Predictive Analytics meets Big Data

What’s making the current Artificial Intelligence (AI) “Summer” Possible?

Rodriguez and Da Cunha (2018) describe an interrelationship between the elements of Big Data and Predictive Analysis (PA) which they hybridize into the term Big Data-Predictive Analytics (BDPA). They describe BDPA as “a holistic approach to manage, process, and analyze data regarding high volume, variety, velocity, veracity, and value to create actionable insights…” which is also described as the 5Vs of Big Data (p. 152). (See the more abbreviated definitions offered by Gupta and Rani’s (2018) works specific to the 3Vs).

Rodriguez and Da Cunha (2018) introduce a concept they describe as absorptive capacity, which is an organization’s ability to gather the data and create value and wisdom for the benefit of an organization or entity (Rodriguez & Da Cunha, 2018). Big Data essentially creates an environment where larger population data in effect provides better predictive outcomes than could be possible with past technologic limitations. Greater data affords a higher sample size that will more likely increase the predictive capacity of most analytic approaches (Hubbard & Seiersen, 2016).

Rodriguez and Da Cunha’s (2018) connect the capability of BDPA to support overall performance and innovation outputs (Russo, 2019). The four elements they describe include 1) innovation drivers—create increased profitability and sustainability, 2) new technologies—provide improved predictive analytics of emerging data science tools and capabilities, 3) relevance—awareness of profitability and social impacts, and 4) absorptive capacity—enable a company to accept, store, and process large volumes of data (Rodriguez & Da Cunha, 2018). Their framework emphasizes the direct importance of Big Data in providing critical forecasts to corporate leadership.

They conclude that their framework links sustainability, BDPA, and absorptive capacity in a way that uses the power of PA with the full capabilities of Big Data to analyze large amounts of data rapidly. Rodriguez and Da Cunha’s (2018) work is relevant because a general understanding of the topics of Big Data (the input component) and Predictive Analytics (the output) are vital to the study’s experimental design. Specifically, the academic community’s work with these conjoined capabilities are key enabling factors for the purpose of this study.

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The most vital technological advancement in data science has been the growth of Big Data capabilities.

(Gupta & Rani, 2018)

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Predictive analytics’ value.

Historically, Hair (2007) discusses PA and how it is applied to knowledge creation in the area of marketing. The nature of his study is beyond the purely descriptive and attempts to predict future outcomes. The following are several reviews of how important it is to have a predictive objective in supporting decision-making that effectively and efficiently uses data science.

Hair (2007) describes the importance of a knowledge-based economy and how it requires information that must be actionable. He notes the historical accumulation of data that has been lost or discarded in the past because of the inability to store and process analytical data. (Hair, 2007). He identifies that PA is not a revolutionary approach and dates it to the 1920s. In particular, he foresaw that increases in data volume collection would improve the ability of businesses to identify, understand, and predict customer needs and market developments better. He concludes with the essential need for PA and the ongoing demand for more accurate and refined information to support knowledge creation (Hair, 2007).

Is there a need for a Cybersecurity-Data Science Conceptual Framework??

K and Shivakumar (2014) describe data analytics as practices, skills, and technologies for the study of business information and intelligence designed to support corporate decision-making. They discuss the crucial insights derived from Big Data sets collected and analyzed as part of the growing efforts in data science (K & Shivakumar, 2014). The authors’ focus is on actionable results that can improve business decisions and increase profitability.

K & Shivakumar (2014) further describe the three types of analytics: 1) descriptive, 2) predictive, and 3) prescriptive; neither of these is any more important than the others; they provide answers to real-world questions based upon the customer’s needs. These three analytics types offer business intelligence the extract and describe a picture of a stated problem. While descriptive illustrates what has happened in the past, predictive—an emphasis of this study—extrapolates to what may occur in the future (K & Shivakumar, 2014).

Lee (2015) calls for a systems approach for projects requiring data modeling, especially those used in the areas of waste, fraud, and abuse detection. He sees PA as a process that involves a series of methodologies to increase awareness and actionability; it requires a combination of statistical sampling, modeling, and estimation. In Lee’s (2015) Predictive Analysis System (PAS) model, he describes a cycle of  1) data collection and sampling, 2) model estimation—what are the likely results, 3) prediction and model evaluation—more refined statistical analyses to identify future outcomes, and 4) sampling and modeling adjustments—recursively adjust the model to produce better results by repeating Step 1 of the PAS cycle (Lee, 2015).  Lee (2015) posits his PAS approach leads to greater effectiveness, efficiency, and transparency in the realm of his fraud prediction efforts.

PA is vital for the experimental design and the objective of this research.  It employs the use of the Artificial Neural Network (ANN) as the basis of a systematic solution to cybersecurity defense. Machine Learning (ML) and ANN afford an extraordinary means to go beyond the more descriptive nature of classic statistics and affords an expanded and more capable use of data science to create new knowledge through its experimental design (Fang et al., 2019; Soni & Vyas, 2010).

Big data meets external data.Gupta and Rani’s (2018) work begins with an explanation of the 3Vs of Big Data: “volume, velocity, and variety” (p. 4). Volume is the magnitude of data that is available to the public and private sector, velocity is associated with the ability to accomplish real-time analytics rapidly, and variety addresses the problem of the manipulation of differing data types that may include unstructured data that comprises, for example, text messages, images, and tweets (Gupta & Rani, 2018; Zuech et al., 2015). They recognize that Big Data technologies and tools are still evolving and requiring greater commitment by both academia and industry (Gupta & Rani, 2018). 

Specifically, the most vital technological advancement in data science has been the growth of Big Data capabilities (Gupta & Rani, 2018). Gupta and Rani (2018) explain with the increasing availability of information and ability to store and process data, there are also associated opportunities and challenges (Gupta & Rani, 2018; Marz & Warren, 2015; Taylor-Sakyi, 2016). Gupta and Rani (2018) describe, for example, with over seven billion searches on Google, the demand for information processing continues to increase. Big Data has become a centerpiece for the rapid evolution of data science solutions in the 21^st Century. 

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Gupta and Rani (2018) offer a significant roadmap that is more than an informative research paper on the topic of Big Data (Russo, 2019). They identify how many facets of data science will play a role in the future development of data science. They offer that Big Data provides “useful analysis, [and] better decisions and knowledge discovery [support]” for those using this information to improve future business and societal predictive analytics (p. 17).  While Big Data is pivotal to the explosion of data science, there remains an ongoing  need to employ multiple methodologies and techniques to counter unrelenting nature of daily cyber-threats.

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