What is HR Analytics? All You Need to Know to Get Started
Data is a hot commodity in today’s marketplace. While digital tools generate a vast amount of readily available information, data holds little value in its raw form. That’s where HR analytics comes in – transforming data into insights for resolving workforce and business challenges.
HR analytics allows HR professionals to make informed decisions and create strategies that will benefit employees and support organizational goals. This has a significant impact on organizational performance, leading to as much as a 25% rise in business productivity, a 50% decrease in attrition rates, and an 80% increase in recruiting efficiency.
In this article, we will explain what HR analytics is, its benefits, as well as how to get started and grow in your HR analytics capabilities.
Contents
What is HR analytics?
What is HR analytics used for?
Importance of HR analytics
HR analytics examples
Key HR metrics
Data analytics in HR: How to get started
How to transition from descriptive to predictive and prescriptive analytics in HR
HR analytics certification
FAQ
What is HR analytics?
HR analytics, also referred to as people analytics or workforce analytics, involves gathering, analyzing, and reporting HR data to drive business results. It enables your organization to better understand your workforce, make decisions based on data, and measure the impact of a range of HR metrics, ultimately improving overall business performance. In other words, HR analytics is a data-driven approach to Human Resources Management.
Although the term “HR analytics” is widely used, there is a growing trend of referencing “people analytics” as well. The two may often be used interchangeably, but technically there is a subtle difference. HR analytics originates from data housed within Human Resources and is aimed at optimizing HR functions. People analytics expands beyond HR to incorporate data from other sources within the organization, such as marketing, finance, and customer statistics, to address a wider scope of business issues.
In the past century, Human Resource Management has made a dramatic shift from an operational discipline to a more strategic one. The popularity of using the phrase Strategic Human Resource Management exemplifies this. The data-driven approach that characterizes HR analytics is in line with this development.
Analytics enables HR professionals to make data-driven decisions instead of relying solely on instinct and opinions. Furthermore, analytics helps test the effectiveness of HR policies and interventions.
Types of HR analytics
Different data analysis methods provide insight and identify trends within data. Being familiar with these methods helps you understand how analytics can contribute to HR planning and decision-making.
Here’s a brief overview of the four types of HR analytics:
- Descriptive HR analytics: Examines historical data to see what has occurred during a specific time. (Example: Annual employee turnover rate.)
- Diagnostic HR analytics: Investigates data to ascertain the causes of past occurrences and behaviors. (Example: Examining unplanned absence data to identify absenteeism drivers.)
- Predictive HR analytics: Explores current and historical data and uses statistical models and forecasts to predict future behaviors and events. (Example: Exploring recruitment data to discover the key attributes of an ideal candidate for a particular position.)
- Prescriptive HR analytics: Suggests potential future outcomes and scenarios and proposes recommendations for addressing them. (Example: Developing an algorithm that predicts what type of onboarding a new hire will need according to their experience and skill level.)
What is HR analytics used for?
Analyzing your HR data helps you draw conclusions, uncover insights, and make predictions. Data analytics in HR is used to improve HR functions in a variety of ways.
Here are a few examples:
- Identifying patterns in voluntary and involuntary employee turnover
- Assessing the recruitment effort through candidate and process data
- Evaluating talent management effectiveness with metrics such as engagement and absenteeism rates
- Determining training and development needs from a skills inventory
- Optimizing compensation and benefits through analyzing market trends, internal equity, and effectiveness of current compensation packages
- Predicting future workforce needs by analyzing current workforce demographics, skill sets, and retirement projections.
We discuss more real-life examples below.
Importance of HR analytics
Leveraging data has become essential to expanding HR’s role within organizations by moving it from an operational function to a strategic partner. Knowing the impact of HR policies helps HR align its strategy with business goals and quantify the value it adds. Increasing what HR has to offer benefits employees and makes a positive impact on business results.
Engaging in HR analytics enables HR to:
- Make better decisions that impact employees and the organization using the evidence data reveals
- Uncover and remedy inefficiencies to improve employee and organizational productivity and reduce costs
- Create a business case for HR interventions
- Evaluate the effectiveness of HR interventions and people policies
- Assess and strengthen DEIB efforts
- Be proactive in navigating change, disruption, and uncertainty.
At AIHR, we see HR analytics as identifying the people-related drivers of business performance. It takes the guesswork out of employee management and is, therefore, the future of HR. Or, to put it in the words of Edwards Deming: “Without data, you’re just another person with an opinion.”
HR analytics examples
To get an idea of how HR data analysis can make a difference in your organization, here are three companies that have successfully put HR analytics into practice:
1. HR analytics in recruitment at Google
Multinational technology company Google embraced predictive analytics in its recruitment efforts to reduce costs and shorten the hiring process.
Google had previously required candidates to endure 15 to 25 rounds of interviews and testing. However, an analysis of the hiring process revealed that successful candidates could be predicted with 86% confidence from just four interviews. This reduced the number of hours and staff required to screen applicants effectively.
In addition, Google formulated an algorithm that analyzes resumes that had been rejected for one position to source potential candidates for another opening.
HR tip
If you’d like to read more about how data can change hiring practices, we recommend Laszlo Bock’s book ‘Work Rules’. Laszlo Bock was the senior VP of People Operations at Google and describes in more detail how hiring practices changed at Google after analyzing recruitment data.
2. HR analytics in employee attrition at Under Armour
American athletic footwear and apparel company Under Armour wanted to reduce its employee attrition rate. They used an integrated workforce analytics tool to sort through data and detect the top causes of attrition. They were also able to forecast departures at Under Armor’s different locations and predicted that within the next six months, 500 out of the 5,000 employees would resign.
With the attrition drivers identified, Under Armour was able to make improvements to its employee retention efforts with enhanced people strategies, including incentives and rewards. With these interventions, the employee attrition rate ended up being 50% lower than the initial prediction.
3. HR analytics in absenteeism at E.ON
German electric utility provider E.ON needed to address an elevated absenteeism rate within its 78,000-person workforce. A team of analysts worked with the available data to find the main factors contributing to the increase in unscheduled absences.
They discovered that absences were more frequent among employees who didn’t take their allotted vacation time. With this insight, E.ON made policy changes to support and accommodate employees in planning their time off. The company encourages employees to take at least one longer period of time off per year, as well as multiple shorter breaks.
For more real-world HR analytics examples, you can refer to the case studies we published in the past. Here are links to three of them:
- Case Study 1: Key Drivers of Retail Sales Performance
- Case Study 2: Reducing Workplace Accidents Using People Analytics
- Case Study 3: How We Determined Optimal Staffing Levels
Key HR metrics
HR metrics are essential data points for tracking human capital and measuring the value of HR initiatives. There are numerous HR metrics used in HR analytics, but here is a brief overview of a few of the more common ones:
Employee turnover
This is the percentage of employees who leave the organization. This is typically calculated for a one-year period. A closer look at employee turnover can reveal helpful insights, such as which departments, positions, or managers lose the most workers.
Employee turnover = (Number of terminations during period / Number of employees at beginning of period) x 100
Absenteeism
Absenteeism refers to the habitual non-presence of an employee at their job without valid reason or notification. A high number of unplanned absences can be a sign that employees are unhappy and point out which areas of the organization need attention before it leads to more turnover.
Absenteeism rate = (Number of absent days / Total working days) x 100
Revenue per employee
This is the average revenue generated per employee, usually calculated on an annual basis. It reflects the organization’s overall efficiency.
Revenue per employee = Total revenue / total number of employees
Employee net promoter score (eNPS)
This metric reflects employee loyalty and satisfaction with the organization as an employer. The higher the score is, the more likely that employees are satisfied and willing to promote the organization and recommend that people they know work for it.
eNPS is based on the results of an employee survey. Responses are given on a scale of 0-10 as follows:
- 9-10 = Satisfied (Promoters)
- 7-8 = Neutral (Passive)
- 0-6 = Dissatisfied (Detractors)
The eNPS score is determined by subtracting the detractor percentage from the promoter percentage:
eNPS = % promoters – % detractors
Cost per hire
This metric illustrates what it costs to recruit an employee. It factors in all of the associated expenses such as recruitment advertising, background checks, referral or sign-on bonuses, and administrative and staffing costs,
Cost per hire = (Internal costs + External costs) / Total number of hires
Data analytics in HR: How to get started
HR data analysis has several phases. You must understand the process to be able to apply HR analytics effectively.
Here is a simplified overview of the five steps:
1. Asking a relevant business question
Your goal for using HR analytics should be to enable HR to impact business outcomes. For this reason, you need to start with the end goal in mind.
Clarify which area you’re focusing on and what you need the data to tell you and then put it in the form of a question. For example, if you want to optimize succession planning, the right question could be, “Which employees have the highest potential for progression and leadership?”
2. Data selection
The second step is to identify which information you need to answer the question and where you will find it. Your HR tech stack or other internal data sources should house most of what you need. However, certain circumstances may require incorporating external benchmarking data.
This stage will be cumbersome without a system that can sort and organize the data. Ideally, it should also be integrated with a reporting system.
3. Data cleaning
Once you’ve collected the right data, you’ll likely have some that are duplicated or incorrectly formatted. Without identifying and correcting this you may end up with a faulty analysis.
The data cleaning process depends on the data set, but it typically involves removing or fixing duplicate, corrupted, incorrect, or incomplete data. You should also review it for any missing data and structural errors.
4. Data analysis
Next comes summarizing and analyzing the data to reveal trends, correlations, and patterns that help you draw conclusions. This can be done using various analysis techniques or tools such as Excel, ChatGPT, R, or Python.
The results of your analysis will show what the data tells you about your original question.
5. Actionable insights
Now it’s time to interpret what the data is telling you and turn that into courses of action. Based on the findings, you can evaluate the impact of HR processes and policies and make decisions or recommendations for improving them.
How to transition from descriptive to predictive and prescriptive analytics in HR
With data now at the heart of business operations, organizations must learn to take full advantage of what it offers. It’s time to move beyond simple descriptive analytics and harness more advanced data analysis capabilities, yet the level of analytics maturity varies by company. (There are HR analytics maturity models that can assess your organization’s status in this area.)
An Oracle report that surveyed HR executives on trends in HR analytics showed the most sophisticated type of analytics being used by their organizations was as follows:
- Novice = 6%
- Descriptive = 17%
- Diagnostic = 26%
- Predictive = 32%
- Prescriptive = 19%
Organizations can choose to put their data to work more effectively by making data analytics a priority and embracing the use of diagnostic, predictive, and prescriptive analytics.
Following are some ideas for developing your organization’s HR analytics maturity:
- Develop analytical capabilities: Invest in training and development programs that will enhance the data literacy and statistical knowledge of HR employees and HR analysts. Incentivize staff to pursue external education and certification in HR data analytics.
- Assess data infrastructure: Ensure that your data infrastructure is capable of handling predictive and prescriptive analytics. It should be able to integrate data sources, clean data, create reports, and establish data governance protocols.
- Invest in the right tools: If necessary, invest money and effort in the tools it will take to ensure you can collect quality data and conduct predictive modeling. Examples include data visualization and analysis tools like Visier and Tableau, advanced HRIS, and statistical analysis tools like R and Python.
- Pilot projects and iterate: Start with small-scale pilot projects for testing predictive and prescriptive models. Gather feedback on the project and then iterate based on the insights and outcomes. Then you can scale up with initiatives that impact the entire organization.
- Establish a data-driven culture: Foster a culture that values the use of data in achieving success. Equip employees with the skill set required to use data while carrying out their responsibilities. Ensure everyone has access to data through transparency, collaboration, and experimentation across departments. Leaders should champion and set an example of data-driven decision-making.
Put simply, HR data analytics holds enormous value for an organization. By applying complex statistical analyses, HR can predict and change the future of the workforce and create real financial impact of Human Resource practices.
HR analytics certification
With HR Analytics Manager being one of the fastest growing jobs, becoming adept at HR and people analytics is a great way to expand your career opportunities. According to Global Market Insights, the worldwide HR data analytics market size was valued at $3.7 billion in 2023 and is projected to grow to $11.1 billion by 2032.
Upskilling yourself with an HR analytics certification gives you the knowledge and credentials you need to develop and succeed in this evolving HR field.
AIHR’s People Analytics Certificate Program delivers the core analytics comprehension, skills, and experience it takes to leverage HR data for improved talent decisions and initiatives that render strategic value.
Highlights of what this program equips learners with include:
- An understanding and application of key statistical concepts and analyses
- The ability to capitalize on what HR data reveals to improve business outcomes
- How to create interactive HR dashboards and reports using Microsoft PowerBI
- How to assess an organization’s analytics maturity.
This engaging, in-depth course is 100% online and self-paced. It includes competency assessments to apply what you’ve learned and case studies that bring HR analytics to life.
As an AIHR member, you’ll also have access to a community of worldwide HR professionals and our vast HR resource library of tools, templates, and playbooks.
In closing
The contemporary HR environment is both people-focused and data-oriented. HR data holds unbiased information and insights for crafting strategies and best practices that lead to more efficient and valuable HR services. This promotes higher employee engagement and productivity for better overall business achievement.
HR professionals who embrace the role of HR analytics and can decipher its insights help their organizations thrive and set themselves up for success in the future of HR.
FAQ
The four types of HR analytics are descriptive (what has happened), diagnostic (causes of what has happened), predictive (what could happen), and prescriptive (how to handle what could happen).
Which type of HR analytics to use depends on the capability level and the nature of what is needed from the data.
A Human Resources Information System (HRIS) is software that gathers and houses employee data. HR analytics is the process of examining HR data to extract insights.
The main responsibilities of an HR analyst are to collect, compile, organize, clean, analyze, and report HR data. They also develop conclusions from their analysis findings, discuss them with HR leaders, and collaborate on how to apply them to policies and programs.
Relevant skills for HR analytics include business consulting to identify critical issues, analytical skills to run the analysis, stakeholder management to bring everyone together and enable the analytics project, and storytelling and visualization in order to communicate effectively with the business and share results.
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