Data Analytics and Predictive Analytics: How Technology Fits into the Equation

  • Brian J. GalliEmail author
  • Gabrielle Muniz


The purpose of this research is to inform readers about data analytics and predictive analytics through their various applications and examples of their benefits. Technology is becoming more integrated into daily life, and the amount of data that is obtained and processed by that technology is quite robust. Most people with accounts that are connected to the Internet have their data collected by these companies. Then, they either package and sell the data or use it for marketing purposes. Also, data analytics is an integral part of artificial intelligence development. Despite predominantly being used for marketing other companies, such as healthcare, providers can use existing healthcare information to predict the development of other future complications. This field is vast and growing at a rapid rate, with more technological devices becoming commonplace. Thus, it is essential to understand the benefits and drawbacks of this technology, as it will be an integral aspect of life in the coming years.


Analytics Data Information Predictive analytics Technology 


  1. Ahmed, N. K., & Kapadia, J. (2017). Big data analytics: How big data is shaping our understanding of electrified vehicle customers. SAE International Journal of Materials & Manufacturing, 12, 99–107.Google Scholar
  2. Blossom, J. (2014). The Signal Economy: How to target new revenues through predictive analytics. Information Services & Use, 34, 17–25.CrossRefGoogle Scholar
  3. Buchanan, E. (2017). Considering the ethics of significant data research: A case of Twitter and ISIS/ISIL. PLoS ONE, 12, 1–6.Google Scholar
  4. Davenport, T. H. (2014). A predictive analytics primer. Harvard Business Review.Google Scholar
  5. Farooq, M. (2016). Applications of predictive analytics in various industries. Retrieved from
  6. Flynn, A. J., & Stevenson, J. G. (2018). The future of data, analytics, and information technology. Pharmacy Forecast, pp. 31–34.Google Scholar
  7. Ghofrani, F., He, Q., Goverde, R. M., & Liu, X. (2018). Recent applications of big data analytics in railway transportation systems: A survey. Transportation Research: Part C, 90, 226–246.Google Scholar
  8. Giacumo, L. A., & Breman, J. (2016). Emerging evidence on the use of big data and analytics in workplace learning. Quarterly Review of Distance Education, 17, 21–38.Google Scholar
  9. Houser, K. A., & Sanders, D. (2017). The use of big data analytics by the IRS: Efficient solution or the end of piracy as we know it? Vanderbilt Journal of Entertainment & Technology Law, 14, 817–872.Google Scholar
  10. Klimberg, R. K. (2016). Fundamentals of predictive analytics with JMP, Second edition. Cary, NC: SAS Institute.Google Scholar
  11. Manisha, A., & Lathwal, P. (2016). Exploring classification & clustering techniques for predictive analytics. International Journal of Recent Research Aspects, 6, 76–78.Google Scholar
  12. Marvin, R. (2016). Predictive analytics, big data, and how to make them work for you. PC Magazine.Google Scholar
  13. Matheson, R. (2017, December 19). Inventing the “Google” for predictive analytics. MIT News. Retrieved from
  14. Moghaddass, R., Zuo, M., Liu, Y., & Huang, H.-Z. (2015). Predictive analytics using a nonhomogenous semi-Markov model and inspection data. IIE Transactions, 47, 505–520.CrossRefGoogle Scholar
  15. Siegel, E. (2013). Predictive analytics: The power to predict who will click, buy, lie or die. Hoboken, NJ: John Wiley & Sons, Inc.Google Scholar
  16. Vesely, R. (2017). Predictive analytics: IU Health knows the patient in Room 103 is at high-risk for CLABSI (cover story). H&HN: Hospitals & Health Networks, 91, 20–25.Google Scholar
  17. Wagner, E., & Longanecker, D. (2016). Scaling student success with predictive analytics: Reflections after four years in the data trenches. Change, 48, 52–59.CrossRefGoogle Scholar
  18. Yuksel, A. S., Cankaya, S. F., & Uncu, I. S. (2017). Design of a machine learning predictive analytics system for spam Problem. Acta Physica Polonica, A, 132, 500–504.CrossRefGoogle Scholar
  19. Zhao, W., Gao, L., & Liu, A. (2018). Programming foundations for scientific big data analytics. Scientific Programming, 5, 1–2.Google Scholar

Copyright information

© The Author(s) 2020

Authors and Affiliations

  1. 1.Long Island UniversityBrookvilleUSA

Personalised recommendations