Managerial Controversies in Artificial Intelligence and Big Data Analytics

2022 ◽  
pp. 1745-1764
Author(s):  
Kenneth David Strang ◽  
Zhaohao Sun
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Action Research ◽  
Literature Review ◽  
Systems Thinking ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Business Services ◽  
Volume Velocity ◽  
Technology Process

This chapter discusses several fundamental and managerial controversies associated with artificial intelligence and big data analytics which will be of interest to quantitative professionals and practitioners in the fields of computing, e-commerce, e-business services, and e-government. The authors utilized the systems thinking technique within an action research framework. They used this approach because their ideology was pragmatic, the problem at hand, was complex and institutional (healthcare discipline), and they needed to understand the problems from both a practitioner and a nonhuman technology process viewpoint. They used the literature review along with practitioner interviews collected at a big data conference. Although they found many problems, they considered these to be already encompassed into the big data five V's (volume, velocity, variety, value, veracity). Interestingly, they uncovered three new insights about the hidden healthcare artificial intelligence and big data analytics risks; then they proposed solutions for each of these problems.

Managerial Controversies in Artificial Intelligence and Big Data Analytics

2019 ◽  
pp. 55-74
Author(s):  
Kenneth David Strang ◽  
Zhaohao Sun
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Action Research ◽  
Literature Review ◽  
Systems Thinking ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Business Services ◽  
Volume Velocity ◽  
Technology Process

This chapter discusses several fundamental and managerial controversies associated with artificial intelligence and big data analytics which will be of interest to quantitative professionals and practitioners in the fields of computing, e-commerce, e-business services, and e-government. The authors utilized the systems thinking technique within an action research framework. They used this approach because their ideology was pragmatic, the problem at hand, was complex and institutional (healthcare discipline), and they needed to understand the problems from both a practitioner and a nonhuman technology process viewpoint. They used the literature review along with practitioner interviews collected at a big data conference. Although they found many problems, they considered these to be already encompassed into the big data five V's (volume, velocity, variety, value, veracity). Interestingly, they uncovered three new insights about the hidden healthcare artificial intelligence and big data analytics risks; then they proposed solutions for each of these problems.

The Impact of Smart Technologies on theManagement and Strategic Control: A Structured Literature Review

2021 ◽  
pp. 11-30
Author(s):  
Rosa Lombardi ◽  
Raffaele Trequattrini ◽  
Federico Schimperna ◽  
Myriam Cano-Rubio
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Literature Review ◽  
Business Intelligence ◽  
Data Analytics ◽  
Business Management ◽  
Big Data Analytics ◽  
Strategic Control ◽  
Smart Technologies ◽  
The Impact

This research proposes a systematic literature review (SLR) of the application of big data, analytics, business intelligence, and artificial intelligence to company management and strategic control. Thus, this paper attempts to answer the following research questions: 1) How is the literature on the application of big data, analytics, business intelligence, and artificial intelligence to management and strategic control developed in the business, management and accounting fields? 2) On which aspects of this application does the literature focus? 3) What are the implications that arise for companies? In this paper, we used a longitudinal study of research documents in the form of last-decade literature collected from Scopus database as the leading source for the international scenario. After, we selected business, management, and accounting areas, and screened the titles and abstracts of the research documents, we based the final result on 60 scientific documents as sources relevant to the aim of this SLR. The findings highlight four main topic clusters. We specifically explain smart technologies' usefulness for each analyzed business function, and, while adopting a critical perspective, we point out the interesting current streams of research resulting from the application of new sources of technology. We conclude by proposing valuable insights gleaned from the study. Thus, our results are useful for both the academic and the professional community.

Trends and Opportunities in Health Analytics as a Service and Implications for Use in Low Resource Settings: A Literature Review Abstract (Preprint)

2019 ◽  
Author(s):  
Meghana Bastwadkar ◽  
Carolyn McGregor ◽  
S Balaji
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Intensive Care ◽  
Literature Review ◽  
Health Monitoring ◽  
Data Analytics ◽  
Neonatal Intensive Care ◽  
Big Data Analytics ◽  
Healthcare Facilities

BACKGROUND This paper presents a systematic literature review of existing remote health monitoring systems with special reference to neonatal intensive care (NICU). Articles on NICU clinical decision support systems (CDSSs) which used cloud computing and big data analytics were surveyed. OBJECTIVE The aim of this study is to review technologies used to provide NICU CDSS. The literature review highlights the gaps within frameworks providing HAaaS paradigm for big data analytics METHODS Literature searches were performed in Google Scholar, IEEE Digital Library, JMIR Medical Informatics, JMIR Human Factors and JMIR mHealth and only English articles published on and after 2015 were included. The overall search strategy was to retrieve articles that included terms that were related to “health analytics” and “as a service” or “internet of things” / ”IoT” and “neonatal intensive care unit” / ”NICU”. Title and abstracts were reviewed to assess relevance. RESULTS In total, 17 full papers met all criteria and were selected for full review. Results showed that in most cases bedside medical devices like pulse oximeters have been used as the sensor device. Results revealed a great diversity in data acquisition techniques used however in most cases the same physiological data (heart rate, respiratory rate, blood pressure, blood oxygen saturation) was acquired. Results obtained have shown that in most cases data analytics involved data mining classification techniques, fuzzy logic-NICU decision support systems (DSS) etc where as big data analytics involving Artemis cloud data analysis have used CRISP-TDM and STDM temporal data mining technique to support clinical research studies. In most scenarios both real-time and retrospective analytics have been performed. Results reveal that most of the research study has been performed within small and medium sized urban hospitals so there is wide scope for research within rural and remote hospitals with NICU set ups. Results have shown creating a HAaaS approach where data acquisition and data analytics are not tightly coupled remains an open research area. Reviewed articles have described architecture and base technologies for neonatal health monitoring with an IoT approach. CONCLUSIONS The current work supports implementation of the expanded Artemis cloud as a commercial offering to healthcare facilities in Canada and worldwide to provide cloud computing services to critical care. However, no work till date has been completed for low resource setting environment within healthcare facilities in India which results in scope for research. It is observed that all the big data analytics frameworks which have been reviewed in this study have tight coupling of components within the framework, so there is a need for a framework with functional decoupling of components.

Strategic issues of big data analytics applications for managing health-care sector: a systematic literature review and future research agenda

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rajesh Kumar Singh ◽  
Saurabh Agrawal ◽  
Abhishek Sahu ◽  
Yigit Kazancoglu
Keyword(s):  
Health Care ◽  
Big Data ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Health Care Sector ◽  
Future Research ◽  
Research Gaps ◽  
Content Type

PurposeThe proposed article is aimed at exploring the opportunities, challenges and possible outcomes of incorporating big data analytics (BDA) into health-care sector. The purpose of this study is to find the research gaps in the literature and to investigate the scope of incorporating new strategies in the health-care sector for increasing the efficiency of the system.Design/methodology/approachFora state-of-the-art literature review, a systematic literature review has been carried out to find out research gaps in the field of healthcare using big data (BD) applications. A detailed research methodology including material collection, descriptive analysis and categorization is utilized to carry out the literature review.FindingsBD analysis is rapidly being adopted in health-care sector for utilizing precious information available in terms of BD. However, it puts forth certain challenges that need to be focused upon. The article identifies and explains the challenges thoroughly.Research limitations/implicationsThe proposed study will provide useful guidance to the health-care sector professionals for managing health-care system. It will help academicians and physicians for evaluating, improving and benchmarking the health-care strategies through BDA in the health-care sector. One of the limitations of the study is that it is based on literature review and more in-depth studies may be carried out for the generalization of results.Originality/valueThere are certain effective tools available in the market today that are currently being used by both small and large businesses and corporations. One of them is BD, which may be very useful for health-care sector. A comprehensive literature review is carried out for research papers published between 1974 and 2021.

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