What Is HR Analytics? | Definition, Examples, Metrics - Valamis

How does HR analytics work?

Understanding the process of HR analytics

HR Analytics is made up of several components that feed into each other.

1. To gain the problem-solving insights that HR Analytics promises, data must first be collected.
2. The data then needs to be monitored and measured against other data, such as historical information, norms or averages.
3. This helps identify trends or patterns. It is at this point that the results can be analyzed at the analytical stage.
4. The final step is to apply insight to organizational decisions.

Let’s take a closer look at how the process works:

1. Collecting data

Big data refers to the large quantity of information that is collected and aggregated by HR for the purpose of analyzing and evaluating key HR practices, including recruitment, talent management, training, and performance.

Collecting and tracking high-quality data is the first vital component of HR analytics.

The data needs to be easily obtainable and capable of being integrated into a reporting system. The data can come from HR systems already in place, learning & development systems, or from new data-collecting methods like cloud-based systems, mobile devices and even wearable technology.

The system that collects the data also needs to be able to aggregate it, meaning that it should offer the ability to sort and organize the data for future analysis.

What kind of data is collected?

• employee profiles
• performance
• data on high-performers
• data on low-performers
• salary and promotion history
• demographic data
• on-boarding
• training
• engagement
• retention
• turnover
• absenteeism

2. Measurement

At the measurement stage, the data begins a process of continuous measurement and comparison, also known as HR metrics.

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

The data also needs a comparison baseline. For example, how does an organization know what is an acceptable absentee range if it is not first defined?

In HR analytics, key metrics that are monitored are:

Organizational performance Data is collected and compared to better understand turnover, absenteeism, and recruitment outcomes.

Operations Data is monitored to determine the effectiveness and efficiency of HR day-to-day procedures and initiatives.

Process optimization This area combines data from both organizational performance and operations metrics in order to identify where improvements in process can be made.

Examples of HR analytics metrics

Here are some examples of specific metrics that can be measured by HR:

• Time to hire – The number of days that it takes to post jobs and finalize the hiring of candidates. This metric is monitored over time and is compared to the desired organizational rate.
• Recruitment cost to hire – The total cost involved with recruiting and hiring candidates. This metric is monitored over time to track the typical costs involved with recruiting specific types of candidates.
• Turnover – The rate at which employees quit their jobs after a given year of employment within the organization. This metric is monitored over time and is compared to the organization’s acceptable rate or goal.
• Absenteeism – The number of days and frequency that employees are away from their jobs. This metric is monitored over time and is compared to the organization’s acceptable rate or goal.
• Engagement rating – The measurement of employee productivity and employee satisfaction to gauge the level of engagement employees have in their job. This can be measured through surveys, performance assessments or productivity measures.

3. Analysis

The analytical stage reviews the results from metric reporting to identify trends and patterns that may have an organizational impact.

There are different analytical methods used, depending on the outcome desired. These include: descriptive analytics, prescriptive analytics, and predictive analytics.

Descriptive Analytics is focused solely on understanding historical data and what can be improved.

Predictive Analytics uses statistical models to analyze historical data in order to forecast future risks or opportunities.

Prescriptive Analytics takes Predictive Analytics a step further and predicts consequences for forecasted outcomes.

Examples of analytics:

Here are some examples of metrics at the analytics stage:

• Time to hire – The amount of time between a job posting and the actual hire is a metric that enables HR to gain insight into the efficiency of the hiring process; it prompts investigation into what is working and what is not working. Does it take too long to find the right candidate? What factors could be impacting the result?
• Turnover – Turnover metrics that indicate the rate at which employees leave the organization after hire can be analyzed to determine what specific departments within the organization are struggling with retention and the possible factors involved, such as work environment dissatisfaction or lack of training support.
• Absenteeism – The metric indicating how often and how long employees are away from their jobs as compared to the organization’s established norm could be an indicator of employee engagement. As absenteeism can be costly to the productivity of an organization, the metric enables HR to investigate the possible reasons for high absence rates.

4. Application

Once metrics are analyzed, the findings are used as actionable insight for organizational decision-making.

Examples of how to apply HR analytics insights:

Here are some examples of how to apply the analysis gained from HR analytics to decision-making:

• Time to hire – If findings determine that the time to hire is taking too long and the job application itself is discovered to be the barrier, organizations can make an informed decision about how to improve the effectiveness and accessibility of the job application procedure.
• Turnover – Understanding why employees leave the organization means that decisions can be made to prevent or reduce turnover from happening in the first place. If lack of training support was identified as a contributing factor, then initiatives to improve on-going training can be put together.
• Absenteeism – Understanding the reasons for employee long-term absence enables organizations to develop strategies to improve the factors in the work environment impacting employee engagement.

Pros and cons of HR analytics

HR analytics is fast becoming a desired addition to HR practices.

Data that is routinely collected across the organization offers no value without aggregation and analysis, making HR analytics a valuable tool for measured insight that previously did not exist.

But while HR analytics offers to move HR practice from the operational level to the strategic level, it is not without its challenges.

Here are the pros and cons of implementing HR analytics:

Pros:

• More accurate decision-making can be had thanks to a data-driven approach, which reduces the need for organizations to rely on intuition or guess-work in decision-making.
• Strategies to improve retention can be developed thanks to a deeper understanding of the reasons employees leave or stay with an organization.
• Employee engagement can be improved by analyzing data about employee behavior, such as how they work with co-workers and customers, and determining how processes and environment can be fine-tuned.
• Recruitment and hiring can be better tailored to the organization’s actual skillset needs by analyzing and comparing the data of current employees and potential candidates.
• Trends and patterns in HR data can lend itself to forecasting via predictive analytics, enabling organizations to be proactive in maintaining a productive workforce.

Cons:

• Many HR departments lack the statistical and analytical skillset to work with large datasets.
• Different management and reporting systems within the organization can make it difficult to aggregate and compare data.
• Access to quality data can be an issue for some organizations who do not have up-to-date systems.
• Organizations need access to good quality analytical and reporting software that can utilize the data collected.
• Monitoring and collecting a greater amount of data with new technologies (eg. cloud-based systems, wearable devices), as well as basing predictions on data, can create ethical issues.