HR analytics is the process of collecting and analyzing Human Resource (HR) data in order to improve an organization’s workforce performance. The process can also be referred to as talent analytics, people analytics, or even workforce analytics.
This method of data analysis takes data that is routinely collected by HR and correlates it to HR and organizational objectives. Doing so provides measured evidence of how HR initiatives are contributing to the organization’s goals and strategies.
HR analytics provides data-backed insight on what is working well and what is not so that organizations can make improvements and plan more effectively for the future.
Utilizing data relevant to HR strategies enables HR leaders to identify successful practices and pinpoint weak areas in need of improvement. HR analytics enables strategic decision-making that can drive business solutions through improving:
- Create a plan. Determine the business issues to focus on, ranking the most pressing ones first. Include a detailed breakdown of the HR functions and how to adjust them to improve the business problems. Identify metrics to use that will promote results and elevate HR functions to reach long-term goals.
- Involve data scientists. Welcoming data scientists into the process enhances HR analytics immensely. Data scientists can monitor the quality and accuracy of the data and help HR professionals implement the data to their benefit; using the information to prove a point or support a game plan is a crucial aspect of HR analytics. Furthermore, data scientists can coach and instruct employees through the nuances of the HR analytics process.
- Prepare HR personnel. Request that HR personnel evaluates the current analytics level of the company. 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 equip themselves with the knowledge to ride the oncoming tech waves. HR leaders can help HR generalists and business partners adapt to the digital transformation by facilitating their analytics education. In this way, employees can gradually acclimate to the rise of analytics in the work culture.
- Ensure legal compliance. Explain the legal guidelines to managers, executives, and HR personnel to avoid breaching employees’ rights and privacy. It’s crucial to behave with transparency concerning the type and amount of data that the company collects. HR leaders should consult a specialist in employment law to assist them in following regulations and implementing bylaws.
An article titled ‘The measurement imperative’ proposed the idea of measuring the impact of HR activities with collected data on the bottom line of the business. The proposed activities included staff retention, staffing, compensation, competency development, etc.
The idea marks the beginning of the data capturing activity in HRM and its application in organizations.
With growing development in the field of and HR measurement integration with more business dimensions, the predictive and assessment models became a subject of study. But still, the field of HR analytics remained unknown to many organizations and they couldn’t realize its potential.
The developments leading to the concept of ‘Bench-marking’ to compare the HR measurement data in various functions and with other companies. Though companies soon realized that while in theory ‘Bench-marking’ promises to provide strategic business insights, in real business scenario it fails to do the same and Bench-marking lost its recognition by early 2000.
The emergence of HR accounting and utility analysis was witnessed and this added new dimension and measurement data to quantify HR. Researchers not only drew the inference from business firms but from other sources too. One such research is on the metric model adopted by Billy Beane, the general manager of the USA baseball team to select team members.
The study led to a breakthrough metric-based selection model development called as ‘Moneyball’ concept in 2003 and found its adoption at large scale by organizations since 2006.
Though HR Analytics found its growth by late 2000, many organizations were still confused with its adoption and its implementation. Some known MNCs were able to foresee its potential of HR analytics and its benefits to the organization and took initiatives to deep dive into this field.
In 2009, Google started ‘Project Oxygen’ to find the qualities and attributes of an effective manager. The project gained global recognition in 2011 when it published the data-based findings and was found to very relevant and effective across different industries.
The success of the project boosted research regarding the benefits of analytics in workforce management. Around 20 articles were published on topics of Talent and workforce analytics by Harvard Business Review, Wall Street Journal, Forbes, Fortune magazines, etc.
The articles not only supported the application of analytics in workforce management but also found some shortcomings of the ‘Project Oxygen’ like positive co-relation between academic grades and employee performance. But ‘Project Oxygen’ laid the foundation for a dynamic shift from traditional metrics-based HR measurement to Predictive analysis of HR analytics.
IBM acquired an employment and retention service company, Kenexa in 2012. With its cloud-based solutions combined with Oracle, Tableau, and SAP, IBM discovered ways for talent management by analyzing the voluminous big data of HR.
With organizations observing the benefits of HR analytics in business strategic decisions, many have implemented HR analytics within the organization. Some known players in the industry are:
Microsoft found the employee attrition as a major challenge across its various business units. It deployed HR analytics tools to generate a statistical profile of employees who were likely to leave the organizations.
The company found that majority of these profiles were of the direct college hires and those who had not been promoted even after being with the company for 3 years. These insights allowed Microsoft to take several HR interventions like the assignment of mentors, changes in stock vesting, and income hikes to better manage the employee and control attrition.
To observe the effectiveness of these interventions, Microsoft implemented them only in two business units with high attrition rates and observed a significant reduction in attrition rates by more than half in both the business units.
Mindtree is using HR analytics to make strategic decisions about:
- Employee Turnover
- Risk assessment
- Profile management
- Productivity index
With HR analytic tools, Mindtree can predict employee turnover for the next 90 days from employee data. This has enabled them to generate insights from data analysis and fed those insights into forecasting models for employee hiring.
Using analytic tools, HR also manages high-risk employees and uses the data to make better management decisions.
- ConAgra Foods
ConAgra Foods Inc. saw many of its key employees leaving the organization. The company then deployed predictive analytics software to predict the likeliness of an employee leaving the organization. With this data, the company then created a model to identify the factors behind employee attrition, and around 200 factors were fed into the model.
The analysis reflected that pay isn’t among the top 10 significant factors contributing to employee attrition while it is internal recognition that is having a high correlation with employee attrition.
- Wipro Ltd.
Wipro is using HR analytics to boost employee retention and combined with social media analytics, it is also finding new skills and talent through Human Capital Management. With labor mobility, Wipro has transitioned from a cloud-based oracle system to its own Wipro HR sprinter for augmenting talent management. Using this HR analytics software, Wipro can see the trends of each employee, the data of employees, and their predicted behavior with just a click.