In this scenario, data can help HR professionals better understand who would be the best fit for both a specific role and with the company’s overall workplace culture. By getting a hire “right” the first time around, HR professionals can spend more of their time focusing on employee retention, and less time replacing a hire who didn’t work out.
Human Resources Analytics: What It Is and Why It’s Important
The field of human resources management has changed dramatically over time, and continues to rapidly evolve each and every day. While HR professionals are still focused primarily on the “human” aspects of running an organization, they have also become increasingly reliant on technology and data that, just a decade ago, didn’t even exist.
The emergence of this data—and the influence it holds over HR processes—has given rise to a new term and discipline: human resources analytics . Here, we explore what HR analytics entails and the critical role it is playing in the field of human resources management.
What is HR Analytics?
“Human resources analytics falls within the realm of digital HR,” says Tom Penque , lecturer for the Master of Science in Human Resources Management program at Northeastern University. “As HR technologies and platforms evolve, there is more information [available for us to] capture electronically. HR analytics is about the different ways that we capture, measure, and organize that information to create valuable insights for an organization.”
Due to the proliferation of this data, data-driven decision making has become a standard component of many human resources processes. The reasoning is simple: Access to numbers that can back up decisions regarding recruitment, employee performance, quality of human resource software, and other areas of HR, can often lead to improved business strategies .
Keep in Mind: While human resources analytics is also sometimes known as “people analytics,” “talent analytics,” and “workforce analytics,” it is important to remember that these terms refer to more specific sets of data that are used within the larger HR analytics discipline.
People Analytics Resource Library
By using people analytics you don’t have to rely on gut feeling anymore. Analytics enables HR professionals to make data-driven decisions. Furthermore, analytics helps to test the effectiveness of HR policies and different interventions.
Being able to use data in decision-making has been growing in importance throughout the global pandemic. Moving towards a post-pandemic world, there are many changes happening in employment – whether it is the growing popularity of hybrid work or the increased use of automation. In this age of disruption and uncertainty, it is vital to make the correct decisions in order to navigate our new realities.
Map out your
Path to HR Leadership
As a HR professional, you collect vast amounts of data. Unfortunately, this data often remains unused. Once you start to analyze human resource challenges by using this data, you are engaged in HR data analytics.
A few examples of HR Analytics
To get started with HR analytics, you need to combine HR data from different systems. Say you want to measure the impact of employee engagement on financial performance. To measure this relationship, you need to combine your annual engagement survey with your performance data. This way you can calculate the impact of engagement on the financial performance of different stores and departments.
Imagine that you can calculate the business impact of your learning and development budget! Or imagine that you can predict which new hires will become your highest performers in two years. Or that you can predict which new hires will leave your company in the first year. Having this information will change your hiring & selection procedures and decisions.
If you want to read more about how data can change hiring practices, check out Laszlo Bock’s book ‘Work Rules’. Laszlo Bock was the senior VP of People Operations at Google. In his book, he describes how hiring practices changed at Google after they started to analyze their recruitment data.
Predictive HR Analytics
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.
How does it work?
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.
Predictive HR Examples
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.
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.
The benefits and challenges of predictive HR analytics
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.
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.
The different types of human resource analytics used varies from company to company. Still, the five types mentioned above are some of the most important factors that will enable organizations to carry out effective HR analytics.
HR Analytics: Definition, Example, HR Metrics Dashboard and Predictive HR Analytics
Definition: Human Resource analytics (HR Analytics) is defined as the area in the field of analytics that deals with people analysis and applying analytical process to the human capital within the organization to improve employee performance and improving employee retention .
HR analytics doesn’t collect data about how your employees are performing at work, instead, its sole aim is to provide better insight into each of the human resource processes, gathering related data and then using this data to make informed decisions on how to improve these processes.
Most human resource professionals will be easily able to answer the first question for their organization. However, answering the other two questions will be tricky, especially if you don’t have a detailed data for it.
In order to answer the other two questions, as a professional, you would need to combine different data and analyze it thoroughly. Human resources tend to collect a good amount of data but are unaware of how to use this data. Well, here is the answer! Use it now to analyze your human capital and make informed decisions. As soon as an organization starts to analyze their people problems using the collected data, they are engaged in active HR analytics.
Top 5 types of HR Analytics Every Human Resource Manager Should Know
It goes without saying, that employees are an asset and vital to the success of any organization. I can say without a doubt, that any business that can attract the right resources, manage talent acquisition, and utilize their resources to the optimum is setting a long-term path for success.
1. Employee Churn: Huge investments are involved when it comes to human resources and this holds true for any business or organization. Employee churn analytics is the process of assessing your workforce turnover rate. Employee churn analytics helps predicts the future and reduces employee churn. Historical employee churn is the data collected from the past and specifies the employee churn rate since the start of employment. Predictive and historical churn data both are important for employee churn analytics.
2. Capability: Undoubtedly, the success of any business to an extent depends on the level of expertise of the employees and their skills. Capability analytics refers to the talent management process that helps you identify the core competencies of your workforce.
3. Organizational Culture: Culture is not only notorious to pinpoint but also, tough to change. It is often the collective unspoken rules, systems, and patterns of human behavior that make up for the culture of your organization or business.
Organizational culture analytics is a process of assessing and understanding better the culture at your workplace. When you know what is the culture of your organization, you can then evaluate and keep a track of the changes you might observe. Tracking culture changes helps to understand the early signs if the culture is getting toxic.
4. Capacity: It’s true, capacity affects revenue. The aim of capacity analytics is to establish how operationally efficient is your workforce. For example, in an organization that specializes in designing clothes, people are spending too much time on meetings and discussions than spending that time in more profitable work, or are individuals way too casual about their tasks? This behavioral analysis is capacity analytics that determines how much capacity they as individuals have to grow.
5. Leadership: Poor leadership is as good as no leadership at all. Poor leadership costs money, time and employee churn. Employee retention for such an organization becomes extremely difficult and prevents a business to perform at its full potential. Leadership analytics analyzes and unpacks various aspects of leadership performance at a workplace to uncover the good, bad and the ugly! Data can be collected through qualitative research and quantitative research by using a mix of both methods like surveys , polls , focus groups or ethnographic research.
What Is Human Resources Analytics?
- Capability analytics enables managers to identify core competencies their business would benefit from.
- Competency acquisition analytics assesses how well the business succeeds at acquiring those competencies.
- Capacity analytics measures the operational efficiency of individual employees.
- Employee churn analytics assesses turnover rates, which is the first step in figuring out how to decrease them.
- Corporate culture analytics examines corporate culture across an organization, attempting to pinpoint potentially toxic environments.
- Recruitment channel analytics seeks to determine where top-performing employees tend to come from.
- Leadership analytics and employee performance analytics assess the overall performance of managers and workers based on information like interviews.
Each field of human resources analytics relies on data. Consider leadership analytics. An HR professional may gain insights about a leader’s performance by gathering information through surveys, focus groups, and employee interviews (ideally anonymously). For example, employees may be asked to rank how motivated they feel on a scale of 1 to 10. The responses can then be charted using digital tools, and statistical modeling can be used to determine if a leader is successfully inspiring the team. Such information can improve the leader’s future performance. The HR department can identify gaps and organize leadership training sessions to address these gaps and build positive behaviors in leaders.
Human resources analytics is used to improve company performance and profitability in various ways. For example, employee performance analytics determines if individual workers are a good investment, specifically if they’re executing strategic plans and generating revenue while simultaneously minimizing expenses and risks. Meanwhile, recruitment channel analytics helps HR managers determine where to look for highly productive employees. This adds up to a more thoroughly vetted workforce that will better serve the company and meet its needs in terms of performance and profitability.
Examples of HR Analytics
- HR analytics can significantly improve the HR team’s decision-making skills. Companies can be more accurate with their decisions due to the data-driven approach offered by analytics. Companies will no longer need to rely on guess-work to make critical changes.|
- HR can utilize the data provided by analytics to really get to the bottom of employee turnover rates. Analytics can help businesses identify severe lapses and improve their retention rate.
- One of the leading causes of employee absenteeism is a low level of employee engagement. With the help of HR data analytics, companies can better understand employee behaviour and even strive to create changes in processes and work environments that can boost engagement.
- Analytics can go a long way in helping companies improve their recruitment processes. HR can determine the exact skillsets required by the organization to promote growth, and the profiles of current employees and potential candidates can be matched up to the standard.
- The predictive analysis tool displays trends and patterns that can help organizations stay prepared in their vision and make informed decisions for all future needs.
1. Automated recruiting
Companies are sure to begin using AI or automation to find candidates that match the job profile and organizational skillsets perfectly. This will save companies a lot of time and human effort in identifying the right match for the job.
2. Interactive training
Sometimes, inadequate or inefficient training may be one of the major causes of poor employee performance. The data collected using HR analytics can be used to create an interactive training program that is suitable for both existing employees and new recruits.
3. Personalized employee experience
Candidates are very proactive in seeking out job opportunities that offer them employee satisfaction. The level of employee satisfaction is a significant determiner to understand the employee’s intention to stay within the organization and their dedication to their job. HR analytics can personalize and enhance the employee experience, which can go a long way in tackling high turnover rates and absenteeism.
4. Performance Measurement
When companies are looking for new people to fill in certain positions, they may be overlooking some current employees who are better suited for the position. It is always better to retain and promote existing employees, as it reduces the effort and time that comes with training new candidates. For this reason, automated performance measurement can go a long way in identifying employees’ efforts and bring them to the notice of the authorities.
5. Reduce turnover rates
The job market is quite unpredictable, and in tough times, companies cannot afford to have high turnover rates. With the help of predictive reporting, companies can analyze data that is specific to employee turnover and begin taking the necessary action to decrease the high rates.
Human resource analytics
Any sort of background in business, whether it’s working, taking classes, or starting your own as an entrepreneur, can be helpful when learning about HR analytics. A background in human resources or marketing can help even more. And any experience you have working with data, analytics, or statistics can be extremely beneficial. You’ll also need experience with computers and technology since this is usually how data is gathered for HR analytics. Work experience, training, or volunteer work in the fields of psychology and communications may also help you better understand the data that drives HR analytics.
HR Analytics: Why Human Resource Departments Need BAs
It’s easy to think about business analysts taking a high-level view of a company or working on production teams to identify waste, but there are some departments that tend to get overlooked in the business analyst field. Human resources is one clear example of this. Business analysts are essential for HR teams, but few organizations have a dedicated analyst for their human resources department.
The field of HR analysis is growing along with the rise of business analysts and investment in big data. Here are some of the benefits BAs bring to HR and the challenges facing human resource departments across the globe.
What is HR Analytics?
Understanding the concept of HR analytics and the value of this tool are the first two steps toward implementing solutions in your own company. The team at Naukri RMS defines HR analytics as the science of gathering and analyzing data related to various functions in human resources. The main objective of HR analytics is to make sense of the data and turn it into valuable insights.
While some companies might not want to invest in HR analytics initially, wondering why they need a whole resource (or even a part-time resource) dedicated to reviewing data, the investment has a strong ROI when newly discovered insights are applied and result in a smoother hiring process or lower attrition levels.
One of the reasons why HR analytics has failed to become a mainstream part of business analytics is because it is called so many things, says workforce analyst Richard Rosenow. He has seen more than 40 terms to define the field, including people analytics, workforce analytics and talent analytics. It’s hard to keep track of a field when 40 people define their job with different titles and roles.
“Where the analyst’s job focuses primarily on the collection, analysis, and reporting of data, the business partner (BP) is more involved in communication with line managers and helping to solve their HR related problems,” writes Erik van Vulpen, cofounder of Analytics in HR. “In practice, the BP relies for 90% on soft skills, while the analyst relies just as much on harder (data) skills as on soft skills, if not more.”
The fact that analysts are less forward-facing does not make them any less valuable. To get an idea of how HR analysts help the operations and management team, workplace analysts Chantrelle Nielsen and Natalie McCullough looked at some case studies in an article for the Harvard Business Review, and one in particular stood out.
A company wanted to become leaner and asked its employees to do more with less. This meant a lot of change in a short period of time and HR starting seeing burnout. This created a problem for the company. The leadership team wanted to push forward but knew employees with burnout can’t handle more changes or transitions. Through people analytics, the HR team was able to identify the “burnout teams,” giving them a break while implementing change on less-stressed employees. Senior leadership was able to reach its change goals without risking employee attrition.
2 Skills Gap
Although many organizations regard HR analytics as strategically important for organizational success, today many of those same organizations face an HR analytics talent shortage. To some extent, the talent shortage can be attributed to data literacy – or the lack thereof. Historically, academic and professional HR training and development opportunities did not emphasize data-literacy skills, and this omission has left organizations today scrambling to hire external talent or to close the skills gap of existing HR professionals.
To address the HR analytics talent shortage and skills gap, organizations have, broadly speaking, two options. First, for some organizations, closing the skills gap may be as straightforward as hiring a “quant” (e.g., data scientist, statistician), provided the individual works closely with HR professionals when working with data associated with HR systems, policies, and procedures, and identifying HR-specific legal and ethical issues. Second, I would argue that for most organizations perhaps a better alternative is to close the skills gap among current HR professionals, as their HR-specific knowledge, skills, abilities, and other characteristics (KSAOs) offer tremendous value when deriving insights from HR data as well as a solid domain-specific foundation for subsequently layering on data-literacy KSAOs. Importantly, those with existing HR domain expertise presumably have working knowledge of prevailing employment and labor laws and experience with anticipating and uncovering ethical issues, both of which are necessary when acquiring, managing, analyzing, visualizing, and reporting HR data.
4 Overview of HRIS & HR Analytics
Working with data does not need to be scary or intimidating; yet, over the years, I have interacted with students and professionals who carry with them what I refer to as a numerical phobia or quantitative trauma. Unfortunately, at some point in their lives, some people begin to believe that they are not suited for mathematics, statistics, and/or generally working with data. Given these psychological barriers, a primary objective of this book is to make data analytics – and HR analytics specifically – relevant, accessible, and maybe even a little fun. In early chapters, my intention is to ease the reader into foundational concepts, applications, and tools in order to build self-efficacy in HR analytics incrementally. The tutorials in each chapter are grounded in common and (hopefully) meaningful HR contexts (e.g., validating employee selection tools). As the book progresses, I introduce more challenging statistical concepts and data-analytic techniques. Reading this book and following along with the in-chapter tutorials will not lead to expert-level knowledge and skill; however, my hope is that working through this book will do the following:
0.5.1 Rationale for Using R
Today, we have the potential to access and use a remarkable number of statistical and data-analytic tools. Examples of such tools include (in no particular order) R, Python, SPSS, SAS, Stata, MatLab, Mplus, Alteryx, Tableau, PowerBI, and Microsoft Excel. Notably, some of these programs can be quite expensive when it comes to user licensing or subscription costs, which can be a barrier to access for many.
Programming languages like R and Python have several desirable qualities when it comes to managing, analyzing, and visualizing data. Namely, both are free to use, and both have an ever-growing number of free (add-on) packages with domain- or area-specific functions (e.g., data visualizations). It is beyond the scope of this Preface to provide an exhaustive comparison of the relative merits of R versus Python; however, when it comes to the statistical analysis of data, specifically, I argue that R provides a more user-friendly entry point for beginners as well as more advanced capabilities desired by expert users, especially for ad-hoc analyses. Moreover, the integrated development environment program called RStudio (which “sits on top of” base R) offers useful workflow tools and generally makes for an inviting environment.
That said, Python has been catching up in these regards, and I wouldn’t be surprised if Python closes these gaps relative to R in the next few years. I would be remiss if I didn’t mention that the Python language is powerful and has capabilities that extend far beyond the management, analysis, and visualization of data. Fortunately, learning R makes learning Python easier (and vice versa), which means that this book can serve as a springboard for learning Python or other programming languages; in fact, RStudio allows users to create and run Python code. Finally, I believe it to be unlikely that one tool (e.g., program, language) will emerge that is ideal for every task, and thus, I encourage you to build familiarity with multiple tools so that you develop a “toolbox” of sorts, thereby allowing you to choose the best (or at least better) tool for each task.
I have written this book with current or soon-to-be HR professionals in mind, particularly those who have an interest in upskilling their data-analytic knowledge and skills.This book can provide a meaningful context for learning key data-analytic concepts, applications, and tools that are applicable beyond the HR context. Relatedly, this book may serve as a user-friendly gateway and introduction to the programming language called R for those who are interested in other non-HR domains.