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HR analytics is the process of collecting and analyzing Human Resource (HR) data in order to improve an organization’s workforce performance. The process can also be referred to as talent analytics, people analytics, or even workforce analytics.
This method of data analysis takes data that is routinely collected by HR and correlates it to HR and organizational objectives. Doing so provides measured evidence of how HR initiatives are contributing to the organization’s goals and strategies.
For example, if a software engineering firm has high employee turnover, the company is not operating at a fully productive level.
It takes time and investment to bring employees up to a fully productive level.
HR analytics provides data-backed insight on what is working well and what is not so that organizations can make improvements and plan more effectively for the future.
As in the example above, knowing the cause of the firm’s high turnover can provide valuable insight into how it might be reduced. By reducing the turnover, the company can increase its revenue and productivity.
Read: How to Successfully Implement Learning Analytics in Your company
Most organizations already have data that is routinely collected, so why the need for a specialized form of analytics? Can HR not simply look at the data they already have?
Unfortunately, raw data on its own cannot actually provide any useful insight. It would be like looking at a large spreadsheet full of numbers and words.
Without organization or direction, the data appears meaningless.
Once organized, compared and analyzed, this raw data provides useful insight.
They can help answer questions like:
Having data-backed evidence means that organizations can focus on making the necessary improvements and plan for future initiatives.
With the ability to answer important organizational questions without any guesswork, it is not surprising that many businesses using HR analytics are attributing performance improvement to HR initiatives.
How can HR Analytics be used by organizations?
Let’s take a look at a few examples using common organizational issues:
When employees quit, there is often no real understanding of why.
There may be collected reports or data on individual situations, but no way of knowing whether there is an overarching reason or trend for the turnover.
With turnover being costly in terms of lost time and profit, organizations need this insight to prevent turnover from becoming an on-going problem.
HR Analytics can:
Organizations are seeking candidates that not only have the right skills, but also the right attributes that match with the organization’s work culture and performance needs.
Sifting through hundreds or thousands of resumes and basing a recruitment decision on basic information is limiting, more so when potential candidates can be overlooked. For example, one company may discover that creativity is a better indicator of success than related work experience.
HR Analytics can:
HR Analytics is made up of several components that feed into each other.
Let’s take a closer look at how the process works:
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?
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.
Here are some examples of specific metrics that can be measured by HR:
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.
Here are some examples of metrics at the analytics stage:
Once metrics are analyzed, the findings are used as actionable insight for organizational decision-making.
Here are some examples of how to apply the analysis gained from HR analytics to decision-making:
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.
Pros:
Cons:
Predictive Analytics analyzes historical data in order to forecast the future. The differentiator is the way 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, data is also collected but is used to make future predictions about employees or HR initiatives.
This can include anything from predicting which candidates would be more successful in the organization, to who is at risk of quitting within a year.
Advanced statistical techniques are used to create algorithmic models capable of identifying trends and future behaviors. These future trends can describe possible risks or opportunities that organizations can leverage in long-term decision-making.
Let’s take a look at how predictive analytics can be used:
Turnover
With predictive analytics, an algorithm can be devised to predict the likelihood of employees quitting within a given timeframe. Being able to flag which employees are at risk enables organizations to step in with preventative measures and avoid the cost of losing productivity and the cost of re-hiring.
Organizational Performance
Historical data can pinpoint reasons for poor performance, but predictive analytics can make predictions about what initiatives are most likely to improve performance. If engagement levels are identified as being correlated with performance, then organizations can implement specific initiatives that boost employee engagement.
Benefits: Predictive HR analytics enables organizations to become proactive in their use of data.
Instead of fixing past problems, organizations can create a future that prevents problems and solves future challenges before they even happen. This can save on future costs, both in revenue, goals, and productivity.
Challenges: Predictive HR analytics requires a level of skill, technology and investment that many organizations do not yet have.
Many factors also need to be taken into consideration in order to make predictions about employees or potential candidates.
Human beings can be unpredictable and have different personalities, backgrounds and experiences. Slotting people into a black and white algorithm in order to make predictions about their job performance or future poses not just a risk, but an ethical question.