Big Data in Global Business- Analysis and Future Directions in HRM

Big Data in Global Business- Analysis and Future Directions in HRM

Big data refers to structured and unstructured data organizations collect from individuals daily. Some of the sources of such data include internet sites, social media networks, and information collected from sensors, among other sources. Big data often comes in in large volumes, rapidly, and in various forms. Therefore, an organization dealing with big data must employ specialized personnel and methods to make optimum use of the data. Some of the common uses of big data include personalization and predictive analysis. This paper will investigate Amazon’s use of big data, the types of data it analyzes, why and how the company transforms big data into useful information, and the company’s future directions in its use of big data, especially in human resource management.

Customer Data

Amazon uses consumer information to help improve its operations. The data is utilized to assess client behavior, preferences, purchasing history, and site engagements. This enables Amazon to customize product suggestions, adjust marketing efforts, and enhance the entire purchasing experience.

 Transforming Customer Data into Useful Information

Amazon uses customer information to provide tailored sales suggestions. As an e-commerce retailer, the company uses previous purchases, browsing habits, and demographic information to propose goods that are relevant to each user (Chen, 2017). This improves the buying experience by displaying things consumers are more likely to be interested in. For instance, if a customer tends to search for electronics on Amazon’s website, the company will leverage the data and display electronic devices on its website, including those with discounts and offers.

Additionally, client data facilitates customized marketing. Amazon utilizes consumer information to provide customized adverts on its website and via its advertising network (Chen, 2017). These advertisements are targeted to specific interests and behaviors, boosting the probability of interaction and conversion. Suppose a customer has an Amazon application on their digital devices and allows pop-up notifications. In that case, they will be targeted with ads based on their previous searches and interests. Another use of consumer data is to enhance the quality of products and services. By monitoring user comments, assessments, and usage trends, Amazon learns how to improve its goods and services. The company monitors customers’ feedback on various social media platforms and leverages the feedback to improve its products and services. This data-driven strategy enables Amazon to fine-tune its services to fit consumer demands and interests.

Transactional Data

Amazon gathers enormous quantities of transactional data. The corporation uses transactional data to monitor sales, inventory levels, price patterns, and the performance of products. This allows the organization to improve pricing tactics, manage inventory more effectively, and discover popular goods.

Transforming Transactional Data into Useful Information

Transactional data aids inventory management. Information from transactions enables the company to estimate demand and keep track of inventories more efficiently (Hewage et al., 2018). By examining purchase habits and patterns, Amazon can optimize the supply chain network and guarantee that the most requested goods or services are effectively supplied. Another significant use of transactional information is to aid in new product development. Amazon analyzes transactional data to gather insights into customer preferences and market dynamics, which influence product development choices. This data helps discover areas for innovation and development, ensuring that Amazon continues to provide goods and services that match the demands of its customers.

Finally, transactional data is crucial for pricing optimization. Amazon alters prices regularly depending on transaction data, competitive pricing, and other considerations. The company maximizes income while staying competitive by analyzing purchasing behavior and market circumstances (Hewage et al., 2018). The company faces significant competition from other participants in e-retailing, including eBay, Alibaba, Walmart, Apple, and Google, hence the need to optimize prices to get a competitive edge.

The Future of Data Collection and Decision-Making in HRM Management

Predictive Analysis in Hiring

According to Lawler (2017), the modern human resource landscape demands that hiring should be more than just filling positions. Amazon may use big data for predictive analytics in recruiting by first collecting massive volumes of data from a variety of sources, including resumes, job applications, performance evaluations, and employee surveys. This information is then evaluated using powerful algorithms to uncover patterns and connections between applicant characteristics and effective organizational job performance.

This analysis allows Amazon to create predictive models that anticipate which individuals are most likely to thrive in certain areas based on abilities, experience, education, personality attributes, and cultural fit (Lawler, 2017). These prediction models may also use previous recruiting data, turnover rates, and other pertinent indicators to improve their accuracy over time.

Amazon can use predictive analytics to improve the recruiting process, find the best candidates more effectively, minimize turnover, and ultimately establish a staff that fosters creative thinking and achievement for the firm. In the same breath, continual tracking and updating of these forecasting techniques ensures they successfully adjust to changing market circumstances and organizational demands.

 Employee Performance Monitoring

Amazon uses big data in worker performance assessment in various ways. An organization using big data for employee improvement gathers massive volumes of data from various sources, including staff workstations, warehouse sensors, and internal structures (Du Plessis & De Wet Fourie, 2016). This data includes indicators like productivity, mistake rate, time management, and adherence to corporate regulations.

Further, Amazon uses advanced analytics technologies to process and assess information in real time. These tools provide insights into individual and team performance patterns, highlighting components of strength and opportunities for growth (Du Plessis & De Wet Fourie, 2016). Machine learning algorithms are often used to identify trends and predict future performance results.

In addition, the monitoring tools are highly automated, enabling continuous monitoring and feedback loops. Managers receive automatic notifications and reports that indicate performance deviations or issues, allowing them to act quickly and give targeted coaching or assistance to staff (Du Plessis & De Wet Fourie, 2016). Such insights will inform customized training initiatives to improve employee performance.

Conclusion

Big data usage in organizations is increasingly gaining prominence. Due to the Internet penetration, digitalization of the economy, and the Internet of Things (IoT), big data has become helpful in enhancing operations. As shown above, Amazon utilizes insights from big-data analytics, including customer and transactional data, to enhance customer experience. Additionally, a new paradigm has emerged, which encompasses the use of data analytics to bolster human resource management. The company uses highly automated tools to conduct employee fitness based on information collected from resumes and surveys. In the same breath, the company monitors employee productivity in real time and identifies areas of improvement that may be met through customized training and employee improvement.

References

Chen, Y. (2017, February). Application and research of big data in E-commerce enterprises. Amazon is the case. In 2016 7th International Conference on Education, Management, Computer and Medicine (EMCM 2016) (pp. 481-487). Atlantis Press.

Du Plessis, A. J., & De Wet Fourie, L. (2016). Big data and HRIS used by HR practitioners: Empirical evidence from a longitudinal study. Journal of Global Business & Technology, 12(2).

Hewage, T. N., Halgamuge, M. N., Syed, A., & Ekici, G. (2018). Review: Big data techniques of Google, Amazon, Facebook and Twitter. Journal of Communications, 13(2), 94–100. https://doi.org/10.12720/jcm.13.2.94-100

Lawler, E. E. (2017). Reinventing talent management: Principles and practices for the new world of work. Berrett-Koehler Publishers.

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Module 4 – SLP
PEOPLE/PREDICTIVE ANALYTICS
For this SLP, you have the opportunity to focus on a private-sector organization doing business in another country (a company that you have not previously examined).

Big Data in Global Business- Analysis and Future Directions in HRM

Discuss how this organization is approaching the issue of “big data” and analysis of big data. What data is it analyzing and why? How is it transforming the data into useful information? What future directions do you see this global company taking about data collection and decision-making insofar as HRM is concerned?