Human resource analysis, or HR analytics, is the process of collecting and analyzing human resources data to improve the performance of an organization's workforce. This data analysis method takes the data that HR routinely collects and correlates it with its and the organization's objectives. Doing so provides evidence of how HR initiatives are contributing to the organization's goals and strategies.
It takes time and investment to bring employees to a fully productive level, but HR analytics provides data-backed insights into what's working well and what's not, so organizations can make improvements and more effectively plan for future action.
This is beneficial for understanding the cause of a company's high turnover and providing other valuable information for reducing common problems. By reducing turnover, the company can increase its revenue and productivity.
Most organizations already have data that is routinely collected, but a more comprehensive analysis is needed to truly arrive at a solution. Contrary to popular belief, HR can't simply look at the data it already has.
In and of itself, raw data can't provide any useful information, as it would be like looking at a large spreadsheet full of numbers and words. This means that without organization and direction, data seems meaningless. However, once managed, this data can help answer relevant questions. Below are some of them:
Generally, having data-backed evidence means organizations can focus on making necessary improvements and planning future initiatives.
With HR analytics' ability to answer important organizational questions without guesswork, it's no surprise that many companies using HR analytics attribute improved performance to HR initiatives.
Let's take a look at some examples using common organizational problems:
When employees resign, it's common to lack a real understanding of why this happens. Case reports are then used, although these still don't provide a solution to the problem.
Since turnover is costly in terms of lost time and profit, organizations need this information to prevent turnover from becoming an ongoing problem.
HR Analytics can:
Organizations seek candidates who not only have the right skills, but also the right attributes that match the organization's work culture and performance needs.
Reviewing hundreds or thousands of resumes and basing a hiring decision on basic information is limiting, even more so when potential candidates may be overlooked. For example, a company may discover that creativity is a better predictor of success than related work experience.
HR Analytics can:
HR Analytics is made up of several components that feed into each other. To gain the problem-solving insights that HR Analytics promises, data must first be collected. The data must then be monitored and compared with other data, such as historical information, norms, or averages. This helps identify trends or patterns. At this point, the results can be analyzed in the analytics stage. Finally, the insights must be applied to organizational decisions .
Let's take a closer look at how the process works:
HR Analytics is made up of several components that feed into each other to gain the problem-solving insights that HR Analytics promises. To do this, data must first be collected, then monitored and compared with other data, such as historical information, norms, or averages. This helps identify trends or patterns. It is at this point that the results can be analyzed in the analytics stage. Finally, the obtained knowledge must be applied to organizational decisions.
Let's take a closer look at how the process works:
Big data refers to the vast amount of information that HR collects and aggregates to analyze and evaluate key practices, including recruiting, talent management, training, and performance.
Collecting and tracking high-quality data is the first vital component of HR analytics.
Data must be easily obtainable and able to be integrated into a reporting system. Data can come from existing HR systems, learning and development systems, or new data collection methods, such as cloud-based systems, mobile devices, and even wearable technology.
The tool that collects the data must also be able to aggregate it, meaning it must offer the ability to sort and organize the data for future analysis.
In the measurement stage of HR analytics, data begins a process of continuous measurement and comparison, also known as human resources metrics. This process compares the collected data with historical norms and organizational standards. Therefore, the process cannot be based on a single snapshot of data, but requires a continuous data feed over time.
Data also needs a baseline for comparison. For example, how does an organization know what an acceptable range of absenteeism is if it isn't first defined? In HR analytics, the key metrics monitored are:
Data is collected and compared to better understand turnover, absenteeism, and hiring outcomes.
Data is monitored to determine the effectiveness and efficiency of day-to-day HR procedures and initiatives.
Process optimization:
In this area, data from both organizational performance and operational metrics are combined to identify where process improvements can be made.
The number of days it takes to post jobs and finalize hiring. This metric is monitored over time and compared to the desired organizational rate.
The total cost associated with recruiting and hiring candidates. This metric is monitored over time to track typical costs associated with hiring specific types of candidates.
This metric refers to the employee turnover rate after one year of employment within the organization. It is then monitored over time and compared to the organization's acceptable rate or target.
The number of days and frequency with which employees are away from their jobs is a metric that is monitored over time and compared to the organization's established rate.
Employee productivity and satisfaction measurement is used to gauge employees' level of engagement in their work. This can be measured through surveys, performance evaluations, or productivity measures.
The analytics stage reviews the results of the metrics reports to identify trends and patterns that may have an organizational impact. Different analytical methods are used, depending on the desired outcome. These include descriptive analytics, prescriptive analytics, and predictive analytics.
When metrics are analyzed, the findings are used as actionable information to drive more accurate organizational decisions.
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