scholarly journals Data Economy 2.0: From Big Data Value to AI Value and a European Data Space

2021 ◽  
pp. 379-399
Author(s):  
Sonja Zillner ◽  
Jon Ander Gomez ◽  
Ana García Robles ◽  
Thomas Hahn ◽  
Laure Le Bars ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Point Of View ◽  
Driven Systems ◽  
Significant Growth ◽  
Data Space ◽  
European Data ◽  
Data Value ◽  
Data Volume

AbstractArtificial intelligence (AI) has a tremendous potential to benefit European citizens, economy, environment and society and already demonstrated its potential to generate value in various applications and domains. From a data economy point of view, AI means algorithm-based and data-driven systems that enable machines with digital capabilities such as perception, reasoning, learning and even autonomous decision making to support people in real scenarios. Data ecosystems are an important driver for AI opportunities as they benefit from the significant growth of data volume and the rates at which it is generated. This chapter explores the opportunities and challenges of big data and AI in exploiting data ecosystems and creating AI value. The chapter describes the European AI framework as a foundation for deploying AI successfully and the critical need for a common European data space to power this vision.

Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Digital Transformation ◽  
Decision Processes ◽  
Decision Makers ◽  
Content Type ◽  
Human Decision ◽  
Practical Implications

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

DataPro

2021 ◽  
pp. 98-108
Author(s):  
Vala Ali Rohani ◽  
Sedigheh Moghavvemi ◽  
Tiago Pinho ◽  
Paulo Caldas
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Unemployment Rate ◽  
Recommender System ◽  
Social Problems ◽  
National Level ◽  
Point Of View ◽  
Web Based ◽  
Four Dimensions ◽  
Online Portal

Due to the COVID‐19 pandemic, most countries are exposed to unprecedented social problems in the current global situation. According to the official reports, it caused a dramatic increase of 44% in graduates' unemployment rate in Portugal. Moreover, from the human resource point of view, the whole of Europe is expected to face a shortage of 925,000 data professionals by 2025. Given the existing situations, the DataPro aims to propose a national-level reskilling solution in big data to mitigate both social problems of unemployability and the shortage of data professionals in Portugal. DataPro project consists of four dimensions, including an online portal for the hiring companies and unemployed graduates, along with a web-based analytics talent upskilling (ATU) platform empowered by an artificial intelligence recommender system to match the reskilled data professionals and the hiring companies.

scholarly journals How can we discover the most valuable types of big data and artificial intelligence-based solutions? A methodology for the efficient development of the underlying analytics that improve care

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lytske Bakker ◽  
Jos Aarts ◽  
Carin Uyl-de Groot ◽  
Ken Redekop
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Intensive Care ◽  
Best Practice ◽  
Investment Decisions ◽  
Cost Savings ◽  
Economic Evaluations ◽  
Successful Implementation ◽  
Practice Methods

Abstract Background Much has been invested in big data and artificial intelligence-based solutions for healthcare. However, few applications have been implemented in clinical practice. Early economic evaluations can help to improve decision-making by developers of analytics underlying these solutions aiming to increase the likelihood of successful implementation, but recommendations about their use are lacking. The aim of this study was to develop and apply a framework that positions best practice methods for economic evaluations alongside development of analytics, thereby enabling developers to identify barriers to success and to select analytics worth further investments. Methods The framework was developed using literature, recommendations for economic evaluations and by applying the framework to use cases (chronic lymphocytic leukaemia (CLL), intensive care, diabetes). First, the feasibility of developing clinically relevant analytics was assessed and critical barriers to successful development and implementation identified. Economic evaluations were then used to determine critical thresholds and guide investment decisions. Results When using the framework to assist decision-making of developers of analytics, continuing development was not always feasible or worthwhile. Developing analytics for progressive CLL and diabetes was clinically relevant but not feasible with the data available. Alternatively, developing analytics for newly diagnosed CLL patients was feasible but continuing development was not considered worthwhile because the high drug costs made it economically unattractive for potential users. Alternatively, in the intensive care unit, analytics reduced mortality and per-patient costs when used to identify infections (− 0.5%, − €886) and to improve patient-ventilator interaction (− 3%, − €264). Both analytics have the potential to save money but the potential benefits of analytics that identify infections strongly depend on infection rate; a higher rate implies greater cost-savings. Conclusions We present a framework that stimulates efficiency of development of analytics for big data and artificial intelligence-based solutions by selecting those applications of analytics for which development is feasible and worthwhile. For these applications, results from early economic evaluations can be used to guide investment decisions and identify critical requirements.

Intelligent Big Data Analytics

2019 ◽  
pp. 1-19 ◽  
Author(s):  
Zhaohao Sun
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Business Decision ◽  
Technology System ◽  
System Service ◽  
Managerial Perspective

Intelligent big data analytics is an emerging paradigm in the age of big data, analytics, and artificial intelligence (AI). This chapter explores intelligent big data analytics from a managerial perspective. More specifically, it first looks at the age of trinity and argues that intelligent big data analytics is at the center of the age of trinity. This chapter then proposes a managerial framework of intelligent big data analytics, which consists of intelligent big data analytics as a science, technology, system, service, and management for improving business decision making. Then it examines intelligent big data analytics for management taking into account four managerial functions: planning, organizing, leading, and controlling. The proposed approach in this chapter might facilitate the research and development of intelligent big data analytics, big data analytics, business intelligence, artificial intelligence, and data science.

Toward a Conceptualization of Big Data Value Chain

2019 ◽  
pp. 76-92 ◽  
Author(s):  
Rim Louati ◽  
Sonia Mekadmi
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Big Data ◽  
Value Chain ◽  
Data Capture ◽  
Digital Devices ◽  
Business Executives ◽  
Data Value ◽  
Near Future ◽  
Different Sources

The generation of digital devices such as web 2.0, smartphones, social media and sensors has led to a growing rate of data creation. The volume of data available today for organizations is big. Data are produced extensively every day in many forms and from many different sources. Accordingly, firms in several industries are increasingly interested in how to leverage on these “big data” to draw valuable insights from the various kinds of data and to create business value. The aim of this chapter is to provide an integrated view of big data management. A conceptualization of big data value chain is proposed as a research model to help firms understand how to cope with challenges, risks and benefits of big data. The suggested big data value chain recognizes the interdependence between processes, from business problem identification and data capture to generation of valuable insights and decision making. This framework could provide some guidance to business executives and IT practitioners who are going to conduct big data projects in the near future.

An empirical study on artificial intelligence technology based on big data to assist enterprise management decision

2021 ◽  
pp. 002072092098354
Author(s):  
Jie Guo ◽  
Dong Wang
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Major Change ◽  
Enterprise Management ◽  
Management Decision ◽  
Management Decisions ◽  
Artificial Intelligence Technology ◽  
The Times ◽  
China’S Economy

With the continuous development of China's economy, the level of science and technology has been improved to a great extent. The advent of the era of Internet and cloud computing has brought a major change to China. However, with the advent of the era of big data, a bigger technological change is coming. The arrival of the era of big data has brought a certain impact on China's enterprise management and decision-making, and put forward higher requirements for China's enterprise management and decision-making. Therefore, enterprises need to constantly strive to improve themselves so as to better adapt to the era of big data. In order to keep pace with the development of The Times, major companies and enterprises need to constantly change their internal management methods in order to achieve sustainable and stable development in their own fields and make their management decisions smoother. Among them, optimization and reform of the application of big data are particularly important. Starting from the characteristics of big data and its role in enterprise management decisions, this paper analyzes the current situation of big data management within enterprises and discusses the influence of big data on enterprise management decisions from five aspects, namely, environment, data, participants, organization and technology. And this paper analyzes the construction method and design idea of enterprise decision support system based on artificial intelligence.

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