Analyzing for the People: The Whys and Hows of it
HR analytics are the present and future of the workforce. It is better to get on the bandwagon now.
Introduction
HR Analytics is defined as “a systematic collection, analysis, and interpretation of data to improve talent management decisions”.
There is a debate among scholars about what HR analytics should include. For instance, Soundararajan and Singh (2017) argue that HR analytics should focus only on predictive and prescriptive elements. Predictive analytics involves using data to forecast future trends, while prescriptive analytics provides recommendations based on those predictions.
On the other hand, some experts believe that descriptive analytics should also be considered part of HR analytics. Descriptive analytics deals with interpreting historical data to understand what has happened in the past. This broader view includes analyzing past data (descriptive) alongside predicting future outcomes (predictive) and suggesting actions (prescriptive).
Overall, it is understood that HR analytics is a continuum, and it can range from simple headcounts and calculating averages such as rate of attrition (how workforce is reducing, likely due to resignations; a concern for organizations) to progressively harder measurements of the effect a particular HR program has on organizational effectiveness.
Where a firm lies on this continuum depends on the maturity level of its HR functions, accessibility to technological resources, and the quality of raw data being generated that will be used for analysis.
Historically considered as an art, intuition was mainly at play when it came to the application of HR analytics. However, now it is generally agreed that HR analytics comprises 20% intuition (to ensure situational suitability) and 80% data analysis.
A research study carried out by MIT (2010) concluded that the highest-performing corporations have a 500% higher usage of HR analytics as compared to low performing firms.
Furthermore, research has shown that HR analytics has enabled firms to attain 8% to 15% higher results in metrics such as profits per employee, customer loyalty, client satisfaction, etc.
Usually, the management decides to cut HR expenses in order to reduce costs as the C-suite considers the HR function to be a non-strategic one. However, with the help of HR analytics, the HR function can demonstrate the business value it creates and the ROI it brings. Therefore, using HR analytics can ultimately result in higher investment in the HR function.
Types of Analytics Used in HR
During the initial period of HR analytics, the application of basic statistical procedures to derive meaning from the data was the norm.
This basic approach to HR analytics evolved to include descriptive statistics, with the new aim being validating past decisions and enhancing future ones. Under this approach, simple statistics such as measures of central tendency and measures of dispersion are used to calculate metrics like absentee rate, average compensation, revenue per employee, etc.
These statistics are used to determine patterns in data and explain relationships that describe the organization’s state at any given time, for example, high attrition rate, low productivity per employee, etc. However, these analytics do not explain the reasons behind the organization’s state.
Moreover, these analytics do not facilitate the use of triangulation (using multiple sources/methods to understand something) to determine other possible causes that might be leading to the said current state. Plus, it is hard to ascertain the validity and reliability of the data using descriptive analytics. Also, descriptive analytics do not forecast the patterns that might be in the future.
These limitations of descriptive analytics gave rise to predictive analytics, which help the firm predict future patterns like employee productivity, attrition rate, etc.
The application of predictive analysis is significant to establish causal relationships between two variables, for example, appreciation and acknowledgement causing a boost in the performance of the employees, and predicting future trends, for instance, appreciation and acknowledgement by the managers would result in high employee performance.
Predictive analytics requires clean and relevant data sets to be able to predict future trends. Therefore, data mining and data cleaning from a large amount of raw data is vital to derive accurate results. Even though predictive analytics is highly relevant and a boost from descriptive analytics, it is limited by the fact that it does not elaborate on a multi-perspective viewpoint.
Prescriptive analytics, the next phase of HR analytics, is not bound by this limitation as it provides alternative ways to attain the firm’s goals. It tells HR what should be done or which options are available to achieve the desired results.
Prescriptive analytics depends upon artificial intelligence like deep learning and machine learning to carve out possible courses of action based on historical and current data.
For example, Experian, an American-Irish MNC, has applied prescriptive analytics based-AI to identify employee attrition patterns and reduce the attrition rate. They have determined behaviors that are prevalent in former employees before they quit the firm, and based on these behaviors, they have identified high-risk employees.
Prescriptive analytics also helps reduce human error and its associated costs, thus helping HR become a strategic function.
Evolution of HR Analytics
The concept of measurement in HR was first proposed by Jac Fitz-enz in 1978. The proposal was met with a lot of disagreement and opposition from the industry and academia. Still, Fitz-enz, along with other like-minded scholars, continued to promote HR management.
Their work started with operationalizing basic HR concepts such as compensation, recruitment, etc., and enabled data collection and comparison across industry sectors and corporations and establishing HR metrics reference points (or benchmarks).
In the 1980s and 1990s, scholars chiefly focused on the rectification and enhancement of HR metrics benchmarking. Although, this facilitated comparison across firms, no practical insights were generated that could be applied to make better business decisions.
The change came with the development of technology and the advent of automation and AI, which have allowed the data to become more easily accessible.
McKinsey, for example, has used automation for data-mining, data-cleaning, and self-service, where employees do not have to chase after any particular person to get the data they want but can access it via software. Therefore, automation allows the HR team at McKinsey to save much time, which is then used for generating actionable insights.
Other possible areas of HR analytics where automation has been of help include recruitment, for example, distinguishing candidates who are more likely to accept a job offer from those who are not, identifying high-performing personnel, etc.
Metrics Used in HR Analytics
Historically considered a people function, HR has found it challenging to inculcate data literacy in its domain.
Traditionally, only simple metrics were used in HR, such as key performance indicators that focused on measuring the rate of absenteeism, average recruiting cost per hired employee, etc.
- Efficiency metrics — These metrics measure how efficiently the basic HR tasks are being performed. They have the most uncomplicated data-collection procedure. Productivity and expense metrics are some of the metrics that indicate HR efficiency. A significant drawback of efficiency metrics is that they fail to acknowledge the effect HR functions have on the overall effectiveness of the firm.
- Effectiveness Metrics — These metrics provide data on whether a specific HR program has brought about the desired result or not. For example, metrics measuring a training program would reveal whether the employees have developed the required skills or not.
- Impact Metrics — These metrics measure specific HR programs and functions and determine the ability of HR to become a strategic partner in the organization and to demonstrate a high Return on Investment (ROI). These metrics determine whether particular HR practices are enhancing a firm’s performance or not or the degree to which these practices are helping the firm develop its core competencies.
Strategy Map
A strategy map assimilates the performance indicators of every department in the firm and presents a concise picture of the current status of the organization. For the HR (or any other) department, it provides data to the employee about the part that their department plays in the overall organizational performance and the attainment of the firm’s strategic aims.
Taking the case example of SouthWest Airlines, the firm’s strategic aims include keeping expenses low. To ensure that the firm accomplishes its goals of low cost, high revenue and profits, the strategy map would outline a range of practices that align with these goals.
These might include providing excellent customer service like non-delayed flights at low costs, flying less number of planes, etc. In order for these activities to be executed successfully, there would be another range of activities mentioned in the strategy map which must be carried out efficiently. These might include well-trained and highly motivated crews.
HR Scorecard
The HR Scorecard includes metrics that are linked with the activities mentioned in the strategy map. In the case of SouthWest Airlines, the metrics measuring the activities would include the percentage of delayed flights, crew productivity rate, etc. Here effectiveness metrics are employed, and relationships between HR practices (for example — training) and leading behaviors (for example — productivity) are measured.
HR Dashboard
A dashboard provides visual data to the employee after taking into account all the metrics measured. Thus, it presents the complete picture of how well the organization is performing at any given time. Continuing the SouthWest Airlines example, the manager would be able to view the real-time performance associated with the range of activities mentioned in the strategy map and determine where improvement is required.
Future Possibilities
It is predicted that the significance of data-driven decision making in HR will be on the rise with a special focus on the promoting data literacy in the HR function and the advent of a data culture in the entire organization.
Data and metrics would also be used to deal with the issues that currently plague the HR domain, including but not limited to, the Great Resignation, 4-day workweek, flexible working hours, remote working, the gig economy, etc. The organizations will make use of prescriptive analytics to determine what factors contribute to phenomena such as quiet quitting, mid-life career transitions, etc. The focus would be on improving employee experience.
Furthermore, the analytics practices used by the firms will help them determine the effectiveness of their diversity and inclusion programs and policies. It will also help the firms gather insights about the ROI being generated from the money they put in their DEI initiatives.
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