Is data-driven human resource practices misleading HR professionals and experts in hiring the right people today?

Is data-driven human resource practices misleading HR professionals and experts in hiring the right people today?

The human resources career field is changing every day. More changes are set to be seen in the days to come. Today, the human resource field and the workforce, in general, are shaped by globalization, tighter labor markets, and economic uncertainty (Navarro, 2019). A vital issue that is facing the human resource field is data-driven human resource practices. The growing significance of big data analytics currently presents human resource experts with an opportunity and is putting them under pressure at the same time. Business executives are increasingly demanding that human resource practitioners, like their colleagues and members in other business departments, use in-depth analysis and metrics to make decisions concerning hiring and recruitment, training and development of staff (Lam & Chan, 2017). This argumentative essay explains why data-driven human resource practices are vital for the field.

The time for data-driven human resources is now. While some people consider data-driven practices not suitable for hiring and recruitment related practices, the significance of using data in making some HR decisions is increasingly becoming significant. For several years, human resource experts have lived by the adage,” go with your gut”- trusting their guts when making decisions on hiring, recruitment, and compensation packages. Even when completing employee evaluations and assessments, HR has, for several decades, living by the “go with your gut.” Data-driven human resource practices are here, and the time for practitioners in the field to adopt it is now. According to Lam and Chan (2017), public organizations that use data-driven human resource practices or what is considered “people analytics” show more than 30 percent higher stock market returns than standard methods.

Talent analytics is improving recruiting and hiring. Social media is putting more job opportunities and prospects in the path of the human resource field today than ever before. The possibilities are so many so much such that human resource professionals can’t sift through the massive pool of potential recruits or applicants to find the right c fit with the best chance of becoming long-term staff. However, when big data and deep-learning algorithms are combined, not only can human resource professionals can easily and quickly sift through the data and select the best candidate for the role. The degree of accuracy that comes with big data and learning algorithms in sifting through the data is high. HR staff can also find candidates who are likely to be top performances. As such, they can be given the required resources and tools needed to shine and become the best in their fields.

The second advantage of big data is using predictive analytics to aid the retention of staff. Competent human resource practitioners assume that they can keep a finger on the pulse of their companies. However, it is more uncomplicated and more comfortable to be blindsided by good employees who decide to leave without noting the reasons for such a decision. HR directors and managers do not have to rely on their instincts to spot unmotivated and unhappy staff. Instead, predictive analytics can help practitioners determine which of their teams are most at risk for exiting based on different indicators. Surveys, regular employee feedback, and sample interviews can help determine what employees feel about the organization and the corporate culture being implemented at the firms. From the results of predictive analytics, the HR staff can then decide how to address the risk factors, whether to reduce their overtime hours or provide stock options.

Thirdly, big data is shaping how HR directors plan their workforce. Some of the most critical jobs in America and the rest of the world, from truck drivers to technology experts to nurses and security personnel, face acute talent shortages. In the past, HR managers considered workforce planning and the issue of headcount vs. budget. However, organizations that evaluate only these factors overlook the most essential and vital components. Instead of just balancing the budget to match the headcount, data can be used to predict the staff who are more likely to leave the organization first, when they are likely to go and how their withdrawing will affect the organization. Big data can help the organization, and the HR department foresees a mass exodus or retirements, what skills will be needed to conduct future hiring, and how the workforce can be kept stable in the current environment, which is dominated by change.

Today, it is vital for HR managers and directors to relate human resources data to business results and planning. Most significantly, hiring, workforce planning, and retention should be correlated to the organization’s business outcomes. The departmental silos can be broken down to analyze data from accounting, marketing, and sales to determine the organization’s overall business goals for the next three to five years. Then the HR personnel can think about the type of skill sets needed to add to the organization and the actions to be taken by the department to meet these different goals.

However, as organizations and HR personnel move towards adopting big data and analytics in the field of human resource management, the risk of failure to understand where to focus the investments in human resources. The risk of inaccurate data, not just from the candidates’ perspective but also from businesses, can lead to poor decisions. Such poor decisions make opponents of big data cite gut feeling as the best way to achieve success in the field.

There is no doubt that big data has changed the course of decision making and planning in human resources as a career. However, the biggest question that practitioners in the field of HR should ask themselves is the type of data to use and how best the data can be employed to give the best decisions. HR practitioners must be accustomed to using historical data, for example, analyzing employee feedback during exit interviews to find out the reasons for turnover.