In a world that becomes more and more data driven each day, businesses are turning to HR data analytics to be able to evolve what used to seem like an un-quantifiable aspect of their organizations.
HR data analytics is a data driven approach to highlighting successful practices, identifying weak spots or areas for improvement, and better supporting people throughout the employee lifecycle. Collecting and analyzing HR data can help a company generate more revenue, minimize their HR expenses, mitigate potential risks, and execute more successful, internal, strategic plans.
There are four levels of HR data analytics, the first three being operational reporting, advanced reporting, and strategic analytics. Ultimately, businesses should strive to achieve the fourth level, predictive analytics, where analyzing HR data allows them to predict what might happen in the future, and more importantly, plan for those predictions. HR data analytics is becoming a critical part of understanding how human capital investment strategies can improve the bottom line of a business.
What are the benefits of HR analytics?
It is undeniable that the implementation of HR data analytics can make monumental improvements to a business, ultimately leading to growth for the business and its bottom line. Specifically, HR data analytics can help enhance hiring and training practices, improve employee performance and experience, and maximize business outcomes. A business may be able to gain insights into how successful they are at attracting ideal employees, or if their employees are experiencing job satisfaction which can lead to a healthy, engaged company culture.
What metrics are measured in HR data analytics?
There are a variety of different HR data analytics metrics that a business can choose to focus on. Collaboration between the C-suite and HR leaders is essential to determining the key performance indicators (KPI) of the organization and which metrics would provide the most insight on those KPIs. Some of the more common HR data analytics metrics to consider include employee satisfaction, turnover rates, both voluntary and involuntary, absenteeism, revenue per employee, time to hire, cost per hire, and cost of HR per employee, to name a few.
Examples of HR data analytics that you can implement in your business
The goal of implementing HR data analytics is to generate actionable insights to improve a business on every level. These improvements can be categorized into the following:
Important things to measure and consider include the satisfaction of employees, voluntary and involuntary employee turnover, and work-life balance. These HR data analytics metrics can provide guidance into the work environment and company culture, both of which can impact productivity, employee engagement, and efficiency of the business.
Some key metrics might include the number of part-time vs. full-time employees, employee costs, age, and gender of employees. Measuring these things can provide insights into how to diversify a workforce, and patterns between the metrics and turnover, for example.
Collecting information involves looking at metrics such as absenteeism and revenue per employee. These things can provide insights into the ROI of investing in human capital, specifically the current workforce or individual employees. Absenteeism, specifically, may shed light on how to better the work-life balance for employees to ensure they are engaged and motivated to perform.
Types of HR data collected for this purpose might include the cost of recruitment, recruitment conversion rate, and the ratio of applications to interviews to assessments to hires. These metrics can help show how well a company is attracting ideal talent and how well their resources are being allocated within the recruitment process.
The best HR data analytics software and tools
There is an abundance of HR data analytics software and tools that businesses can adopt to gather, analyze, and present their findings depending on their organization’s personalized needs. R, Python, and Excel are of the most widely used big data and HR analytics tools. R is best suited for large data sets and features different packages which allow users to run different analyses and create easy-to-read visuals to convey findings. Python has less functionalities than R, however, it may be more user-friendly.
Finally, businesses should not neglect the basics, and perhaps the tool their HR teams are most comfortable and familiar with is Excel. These are just a couple of the wide array of platforms, tools, and software used for conducting HR data analytics and it is ultimately up to the business to determine what fits their needs best.
Implementing HR data analytics will allow businesses to stay up to date within their own HR departments, but also externally, as this new tool will undoubtedly contribute to an organization’s profitability.
Customizing HR data analytics to fit the needs of your business is critical to a successful implementation which can be achieved through thoughtful selection of key metrics and the platforms and software that will enhance your ability to gather, analyze, and present your findings.