More workplace are becoming more tech-driven and fast-paced and thus there is increased need for data analysis and skills that play a significant role in business. A recent study showed that 73% of companies have invested or are planning to invest in big data in the coming years, which makes data analytics and business analytics very crucial in the business world (Gandomi & Haider, 2015).
Data analytics play an important role in promoting business growth because it improves performance whereby provides a clear picture of the things that are working and those that are not. Additionally, it offers valuable insights by revealing how the business if doing. Moreover, it sparks change and innovation by enabling business to have knowledge of consumers’ wants and needs. Furthermore, minimizes risks by providing the business with valuable insights that are useful in helping in decision making based on trends, consumer behaviors, and performance (Bradlow, Gangwar, Kopalle & Voleti, 2017).
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Business analytics helps to provide better return to a shareholder, increase revenue and market share, and improve profitability of the business since it functions as a tool used for making a sound commercial decision. Additionally, it offers the business with a competitive advantage by combining the available data with different rational models to enhance business decisions. Moreover, it promotes improved understanding of the available data both secondary and primary that impact on the operational efficiency of the business. Furthermore, it transforms the available data into valuable information, which can be presented in any format that the decision maker is comfortable with as required (Shumeli et al., 2017).
It can be concluded that data analytics and business analytics are very critical in business. Data analytics is critical in improving growth for the business while business analytics is plays an important role of enhances competitiveness of a business in the market. Therefore, analytic skills are a necessity in the increasingly tech-driven and fast-paced business environment.
References
Bradlow, E. T., Gangwar, M., Kopalle, P., & Voleti, S. (2017). The role of big data and predictive analytics in retailing. Journal of Retailing , 93 (1), 79-95.
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management , 35 (2), 137-144.
Shmueli, G., Bruce, P. C., Patel, N. R., Yahav, I., & Lichtendahl Jr, K. C. (2017). Data mining for business analytics: concepts, techniques, and applications in R . John Wiley & Sons.
