How Data Analytics is Transforming HR Practices

Janice Au
Janice Au

HR Photo

Firms experience internal complications annually, such as key staff leaving and business performance stagnating despite new hires and fires. Discovering the exact reasons behind these key occurrences can save companies millions in the long-term.

A few years ago, some enterprising HR leaders recognised they were sitting on a lot of insightful information that could exponentially drive business performance. According to a 2016 report by Deloitte on global human capital trends, 44% of companies surveyed were using workforce data to predict business performance. Some small businesses are beginning to realise the utility of data analysis to gain insights into their workforce, staff policies and practices, in particular the use of machine learning and its algorithms that detect patterns and make predictions.


Enabling data-driven recruiting and better talent management

Many organisations use data analytics in human resources to guide hiring decisions in the hope of making more informed decisions and eliminating any unconscious human biases in the hiring process. Data analytics can range from searching through a stack of data to define the characteristics of a successful candidate, match candidates with jobs based on their specific skill sets, and even determine which candidates are most suitable. Algorithms can also help identify which new hires will be high performers, allowing managers to determine, for example, if an employee should be marked for fast-track performance programmes.

Analysis can be presented visually in graphics or statistical reports in a way that makes it easier to understand and to take action. Data analytics makes data useable, and brings relevant information to the attention of decision makers, helps to limit unconscious bias in recruitment, and to ultimately make the most informed decision.


Tackling the employee turnover challenge

Machine learning is helping companies to deal with staff turnover. Using a company’s past data, machine learning pinpoints the departments and timelines where turnover risks are at its highest, as well as the exact candidates who are likely to resign. It can also help to identify factors that have the most significant influence on employee turnover, such as work hours and compensation.

These data analytics tools can help to take out some of the guesswork and speculation, enabling companies to use that information to:

  • segment at-risk employees
  • engage with them
  • address any concerns or needs
  • offer relevant training or promotions
  • improve the overall work experience.

 As a result, managers are now able to address potential issues before losing any staff, significantly lowering the percentage of employee turnover.


Improving the workplace experience

From workplace design to work procedures and work habits, the new generation of employees increasingly want to work in a way that suits them. Employee data can be used to create personalised experiences. Workforce data, including salaries, team structures, demographics and educational background can be consistently analysed, and suggestions can be made based on those analyses. For example, there may be a correlation between where employees are based and how they perform, which could tell managers that a particular office is underperforming due to low morale in that office.

 Workplace management is serious business. As Microsoft founder Bill Gates once said, "You take away our top 20 employees and we become a mediocre company." The challenge then, is incorporating data analytics into HR practices, and encouraging the transformation together.




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