HR departments collect valuable data in recruiting, payroll, attendance, performance evaluations, learning, engagement surveys, and employee exits. However, the key task is making it useful in decision-making to boost employees' and companies' performance.
People analytics and HR analytics can be easily confused and often used interchangeably, but there is a difference between them. People analytics encompasses more topics than HR analytics; it looks at employee behavior and organizational outcomes.
HR analytics definition: HR analytics involves collecting, analyzing, and interpreting workforce information in order to enhance HR procedures and decision-making.
This may include various areas such as recruiting, absenteeism, turnover, compensation, training, performance, and others. The focus of HR analytics is operational, including such questions as what has occurred, what area of the process is working poorly, and how can HR make this process more effective?
For instance, HR may examine hiring duration on different recruitment platforms in order to see where the investment of resources in recruiting pays off.
Commonly used HR metrics are turnover rate, retention rate, time-to-fill, absenteeism, cost per hire, offer acceptance rate, training completion, and internal promotion rate.
The definition of people analytics is more extensive. It looks at data about the employees in conjunction with business, behavioral, and organizational data, analyzing how people-related factors affect the results.
Rather than saying, "turnover went up," the organization could look at whether there is a relationship between turnover and changing managers, workload, pay, career development, engagement, or team configuration, and if any of these affect productivity or customer results.
People analytics can inform not only HR but also issues such as workforce productivity, organizational design, leadership, skills shortages, employee experience, and future needs.
HR analytics vs people analytics can be easily distinguished by comparing their main aims, scope of usage, data, and decision-making.
| Factor | HR Analytics | People Analytics |
| Key aim | HR processes and administration of the workforce | Behavior of the workforce and business results |
| Scope | Limited mainly to HR processes | Cross-functional |
| Data | HRIS, ATS, payroll, attendance, performance | HR data plus financial, operational, sales, surveying, etc. |
| Perspective | Historical and diagnostic | Diagnostic, predictive, and sometimes prescriptive |
| Main users | HR professionals | HR, executives, and business managers |
| Outcomes | Effective HR management and service | Effective workforce strategy and business success |
It is not an absolute separation. Contemporary HR departments use more and more predictive approaches, and people analytics relies on precise HR data. Thus, one can consider it as emphasis rather than two separate technologies.
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A workable HR analytics approach begins with posing a query. Data that is pertinent to the query will be collected by HR, analyzed for quality and patterns, and converted into an actionable insight.
If turnover rates are increasing, the team will analyze the exit based on the age of the employee, his/her job function, manager, location, compensation, and performance. This may reveal whether there is a problem only among recruits or within a certain category of workers.
There is no necessity for artificial intelligence when conducting HR analytics. Well-designed dashboards can help to respond to queries as long as the data itself is sound. The key lies in the connection between the analysis and decision-making.
People analytics shares the same underlying principles but typically involves more information sources and wider-ranging questions.
Let's say that customer satisfaction is falling. A people analytics study may involve looking at staffing, workload, training, engagement, organizational structure, and customer performance all at once. This is done in order to determine whether there are any workforce factors behind the business outcome.
This method becomes predictive, allowing for estimation of turnover likelihood or skills gaps. Predictions should always remain what they are, a decision-support tool.
The most valuable HR analytics methods depend upon the type of question. Organizations usually go through four levels of analysis as follows:
Descriptive analytics: Describes what happened in the past, for example, the turnover rate in the last quarter.
Diagnostic analytics: Investigates the reasons behind a certain event, for example, departments with excessive attrition rates.
Predictive analytics: Provides estimates about future events, for example, hiring needs and potential turnover.
Prescriptive analytics: Suggests certain actions using evidence and models.
It is not a race to reach level four. An organization that has inconsistent data on its employees will benefit more from refining its data quality first before applying predictive analytics techniques.
The key advantages of HR analytics stem from its ability to make HR decisions quantitative and standardized. Analytics may assist in improving recruitment efficiency, increasing retention, and spotting patterns of absenteeism, performance, pay, or training.
For instance, reviewing time-to-fill statistics for particular positions may allow HR to determine bottlenecks in the recruiting process.
The point here is to relate any metrics to the decision. A dashboard with many metrics does not always mean analytics; sometimes a few relevant ones are better.
People analytics is also able to assist leaders in determining the effect that team structure has on productivity, whether engagement is connected to retention, where there will be skill gaps, and what kinds of workforce investments should be made.
Another benefit is context. The information that there is 15% turnover has no meaning in isolation. People analytics can determine if the turnover is in key positions or with certain managers.
Employee analytics is where HR and people analytics meet in practice. The analysis can be performed on patterns in engagement, performance, career, learning, compensation, absenteeism, and retention.
For instance, an HR-focused retention analysis would show which teams historically had the most turnover. A people analytics analysis could explore why that pattern exists, whether it’s because of manager behavior, workload, career mobility, compensation, engagement, or some other factor.
Engagement analysis can become more valuable when analyzed alongside retention, productivity, performance, or any other outcome measure.
Privacy and governance are key considerations here. Companies need to control access to sensitive employee data, use the data for appropriate uses, avoid unnecessary employee monitoring at the individual level, and clearly explain how the company gathers and uses workforce data.
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In many cases, there is no either/or for most companies.
Begin with robust HR analytics. Create standard definitions, good employee files, valid dashboards, and a handful of key metrics. When those building blocks are solid, integrate the workforce data with the company's data to explore strategic issues.
When executives cannot agree on even basic staffing or turnover numbers, it is too soon for predictive analytics. When HR reporting is solid, people analytics will link workforce decisions to productivity, customer experience, risk management, and growth.
The key is in asking the right questions - not in employing the fanciest analytical tool available.
While HR analytics and people analytics share the same underlying approach to being data-driven, they have distinct functions. HR analytics is more appropriate when it comes to analyzing HR processes, whereas people analytics has a broader scope of application since it includes analysis of employee performance, organizational dynamics, and business results. In order to take advantage of both approaches, organizations need to start by developing trustworthy data sources and move on to further analysis.
HR analytics essentially focuses on the HR processes and information regarding the workforce, including recruitment, turnover, attendance, and performance, among others. People analytics, however, has a much broader focus in linking employee/workforce trends with organizational outcomes, behavior, and strategy.
Popular metrics are turnover rate, time to fill, absenteeism, retention rate, cost of hire, acceptance rate of offers, training success rate, and internal mobility. These become even more useful when HR is comparing them between appropriate groups/periods.
First, ensure consistency in defining important measures, eliminate duplicates, test for missing data, standardize employee data, and assign data ownership. Audits and data governance can increase the reliability of dashboards.
People analytics can link results from employee engagement surveys to metrics like retention, performance, absenteeism, and productivity. That way, management will be able to discover what drives engagement and whether workplace programs have any impact.
HR analytics usually deals with HR operations and operational workforce data. People analytics analyzes the impact of the behavior of employees and the dynamics of the workforce on business performance. Workforce analytics normally looks at talent supply, demand, capacity, and future workforce needs.
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