July 30, 2021
HR analytics involves the collection and evaluation of human resources data for the purpose of enhancing HR processes and other general business operations. Unlike people analytics or workforce analytics, HR analytics aims to directly impact HR functions and strategies that directly affect revenue, expenses, risk, and planning. Both B2B and B2C companies benefit from HR analytics processes.
For example, on the one hand, HR tech companies can leverage alternative data to help fuel HR analytics software and services. On the other hand, organizations are able to take the actionable insights gained from HR analytics software to implement revenue-building strategies surrounding human capital. Let’s take a closer look at HR analytics, its data sources, benefits, and trends for 2021.
More specifically, HR analytics involves collecting and evaluating data pertaining to human resources to enhance HR processes and more general business operations. While human resource data appears to cover a broad range of information, HR data can be broken down into three categories: HR management system data, people data, and firmographic data.
Not unlike other use cases, HR analytics leverages these data categories in a variety of ways. However, it is important to note the overarching goal of HR analytics is to help smooth people-related business operations, not just to help human resources departments.
There are three main categories used to describe HR analytics data: human resources management systems data, people data, and company data. Further, many organizations refer to these data types as human capital data. When combined, these three data categories can provide businesses with hundreds of data points about their employees, company-wide structures, and financial data. When appropriately analyzed and implemented in conjunction with successful existing HR strategies, all of which can ultimately improve more than just employee retention and satisfaction. Strategies and implementations derived from analytics can also improve revenue and sales.
HR management system (HRMS) data, also known as a human resource information system (HRIS), is internal data stored and managed within a human resource software system. This data includes data such as individual employee information, benefits information, payroll information, and time cards, confidential employee and employer documentation, and more.
With HRMS data, companies are able to perform human resource analytics processes, including predictive HR analytics processes. For example, companies utilize HR management systems such as Oracle and PeopleSoft to log and manage recruiting, talent development, entrance interviews, exit interviews, and benefits information, just to name a few.
People data covers a broad range of data categories, sources, and data fields. Nonetheless, HR analytics utilizes people data such as feedback surveys, professional development information, absence information, and even wellness information in order to gain deeper qualitative insights about specific indicators for retention, turnover, and employee satisfaction.
Company data, also sometimes referred to as firmographic data, is useful within HR analytics as it can provide insights into company-wide organizational structures, revenue, sales, and more. In addition to firmographic data, which is typically purchased externally from verified third parties, some companies utilize company data that is internally collected within the organization’s larger project management systems and CRM.
Employee turnover, also known as employee churn, is a measure of how long employees work at a particular company before termination of employment. When analyzing this number over time, organizations are able to determine their average turnover rate. This provides the company with insights on how to improve this number, ultimately increasing employee retention and saving money and resources on recruiting, training, etc.
Cost-per-hire is the measurement of how much it costs a company to hire a new employee. According to a Zety study, it was found that in 2016 companies spent on average over $4,000 to hire new employees. As with understanding and improving employee turnover, tracking this number is crucial, as it can provide direct information about how much money is either being wasted or properly dedicated to the recruiting and training processes within an organization.
Acceptance rates are calculated by dividing the number of job candidates given a job offer by the number of job candidates who accepted a particular company’s job offer. Acceptance rates provide rich insights into how to optimize recruitment and talent sourcing strategies for future hires.
Opposite of employee turnover is the measure of employee retention. Employee retention is measured by dividing the number of total employees that remained employed at a particular company by the total number of employees that were hired during a given period of time.
Specifically, this information is useful for better understanding workplace health, engagement, and even the quality of hire (human resources analytics implies a significant correlation between the quality of hire and employee retention, as the quality of hire is measured with various data points). For instance, according to Achievers, organizations that provide consistent recognition see a 34% increase in employee retention, directly improving one’s employee retention rate.
In addition to collecting quantitative data surrounding hiring, recruiting, and training processes, HR analytics is also interested in analyzing qualitative information about current and potential employees. For example, company’s are able to collect and analyze their current employees’ demographic information such as age, gender, experience level, external interests, and from that generate insights about the overall cultural make-up of the company.
Further, suppose HR professionals for a particular company notices a lack of younger employees that might have fresh or different perspectives on outreach and marketing. In that case, it is in the company’s best interest to expand the age range of their employees, as a diverse workforce offers many revenue-generating benefits.
Lastly, but certainly one of the most crucial metrics of HR analytics is employee productivity, sometimes referred to as employee capability. Employee productivity is measured in a variety of ways, such as time-to-productivity, employee capability scores, competency scores, etc.
For example, a company with a high turnover rate might also be interested in measuring time-to-productivity, as a high turnover rate coupled with a high time-to-productivity rate can lead to a chaotic and unstable workplace, negatively affecting the overall productivity of the company.
Similar to measuring demographics, getting a better understanding of company culture relies on the collection of qualitative employee information. For instance, some HR professionals collect regular surveys from employees, record check-ins between employees and their managers, and track employee engagement with team building activities, etc.
The HR analytics process, similar to workforce analytics and other internal company analytics processes, involves three major steps: data collection, evaluation, and implementation. Today, many companies outsource this process to HR technology companies, which provide businesses with automated tools and software to help smooth this process. According to AIHR, some of the most popular tools utilized internally and externally by HR tech companies include R, Python, Tableau, and Excel. Additionally, To expand on this process, let’s take a look at the steps involved in HR analytics.
For many companies, the data collection process involves both the retrieval of internal and external data. Internal data collection might include collecting and storing qualitative information about employees’ names, ages, interests, and even survey responses. External or third-party data collection might consist of purchasing human capital-related data from alternative data providers or HR tech companies. Further, external data is helpful for recruiting, understanding a competitor’s workforce, and fueling AI-based HR tools that need larger samples of data beyond just one company’s internal data.
The evaluation process involves not only HR analysts but also other HR team members, as well as HR software and tools. HR tools and software are able to help human resources by collecting and analyzing people data, performance data, and business data, generating insights on the impact current systems, strategies, and team members have on the business’ overall success.
The evaluation process for quantitative data can be processed quickly with software; however, qualitative data, such as survey results, take longer to analyze but are just as important. Likewise, it is important for HR teams to develop an evaluation process that dedicates ample time to utilizing AI-based tools and software for quick insights and unpacking more lengthy qualitative information.
After the collection and analysis of HR-related data, HR teams are able to implement the actionable insights generated from the above steps. This might look like implementing more team-building events, increasing the number of employees at a given company, or even simply adding more amenities in work offices.
Companies are able to leverage HR analytics processes and software for the following benefits:
Just as many other departments within an organization turn to data-driven solutions, so are human resources departments. HR analytics trends in 2021 suggest that HR managers realize the importance of alternative data, data-driven HR tools, and AI-based solutions.
As new technology surrounding HR analytics and similar processes roll out, companies will need to shift or update their current AI-based HR tools in order to stay on top of their competition. For instance, HR tech companies such as Tableau and Sisense, provide users (companies) with HR data solutions, including data visualization dashboards, HR data reports, and management tools.
Predictive analytics, a subset of AI analytics processes, involves analyzing data in order to generate insights that will be useful in the future. Many companies are able to stay up-to-date and implement new HR strategies that are relevant today. However, only a few companies are proactive in making HR decisions. Organizations that utilize HR analytics to predict workforce trends, performance, and needs are able to stay ahead of unexpected shifts within the organization or even within a given industry. For example, companies that implemented virtual training, hiring, and working were able to stay ahead of many challenges caused by the COVID-19 pandemic.
In all, HR analytics is a gateway for companies to expand and improve their human capital and overall people-related functions and strategies. However, in order to see real growth and progress from utilizing HR analytics, companies must harness alternative data, HR analytics tools and implement the actionable insights generated from such resources.
Analytics, the process of interpreting data for a better understanding of functions and improved decision making, is used in HR to improve the business performance and operations of an organization’s employees and people-related functions.
The Human Resource (HR) Analyst will collect, compile, and analyze HR data, metrics, and statistics and apply this data to make recommendations related to recruitment, retention, and legal compliance.
HR analytics is necessary for businesses because it provides information and feedback surrounding human capital-related internal and external processes that can be improved. Overall, HR analytics provides the necessary tools to help HR teams improve HR functions and strategies surrounding revenue, expenses, risk, and planning.
Predictive analytics involves implementing statistical and mathematical techniques by analyzing and discovering insights about current and historical data to help make predictions about the future.
Some examples of HR metrics include, but are not limited to, cost-per-hire, turnover rate, retention rate, revenue per employee, and more.
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