reporting, such as tracking turnover rates or time-to-hire. Then add more complex HR analytics and AI as your team gets more comfortable with the process.
• Choose Tools That Grow With You: Cloud systems reduce IT costs, update automatically, and make HR data securely accessible anywhere. Make sure that whatever you choose integrates smoothly with your payroll, benefits, and time-tracking software to prevent data silos.
• Maintain Data Accuracy and Privacy: Standardize data entry, conduct regular audits, and follow current regulations. Your employees need to trust that their information is handled responsibly and securely.
• Train and Communicate: Build HR staff confidence through analytics training and be transparent with employees about how data supports fair, informed decisions. When people understand the why, adoption is easier.