Data Analytics
The science of studying unprocessed information in order to pursue significant outcomes with the help of that information is called data analytics. The prior research and study shows how remarkable results Data Analytics bring to the retail and hospitality industry as well as big manufacturing multinational organizations. However, many also claimed that Data Analytics cannot provide accurate data and focuses on the loopholes which make it hard to make right decisions (Ali et. al., 2013). There are different data types whereas raw data can be in the form of Audio, Video, Text, location etc. Then, Data Analytics focuses on managing it with techniques to filter obsolete data and use the useful data. Furthermore, Data Analytics helps researchers as much as it helps businesses to grow; it helps analyze the current state of business and aid in making useful report that can help business to grasp the importance of Data Analytics (Waller & Fawcett, 2013).
According to an article (Marr, 2016), one of the giant company today, Amazon seems to benefit highly from the data collections from tens of millions customers based on their item selection, demographic data, purchase pattern and feedback as a result its web services had its revenue increased by 81% than previous year to 1.8 billion dollars. In fact, Business analytics provides structural framework and insights for business to derive values and gain the output which helps developing new methods to serve quality products or services to customers. Similarly, big data is a major tool for measuring performance, customer behaviors and destinations using various tools such as web-navigation, booking system and feedback. Big data can be helpful to understand the trends of any business, where does it stand, what kind of complaints or dissatisfactions customer report and how to take effective action to solve it. Therefore, proper understanding of the customer review or rating matters for any business organization to grow maximum profit as the customer satisfaction can only help sustain any businesses in today’s competitive world.
Reference:
Ali, L., Asadi, M., Gasevic, D., Jovanovic, J., & Hatala, M. (2013). Factors Influencing Beliefs for Adoption of a Learning Analytics Tool: An Empirical Study. Computers & Education, 62, 130. Retrieved from https://www-sciencedirect-com.ezproxy.csu.edu.au/science/article/pii/S0360131512002515
Marr, B. (2016). Big data in practice: how 45 successful companies used big data analytics to deliver extraordinary. West Sussex, United Kingdom: John Wiley and Sons Ltd. Retrieved from https://onlinelibrary-wiley-com.ezproxy.csu.edu.au/doi/book/10.1002/9781119278825
Waller, M. A., & Fawcett, S. E. (2013). Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management. Journal of Business Logistics, 34(2), 77-84. doi:doi:10.1111/jbl.12010