INTRODUCTION
1.1 Human Resource Analytics
To ensure efficient decision-making in all these fields, HR analytics is the science of capturing, arranging, and reviewing data relevant to HR roles such as recruiting, talent acquisition, employee participation, success, and retention. HR departments generate a vast volume of data every day with the usage of multiple forms of HR applications and technologies. The goal of HR analytics, though, is to make sense of this knowledge and transform it into a meaningful perspective (Davenport, Harris, and Morison, 2010).
‘HR analytics is a technique for developing information into how human resource asset acquisitions lead to the performance of four key outcomes: (a) sales production, (b) cost minimization, (c) risk avoidance, as well as (d) strategic strategy implementation. In an exclusive interview with HR Technologist, Collins notes that this is achieved by applying statistical methodology to interconnected HR, talent management, financial, as well as organizational results (Boudreau & Ramstad, 2005). HR analytics focuses specifically on the HR feature and is not quite synonymous with person analytics or workforce analytics, as is commonly assumed.
The key aim of an organization’s HR analytics is to enable human resources workers to make the best of their gathered data. Firms usually have a lot of details about their personnel, like their demographics, reports of training, as well as, so on (Bharti, 2017). To make sense of this knowledge and then use it to help their business, HR analytics has been used. HR analytics can help to improve the total efficiency of workers as well as, also help to forecast the models that fit well for the company. In decision making, there is no user failure today (Boakye & Lamptey, 2020).
1.2 Industrial Revolution IR 4.0
‘Industry 4.0 includes cyber-physical systems (CPS) networking, where current and modern manufacturing devices are equipped with sensors that gather several specific data as well as, use QR codes or RFID tags to distinguish various items. By being more agile and responding to shifts in the sector faster, it supports farmers with existing challenges. It will improve the speed of imagination and is very customer-centered, contributing to smoother processes of design. At the heart of development, staff can become coordinators, potentially enhancing the work-life balance of the workforce. Industry 4.0 is long-term, competitive, trying to identify answers to any problem that emerges (Patre, 2016).
2.0 The effectiveness of Organization if HR Analytics is implemented
2.1 Monitors Employee Training Data
Continuous career growth in most industries is important for recruiting and maintaining qualified workers. It will contribute to employee turnover, a loss of direction, and low employee productivity if there is nothing provided in the form of professional growth. Integrating HR analytics will aid in improving job satisfaction and efficiency in the workplace (Angrave, et. al., 2016).
For example, in Amazon company, the introduction of an employee career development initiative shows that this organization is truly involved in fulling the maximum potential of its employees and helps them in their specific area of specialization to acquire relevant knowledge and credentials. In Amazon, it is a symbol of a forward-thinking and creative organization because it values its workforce and they give commitments to the organization (Ejo-Orusa & Okwakpam, 2018).
HR analytics also helps Amazon company to determine the most suitable courses for individuals in career learning and to assess their success. During the training session in Amazon, HR analytics offers a run-down of the success of the training as well as the expense per individual. Amazon will then find out why it is cost-effective for the organization by knowing the number of workers who utilize training strategies and the expenses associated (Calvard & Jeske, 2018).
2.2 Identifies Employee Retention Rate
A potential indicator that should be tackled may be high staff turnover. While the factors behind high employee turnover can be large and varied, there is a need to develop an understanding of employee retention. Hence, to incorporate measures to avoid the decline, it is important to conduct a comprehensive review of the company’s community, governance, remuneration, as well as a business model (Rajbhar, Khan, & Puskar, 2017). For example, in IBM, HR analytics will aid by offering data-guided insights into the factors why workers remain loyal or why they want to quit. The explanations for this can be attributed to a wide range of factors, like lack of skills, bad results, difficulties with benefits, or other situations (King, 2016).
IBM will figure out possible challenges inside the business that can impact productivity and the feeling of connection of the staff by utilizing exit and remain interviews, team reviews as well as, satisfaction surveys. Proactively solving these challenges will help IBM to increase the total bottom line, instead of claiming that they do not occur. IBM company can prevent incurring additional expenses involved with the hiring and training of new workers (Qadir & John, 2019).
2.3 Unlocking Potential
Just like high job turnover can be increased and the recruiting of underperforming workers stopped, the main objective of human resources is the selection, recruiting, and advancement of the best team members. For example, in HCL, HR analytics help to recognize certain attributes within a company or a team that is indicators of progress, so it can target the quest for talent and stop creating errors that could be costly as well as, time-consuming to fix (Stone, Neely, & Lengnick-Hall, 2018).
The correct mix of personalities as well as talents will also be the ideal formula to magnify a group’s productivity or production. Moreover, in HCL, analytics will automate the method instead of operating by trial and error, meaning that effective teams can be assembled as well as released more efficiently. This method is about more than making mistakes and it is about inspiring people in IBM for the sake of the business and the consumers to use their talents the best they can (Mishra, Lama, & Pal, 2016).
2.4 Improves Hiring Decisions Throughout the Company
Most companies find the method of recruiting difficult. Through leveraging data obtained from past employee recruiting sessions, HR analytics can help enhance the hiring decisions (Martin-Rios, Pougnet, & Nogareda, 2017). Consider the scenario that follows. For example, in IBM, after interviewing 15 applicants for a previous role in the sector, the company can find that seven share common characteristics that are not consistent with the business values or culture of the firm. IBM will change the business profile using this knowledge to automatically exclude candidates who hold certain qualities from potential openings, thus increasing the quality of the recruiting process. This tends to accelerate the process by reducing time wasted reviewing unacceptable candidates and having more time to work on selecting employees that are more relevant to the community, ethos, as well as, the atmosphere of the business (Malisetty, Archana, & Kumari, 2017).
2.5 Better Insights
HR reporting supports the organization by monitoring, exchanging, and reviewing performance-associated data to look at the personal life of the employee. For example, Amazon company monitors and documents their employees ‘ behavior toward clients, co-workers as well as, how they invest their time. Besides, an individual employee success information could be used to identify fantastic talents from the recruiting manager. This knowledge not only offers further employee perspectives, but it also forms the techniques for improving the morale, retention, as well as the involvement of workers in Amazon (Bhardwaj & Patnaik, 2019).
3.0 The Challenges in Implementing HR Analytics in an Organization with IR 4.0
3.1 Multiple sources of data:
There are so many sources of information operating in isolation for various HR resources appealing to various HR roles. Each platform generates its data and combining it with other databases is a big challenge, whether it is the HR information system, candidate monitoring system, learning management system, or employee referral program. For example, IBM might be shocked to hear that a longer time is consumed by data analysts only gathering and cleaning data, i.e. extracting valuable data from the sound. Often, when making a strategic business judgment, IBM should not use information from one source in isolation. Thus, combining these silo structures and making them interact with each other is a huge challenge (Khan & Tang, 2016).
3.2 Lack of Skills and Training:
Although it is accurate that data analysts are at the forefront of this movement in data & analytics, recruiting executives and even companies have a pre-existing impression that HR teams have little to no role to play in the data processing. They refuse to study and adopt these instruments, aided by a mentality that is afraid of the learning curve, given the technicalities considered. For example, in HCL, instead of preparing with an HR analytical device, HR managers will rather focus on their intelligence and comprehension. HCL can also fall back on a false claim that artificial intelligence, this is not at all valid would strip human capital out of human resources (Hamilton & Sodeman, 2020).
3.3 Differential abilities of people
There are barriers to incorporating an HR Analytics feature with each new project inside HR. The greatest problem is the unequal ability of individuals to consume information easily. Although it is my opinion that the path forward is data-based HR, not everybody can interpret data. For example, in IBM, allowing customers and policymakers to interpret data on their own without any of the support of a trained data scientist may be risky (Malisetty, Archana, & Kumari, K. 2017).
Several prejudices may skew the perspective prism whereby descriptive data is interpreted by an unqualified recipient of data. One case in IBM is confirmation bias; this is where, in defences of their view, the stakeholder combs the information for proof. While it may not be deliberate, it is counterproductive to the efficiency of the role of HR Analytics. Because, in IBM, information may be manipulated to promote a pre-determined view or judgment hence, it may not be the right choice for this organization rather than making a trained data analyst search through the information to analyse the facts (Bhardwaj & Patnaik, 2019).
3.4 Integrate people analytics
Identifying who is accountable for execution is one of the more popular obstacles for Amazon company encounter while attempting to incorporate employee metrics into the recruiting process. Management of Amazon can compile the details they believe they need in certain instances and then drop it on the desk of the HR staff. This technique could succeed in an organization (Khan & Tang, 2016).
3.5 Privacy and compliance
To achieve the required outcomes, analytics allows a sufficient number of data to be obtained from multiple reputable sources. For example, in IBM, HR practitioners must recognize privacy when obtaining data about an employee as well as, a potential employee-particularly from diverse information. Moreover, in IBM, collecting personal employee data may often create problems for the organization (Hamilton & Sodeman, 2020).
4.0 Recommendation
4.1 Invite a representative from the People Analytics group
Organizations should make sure to invite a member from the HR Analytics community as a better practice to clarify the data collection findings and include an overview of the context as well as any activity suggestions. For example, in Amazon, recruiting the representative of the HR Analytics team would allow customers to be informed on the value of the data and avoid the unintended misuse of data (Martin-Rios, Pougnet, & Nogareda, 2017).
4.2 Create a Teamwork Environment from the Top Down
Throughout the whole construction process, management and HR of Amazon should function together as a team. Until management, as well as HR of Amazon, have worked together to establish a specific set of priorities and priorities, and to build a plan to accomplish these goals, the data collection phase does not even begin. This would make sure that the correct data is gathered to achieve the outcomes needed (Stone, et. al., 2018).
4.3 Data privacy and compliance
As data on an employee or prospective employee is gathered, the privacy of the same must be held in mind as breaching the privacy of its workers. It could also lead the Amazon business to be in trouble (Qadir & John, 2019).
4.4 Promote collaboration
It is important to make sure the teams have committed to the cause, in addition to ensuring strong communication. For example, in HCL, collaboration is an integral aspect of the plan for HR and one that has a significant effect on the larger business. Via streamlined work processes or also psychological, cultural as well as team-building efforts, HR of HCL will serve as the glue that holds teams united (King, 2016).
5.0 References.
Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M. (2016). HR and analytics: why HR is set to fail the big data challenge. Human Resource Management Journal, 26(1), 1-11.
Bhardwaj, S., & Patnaik, S. (2019). People Analytics: Challenges and Opportunities-A Study Using Delphi Method. IUP Journal of Management Research, 18(1).
Bharti, A. (2017). Human resource analytics. South Asian Journal of Marketing & Management Research, 7(5), 68-77.
Boakye, A., & Lamptey, Y. A. (2020). The Rise of HR Analytics: Exploring Its Implications from a Developing Country Perspective. Journal of Human Resource Management, 8(3), 181-189.
Boudreau, J. W., & Ramstad, P. M. (2005). Talentship, talent segmentation, and sustainability: a new HR decision science paradigm for a new strategy definition. Human Resource Management, 44(2), 129-136.
Calvard, T. S., & Jeske, D. (2018). Developing human resource data risk management in the age of big data. International Journal of Information Management, 43, 159-164.
Davenport, T.H., Harris, J.G., and Morison, R. (2010). Analytics at Work: Smarter Decisions, Better Results, Harvard Business School Press, Boston, MA.
Ejo-Orusa, H., & Okwakpam, J. A. A. (2018). Predictive HR analytics and human resource management amongst human resource management practitioners in Port Harcourt, Nigeria. Global Scientific Journal, 6(7), 254.
Hamilton, R. H., & Sodeman, W. A. (2020). The questions we ask Opportunities and challenges for using big data analytics to strategically manage human capital resources. Business Horizons, 63(1), 85-95.
Khan, S. A., & Tang, J. (2016). The paradox of human resource analytics: being mindful of employees. Journal of General Management, 42(2), 57-66.
King, K. G. (2016). Data analytics in human resources: A case study and critical review. Human Resource Development Review, 15(4), 487-495.
Malisetty, S., Archana, R. V., & Kumari, K. V. (2017). Predictive analytics in HR management. Indian Journal of Public Health Research & Development, 8(3), 115-120.
Martin-Rios, C., Pougnet, S., & Nogareda, A. M. (2017). Teaching HRM in contemporary hospitality management: a case study drawing on HR analytics and big data analysis. Journal of teaching in travel & tourism, 17(1), 34-54.
Mishra, S. N., Lama, D. R., & Pal, Y. (2016). Human Resource Predictive Analytics (HRPA) for HR management in organizations. International Journal of Scientific & Technology Research, 5(5), 33-35.
Patre, S. (2016). Six thinking hats approach to HR analytics. South Asian Journal of Human Resources Management, 3(2), 191-199.
Qadir, A., & John, J. (2019). Mapping Theory and Practice of HR Analytics for Strategic Human Resource Management (SHRM). Editorial Committee, 6.
Rajbhar, A. K., Khan, T., & Puskar, S. (2017). A study on HR analytics transforming human resource management. Journal of Investment and Management, 6(4), 92-96.
Stone, C. B., Neely, A. R., & Lengnick-Hall, M. L. (2018). Human resource management in the digital age: Big data, HR analytics, and artificial intelligence. In Management and technological challenges in the digital age (pp. 13-42). CRC Press.
