Management information systems in business can use HR data to reduce the cost in various aspects of running the operations. Business MIS department can be beneficial in different ways to maximize productivity and minimize costs. According to Sebastian (2020), HR data enables the generation of key performance indicators that enhance change in the working environment by boosting efficiency. MIS utilizes the analytic tools in HR data to cut the cost of metrics and techniques for recruitment and measurement of turnover rates. Analytic tools enable the management department to monitor the HR data on cost, help to predict areas that consume more resources, and mitigate through proper allocation to manage the cost.
MIS department can also engage in cost analysis programs using HR data, which helps to manage the human resource programs that help to reduce the cost. Businesses develop initiatives that train and empower the workforce, such as career development and growth for employees, compensating packages, and training strategies (Upadhyay, 2021). HR data enables the MIS department to analyze and generate accountability reports that enhance the development of strategies that save costs without affecting employee benefit packages and their well-being. MIS department uses the HR data to determine the potential of employees and create initiatives to retain the performance, saving the recruitment cost for replacements.
Business HR departments can encourage objective decision-making through data by ensuring data privacy and security. Employees and business data can be secure, and protection enhances trust in the human resource department and ensures compliance and maintenance of performance (Troisi, 2020). HR data enables the department to provide continuous assessment and monitoring of decisions and development programs such that problems are resolved to manage the outcome of the data-driven initiatives. Data analysis is essential for predicting the results of objectives, which enhances informed decision-making in choices that affect the effectiveness of the programs in the company.
The business HR department can also encourage decision-making through data by training the HR professionals to equip them with skills that help in data analysis and decision-making. The department can encourage decision-making by adapting evidence-based decision initiative to ensure that a data-driven program leads the HR personnel to provide evidence for validation that influence decision-making (Sebastian, 2020). Predictive models using HR data encourage decision-making through analysis of the objectives and plans that focus on the growth and productivity of the business. Including reviews on analytics in data enables informed decision-making in business based on the validation of HR data.
Organizations face challenges when using HR data for decision-making due to limiting factors that can be addressed to resolve the problems. Poor data collection and analysis is a challenge that affects the effectiveness of the decisions based on the data (Upadhyay, 2021). Inaccurate data is misleading for organizations that rely on data for essential objectives since the errors create inconsistency and mismatch of the information. Data protection and privacy is another challenge that can cause law interference with company productivity. HR department’s failure to adhere to the data protection policies leads to ethical and regulation compliance governance issues that affect the reputation of the firms.
Data complexities are another challenge organizations face in using HR data for decision-making due to complications in the system. HR systems develop complex processing issues due to large data sets that generate computation problems (Nocker, 2019). The large data present problems of incompatibility and volume when merging and changing the system for analysis, which results in errors and hinders the high quality of integration processes. The existence of bias in HR data in the evaluation of performance and resource allocation is a problematic issue that leads to discrimination that affects the achievement of the objectives.
References
Nocker, M., & Sena, V. (2019). Big data and human resources management: The rise of talent analytics. Social Sciences, 8(10), 273. https://www.mdpi.com/2076-0760/8/10/273
Sebastian, I. M., Ross, J. W., Beath, C., Mocker, M., Moloney, K. G., & Fonstad, N. O. (2020). How big old companies navigate digital transformation. In Strategic information management (pp. 133-150). Routledge. https://www.taylorfrancis.com/chapters/edit/10.4324/9780429286797-6/big-old-companies-navigate-digital-transformation-ina-sebastian-jeanne-ross-cynthia-beath-martin-mocker-kate-moloney-nils-fonstad
Troisi, O., Maione, G., Grimaldi, M., & Loia, F. (2020). Growth hacking: Insights on data-driven decision-making from three firms. Industrial Marketing Management, 90, 538-557. https://www.sciencedirect.com/science/article/pii/S0019850118308496
Upadhyay, D. R. K., Tyagi, M. E., & Malhotra, R. K. (2021). HR analytics in business: role, opportunities, and challenges of using it. Ilkogretim Online, 20(1), 6888-6899. https://www.ilkogretim-online.org/fulltext/218-1662732602.pdf
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