scholarly journals Correction to: Big data and business analytics ecosystems: paving the way towards digital transformation and sustainable societies

2021 ◽  
Author(s):  
Ilias O. Pappas ◽  
Patrick Mikalef ◽  
Michail N. Giannakos ◽  
John Krogstie ◽  
George Lekakos
Keyword(s):  
Big Data ◽  
Digital Transformation ◽  
Business Analytics ◽  
Sustainable Societies ◽  
The Way

scholarly journals Big data and business analytics ecosystems: paving the way towards digital transformation and sustainable societies

2018 ◽  
Vol 16 (3) ◽  
pp. 479-491 ◽  
Author(s):  
Ilias O. Pappas ◽  
Patrick Mikalef ◽  
Michail N. Giannakos ◽  
John Krogstie ◽  
George Lekakos
Keyword(s):  
Big Data ◽  
Digital Transformation ◽  
Business Analytics ◽  
Sustainable Societies ◽  
The Way

HIMSS Impact18: Leading Digital Transformation und Big Data in Medicine

2018 ◽  
Vol 23 (09) ◽  
pp. 25-25
Author(s):  
Sabine Schützmann
Keyword(s):  
Big Data ◽  
Digital Transformation

Am 17. und 18. Oktober findet im Hasso-Plattner-Institut (HPI) in Potsdam zum zweiten Mal die HIMSS Impact statt: Ein englischsprachiges Symposium, welches aktuelle Trends im Gesundheitswesen, digitale Strategien und jüngste Forschungserkenntnisse beleuchtet.

Artistic Challenges within Control Societies: Big Data and Democratic Resistance

MedienJournal ◽  
2017 ◽  
Vol 38 (4) ◽  
pp. 50-61 ◽  
Author(s):  
Jan Jagodzinski
Keyword(s):  
Big Data ◽  
Gilles Deleuze ◽  
Data Archive ◽  
Current State ◽  
The Future ◽  
The Way

This paper will first briefly map out the shift from disciplinary to control societies (what I call designer capitalism, the idea of control comes from Gilles Deleuze) in relation to surveillance and mediation of life through screen cultures. The paper then shifts to the issues of digitalization in relation to big data that have the danger of continuing to close off life as zoë, that is life that is creative rather than captured via attention technologies through marketing techniques and surveillance. The last part of this paper then develops the way artists are able to resist the big data archive by turning the data in on itself to offer viewers and participants a glimpse of the current state of manipulating desire and maintaining copy right in order to keep the future closed rather than being potentially open.

Rediseñando las Organizaciones del Futuro. El impacto de la transformación digital en el mundo del trabajo

2019 ◽  
Vol 1 (1) ◽  
pp. 199-226
Author(s):  
Ricardo M. Piñeyro Prins ◽  
Guadalupe E. Estrada Narvaez
Keyword(s):  
Industrial Revolution ◽  
New Technologies ◽  
Third Sector ◽  
Digital Transformation ◽  
Digital Culture ◽  
Exploratory Research ◽  
The People ◽  
Technological Advances ◽  
Current Scenario ◽  
The Way

We are witnessing how new technologies are radically changing the design of organizations, the way in which they produce and manage both their objectives and their strategies, and -above all- how digital transformation impacts the people who are part of it. Even today in our country, many organizations think that digitalizing is having a presence on social networks, a web page or venturing into cases of success in corporate social intranet. Others begin to invest a large part of their budget in training their teams and adapting them to the digital age. But given this current scenario, do we know exactly what the digital transformation of organizations means? It is necessary? Implying? Is there a roadmap to follow that leads to the success of this process? How are organizations that have been born 100% digital from their business conception to the way of producing services through the use of platforms? What role does the organizational culture play in this scenario? The challenge of the digital transformation of businesses and organizations, which is part of the paradigm of the industrial revolution 4.0, is happening here and now in all types of organizations, whether are they private, public or third sector. The challenge to take into account in this process is to identify the digital competences that each worker must face in order to accompany these changes and not be left out of it. In this sense, the present work seeks to analyze the main characteristics of the current technological advances that make up the digital transformation of organizations and how they must be accompanied by a digital culture and skills that allow their successful development. In order to approach this project, we will carry out an exploratory research, collecting data from the sector of new actors in the world of work such as employment platforms in its various areas (gastronomy, delivery, transportation, recreation, domestic service, etc) and an analysis of the main technological changes that impact on the digital transformation of organizations in Argentina.

scholarly journals CoPart: a context-based partitioning technique for big data

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sara Migliorini ◽  
Alberto Belussi ◽  
Elisa Quintarelli ◽  
Damiano Carra
Keyword(s):  
Big Data ◽  
Quality Criteria ◽  
Query Execution ◽  
Data Correlation ◽  
Huge Amount ◽  
Programming Paradigm ◽  
Computation Node ◽  
Contextual Dimension ◽  
Partitioning Technique ◽  
The Way

AbstractThe MapReduce programming paradigm is frequently used in order to process and analyse a huge amount of data. This paradigm relies on the ability to apply the same operation in parallel on independent chunks of data. The consequence is that the overall performances greatly depend on the way data are partitioned among the various computation nodes. The default partitioning technique, provided by systems like Hadoop or Spark, basically performs a random subdivision of the input records, without considering the nature and correlation between them. Even if such approach can be appropriate in the simplest case where all the input records have to be always analyzed, it becomes a limit for sophisticated analyses, in which correlations between records can be exploited to preliminarily prune unnecessary computations. In this paper we design a context-based multi-dimensional partitioning technique, called CoPart, which takes care of data correlation in order to determine how records are subdivided between splits (i.e., units of work assigned to a computation node). More specifically, it considers not only the correlation of data w.r.t. contextual attributes, but also the distribution of each contextual dimension in the dataset. We experimentally compare our approach with existing ones, considering both quality criteria and the query execution times.

How to turn managers into data-driven decision makers

2019 ◽  
Vol 25 (3) ◽  
pp. 553-578 ◽  
Author(s):  
Kevin Daniel André Carillo ◽  
Nadine Galy ◽  
Cameron Guthrie ◽  
Anne Vanhems
Keyword(s):  
Measurement Instrument ◽  
Data Driven ◽  
Business Analytics ◽  
Business World ◽  
Content Type ◽  
Multi Stage ◽  
Attitudes Toward Statistics ◽  
Attitude Toward Business ◽  
Professional Contexts ◽  
The Way

Purpose The purpose of this paper is to emphasize the need to engender a positive attitude toward business analytics in order for firms to more effectively transform into data-driven businesses, and for business schools to better prepare future managers. Design/methodology/approach This paper develops and validates a measurement instrument that captures the attitude toward business statistics, the foundation of business analytics. A multi-stage approach is implemented and the validation is conducted with a sample of 311 students from a business school. Findings The instrument has strong psychometric properties. It is designed so that it can be easily extrapolated to professional contexts and extended to the entire domain of business analytics. Research limitations/implications As the advent of a data-driven business world will impact the way organizations function and the way individuals think, work, communicate and interact, it is crucial to engage a transdisciplinary dialogue among domains that have the expertise to help train and transform current and future professionals. Practical implications The contribution provides educators and organizations with a means to measure and monitor attitudes toward statistics, the most anxiogenic component of business analytics. This is a first step in monitoring and developing an analytics mindset in both managers and students. Originality/value By demonstrating how the advent of the data-driven business era is transforming the DNA and functioning of organizations, this paper highlights the key importance of changing managers’ and all employees’ (to a lesser extent) mindset and way of thinking.

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.

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