Human Resources Analytics (HR Analytics)

What is HR Analytics?

Human Resource Analytics (HR analytics) is an area in the field of analytics that refers to applying analytic processes to the human resource department of an organization in the hope of improving employee performance and therefore getting a better return on investment. HR analytics does not just deal with gathering data on employee efficiency. Instead, it aims to provide insight into each process by gathering data and then using it to make relevant decisions about how to improve these processes.

Mick Collins, Global Vice President, Workforce Analytics & Planning Solution Strategy and Chief Expert at SAP SuccessFactors defines HR Analytics as “a methodology for creating insights on how investments in human capital assets contribute to the success of four principal outcomes: (a) generating revenue,
(b) minimizing expenses,
(c) mitigating risks, and
(d) executing strategic plans.
This is done by applying statistical methods to integrated HR, talent management, financial, and operational data.”

With the continuous influx of tech innovations challenging the workplace, HR analytics enables intelligent decision-making for better talent and business management. Equipped with analytics, HR leaders can elevate their strategy to improve the employee experience, reduce attrition, and boost the company’s bottom line.

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. It encompasses many different fields:

• 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.

Core Functions of HR Analytics

HR analytics demonstrates the causal relationship between the activities exacted by an HR department and the business outcomes that result from this activity. Although the realm of human resources analytics can involve a wide range of activities, there are generally four core functions that manifest within the field. The core functions are the acquisition, optimization, development, and paying of the employees within a business or organization. To optimize each of these core functions, human resources analytics representatives will work with managers by gaining information from them regarding the issues and problems that pertain to their unique workforce.

The difference between HR Analytics, People Analytics, and Workforce Analytics

• HR analytics: HR analytics specifically deals with the metrics of the HR function, such as time to hire, training expense per employee, and time until promotion. All these metrics are managed exclusively by HR for HR.
• People analytics: People analytics, though comfortably used as a synonym for HR analytics, is technically applicable to “people” in general. It can encompass any group of individuals even outside the organization. For instance, the term “people analytics” may be applied to analytics about the customers of an organization and not necessarily only employees.
• Workforce analytics: Workforce analytics is an all-encompassing term referring specifically to employees of an organization. It includes on-site employees, remote employees, gig workers, freelancers, consultants, and any other individuals working in various capacities in an organization.

How HR Analytics Works

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 then needs to be monitored and measured against 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 at the analytical stage.
• The final step is to apply insight to organizational decisions.

Let’s take a closer look at how the process works:

1. Collecting data: 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?
     • employee profiles
     • performance
     • data on high-performers
     • data on low-performers
     • salary and promotion history
     • demographic data
     • on-boarding
     • training
     • engagement
     • retention
     • turnover
     • absenteeism
2. Measurement: 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 the process can be made.

Human Resources Analytics Types and Processes

Human resources analytics doesn’t just encompass various fields, like competency acquisition analytics and employee churn analytics. Different types of human resources analytics can be identified and applied across these different fields. These distinct processes serve different purposes, but they all share one thing: a reliance on data. The primary types of human resources analytics are:

• Operational reporting. Operational reporting is used to examine what’s happened in an organization’s past. It relies on existing data, which is then analyzed to determine what it means for the company. For example, operational reporting related to employee churn can be used to understand why a large number of employees left an organization in the previous year. Since most companies require exit interviews, HR professionals can examine this data to determine trends.
• Advanced reporting. Advanced reporting is proactive, casting an eye toward the future rather than looking back. It’s an automated process and regularly examines relationships between variables. For example, advanced reporting related to competency acquisition can track competencies that are in demand in a company to guide future hiring. As companies become more digitally driven, they may have less need for administrative staff to handle tasks—like printing, transcribing, collating, and copying—and require more tech-savvy individuals instead.
• Strategic analytics. Strategic analytics consider financial, organization-specific, historical, or employee-driven data to inform business planning. In studying corporate culture, it might analyze the correlation between financial investment in team-building events and the results of employee satisfaction surveys. If the analysis shows a link between increased frequency of team-building events and higher employee satisfaction, it would suggest that this is money well spent.
• Predictive analytics. Predictive analytics is the most mature type of human resources analytics. Instead of just analyzing data, this approach evaluates data in order to make predictions about the future. The resulting knowledge can be used to plan ahead. For instance, a strategic look at capacity analytics may indicate that employee productivity lags around the holidays. In this case, HR can propose that the company offer added incentives, like a performance-based bonus, to keep productivity on track at that time of year.

HR Analytics Process

Incorporating the following practices can pave a smooth path for the effective integration of HR analytics:

• Create a plan. Determine the issues to focus on, ranking the most pressing ones first. Include a detailed breakdown of the HR functions and how to adjust them to overcome the company’s business challenges. Identify metrics to drive results and elevate HR functions to reach long-term goals.
• Involve data scientists. Welcoming data scientists into the process enhances HR analytics. Data scientists can monitor the quality and accuracy of the data while helping HR professionals understand the information and implement it strategically. With well-organized and clearly displayed information, HR leaders easily share the information with stakeholders to promote an agenda.
• Prepare HR personnel. Request that HR team members evaluate how influential the role of HR analytics is in developing the company’s business strategy. Once they cultivate an awareness of their standing and determine what they need to do to reach the next level, they can take steps to progress.
• Educate HR professionals. Analytics brings an abundance of AI that challenges the status quo at work, so HR professionals must educate themselves about developing technological trends. HR leaders can help HR generalists and business partners adapt to digital transformation by facilitating professional development opportunities.
• Ensure legal compliance. It’s up to HR to ensure managers, executives, individual contributors, and other HR team members understand the importance of complying with local and national labor and privacy laws. Be transparent concerning the type and amount of data the company collects. HR leaders can consult a specialist in employment law to assist them in following regulations and implementing bylaws.

HR Analytics Maturity Model

An analytics model made popular by Deloitte describes the four levels of HR analytics "maturity" — in other words, the complexity of the data analytics the company uses to solve problems. Here is how the levels are interpreted:
Level 1: operational reporting. Level 1 HR analytics is defined as using data to understand and reflect on what happened in the past—and maybe going further to draw conclusions as to why past events played out in the ways they did. The fundamentals of this level of HR analytics are understanding already available data and eventually coming to an agreement as to what the data mean for the company.
Level 2: advanced reporting. The significant difference that separates Level 2 from Level 1 is the frequency of the data reporting. The authors define this level of reporting as proactive, routine, or even automated. The top functionality at this level is simply looking at relationships between variables.
Level 3: strategic analytics. HR departments operating at Level 3 are at the beginning of a thorough analysis. These analyses may occur in the form of developing causal models or looking at how relationships between variables affect outcomes.
Level 4: predictive analytics. The highest level of the HR analytics maturity model is defined by making predictions. HR departments functioning at Level 4 are gathering data and using it not only to predict what will happen in the future but also to plan for it. An example of Level 4 operations is "using turnover, promotion, and market data to model scenarios that help with workforce planning," the authors write.

Benefits of HR Analytics

HR Analytics helps your organization become more strategic, data helps you tackle current issues and also plan better for future activities. Let’s look at some of the benefits that HR HR Analytics offers:

• Improve your hiring process: Talent acquisition is a key element of your HR process, it is an all-year-round activity. Be it hiring for a new function, a larger team, or a new role altogether, your TA team is always busy. Finding the right candidate is always a task, and when they do, one can only hope everything goes well and they actually join the organization. How many candidates actually join, and how many drop off at what stage? What job boards work best for you? How many candidates do you need to reach out to close a position? These are just some questions that you could look at resolving through analytics. This data will help you see the bigger picture and fill in whatever gaps are causing delays.
• Reduce attrition: Employee retention is becoming harder every day, especially with the younger workforce not afraid of switching jobs frequently. Conduct exit interviews, gather data, look at the reasons, and patterns and find a way to arrest the attrition rate. HR Analytics here will go a long way in identifying what are the factors contributing to attrition and what remedial measures can be taken to avoid it in the future.
• Improve employee experience: It is imperative for managers and HR reps to meet with employees regularly to understand what factors are affecting employee experiences in positive and negative ways. This is a crucial step in improving employee experience. Many organizations fail to realize that employee experience starts with hiring. Your first interaction with a candidate before hiring is equally important to any other HR-related process. Employee experience is the sum of experiences that an employee feels throughout their journey. Every step, every behavior, and every experience counts.
• Make your workforce productive: Productivity levels will always go up and down and there are a host of factors affecting that. This ranges from office infrastructure, work environment, managers and team-mates, and job satisfaction among other things. Gathering data on what’s affecting productivity will certainly arm you with data to take corrective actions. Employee engagement is a key factor affecting workforce productivity, look at improving engagement. You can start off by implementing a few employee engagement ideas and activities to boost the rate.
• Improve your talent processes: Talent processes are not only about pre-hiring, hiring or annual performance reviews, but they are also much more than that. You need to consider training, recreational activities, and counseling among others. While each organization is unique, there are some processes that should be standard, these can be regular one-on-ones, skip-level meetings, etc. HR should always be monitoring their talent processes, identify challenges and bottlenecks if any, and then work on them. It’s ideal to meet with employees, however, we understand this may not always be possible or feasible. Conducting employee surveys is a good idea, get their feedback and inputs and work on them, let them know they are being heard. Employee surveys don’t always have to only be exit surveys, do it to see what they feel about employee benefits, how employee experience is at your organization, what changes they would like to see for improving it, etc.
• Gain employee trust: Thanks to HR Analytics, you have access to data that lets you see what’s happening in the organization and how employees are perceiving it. When you are armed with data, it lets you fix what’s supposedly broken and improve future processes. You can clearly see what’s working and what’s not. When you bring about changes to processes to make them better and introduce new ones, your employees take notice. They know their feedback is valued and the management team will act on it. This is crucial to building and maintaining employee trust, a critical element to high employee engagement, employee success, and employee retention percentages.

HR Analytics does not mean buying expensive software, setting up a huge team, or long processes. You can start small – have conversations with employees, record their responses, add managers in the loop, involve various functions, make a plan, share it with everybody, and commit to it. Sharing the data is crucial to make sure everyone knows it, understands it, and suggests ideas to improve the employee experience. Use the data to drive initiatives, remedy any existing problems, and bring positive changes to the organization. HR Analytics will help you monitor and improve your employee engagement, employee retention, employee wellness, employee productivity, employee experience, and work culture.

See Also

Human Capital
Human Capital Index (HCI)
Human Resource Management (HRM)
Human Capital Management (HCM)
Human Resources
Performance Management
Applicant Tracking System (ATS)
Personnel Management
Enterprise Resource Planning (ERP)