Big data as a value generator in decision support systems: a literature review

PurposeThis paper aims to analyze how decision support systems manage Big data to obtain value.Design/methodology/approachA systematic literature review was performed with screening and analysis of 72 articles published between 2012 and 2019.FindingsThe findings reveal that techniques of big data analytics, machine learning algorithms and technologies predominantly related to computer science and cloud computing are used on decision support systems. Another finding was that the main areas that these techniques and technologies are been applied are logistic, traffic, health, business and market. This article also allows authors to understand the relationship in which descriptive, predictive and prescriptive analyses are used according to an inverse relationship of complexity in data analysis and the need for human decision-making.Originality/valueAs it is an emerging theme, this study seeks to present an overview of the techniques and technologies that are being discussed in the literature to solve problems in their respective areas, as a form of theoretical contribution. The authors also understand that there is a practical contribution to the maturity of the discussion and with reflections even presented as suggestions for future research, such as the ethical discussion. This study’s descriptive classification can also serve as a guide for new researchers who seek to understand the research involving decision support systems and big data to gain value in our society.

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

The TQM Journal ◽

10.1108/tqm-02-2021-0051 ◽

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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USABILITY OF BIG DATA ANALYTICS WITHIN CLINICAL DECISION SUPPORT SYSTEMS

International Journal of Engineering Applied Sciences and Technology ◽

10.33564/ijeast.2019.v04i05.010 ◽

2019 ◽

Vol 04 (05) ◽

pp. 64-73

Author(s):

Christopher Otieno Okal ◽

Harriet Tsinale Loice

Keyword(s):

Big Data ◽

Decision Support ◽

Decision Support Systems ◽

Clinical Decision Support ◽

Data Analytics ◽

Support Systems ◽

Big Data Analytics ◽

Clinical Decision Support Systems ◽

Clinical Decision

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Decision Support Systems V – Big Data Analytics for Decision Making

10.1007/978-3-319-18533-0 ◽

2015 ◽

Cited By ~ 3

Keyword(s):

Decision Making ◽

Big Data ◽

Decision Support ◽

Decision Support Systems ◽

Data Analytics ◽

Support Systems ◽

Big Data Analytics

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Decision Support Systems

Oxford Research Encyclopedia of Politics ◽

10.1093/acrefore/9780190228637.013.1008 ◽

2020 ◽

Author(s):

Sean B. Eom

Keyword(s):

Decision Making ◽

Big Data ◽

Decision Support ◽

Decision Support Systems ◽

Business Intelligence ◽

Data Analytics ◽

Support Systems ◽

Academic Discipline ◽

Big Data Analytics ◽

Academic Field

A decision support system is an interactive human–computer decision-making system that supports decision makers rather than replaces them, utilizing data and models. It solves unstructured and semi-structured problems with a focus on effectiveness rather than efficiency in decision processes. In the early 1970s, scholars in this field began to recognize the important roles that decision support systems (DSS) play in supporting managers in their semi-structured or unstructured decision-making activities. Over the past five decades, DSS has made progress toward becoming a solid academic field. Nevertheless, since the mid-1990s, the inability of DSS to fully satisfy a wide range of information needs of practitioners provided an impetus for a new breed of DSS, business intelligence systems (BIS). The academic discipline of DSS has undergone numerous changes in technological environments including the adoption of data warehouses. Until the late 1990s, most textbooks referred to “decision support systems.” Nowadays, many of them have replaced “decision support systems” with “business intelligence.” While DSS/BIS began in academia and were quickly adopted in business, in recent years these tools have moved into government and the academic field of public administration. In addition, modern political campaigns, especially at the national level, are based on data analytics and the use of big data analytics. The first section of this article reviews the development of DSS as an academic discipline. The second section discusses BIS and their components (the data warehousing environment and the analytical environment). The final section introduces two emerging topics in DSS/BIS: big data analytics and cloud computing analytics. Before the era of big data, most data collected by business organizations could easily be managed by traditional relational database management systems with a serial processing system. Social networks, e-business networks, Internet of Things (IoT), and many other wireless sensor networks are generating huge volumes of data every day. The challenge of big data has demanded a new business intelligence infrastructure with new tools (Hadoop cluster, the data warehousing environment, and the business analytical environment).

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