Predictive Analytics Revolutionizing Global Industries

Introduction
Predictive analytics—an intelligence software system that infers user trends from stored computer data—statistics, models, etc.—offers innovative alternatives to traditional human reasoning. Historically, people typically combined logic and intuition to infer outcomes from human behavior.

However, with predictive analytics at the vanguard of technological development, exclusive reliance on people for intelligent solutions now remains nearly vestigial.

Since computers perform the same operations presumably quicker and more accurately than possible by people alone, predictive analytics renders unassisted human reasoning almost anachronistic. In other words, predictive analytics relies not on mere conjecture but collects information stored from people within computer systems which assumes patterns to infer human behavior.

This sophisticated software ostensibly mitigates inaccuracy, juxtaposing online websites visited and internet user trends to predict human action with intelligence. Indeed, business users may extrapolate decisions about future human behavior assumed from cumulative data storage with exponentially enhanced effectiveness, efficiency, and punctilious precision.

For example, IBM provides predictive analytics software as a revolutionary resource to help business leaders “look beyond biases in discerning real patterns and anticipating events.” [i] By transcending tendentious sentiments inherent to human thought, predictive analytics, though not unassailable, strengthen reliability with a copious cornucopia of accessible data—information otherwise unavailable if extracted solely from limited human knowledge and processing capacity.

Therefore, predictive analytics/“predictive intelligence”—accurately predicting human decisions from voluminous data accumulation—revolutionizes 21^st Century global industries with advanced inductive/deductive programming unsurpassed in human history.

Six Step Process—Predictive analytics assumes a six-step process:

• Define Project—The first step requires users to define project. Here, users of predictive analytics software define business objective and identify the sets intended for use.
• Data Collection—The second step entails data collection. In this stage the predictive analytics initiates data mining—gathering information from multiple sources, collecting data to generate projections.
• Data Analysis—The third step requires data analysis. Data analysis commences the discovery process, systematically separating usefulness and relevance of information from modeling data to elicit conclusions.
• Statistics—The fourth step employs statistical analysis to corroborate accuracy of information discovered and analyzed in step three. The computer engages heuristic experimentation—trial/error testing process to validate assumptions about information and ensure accurate inferences engendered from those assumptions. [ii]
• Modeling—This stage automatically creates models predicated on the statistical accuracy process engaged in step 4. These models become predictive resources for future events and incorporates a selection of options for multi-model evaluation.
• Deployment—This stage deploys the analytical results previously obtained to facilitate decision-making with human volition based on computer-generated results.

Applications
Predictive intelligence technology applies to almost any field as businesses become increasingly reliant on quick, cogently consolidated, computer generated logic for critical thinking and problem-solving. Applications assert particular prominence in the following domains:

• Analytical Customer-Relationship Management—Predictive applications implement marketing stratagems to target demographics with aspirations of producing positive, long-term customer value. Computers may store such personal information as consumer purchases to infer plausible retention rates.
• Ex: Competitive Marketing intelligence—marketing techniques that range from observing consumers firsthand to meticulously monitoring social media buzz via internet research helps companies acquire invaluable insights about how consumer interaction may engage with brands. [iii] By observing computer-generated analytics, companies may apply predictive intelligence concerning consumer behavior as an effective offensive/defensive SWOT marketing mechanism, mitigating weaknesses and threats while simultaneously ameliorating strengths to capitalize on potential available opportunities.
• Health Care—Predictive intelligence exercises increasing prominence in health care. The apparent demand to identify certain chronic health conditions seems increasingly evident among patients. For example, physicians may innovatively employ “big data” analytics to accommodate transitioning trends among some tech-savvy consumers who perhaps prefer long-distance diagnoses from dermatologists by transmitting “digital photos” of anomalous skin manifestations. [iv] Assuming arguendo the validity of such surveys—which become susceptible to distortion by falsely disseminated information and/or unrepresentative population samples—one may reasonably infer increasing demand for predictive intelligence. However, to conclude as U.S. News with the verbatim assertion, “Once doctors and providers start viewing their patients as consumers, patients will begin to expect a personalized medical experience that factors in all of their health information, much like many retailers already do today,” assumes without warrant certain expectations about patients perhaps not necessarily true. [v] After all, even if patients truly desire the kinds of long-distance digital interaction for some diagnoses as purported, that same mentality may not necessarily apply for “all health information.” Furthermore, the foregoing assertion also assumes doctors may not already perceive patients as consumers similar to retailers. It also neglects the assumption that some patients possibly already anticipate a personalized medical experience. Ultimately, we lack evidence sufficient to infer future patient expectations. Such conjectural conclusions assume omniscience, a degree of prescience not readily available to people, and thus neglects alternative possibilities. Nonetheless, we may reasonably conclude the application of predictive intelligence extending to healthcare possibly increasing in subsequent years. If true, this inference plausibly portends a paradigmatic shift in how doctors consider treating certain digitally-oriented patients influenced by transitioning technological trends in health care.
• Collection Analytics—predictive analytics may become increasingly prevalent in the legal field. For example, government municipality clerk office websites in local jurisdictions may find more clever methods for lawyers/collection agencies to track down defaulting debtors.
• Cross-Selling Analytics—predictive analytics may even apply to analyzing consumer-spending behaviors among pre-existing customers in targeting a specific customer demographic. Ex: Target’s customer profiling via a Guest ID number that connects to name, credit card, and/or email address in inferring prospective customer purchases. See p. 150 in text. However, the use of financial information to infer purchasing trends may pose damaging legal implications—e.g. customer privacy/trespass violations—consequently culminating in possible lawsuits.
• Fraud Detection—predictive analytics to intercept identity theft and/or fraudulent financial/personal online/offline transactions emerges with particular pertinence as technology predisposes victim vulnerability—risks of hacks from criminals, frequently illegal aliens becomes prevalent. These systems may help implicate motives to identify certain incriminating patterns, which may catch criminals and/or thwart future fraud. Technology may recognize probative historical patterns of fraud/theft or illegal activity, and proactively prevent possible future attempts.
• Risk Management—Predictive analytics applications helps minimize risks on investment options by itemizing price earnings ratios—featuring stock interpolations—to create reasoned decisions in inferring plausible financial outcomes.

Conclusion
Predictive intelligence technology at the forefront of globalization in contemporary society provides innovative ways to help people more effectively solve societal problems. The technology fosters opportunities to guide professionals in their respective fields, targeting career, consumer, and/or personal material needs. Technological advances relegates the traditional use of solely human reasoning to obsolescence, supplanting prior antiquated human intuition with increasingly reliable data. Though predictive analytics may not furnish a completely flawless, fool-proof system, the heightened accuracy and quickness of forecasts extrapolated from data overwhelmingly compensates for imperfections. Therefore, predictive analytics possesses profound implications for future developments, assuming people continue to become increasingly reliant on technology, an inference plausibly suggested from the manifestations of modernization.

[i] IBM, “What’s new on a Smarter Planet.” p.1.
[ii] Predictive Analytics Today, “What is Predictive Analytics?” p. 2-4.
[iii] Gary Armstrong, Philip Kotler, “Principles of Marketing, sixteenth edition”, 2016, 2014, 2012,
Pearson Education, Inc., p. 109.
[iv] Girish Navani, “How Big Data is Driving the Consumerization of Health Care”, US News, p.2.
[v] See Navani Id. at p. 2, final sentence of last paragraph.