Human resources analytics is a series of processes that collect and analyze HR data to improve organizations and the performance of their workforce.
An organization can gather data routinely, but what does the data indicate? Without direction or a process, the data may be meaningless or extraneous. HR analytics breaks down raw human-resources data into actionable insights, answering important questions in the process. These questions include:
HR aggregates data to evaluate key practices, such as recruitment, talent management, performance, and training. The kind of data that is typically collected in HR analytics includes employee profiles, as well as data associated with performance, onboarding, demographics, retention, turnover, absenteeism, promotions, and salaries.
With the correct data on hand, it can now be placed under a microscope of continuous measurement and comparison. HR analytics compares the data to historical norms and standards, building off of a constraint stream (rather than a single-moment snapshot) of information, to develop an accurate, real-time picture of HR metrics. Effective use of HR metrics in HR analysis depends heavily on pre-established baselines, so that organizations can chart their progress.
Important HR analytic metrics include:
This stage analyzes the results from metrics gathered to find trends that may impact the organization as a whole, and can be broken down into three specific forms of analysis. Descriptive analytics help interpret and quantify historical data, predictive analytics uses statistical models to forecast future opportunities, and prescriptive analytics then focuses on using reliable information and insights to anticipate possible consequences and outcomes.
With data collected, quantified, and analyzed, the resultant insights are then used to inform organizational strategy and improve decision-making capabilities.
The data useful for an HR team is a measure of employee performance, experience, and business outcomes. Metrics include pulse surveys, sentiment analysis, employee net promoter score, one-on-one meetings, and exit/stay interviews.
The return on investment increases the business value derived from making better talent decisions.
Satisfied, healthy, engaged employees are less likely to miss work. By tracking absenteeism, organizations can gain insight into employee health and happiness.
Involuntary turnover tracks the rate at which employees are terminated from their positions within the organization. This is found by calculating the number of employees who were terminated involuntarily vs. the number of employees within an organization. The involuntary turnover rate can also provide information on the recruitment strategy to analyze the quality of hires.
Whether or not a prospective hire accepts an offer of employment may depend on a number of deciding factors. However, many of these factors relate directly to a company’s talent acquisition strategy. A low offer acceptance rate may indicate critical problems in the hiring process. Acceptance rate is found by taking the number of formal job offers divided by the number of open job positions.
This metric is found by dividing revenue by the number of employees within an organization, which indicates the average revenue generated by an employee. This is a measurement of operational efficiency and how effective an organization is at generating revenue through employees.
Time to hire describes the amount of time that it takes to find and bring in new talent. A longer time to hire may indicate inefficiencies or bottlenecks on the hiring process, while a shorter time to hire can help deliver a more satisfying experience for future employees.
Training efficiency is determined by analyses of multiple data points, such as performance improvement, upward transition of employee roles, and test scores after training. Measuring training efficiency can provide better insights into the effectiveness of company training programs.
Overly expensive training programs can be nearly as problematic as ineffective ones. Measure the training expenses per employee by finding the total training expenses, and then dividing it by the total number of employees who participated in the training.
The rate at which employees choose to quit their jobs, voluntary turnover rate helps identify any issues in the employee experience. This is calculated by dividing the number of employees who have left their jobs by the number of total employees.
Time to fill describes the length of time between first advertising a job opportunity and conclusively hiring a new employee to fill the position. Organizations benefit when positions are filled quickly, and this metric can help refine strategies for a more efficient process.
Core HR systems have several data points for their analytics tools
obtained from within their HR department—some metrics may include
employee tenure, compensation, training records, performance appraisal,
employee potential and value, disciplinary actions taken, and reporting
An issue with these metrics is that the data may be disconnected and may not be entirely reliable, which is the point where data scientists play an important role in organizing scattered data and grouping buckets of relevant points.
Data gathered externally involves working with other departments within an organization, which is essential for gathering a wider perspective. Vital external data metrics generally fall into the following categories.
Getting started on the path to HR analytics is not difficult. With the right information, strategies, people, and buy-in, organizations will be able to enjoy actionable insight to optimize HR performance and processes. Consider the following best practices:
It’s crucial for HR leaders to prepare teams and organizations for workflows driven by analytics, which must occur before operational and mathematical aspects. Discussing the need for analytics and preparing your team to work with the data are two pieces that must operate in conjunction.
Data scientists are slowly becoming an integral aspect of HR teams. These key players assess analytics solutions and their viability—securing more-robust, more-detailed predictions and statistical models.
Implementing a successful small project can help convince stakeholders that HR analytics have important value to the business. These quick wins may deliver tangible, high-impact results in a shorter amount of time.
Data collected by HR teams is heavily governed by compliance standards and laws. Some legal considerations include:
Although different organizations may have different HR analytics needs, effective HR analytics solutions tend to share certain commonalities:
Although analytics has helped revolutionize nearly every aspect of modern business, HR analytics is, in many ways, still in its infancy. This means that those companies who are able to fully embrace analytics within human resources processes and tasks may enjoy a clear advantage. In the book The Practical Guide to HR Analytics: Using Data to Inform, Transform and Empower HR Decisions, the authors identify four levels of HR analytics maturity:
The first level of HR analytics uses data to reflect on the events of the past, and draw conclusions about why those events played out. This level fundamentally understands data that is already available and eventually reconciles what the data means for the company.
The difference between this level and Level 1 is how frequently data is reported. Level 2 involves proactive, routine, or automated reporting. The primary functionality is exploring relationships between variables.
This level is the start of a more-thorough analysis. Casual models, data relationships, and more come into play, and their effect on outcomes is carefully evaluated.
The final level is defined by predictive analytics. HR departments at
Level 4 gather data and apply it to predict the future and make the
right plans for it.
Unfortunately, most HR departments within organizations function entirely in Level 1, and only a third are capable of functioning at Level 2. However, for those HR departments that function at levels 3 and 4, increased efficiency, improved processes, and a greater role in business decision making are the natural result.
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