SPOT: Halladay and Predictive Analytics
The Value of Data Science in the 21st Century
Halladay (2013) explores the value of AI/ML predictive capabilities in the area of equipment leasing and the associated financial industry supporting this business segment; his view is much more pragmatic than academic. He sees PA as needed in the commercial sector for making sound judgments about risk—which can also include cyber-threats to corporations and agencies (Halladay, 2013). The value of data science tools cannot be ignored in a continuous battle with global cyber-threats for the foreseeable future (Allyn, 2019; Kaplan, 2016; Starks, 2019). His work is not only intended to observe the problem of defending against a myriad of cyber-threats but how to create a pathway to better anticipate and respond to these attacks using data science tools and capabilities.
Data science has distinct and fundamental advantages to segment the good guys from the bad guys which is an inherent strength of modern-day data analytic toolsets (Fang et al., 2019; Rashid, 2016; Taylor, 2017). Halladay (2013) further emphasizes the importance of data science and its ability to segment data. The strength of data science is to also rapidly and efficiently provide needed classification and predictiveness. Halladay (2013) makes two significant observations and declarations about PA. He states that: 1) industry is currently using PA in an ad hoc manner, and 2) the “resultant information must be actionable” (p. 4); he accentuates concerns that data science needs to meet applicability in the realm of human problem-solving, and must be more than used for observation alone.
References
Allyn, B. (2019, August 20). 22 Texas towns hit with ransomware attack in ‘new front’ of cyberassault. National Public Radio. Retrieved from https://www.npr.org/2019/08/20/752695554/23-texas-towns-hit-with-ransomware-attack-in-new-front-of-cyberassault
Fang, X., Xu, M., Xu, S., & Zhao, P. (2019). A deep learning framework for predicting cyber attacks rates. EURASIP Journal on Information Security, 2019(1), 1–11. Retrieved from http://franklin.captechu.edu:2123/10.1186/s13635-019-0090-6
Halladay, S. D. (2013). Using predictive analytics to improve decisionmaking. The Journal of Equipment Lease Financing (Online), 31(2), 1–6. Retrieved from https://franklin.captechu.edu:2074/docview/1413251757?accountid=44888
Kaplan, F. (2016). Dark territory: The secret history of cyber war. New York, NY: Simon & Schuster.
Rashid, T. (2016). Make your own neural network. Amazon Digital Services, LLC: Tariq Rashid.
Starks, T. (2019, July 9). Cyber incidents were expensive in 2018. Politico. Retrieved from https://www.politico.com/newsletters/morning-cybersecurity/2019/07/09/cyber-incidents-were-expensive-in-2018-675243
Taylor, M. (2017). Neural network math: A visual introduction for beginners. Vancouver, Canada: Blue Windmill Media.
