scholarly journals Why Personal Dreams Matter: How professionals affectively engage with the promises surrounding data-driven healthcare in Europe

2022 ◽  
Vol 9 (1) ◽  
pp. 205395172110706
Author(s):  
Marthe Stevens ◽  
Rik Wehrens ◽  
Johanna Kostenzer ◽  
Anne Marie Weggelaar-Jansen ◽  
Antoinette de Bont
Keyword(s):  
Big Data ◽  
Science And Technology Studies ◽  
Data Science ◽  
Data Driven ◽  
Grand Narrative ◽  
Healthcare Staff ◽  
Critical Data ◽  
Key Players ◽  
Big Data Technologies ◽  
Critical Policy

Recent buzzes around big data, data science and artificial intelligence portray a data-driven future for healthcare. As a response, Europe's key players have stimulated the use of big data technologies to make healthcare more efficient and effective. Critical Data Studies and Science and Technology Studies have developed many concepts to reflect on such overly positive narratives and conduct critical policy evaluations. In this study, we argue that there is also much to be learned from studying how professionals in the healthcare field affectively engage with this strong European narrative in concrete big data projects. We followed twelve hospital-based big data pilots in eight European countries and interviewed 145 professionals (including legal, governance and ethical experts, healthcare staff and data scientists) between 2018 and 2020. In this study, we introduce the metaphor of dreams to describe how professionals link the big data promises to their own frustrations, ideas, values and experiences with healthcare. Our research answers the question: how do professionals in concrete data-driven initiatives affectively engage with European Union's data hopes in their ‘dreams’ – and with what consequences? We describe the dreams of being seen, of timeliness, of connectedness and of being in control. Each of these dreams emphasizes certain aspects of the grand narrative of big data in Europe, makes particular assumptions and has different consequences. We argue that including attention to these dreams in our work could help shine an additional critical light on the big data developments and stimulate the development of responsible data-driven healthcare.

scholarly journals Urban Crime and Security

2021 ◽  
pp. 213-228
Author(s):  
Tao Cheng ◽  
Tongxin Chen
Keyword(s):  
Resource Allocation ◽  
Big Data ◽  
Crime Prevention ◽  
Urban Crime ◽  
Data Driven ◽  
Big Data Technologies ◽  
Urban Security ◽  
Made In

AbstractScientists have an enduring interest in understanding urban crime and developing security strategies for mitigating this problem. This chapter reviews the progress made in this topic from historic criminology to data-driven policing. It first reviews the broad implications of urban security and its implementation in practice. Next, it focuses on the tools to prevent urban crime and improve security, from analytical crime hotspot mapping to police resource allocation. Finally, a manifesto of data-driven policing is proposed, with its practical demand for efficient security strategies and the development of big data technologies. It emphasizes that data-driven strategies could be applied in cities due to their promising effectiveness for crime prevention and security improvement.

scholarly journals Critical Care, Critical Data

2019 ◽  
Vol 10 ◽  
pp. 117959721985656 ◽  
Author(s):  
Christopher V Cosgriff ◽  
Leo Anthony Celi ◽  
David J Stone
Keyword(s):  
Artificial Intelligence ◽  
Critical Care ◽  
Clinical Data ◽  
Continuous Monitoring ◽  
Data Science ◽  
Clinical Decision ◽  
Critical Care Medicine ◽  
Data Driven ◽  
Critical Data

As big data, machine learning, and artificial intelligence continue to penetrate into and transform many facets of our lives, we are witnessing the emergence of these powerful technologies within health care. The use and growth of these technologies has been contingent on the availability of reliable and usable data, a particularly robust resource in critical care medicine where continuous monitoring forms a key component of the infrastructure of care. The response to this opportunity has included the development of open databases for research and other purposes; the development of a collaborative form of clinical data science intended to fully leverage these data resources, and the creation of data-driven applications for purposes such as clinical decision support. Most recently, data levels have reached the thresholds required for the development of robust artificial intelligence features for clinical purposes. The systematic capture and analysis of clinical data in both individuals and populations allows us to begin to move toward precision medicine in the intensive care unit (ICU). In this perspective review, we examine the fundamental role of data as we present the current progress that has been made toward an artificial intelligence (AI)-supported, data-driven precision critical care medicine.

scholarly journals Extracting Value from Industrial Alarms and Events: A Data-Driven Approach Based on Exploratory Data Analysis

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2772 ◽  
Author(s):  
Aguinaldo Bezerra ◽  
Ivanovitch Silva ◽  
Luiz Affonso Guedes ◽  
Diego Silva ◽  
Gustavo Leitão ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Exchange ◽  
Data Science ◽  
Exploratory Data Analysis ◽  
Data Driven ◽  
Industrial Data ◽  
Industrial Big Data ◽  
Data Driven Approach ◽  
Exploratory Data

Alarm and event logs are an immense but latent source of knowledge commonly undervalued in industry. Though, the current massive data-exchange, high efficiency and strong competitiveness landscape, boosted by Industry 4.0 and IIoT (Industrial Internet of Things) paradigms, does not accommodate such a data misuse and demands more incisive approaches when analyzing industrial data. Advances in Data Science and Big Data (or more precisely, Industrial Big Data) have been enabling novel approaches in data analysis which can be great allies in extracting hitherto hidden information from plant operation data. Coping with that, this work proposes the use of Exploratory Data Analysis (EDA) as a promising data-driven approach to pave industrial alarm and event analysis. This approach proved to be fully able to increase industrial perception by extracting insights and valuable information from real-world industrial data without making prior assumptions.

scholarly journals Integrating Data Cleansing With Popular Culture: A Novel SQL Character Data Tutorial

2016 ◽  
Vol 20 (1) ◽  
pp. 13-28 ◽  
Author(s):  
David H. Olsen ◽  
Pamela A. Dupin-Bryant
Keyword(s):  
Big Data ◽  
Popular Culture ◽  
Learning Outcomes ◽  
Data Science ◽  
Data Driven ◽  
Job Market ◽  
Data Cleansing ◽  
Problem Solving Skills ◽  
5E Instructional Model ◽  
Data Market

Big data and data science have experienced unprecedented growth in recent years.  The big data market continues to exhibit strong momentum as countless businesses transform into data-driven companies. From salary surges to incredible growth in the number of positions, data science is one of the hottest areas in the job market. Significant demand and limited supply of professionals with data competencies has greatly affected the hiring market and this demand/supply imbalance will likely continue in the future. A major key in supplying the market with qualified big data professionals, is bridging the gap from traditional Information Systems (IS) learning outcomes to those outcomes requisite in this emerging field. The purpose of this paper is to share an SQL Character Data Tutorial.  Utilizing the 5E Instructional Model, this tutorial helps students (a) become familiar with SQL code, (b) learn when and how to use SQL string functions, (c) understand and apply the concept of data cleansing, (d) gain problem solving skills in the context of typical string manipulations, and (e) gain an understanding of typical needs related to string queries. The tutorial utilizes common, recognizable quotes from popular culture to engage students in the learning process and enhance understanding. This tutorial should prove helpful to educators who seek to provide a rigorous, practical, and relevant big data experience in their courses.

scholarly journals Unlocking the Power of Artificial Intelligence and Big Data in Medicine

10.2196/16607 ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. e16607 ◽  
Author(s):  
Christian Lovis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Decision Support ◽  
Data Science ◽  
Global Environment ◽  
Data Driven ◽  
Cross Linking ◽  
Science And Society ◽  
Slowing Down

Data-driven science and its corollaries in machine learning and the wider field of artificial intelligence have the potential to drive important changes in medicine. However, medicine is not a science like any other: It is deeply and tightly bound with a large and wide network of legal, ethical, regulatory, economical, and societal dependencies. As a consequence, the scientific and technological progresses in handling information and its further processing and cross-linking for decision support and predictive systems must be accompanied by parallel changes in the global environment, with numerous stakeholders, including citizen and society. What can be seen at the first glance as a barrier and a mechanism slowing down the progression of data science must, however, be considered an important asset. Only global adoption can transform the potential of big data and artificial intelligence into an effective breakthroughs in handling health and medicine. This requires science and society, scientists and citizens, to progress together.

scholarly journals Music Industry in the Big Data Era: Sociological and Marketing Research

2018 ◽  
Vol 6 (4) ◽  
pp. 161-172
Author(s):  
Marina G. Snezhinskaya
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Science ◽  
Music Industry ◽  
Marketing Research ◽  
Professional Competencies ◽  
Data Usage ◽  
Big Data Technologies ◽  
Music Market ◽  
Musical Preferences

The Big Data technologies and the potential of their application in the music industry are reviewed in the article. The main questions raised concern the perspectives of the Big Data usage in the sociological and marketing research, the audience data analysis and the musical preferences of the audience. The Big Data allow to discover new artists and to find new ways of stimulating the audience’s loyalty. The author attempts to answer the question: how does the Big Data change the music industry? The possibility of using the Big Data to forecast the audience’s behavior is being reviewed. The examples of the Big Data technologies usage in the marketing research for the music industry are exposed. The author underlines the importance of this technology for sociologists and market researchers and brings into the light the problems of the Big Data usage. The attention is drawn to the development of the new sphere of musical data science and to the necessity of broadening the professional competencies of sociologists and music market researchers.

scholarly journals Adoption of Big Data by Global Chemical Industries

2021 ◽  
Author(s):  
Ashiff Khan ◽  
A Seetharaman ◽  
Abhijit Dasgupta
Keyword(s):  
Big Data ◽  
Data Science ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Time Data ◽  
New Era ◽  
Potential Applications ◽  
Data Driven Approach ◽  
Process Plants ◽  
The Right

The new era of Big Data (BD) is influencing the chemical industries tremendously, providing several opportunities to reshape the way they operate and for shifting towards smart manufacturing. Given the availability of free software, and the large amount of real-time data generated and stored in process plants why many chemical industries are still not fully adopting BD? The industry is just starting to realize the importance of a large amount of data that they own to make the right decisions and to support their strategies. This article is exploring the importance of professional competencies and data science that influence BD in chemical industries for shifting towards smart manufacturing in a fast and reliable manner. This article utilizes a literature review and identifies potential applications in the chemical industry to shift from conventional methods towards a data-driven approach.

scholarly journals Enablers to supply chain performance on the basis of digitization technologies

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Himanshu Gupta ◽  
Sarangdhar Kumar ◽  
Simonov Kusi-Sarpong ◽  
Charbel Jose Chiappetta Jabbour ◽  
Martin Agyemang
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Supply Chain ◽  
Information Technologies ◽  
Data Science ◽  
Content Type ◽  
Big Data Technologies ◽  
Fierce Competition ◽  
Practical Implications ◽  
Improve Supply Chain

PurposeThe aim of this study is to identify and prioritize a list of key digitization enablers that can improve supply chain management (SCM). SCM is an important driver for organization's competitive advantage. The fierce competition in the market has forced companies to look the past conventional decision-making process, which is based on intuition and previous experience. The swift evolution of information technologies (ITs) and digitization tools has changed the scenario for many industries, including those involved in SCM.Design/methodology/approachThe Best Worst Method (BWM) has been applied to evaluate, rank and prioritize the key digitization and IT enablers beneficial for the improvement of SC performance. The study also used additive value function to rank the organizations on their SC performance with respect to digitization enablers.FindingsThe total of 25 key enablers have been identified and ranked. The results revealed that “big data/data science skills”, “tracking and localization of products” and “appropriate and feasibility study for aiding the selection and adoption of big data technologies and techniques ” are the top three digitization and IT enablers that organizations need to focus much in order to improve their SC performance. The study also ranked the SC performance of the organizations based on digitization enablers.Practical implicationsThe findings of this study will help the organizations to focus on certain digitization technologies in order to improve their SC performance. This study also provides an original framework for organizations to rank the key digitization enablers according to enablers relevant in their context and also to compare their performance with their counterparts.Originality/valueThis study seems to be the first of its kind in which 25 digitization enablers categorized in four main categories are ranked using a multi-criteria decision-making (MCDM) tool. This study is also first of its kind in ranking the organizations in their SC performance based on weights/ranks of digitization enablers.

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