The Internet, wireless connectivity, and network communication enable users to generate a constant flow of data in real time. This data includes information about their location, physical and health status, emotional state, and behaviour.
This enormous information, referred to as big data, has become especially valuable for businesses globally and in Malaysia.
Due to the widespread usage of 5G technology and the Internet of Things, the term “big data analytics” is fast becoming a buzzword in Malaysia, and with good reason. In fact, its potential to disrupt different domains is being widely acclaimed—and one such domain is human resources.
The importance of big data in HR practices
Advanced analytics is transforming human resource management by enabling data-driven decision-making and optimizing various processes. Here are examples of how it impacts HR practices:
- Smarter hiring: Allows HR practitioners to make more informed hiring decisions by analysing large amounts of candidate data. This reduces the costs associated with bad hires.
- Workforce planning: By analysing employee data, such as training records and career trajectories, HR departments can forecast workforce needs and identify skills gaps.
- Learning and development: By tracking employee engagement and performance data, HR practitioners can tailor employee programs to individual needs, improving overall performance.
- Retention and engagement: With data, the management can programmatically analyse factors like job performance and compensation to flag employees at risk of leaving.
- Compliance and risk management: Big data can spot possible risks, ensure compliance with labour laws and automate compliance assessments.
- Competitive advantage: By leveraging data analytics, HR departments can stay aligned with overall business objectives. Through strategic, data-driven decision-making, they can drive organisational performance and sustainability.
What kind of data does HR use?
Human resources analytics entails complex analyses of data, both internal (from the HR department) and external (from the company or market). This supports personnel decisions that are connected to organisational performance and business outcomes.
In fact, the use of big data in the HR sphere boils down to two words— talent analytics. In practice, this discipline has emerged as a suite of methodologies. Companies use these approaches to identify patterns in workforce data, drive changes, and eventually create value.
Talent analytics can help organisations answer key questions like:
- What is the relationship between training and productivity?
- Does an organisation’s well-being programme contribute to performance?
- Are permanent employees a better investment than temporary employees?
- How does an organisation best retain employees?
- Are employees with specific degrees more productive than others?
To address these questions, HR departments increasingly use different types of data to uncover trends and correlate them to performance outcomes. For example:
- Employee performance data: Includes metrics from performance reviews, productivity statistics, and feedback from peers and managers.
- Recruitment data: Includes information from the hiring process, like resumes, interview scores, and hiring metrics.
- Employee engagement data: Gathered through surveys, feedback forms, and mechanisms that gauge employee satisfaction and commitment.
- Workforce demographics: Data on employees’ age, gender, education, tenure, and other characteristics help HR understand the workforce’s composition and identify diversity and inclusion gaps.
- Learning and development data: Covers employee training participation, skill assessments, and development program outcomes.
- Compliance and risk management data: Relates to compliance training, employee grievances, and audits. It helps to identify and minimise legal risks and enhances organisational integrity.
HR uses of big data and analytics.
Advanced data analytics has several use cases in the modern organizational marketplace, such as:
Recruitment and talent acquisition
HR practitioners can improve candidate screening and lessen recruiting biases by leveraging big data. Data analytics can be employed to analyse criteria beyond standard resumes, like online activity and social media presence. This approach provides a more objective assessment of candidates.
Thanks to this thorough profiling, recruiters can also find the best candidates from a larger pool. This guarantees full transparency and bases decisions on qualifications and fit rather than personal preferences.
Furthermore, predictive analytics can expedite the hiring process by predicting future recruiting requirements and keeping a pool of competent candidates on hand. This saves time and money. Big data also makes labour market research easier, aiding HR departments in understanding employment patterns.
Employee retention and engagement
A substantial amount of time, effort, and money is usually spent on an employee when a company hires them. When someone resigns, what happens? The whole process starts over when searching for a new applicant.
Advanced algorithms can identify workers who are likely to leave. They do this by analysing their work performance, employment history, online activity, and payroll information. As such, companies can react appropriately to keep a highly valued employee on board by giving them more training, a more fulfilling role, or a raise.
Performance management
How can an HR manager tell whether a potential new hire will fit in well with their workplace culture and be able to carry out their responsibilities to a high standard? Comparing a potential hire to current top performers while considering all of the job’s nuanced requirements takes a lot of work. Relying on gut instincts alone is not always a trustworthy decision.
However, HR analytics algorithms can use the data of productive workers to create a profile of top achievers. This profile can then be used as a benchmark for judging new prospective recruits. It can also be used when assessing promotions and even layoffs inside an organisation.
Appropriate resource utilisations
HR practitioners can improve workforce planning by aggregating demographic data on age, gender, education, and tenure.
In fact, HR departments can anticipate future staffing demands and efficiently address any skills gaps by using big data to find trends and patterns across the workforce. For example, knowing the age distribution of staff members can assist HR in planning for future retirements and creating succession plans within an organisation.
Decision making
Big data enables HR practitioners to replace ‘gut feeling’ with data-driven insights. This augments their ability to find top personnel more effectively, hire with greater knowledge, and proactively manage employee performance and retention.
How can analytics be used for HR decisions?
Predictive analytics can be exploited to reduce prejudices and ensure skills-based recruiting in the hiring process. Furthermore, analytics expedites the hiring process by predicting future company requirements and keeping a pool of competent applicants. It also matches talent with overarching business goals.
In terms of retention, HR departments can leverage big data analytics to devise customised retention tactics and identify at-risk employees based on performance dips.
How can HR be data-driven?
Big data is enabling smarter, more efficient human capital management, which is transforming HR. The potential for people management optimisation and competitive advantage will only increase as long as firms continue to adopt HR analytics.
To become data-driven, HR departments must first implement robust data analytics technologies. These technologies should allow the tracking and analysis of important HR metrics. Examples of these metrics include employee feedback platforms, performance management software, and Human Resource Information Systems (HRIS).
This capacity improves HR operations’ effectiveness. It also enables HR to make well-informed decisions that support company objectives and eventually improve employee outcomes.
Establishing a data-driven culture promotes a seismic shift from intuition-based decision-making to a more analytical approach grounded in insights. Support from the leadership and a dedication to creating an atmosphere where data is appreciated and used in daily operations is necessary for this cultural shift.
By developing competencies in data literacy, HR staff can confidently leverage analytics to inform their strategies and initiatives, like talent acquisition and employee development. This all-encompassing strategy not only makes HR more effective overall but also establishes HR as a key player. HR becomes crucial in accomplishing the organisation’s long-term goals.
Emerging trends in HR analytics
As a developing sphere, HR analytics has no limitations to its possibilities. The incorporation of AI-powered HR analytics is a prominent trend. It aims to improve the capacity to examine enormous volumes of employee data and unearth performance, engagement, and retention.
This trend aims to automate repetitive processes like predictive analytics and resume screening. Thus, freeing up HR personnel to concentrate on strategic projects rather than time-consuming administrative work.
Another significant trend is the growing emphasis on employee experience (EX) measurements, which stresses deeper employee understanding. This trend seeks to enhance the employee journey across the company.
Furthermore, the Internet of Things (IoT) is becoming a potent tool for tracking workplace efficiency. Sensors can gather data on employee interactions and environmental conditions in real-time. By identifying areas for improvement in workplace design and employee collaboration, HR may use this data to increase overall productivity.
While big data has the potential to revolutionise human resource management, there are still questions about how it should be used in the HR function. Organisations must use extreme caution when determining what information to gather and how to use it since workforce data is extremely sensitive.
For instance, it is feasible to monitor what employees are sending via email, but most companies would be reluctant to engage in what they view as a very invasive procedure.
In fact, employers now have much more responsibility due to the growing volume of accessible data. This data comes from sensitive sources such as smartphone records and wearable devices.
Conclusion
Malaysian organisations are increasingly recognising the significance of attracting and retaining exceptional personnel. However, despite the many benefits of talent analytics in human resource management, its adoption as a distinct field of big data analytics has been slow.
A lack of high-quality HR data, analytical competencies, and clarity on integration with traditional HR activities have all contributed to the limited adoption.
Nonetheless, some Malaysian organisations have started investigating the concept. With the right analytics tools, skill development, and well-defined implementation frameworks, Malaysian enterprises can effectively leverage big data.
This approach enhances their human resource management and secures a competitive advantage in the ever-evolving business environment.