HR Analytics

What is HR analytics and how important is it?

Human Resource Managers- the ones known for keeping the dynamics, of a person and amongst people, healthy and efficiently functioning, so that organizational goals are achieved. Traditional Human Resource Management is known for its innate talent to understand people, their intricacies, and intuitively figure out how the person in question, will impact the productivity and profits of an organization. HR Analytics is exactly about shattering this traditional viewpoint pertaining to how HRM of an organization function.

HR Analytics involves the usage of data to analyze patterns and correlations to develop insights and make predictions that serve towards identifying what would work, when it comes to having the right human resources for an organization. Just like general analytics, HR analytics strives to prepare models based on the analyzed data, in order to prepare algorithms that can predict future outcomes based on manipulation of factors involved in the model (Predictive Analytics).

How can HR analytics be used?

HR analytics can be leveraged in all HR functions in myriad ways. For example, your organization is observing an increase in its employee turnover rate. The resignation by a single employee costs a lot to the organization. Studies estimate that the cost to replace an employee can reach to 200% of the cost to have the former employee working. This cost is attributed to, by factors such as training requirements, recruiting cost, cost incurred due to a loss in productivity and loss of morale amongst team members, etc.

In order to find out the reason behind the increasing attrition rate using HR analytics, the HR manager will thus have to observe if there is a correlation between, organizational practices and employee characteristics. The correlation studies will require data such as current attrition rate, similarity in employee characteristics with longer tenure, similarity in employee characteristics with shorter tenure, responses from surveys by current employees, performance data, organizational practices such as type of onboarding experience, and appraisal mechanisms. Having a data driven approach gives a credible insight that can be used to make future decisions, which strategically align with organizational goals, thus making HRM strategic partners to the organization.

Other examples of its application:

  1. Understanding factors affecting employee performance.
  2. Increasing recruiting efficacy via understanding which factors contribute to metrics such as Time to Fill, Time to Hire, Cost per Hire, Source of Hire, Selection Ratio, First Year Turnover Rate, etc.
  3. Anticipate future human resource needs.
  4. To understand which compensation system works and which does not.

Popular job titles or opportunities pertaining to use of analytics in HR include: HR Analyst, HR Information System Analyst, HR Data Scientist, Corporate HR managing workforce analytics, Talent Analytics Manager. Tools for analytics such statistical programming languages (R) and programming languages for automation (Python) are molded to create HR Information System solutions and analytics’ platforms.

Challenges faced when implementing HR analytics:

  • Lack of skills to gather, process and interpret the required data.
  • Lack in data quality or lack of understanding towards knowing which is the right data to be processed.
  • Inability to manage data privacy.
  • Linking insights to actions for increased ROI.
  • Lack of understanding amongst executive leaders towards the importance of adapting analytics in HRM processes.

What does the future hold for HR Analysts:

An amalgam of AI and HR Analytics in HR functions is a popular idea these days. Hence, we can expect that in future, the resultant coalition will aid towards the following:

  • A personalized employee on-boarding experience:Having the right employee on-boarding experience plays a huge role in making an employee feel valued and secure. This directly affects morale. Integration of psychology and AI, will help understand the type of on-boarding experience a new recruit needs to be provided with. It can also help ease and automate general on-boarding procedures such as interaction with other employees and understanding their functions in the organization.
  • Application of cognitive analytics, towards tasks such leave management, performance management, appraisal management, coordination between departments, etc.; which not only will result in automation of these tasks, but also remove biasedness when making decisions. Humans are bound to be biased sometimes, owing to various factors affecting one’s psychology and hence integration of AI in decision making will lead HR Managers to make the most effective and strategic decisions.
  • Neuro-Linguistic Programming (NLP)to monitor employee morale by analyzing voice characteristics and behavior. Key strokes and internet usage will also be monitored to predict if an employee is planning on leaving the organization.
  • Using predictive and cognitive analytics to determine the right fit for a job by understanding personality traits of probable recruits.

HR analytics have provoked organizations in understanding that human resource management needs to be looked upon as a vital strategic partner. With disruptive technologies shaping trends and practices, analytics make human resource management all the more essential to an organization’s need to continuously evolve.

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