Data Analytics in jungen Unternehmen – Ergebnisse einer aktuellen Online-Befragung

2018 ◽  
Vol 59 (2) ◽  
pp. 26-31
Author(s):  
Wolfgang Becker ◽  
Christoph Feichtinger
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Scientific Paper ◽  
Customer Support ◽  
Current State ◽  
State Of Research

Die Digitalisierung ist in aller Munde. Data Analytics soll helfen, neue Erkenntnisse durch Big Data zu generieren, um eine Verbesserung u.a. der Kundenbetreuung und von Entscheidungen zu ermöglichen. In diesem Zusammenhang werden junge Unternehmen betrachtet, da gerade diese oftmals mit innovativen und flexiblen Methoden in Verbindung gebracht werden. Dieser wissenschaftliche Beitrag bilanziert daher den aktuellen Stand der Forschung im Bereich Data Analytics und stellt die Nutzung dieser Methoden dar. Digitalization is on everyone‘s lips. Data Analytics should help to generate new insights through big data in order to improve, i.a. to provide customer support and decision-making. In this context, nascent enterprises are considered, as they are often associated with innovative and flexible methods. This scientific paper, therefore, reviews the current state of research in the field of data analytics and illustrates the use of these methods. Keywords: junge unternehmen, digitalisierung, big data

Examining the potential of textual big data analytics for public policy decision-making: A case study with driverless cars in Denmark

2020 ◽  
Vol 98 ◽  
pp. 68-78 ◽  
Author(s):  
Aseem Kinra ◽  
Samaneh Beheshti-Kashi ◽  
Rasmus Buch ◽  
Thomas Alexander Sick Nielsen ◽  
Francisco Pereira
Keyword(s):  
Public Policy ◽  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Policy Decision ◽  
Driverless Cars ◽  
Policy Decision Making

Big Data and Data Analytics Research

Web Services ◽  
2019 ◽  
pp. 2161-2171
Author(s):  
Miltiadis D. Lytras ◽  
Vijay Raghavan ◽  
Ernesto Damiani
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Business Models ◽  
Internet Technology ◽  
Cognitive Computing ◽  
New Paradigm ◽  
Web Science ◽  
New Business ◽  
Value Propositions

The Big Data and Data Analytics is a brand new paradigm, for the integration of Internet Technology in the human and machine context. For the first time in the history of the human mankind we are able to transforming raw data that are massively produced by humans and machines in to knowledge and wisdom capable of supporting smart decision making, innovative services, new business models, innovation, and entrepreneurship. For the Web Science research, this is a new methodological and technological spectrum of advanced methods, frameworks and functionalities never experienced in the past. At the same moment communities out of web science need to realize the potential of this new paradigm with the support of new sound business models and a critical shift in the perception of decision making. In this short visioning article, the authors are analyzing the main aspects of Big Data and Data Analytics Research and they provide their own metaphor for the next years. A number of research directions are outlined as well as a new roadmap towards the evolution of Big Data to Smart Decisions and Cognitive Computing. The authors do hope that the readers would like to react and to propose their own value propositions for the domain initiating a scientific dialogue beyond self-fulfilled expectations.

Semantic Technologies and Big Data Analytics for Cyber Defence

Web Services ◽  
2019 ◽  
pp. 1430-1443
Author(s):  
Louise Leenen ◽  
Thomas Meyer
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Data Sets ◽  
Semantic Technologies ◽  
Data Types ◽  
Intelligent Decision Making ◽  
Big Data Technologies

The Governments, military forces and other organisations responsible for cybersecurity deal with vast amounts of data that has to be understood in order to lead to intelligent decision making. Due to the vast amounts of information pertinent to cybersecurity, automation is required for processing and decision making, specifically to present advance warning of possible threats. The ability to detect patterns in vast data sets, and being able to understanding the significance of detected patterns are essential in the cyber defence domain. Big data technologies supported by semantic technologies can improve cybersecurity, and thus cyber defence by providing support for the processing and understanding of the huge amounts of information in the cyber environment. The term big data analytics refers to advanced analytic techniques such as machine learning, predictive analysis, and other intelligent processing techniques applied to large data sets that contain different data types. The purpose is to detect patterns, correlations, trends and other useful information. Semantic technologies is a knowledge representation paradigm where the meaning of data is encoded separately from the data itself. The use of semantic technologies such as logic-based systems to support decision making is becoming increasingly popular. However, most automated systems are currently based on syntactic rules. These rules are generally not sophisticated enough to deal with the complexity of decisions required to be made. The incorporation of semantic information allows for increased understanding and sophistication in cyber defence systems. This paper argues that both big data analytics and semantic technologies are necessary to provide counter measures against cyber threats. An overview of the use of semantic technologies and big data technologies in cyber defence is provided, and important areas for future research in the combined domains are discussed.

Coding Inequality

2020 ◽  
pp. 100-117
Author(s):  
Sarah Brayne
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Social Inequalities ◽  
Big Data Analytics ◽  
The Other ◽  
Surveillance Network ◽  
Data Intensive ◽  
The One ◽  
Reproduction Of Inequality

This chapter looks at the promise and peril of police use of big data analytics for inequality. On the one hand, big data analytics may be a means by which to ameliorate persistent inequalities in policing. Data can be used to “police the police” and replace unparticularized suspicion of racial minorities and human exaggeration of patterns with less biased predictions of risk. On the other hand, data-intensive police surveillance practices are implicated in the reproduction of inequality in at least four ways: by deepening the surveillance of individuals already under suspicion, codifying a secondary surveillance network of individuals with no direct police contact, widening the criminal justice dragnet unequally, and leading people to avoid institutions that collect data and are fundamental to social integration. Crucially, as currently implemented, “data-driven” decision-making techwashes, both obscuring and amplifying social inequalities under a patina of objectivity.

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