Use Of Predictive Analytics Research Articles

The power of prediction: predictive analytics, workplace complements, and business performance

Anecdotes abound suggesting that the use of predictive analytics boosts firm performance. However, large-scale representative data on this phenomenon have been lacking. Working with the Census Bureau, we surveyed over 30,000 American manufacturing establishments on their use of predictive analytics and detailed workplace characteristics. We find that productivity is significantly higher among plants that use predictive analytics—up to $918,000 higher sales compared to similar competitors. Furthermore, both instrumental variables estimates and the timing of gains suggest a causal relationship. However, we find that the productivity pay-off only occurs when predictive analytics are combined with at least one of three workplace complements: significant accumulation of IT capital, educated workers, or workplaces designed for high flow-efficiency production. Our findings support claims that predictive analytics can substantially boost performance, while also explaining why some firms see no benefits at all.

Virtual Airport Hub—A New Business Model to Reduce GHG Emissions in Continental Air Transport

The virtual airport hub business model is an innovative solution supported by digital technologies; the implementation of which in continental air transport may lead to a reduction in energy consumption and to a reduction in greenhouse gas emissions. The prerequisites for the implementation of the described solution are as follows: striving to implement the GHG emission reduction strategy laid out in the Paris Agreement (2015) and the European Green Deal (2019) as well as the EU digitalization strategy (2020). The use of predictive analytics to identify the mobility needs of population and operational capabilities of the sector gives an opportunity to plan travel flows and to create an appropriate set of direct connections among regional airports every day. The results of the analysis of data from 2019 on the amount of energy consumption and GHG emissions indicate that in Europe, it would be possible to achieve reduce GHG emissions by 5% without reducing the number of passengers using air transport. The study was prepared after conducting literature studies, data analysis, and using the method of formulating scenarios. The proposed solution has the features of an innovative business model, the implementation of which allows for obtaining more favorable effects using already available resources.

Development and validation of a predictive model to predict and manage drug shortages.

Pharmacy departments across the country are problem-solving the growing issue of drug shortages. We aim to change the drug shortage management strategy from a reactive process to a more proactive approach using predictive data analytics. By doing so, we can drive our decision-making to more efficiently manage drug shortages. Internal purchasing, formulary, and drug shortage data were reviewed to identify drugs subject to a high shortage risk ("shortage drugs") or not subject to a high shortage risk ("nonshortage drugs"). Potential candidate predictors of drug shortage risk were collected from previous literature. The dataset was trained and tested using 2 methods, including k-fold cross-validation and a 70/30 partition into a training dataset and a testing dataset, respectively. A total of 1,517 shortage and nonshortage drugs were included. The following candidate predictors were used to build the dataset: dosage form, therapeutic class, controlled substance schedule (Schedule II or Schedules III-V), orphan drug status, generic versus branded status, and number of manufacturers. Predictors that positively predicted shortages included classification of drugs as intravenous-only, both oral and intravenous, antimicrobials, analgesics, electrolytes, anesthetics, and cardiovascular agents. Predictors that negatively predicted a shortage included classification as an oral-only agent, branded-only agent, antipsychotic, Schedule II agent, or orphan drug, as well as the total number of manufacturers. The calculated sensitivity was 0.71; the specificity, 0.93; the accuracy, 0.87; and the C statistic, 0.93. The study demonstrated the use of predictive analytics to create a drug shortage model using drug characteristics and manufacturing variables.

The Power of Prediction: Predictive Analytics, Workplace Complements, and Business Performance

Anecdotes abound suggesting that the use of predictive analytics boosts firm performance. However, large-scale representative data on this phenomenon have been lacking. Working with the Census Bureau, we surveyed over 30,000 American manufacturing establishments on their use of predictive analytics and detailed workplace characteristics. We find that productivity is significantly higher among plants that use predictive analytics—up to $918,000 higher sales compared to similar competitors. Furthermore, both instrumental variables estimates and timing of gains suggest a causal relationship. However, we find that the productivity pay-off only occurs when predictive analytics are combined with at least one of three workplace complements: significant accumulation of IT capital, educated workers, or workplaces designed for high flow-efficiency production. Our findings support claims that predictive analytics can substantially boost performance, while also explaining why some firms see no benefits at all.

The application of predictive analysis in decision-making processes on the example of mining company’s investment projects

The authors present a method of supporting decision-making processes with the use of predictive analytics. The method is presented with an example of investment project portfolios of a mining company. In the research multiple predictive models have been tested to determine most effective algorithms and key project attributes allowing to predict possible budget deviations. The proposed approach can be applied in other organizations managing projects according to project methodologies with access to structured project data.

Déjà Vu - A New Coronavirus Challenge.

Déjà Vu - A New Coronavirus Challenge.

Narrative Review of Predictive Analytics of Patient-Reported Outcomes in Adult Spinal Deformity Surgery.

Study Design:Narrative reviewObjective:Decision making in surgery for adult spinal deformity (ASD) is complex due to the multifactorial etiology, numerous surgical options, and influence of multiple medical and psychosocial factors on patient outcomes. Predictive analytics provide computational tools to analyze large data sets and generate hypotheses regarding new data. In this review, we examine the use of predictive analytics to predict patient-reported outcomes (PROs) in ASD surgery.Methods:A search of PubMed, Web of Science, and Embase databases was performed to identify all potentially relevant studies up to February 1, 2020. Studies were included based on the use of predictive analytics to predict PROs in ASD.Results:Of 57 studies identified and reviewed, 7 studies were included. Multiple algorithms including supervised and unsupervised methods were used. Significant heterogeneity was observed with choice of PROs modeled including ODI, SRS22, and SF36, assessment of model accuracy, and with the model accuracy and area under the receiver operating curve values (ranging from 30% to 86% and 0.57 to 0.96, respectively). Models were built with data sets of patients ranging from 89 to 570 patients with a range of 22 to 267 variables.Conclusions:Predictive analytics makes accurate predictions regarding PROs regarding pain, disability, and work and social function; PROs regarding satisfaction, self-image, and psychologic aspects of ASD were predicted with the lowest accuracy. Our review demonstrates a relative paucity of studies on ASD with limited databases. Future studies should include larger and more diverse databases and provide external validation of preexisting models.

Exploring Futuring and Predictive Analytics for Developing Organizational Strategy

The study addresses the type of leadership styles that promote Innovation, transformational, and adaptive leadership—in this study of futuring, discussing the evolving role of the futurist within the organization. The discussion of the history of futurists within the organization for the public and private sectors looks at the part of the futurist to determine the relevance of today compared to 20 years ago. Also, it looks at how the futurist can utilize the futurist's techniques in their organization. The study of predictive analytics addresses the use of predictive analytics with examples of companies that use predictive analytics, and the reasons they use predictive analytics—examining predictive analytics to determine if they are driving more informed business decisions. The article explores the relationship between big data and predictive analytics and if big data is responsible for the popularity of predictive analytics.

Use of Predictive Analytics to Inform Integrated Care Programs to Reduce Hospitalizations Among Hemodialysis Patients

Use of Predictive Analytics to Inform Integrated Care Programs to Reduce Hospitalizations Among Hemodialysis Patients

Student and Staff Perspectives on the Use of Big Data in the Tertiary Education Sector: A Scoping Review and Reflection on the Ethical Issues

While universities routinely use student data to monitor and predict student performance, there has been limited engagement with student and staff views, social and ethical issues, policy development, and ethical guidance. We reviewed peer-reviewed and grey-literature articles of 2007 to 2018 describing the perspectives of staff and students in tertiary education on the use of student-generated data in data analytics, including learning analytics. We used an ethics framework to categorize the findings. There was considerable variation but generally low awareness and understanding amongst students and staff about the nature and extent of data collection, data analytics, and use of predictive analytics. Staff and students identified potential benefits but also expressed concerns about misinterpretation of data, constant surveillance, poor transparency, inadequate support, and potential to impede active learning. This review supports the contention that consideration of ethical issues has failed to keep pace with the development of predictive analytics in the tertiary sector.

Analytics as a Tool in Major Selection: Exploring Student Reactions

As the use of predictive analytics is growing in higher education, research is lagging, with research on student perspectives mostly absent. In light of this we drew on theory and practice to posit antecedents that would influence Belief In Analytics (BIA), a measure we introduce to assess the perceptions about the utility of analytics in career-related decisions. We explored BIA’s influence on student desire to have predicative analytic data for college major selection, as well as moderators of that relationship. We found that personality, interests and experience influenced BIA and that BIA influenced the desire to use analytics in major selection. We discuss how Social Cognitive Career Theory, along with research in human resources and technology adoption, and our empirical results support the need to incorporate student perceptions into research on analytics in higher education. Implications for research and practice are discussed.

The case of Canvas: Longitudinal datafication through learning management systems

ABSTRACT The Canvas Learning Management System (LMS) is used in thousands of universities across the United States and internationally, with a strong and growing presence in K-12 and higher education markets. Analyzing the development of the Canvas LMS, we examine 1) ‘frictionless’ data transitions that bridge K12, higher education, and workforce data 2) integration of third party applications and interoperability or data-sharing across platforms 3) privacy and security vulnerabilities, and 4) predictive analytics and dataveillance. We conclude that institutions of higher education are currently ill-equipped to protect students and faculty required to use the Canvas Instructure LMS from data harvesting or exploitation. We challenge inevitability narratives and call for greater public awareness concerning the use of predictive analytics, impacts of algorithmic bias, need for algorithmic transparency, and enactment of ethical and legal protections for users who are required to use such software platforms.

Barriers and Recommendations for Developing a Data Commons for the Implementation and Application of Cardiovascular Disease and Diabetes Risk Scoring in the Philippines

Cardiovascular diseases (CVDs) and diabetes are the primary causes of death in the Philippines. This manuscript reviewed previous studies on the use of predictive analytics for CVD and diabetes risk scoring. This paper also discussed barriers and strategies on how to access/generate available data sets for CVDs and diabetes in the country. CVD and diabetes risk scoring requires the availability of data sets related to such diseases. Although the Philippines has taken strides to implement the Philippine National eHealth solution, such a program does not include strategies toward the use of predictive analytics for CVD and diabetes risk scoring. CVD and diabetes risk scoring research is particularly limited in the Philippines due to challenges related to costs, gaps in policies, and stakeholder involvement. A possible theoretical framework for the analysis and utilization of data sets as well as recommendations and research directions were discussed in this manuscript.

Securing IoT Applications with Smart Objects: Framework and a Socially Assistive Robots Case Study

The expanded deployment of smart objects in IoT applications is pushing existing IoT platform architectures and their security functionalities to their limits. Indeed, smart objects exhibit semi-autonomous behaviours, are not centrally controlled all the time and therefore need more dynamic approaches in protecting them against vulnerabilities and security incidents. In this paper, we introduce a novel framework for securing the latest generation of IoT applications that involve smart objects, while illustrating its application in securing an Ambient Assisted Living (AAL) system that comprises socially assistive robots. The framework’s innovative aspects lie in the use of predictive analytics for anticipating the behaviour of smart objects, including abnormalities in their security behaviour. The importance of anticipating such abnormalities is validated, demonstrated and discussed in the context of the AAL application.

THE CUSTOMER OF THE FUTURE: THE PSYCHOLOGICAL, TECHNICAL, AND EXPERIENTIAL STRATEGIES

AbstractThis article examines how the most successful companies have become increasingly customer‐centric, especially as the evolve to better serve the customer of the future. It is based on my research for a new book, The Customer of The Future: 10 Guiding Principles For Winning Tomorrow's Business, as well as research studies from such organizations as Temkin Group, Gartner, Forrester Research, and McKinsey. The approach used by the profiled companies can be applied to the work of leaders in all sectors of society's organizations.The article divides the customer experience into three categories: psychological, technical, and experiential. Within psychological, examples are drawn from Amazon, Capital One, and Pixar; within technical, from Sephora, Best Buy, and Macy's; and within experiential, from Hasbro, Lyft, and Allstate. The impact of artificial intelligence is noted, with examples from The North Face and Macy's; and the use of predictive analytics by ride‐sharing firm Lyft is explained. Particular importance in the article is given to the strategic and ethical use of customer data. For instance, Allstate's CEO Tom Wilson mentions that customer data is the new currency, and his company is building ways that people can be better aware of their personal data and accounts online.

Why Predictive Algorithms are So Risky for Public Sector Bodies

This paper collates multidisciplinary perspectives on the use of predictive analytics in government services. It moves away from the hyped narratives of “AI” or “digital”, and the broad usage of the notion of “ethics”, to focus on highlighting the possible risks of the use of prediction algorithms in public administration. Guidelines for AI use in public bodies are currently available, however there is little evidence these are being followed or that they are being written into new mandatory regulations. The use of algorithms is not just an issue of whether they are fair and safe to use, but whether they abide with the law and whether they actually work. Particularly in public services, there are many things to consider before implementing predictive analytics algorithms, as flawed use in this context can lead to harmful consequences for citizens, individually and collectively, and public sector workers. All stages of the implementation process of algorithms are discussed, from the specification of the problem and model design through to the context of their use and the outcomes. Evidence is drawn from case studies of use in child welfare services, the US Justice System and UK public examination grading in 2020. The paper argues that the risks and drawbacks of such technological approaches need to be more comprehensively understood, and testing done in the operational setting, before implementing them. The paper concludes that while algorithms may be useful in some contexts and help to solve problems, it seems those relating to predicting real life have a long way to go to being safe and trusted for use. As “ethics” are located in time, place and social norms, the authors suggest that in the context of public administration, laws on human rights, statutory administrative functions, and data protection — all within the principles of the rule of law — provide the basis for appraising the use of algorithms, with maladministration being the primary concern rather than a breach of “ethics”.

Технологии прогнозной аналитики в борьбе с пандемией COVID‑19

Recently, a new coronavirus infection, or COVID‑19, caused by the pathogen SARS-CoV‑2, has been continuing to spread around the world rapidly. According to the World Health Organization (WHO), which declared this outbreak a pandemic, COVID‑19 is a serious public health problem of international concern. Due to the lack of proven effective treatment and vaccination against COVID‑19, precautions are considered by WHO to be strategic goals and a primary response to the pandemic. It is recommended that country guidelines adopt national health care programs aimed at assessing and reducing the risk of infection spread. Predictive analytics have begun to be actively used to compile population and personal forecasts of the progression of morbidity, mortality, assess the severity of the course of the disease, etc. This article provides an overview of available developments and publications on the use of predictive analytics in the management of COVID‑19 pandemic.

The Use of Predictive Analytics to Shift the Culture of Academic Advising Toward a Focus on Student Success

The Use of Predictive Analytics to Shift the Culture of Academic Advising Toward a Focus on Student Success

Предиктивная аналитика в прогностической деятельности полиции современных государств

The relevance of the topic of the study is due to the need to find an optimal model of police activity that meets the requirements and challenges of the present. The transformation of the public organization requires the comprehension of the current situation, new forms of theorizing and the development of new conceptual ideas. The growing importance of police systems in the context of globalization has been determined by a variety of law enforcement practices that requires critical analysis. A historiographic analysis of the problem of the use of predictive analytics in police activities showed that such experience from the theoretical and legal position was not reflected in the researches of Russian scientists. The object of the study was the police systems of foreign countries, where the predictive analytics became widespread and was the basis of the work of the police bodies. Predictive analytics (software that allows to predict crime) and the possibilities of its use in police activities is the subject of the research. A legal analysis of prognostic police activity, on the basis of which a new law enforcement model was developed, which already confirmed its effectiveness in the US, Great Britain, Germany, the Netherlands, China was the purpose of the research. The methodology of the study was drawn up by the scientific principles of dialectics, historicism and objectivity, based on the recognition of the variability in the ways of implementing the law enforcement function of the state in various national political and legal systems, as well as general scientific methods: analysis and synthesis, induction and deduction, analogue method, ascent from abstract to concrete, and private-scientific methods: legal comparativeness, formal-legal (dogmatic), methods of systemic and structural-functional analysis, methods of theoretical modeling and scientific forecasting. In the course of the research, the author have concluded that the use of predictive analytics in the police activity is a perspective direction of work, during which individuals and places with an increased risk of crime can be identified. Prognostic activity does not replace traditional methods and directions of police work, but strengthens them by applying advanced statistical models and algorithms with minimal use of budgetary funds. The police’s prognostic activity requires a new way of thinking, a high level of automation, and the expansion of technological capabilities of mass observation. Only under such conditions it is possible to create a police system that is able to meet the modern needs of society and effectively fulfill its tasks.

Predictive Analytics and Medical Malpractice

Insurance as a discipline has long embraced analytics, and market trends signal an even stronger relationship going forward. This article is a case study on the use of predictive analytics in the context of medical errors. Analyzing medical errors helps improve health care systems, and through a type of insurance known as medical malpractice insurance, we have the ability to analyze medical errors using data external to the health care system. In the spirit of modern analytics, this paper describes the application of data from several different sources. These sources give different insights into a specific problem facing the medical malpractice community familiar to actuaries: the relative importance of upper limits (or caps) on insurance payouts for noneconomic damages (e.g., pain and suffering). This topic is important to the industry in that many courts are considering the legality of such limitations. All stakeholders, including patients, physicians, hospitals, lawyers, and the general public, are interested in the implications of removing limitations on caps. This article demonstrates how we can use data and analytics to inform the many different stakeholders on this issue.