scholarly journals Reputational Risk Associated with Big Data Research and Development: An Interdisciplinary Perspective

2021 ◽  
Vol 13 (16) ◽  
pp. 9280
Author(s):  
Cara Stitzlein ◽  
Simon Fielke ◽  
François Waldner ◽  
Todd Sanderson
Keyword(s):  
Big Data ◽  
Research And Development ◽  
Technology Acceptance ◽  
Data Science ◽  
Reputational Risk ◽  
Individual Privacy ◽  
Traditional Research ◽  
Big Data Technologies ◽  
Value Propositions ◽  
Private And Public

Many private and public actors are incentivized by the promises of big data technologies: digital tools underpinned by capabilities like artificial intelligence and machine learning. While many shared value propositions exist regarding what these technologies afford, public-facing concerns related to individual privacy, algorithm fairness, and the access to insights requires attention if the widespread use and subsequent value of these technologies are to be fully realized. Drawing from perspectives of data science, social science and technology acceptance, we present an interdisciplinary analysis that links these concerns with traditional research and development (R&D) activities. We suggest a reframing of the public R&D ‘brand’ that responds to legitimate concerns related to data collection, development, and the implementation of big data technologies. We offer as a case study Australian agriculture, which is currently undergoing such digitalization, and where concerns have been raised by landholders and the research community. With seemingly limitless possibilities, an updated account of responsible R&D in an increasingly digitalized world may accelerate the ways in which we might realize the benefits of big data and mitigate harmful social and environmental costs.

scholarly journals Managing reputational risk associated with big data research and development: an interdisciplinary perspective

2020 ◽  
Author(s):  
Cara Stitzlein ◽  
Simon Fielke ◽  
François Waldner ◽  
Todd Sanderson
Keyword(s):  
Big Data ◽  
Research And Development ◽  
Technology Acceptance ◽  
Data Science ◽  
Technology Development ◽  
Reputational Risk ◽  
Individual Privacy ◽  
Big Data Technologies ◽  
Value Propositions ◽  
Private And Public

Many private and public actors are incentivized by big data technologies: digital tools underpinned by capabilities such as artificial intelligence and machine learning. While many shared value propositions exist about what these technologies afford, public facing concerns related to individual privacy, algorithm fairness, and access to insights require attention if the widespread use and subsequent value of these technologies are to be fully realized. Drawing from perspectives of data science, social science and technology acceptance, we present an interdisciplinary analysis that reveals the connections between these concerns and traditional research and development (R&D) activities of data collection, technology development and implementation. Given the behaviors associated with digital transformation opportunities, we suggest a reframing of the public-facing R&D ‘brand’ that responds to legitimate concerns related to individual privacy, fairness, and social equity. We offer as a case study Australian agriculture, which is currently undergoing such digitalisation and where concerns have been raised by landholders and the research community. With seemingly limitless possibilities, an updated account of responsible R&D in an increasing digitalized world may accelerate how we might realize benefits of big data and mitigate harmful social and environmental costs.

The Nature of Intelligent Analytics

2021 ◽  
pp. 1-21
Author(s):  
Zhaohao Sun ◽  
Andrew Stranieri
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Research And Development ◽  
Business Intelligence ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Business Analytics ◽  
Novel Approach ◽  
Advanced Analytics

Intelligent analytics is an emerging paradigm in the age of big data, analytics, and artificial intelligence (AI). This chapter explores the nature of intelligent analytics. More specifically, this chapter identifies the foundations, cores, and applications of intelligent big data analytics based on the investigation into the state-of-the-art scholars' publications and market analysis of advanced analytics. Then it presents a workflow-based approach to big data analytics and technological foundations for intelligent big data analytics through examining intelligent big data analytics as an integration of AI and big data analytics. The chapter also presents a novel approach to extend intelligent big data analytics to intelligent analytics. The proposed approach in this chapter might facilitate research and development of intelligent analytics, big data analytics, business analytics, business intelligence, AI, and data science.

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 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.

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.

Advanced Threat Detection Based on Big Data Technologies

2018 ◽  
pp. 1-19
Author(s):  
Madhvaraj M. Shetty ◽  
Manjaiah D. H.
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Data Science ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Threat Detection ◽  
Cyber Threats ◽  
Threat Intelligence ◽  
Big Data Technologies

Today constant increase in number of cyber threats apparently shows that current countermeasures are not enough to defend it. With the help of huge generated data, big data brings transformative potential for various sectors. While many are using it for better operations, some of them are noticing that it can also be used for security by providing broader view of vulnerabilities and risks. Meanwhile, deep learning is coming up as a key role by providing predictive analytics solutions. Deep learning and big data analytics are becoming two high-focus of data science. Threat intelligence becoming more and more effective. Since it is based on how much data collected about active threats, this reason has taken many independent vendors into partnerships. In this chapter, we explore big data and big data analytics with its benefits. And we provide a brief overview of deep analytics and finally we present collaborative threat Detection. We also investigate some aspects of standards and key functions of it. We conclude by presenting benefits and challenges of collaborative threat detection.

Advanced Threat Detection Based on Big Data Technologies

2020 ◽  
pp. 808-822
Author(s):  
Madhvaraj M. Shetty ◽  
Manjaiah D. H.
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Data Science ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Threat Detection ◽  
Cyber Threats ◽  
Threat Intelligence ◽  
Big Data Technologies

Today constant increase in number of cyber threats apparently shows that current countermeasures are not enough to defend it. With the help of huge generated data, big data brings transformative potential for various sectors. While many are using it for better operations, some of them are noticing that it can also be used for security by providing broader view of vulnerabilities and risks. Meanwhile, deep learning is coming up as a key role by providing predictive analytics solutions. Deep learning and big data analytics are becoming two high-focus of data science. Threat intelligence becoming more and more effective. Since it is based on how much data collected about active threats, this reason has taken many independent vendors into partnerships. In this chapter, we explore big data and big data analytics with its benefits. And we provide a brief overview of deep analytics and finally we present collaborative threat Detection. We also investigate some aspects of standards and key functions of it. We conclude by presenting benefits and challenges of collaborative threat detection.

scholarly journals Business Models and Ecosystem for Big Data

2021 ◽  
pp. 269-288
Author(s):  
Sonja Zillner
Keyword(s):  
Big Data ◽  
Virtual Worlds ◽  
Business Models ◽  
Value Added ◽  
Systematic Investigation ◽  
Data Driven ◽  
Strategic Partnerships ◽  
New Business ◽  
Big Data Technologies ◽  
Value Propositions

AbstractWith the recent technical advances in digitalisation and big data, the real and the virtual worlds are continuously merging, which, again, leads to entire value-added chains being digitalised and integrated. The increase in industrial data combined with big data technologies triggers a wide range of new technical applications with new forms of value propositions that shift the logic of how business is done. To capture these new types of value, data-driven solutions for the industry will require new business models. The design of data-driven AI-based business models needs to incorporate various perspectives ranging from customer and user needs and their willingness to pay for new data-driven solutions to data access and the optimal use of technologies, while taking into account the currently established relationships with customers and partners. Successful data-driven business models are often based on strategic partnerships, with two or more players establishing the basis for sustainable win-win situations through transparent resource-, investment-, risk-, data- and value-sharing. This chapter will explore the different data-driven business approaches and highlight in this context the importance of functioning ecosystems on the various levels. The chapter will conclude with an introduction to the data-driven innovation framework, a proven methodology to guide the systematic investigation of data-driven business opportunities while incorporating the dynamics of the underlying ecosystems.

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