How Machine Learning Is Impacting HR Analytics

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HR analytics is revolutionizing the way human resources departments operate, leading to higher efficiency and better results overall.

Human resources has been using analytics for years. However, the collection, processing and analysis of data has been largely manual, and given the nature of human resources dynamics and HR KPIs, the approach has been constraining HR. Therefore, it is surprising that HR departments woke up to the utility of machine learning so late in the game.

Nevertheless, machine learning has been slowly but surely entering the HR domain, and multiple use cases such as attrition prediction, right hiring and human resource training have been established. It is also believed that machine learning can predict the success of a potential candidate. More use cases are likely to be discovered soon. Unlike the manual approach, the machine learning approach is much faster, far more responsive to dynamic situations and provides accurate, actionable and valuable data. (Even though the field of data analytics is becoming increasingly automated, there's no need to worry about unemployment just yet. Learn more in No, Data Analytics Bots Aren’t Going to Steal Your Job Anytime Soon.)

The Role of HR

Human resources is inarguably an organization's most valuable asset. HR is responsible for managing the human resources of an organization so that it gets the most possible value out of its people. The role of HR includes the following:

  • Identifying the right talent for the right role
  • Proper compensation and benefits
  • Managing employee development with training and opportunities
  • Tracking and managing human resources growth with increments, promotions, opportunities and benefits
  • Managing employee motivations, grievances and feelings
  • Managing exits

Case for Machine Learning in HR

Over time, expectations of the HR department have been changing. Previously, HR would find suitable candidates; conduct or facilitate assessments; hand out offers, compensation and benefits based on HR policies; and manage employee careers and exits. Now, HR is expected to add more value to what it already does and do even more, such as predict attrition and candidate success in a role. Is the current approach to fulfilling these expectations enabling or constraining HR?

Prior to the adoption of machine learning, HR would manage data in manual and semi-automated ways. It would collect, store and process data to produce analytics before the data would quickly become irrelevant because the situation had changed and the data needed updating. For example, data collected before the annual appraisal cycle showed low attrition risks. However, post-appraisal, there is a spike in attrition and employee dissatisfaction, mainly because of mismatch in expectations and actual rewards and a rise in opportunities in the job market. Basically, pre-appraisal analytics misled the organization, and the effort can be considered a waste.

Manual and semi-manual methods are not equipped to enable HR to manage data on the rapidly changing variables related to human resources. HR needs regular, updated analytics on relevant factors such as employee sentiments within the organization, employee attitudes toward policies, and attractiveness of market opportunities versus that offered by the organization. This is serious business. Unless the human capital is managed well, an organization can potentially lose valuable employees. Bill Gates once commented, “You take away our top 20 employees and we [Microsoft] become a mediocre company.” Enter machine learning. What can machine learning offer over the old methods? Consider the following:


Faster Response to Changing Dynamics

This is the age of big data. To manage employees, you need data on:

  • Employee attitudes and feelings
  • Credentials or qualifications
  • Employee views toward policies
  • Compensation and benefits trends
  • Relevant external developments such as the job market and rival organizations and their impact on your employees

That adds up to a humongous data volume arriving every moment. Manual management is simply ill-equipped to handle it. However, machine learning is appropriate to consistently accept, store and process such data volumes and provide relevant and actionable insights in the form of simple analytics. (Learn more about big data's role in business with Tackling Big Data Analytics Pain Points.)

Accurate Predictions

Machine learning can predict key developments such as attrition, success in job roles and adverse events such as unethical behavior. For example, the success likelihood of an employee in a new role can be predicted based on an analysis of past data such as past project performance, knowledge base and key initiatives taken to improve the knowledge base, which reflects attitudes. Findings based on these parameters can be converted to analytics and then decisions can be made.

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Candidate Identification and Applicant Tracking

Machine learning can connect the right job to the right candidate based on job role and the candidate's credentials, experience and interests. Machine learning can leverage social networks for that. It significantly reduces manual effort in candidate assessments and tracking.


The HR domain, after a lukewarm response to machine learning, is waking up to its utility. Many use cases are being implemented and more are on the way. A summary of the key developments is given below.

Candidate Identification and Application Tracking

With big data from web sources such as forums and social media, organizations are finding the right candidates for the right roles. While assessing candidature, machine learning considers qualifications, experience, interests, professional connections and memberships, achievements, forum discussions and more. This significantly improves chances of role fitment, if not guaranteeing it. A good example could be the professional networking site, LinkedIn.

Machine learning significantly reduces manual effort in applications management and frees HR to focus on more productive efforts. According to Cristian Rennella, CEO & Cofounder of, a company that compares financial products, "In the past, we spent 67.2 percent of each person's time in HR to read the CVs of each candidate who came to us through our own website and third parties. Thanks to AI, this work today is done automatically by our internal system, which through deep learning using TensorFlow, we can automate this task."

Accurate Predictions

HR analytics can often accurately predict key factors such as attrition, employee performance, and even adverse events such as unethical behavior. For example, data from various forum conversations, social media posts, emails, videos, rival organizations and market opportunities can point to changes in attrition levels. Attrition levels are particularly susceptible to change after appraisal cycles.

Job Success Predictions

Data on a candidate's credentials, memberships, attitudes and performance can point to success probability in job roles. The point is, manually attempting to calculate predictions based on so many variables is simply inadequate. HR analytics can provide accurate insights based on which organizations can find the right candidates for the right job roles.


Organizations are already reaping the benefits of adopting machine learning. While machine learning has already reduced manual effort, ML is expected to become even more accurate and prominent in areas like attrition prediction and management, employee management and success.


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Kaushik Pal
Technology writer
Kaushik Pal
Technology writer

Kaushik is a technical architect and software consultant with over 23 years of experience in software analysis, development, architecture, design, testing and training. He has an interest in new technologies and areas of innovation. He focuses on web architecture, web technologies, Java/J2EE, open source software, WebRTC, big data and semantic technologies. He has demonstrated expertise in requirements analysis, architectural design and implementation, technical use cases and software development. His experience has covered various industries such as insurance, banking, airlines, shipping, document management and product development, etc. He has worked on a wide range of technologies ranging from large scale (IBM…