Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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.
Blossom, J. (2014). The Signal Economy: How to target new revenues through predictive analytics. Information Services & Use, 34, 17–25.
Buchanan, E. (2017). Considering the ethics of significant data research: A case of Twitter and ISIS/ISIL. PLoS ONE, 12, 1–6.
Davenport, T. H. (2014). A predictive analytics primer. Harvard Business Review.
Farooq, M. (2016). Applications of predictive analytics in various industries. BigData-MadeSimple.com. Retrieved from http://bigdata-madesimple.com/applications-of-predictive-analytics-in-various-industries-2/
Flynn, A. J., & Stevenson, J. G. (2018). The future of data, analytics, and information technology. Pharmacy Forecast, pp. 31–34.
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.
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.
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.
Klimberg, R. K. (2016). Fundamentals of predictive analytics with JMP, Second edition. Cary, NC: SAS Institute.
Manisha, A., & Lathwal, P. (2016). Exploring classification & clustering techniques for predictive analytics. International Journal of Recent Research Aspects, 6, 76–78.
Marvin, R. (2016). Predictive analytics, big data, and how to make them work for you. PC Magazine.
Matheson, R. (2017, December 19). Inventing the “Google” for predictive analytics. MIT News. Retrieved from http://news.mit.edu/2017/endor-inventing-google-predictive-analytics-1220
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.
Siegel, E. (2013). Predictive analytics: The power to predict who will click, buy, lie or die. Hoboken, NJ: John Wiley & Sons, Inc.
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.
Wagner, E., & Longanecker, D. (2016). Scaling student success with predictive analytics: Reflections after four years in the data trenches. Change, 48, 52–59.
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.
Zhao, W., Gao, L., & Liu, A. (2018). Programming foundations for scientific big data analytics. Scientific Programming, 5, 1–2.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 The Author(s)
About this chapter
Cite this chapter
Galli, B.J., Muniz, G. (2020). Data Analytics and Predictive Analytics: How Technology Fits into the Equation. In: George, B., Paul, J. (eds) Digital Transformation in Business and Society. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-08277-2_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-08277-2_6
Published:
Publisher Name: Palgrave Macmillan, Cham
Print ISBN: 978-3-030-08276-5
Online ISBN: 978-3-030-08277-2
eBook Packages: Business and ManagementBusiness and Management (R0)