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.

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.

The potential of entry barriers in the context of digitalization

2021 ◽  
Vol 21 (1) ◽  
pp. 128-147
Author(s):  
Aleksey Zazdravnykh
Keyword(s):  
Big Data ◽  
Market Power ◽  
Network Effects ◽  
Economies Of Scale ◽  
Digital Transformation ◽  
Entry Barriers ◽  
Barriers To Entry ◽  
Significant Growth ◽  
Digital Infrastructure ◽  
Market Operation

The article analyzes the practical aspects of the functioning of some barriers to entry in the era of digital transformation of industry markets. It is noted that under the influence of digitalization processes, both positive changes in the mechanism of market operation are recorded, as well as a number of negative circumstances that have become a serious challenge for antitrust agencies. Control of big data, initial investment in digital infrastructure, and broad technological capabilities of digital blocking of users, against the background of powerful network effects and pronounced economies of scale, carry the potential for significant growth in the market power of individual firms. The article substantiates that such trends theoretically pose a significant threat to competition, and can form new types of entry barriers. At the same time, practical arguments are presented that indicate the ambiguity of this position.

scholarly journals Agile Elastic Desktop Corporate Architecture for Big Data

2020 ◽  
Vol 20 (3) ◽  
pp. 15-31
Author(s):  
Valentin Kisimov ◽  
Dorina Kabakchieva ◽  
Aleksandar Naydenov ◽  
Kamelia Stefanova
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Business Environment ◽  
Digital Transformation ◽  
Big Data Processing ◽  
Online Business ◽  
New Challenges ◽  
Corporate Architecture ◽  
Effective Use ◽  
Corporate Business

AbstractNew challenges in the dynamically changing business environment require companies to experience digital transformation and more effective use of Big Data generated in their expanding online business activities. A possible solution for solving real business problems concerning Big Data resources is proposed in this paper. The defined Agile Elastic Desktop Corporate Architecture for Big Data is based on virtualizing the unused desktop resources and organizing them in order to serve the needs of Big Data processing, thus saving resources needed for additional infrastructure in an organization. The specific corporate business needs are analyzed within the developed R&D environment and, based on that, the unused desktop resources are customized and configured into required Big Data tools. The R&D environment of the proposed Agile Elastic Desktop Corporate Architecture for Big Data could be implemented on the available unused resources of hundreds desktops.

scholarly journals Premises of the creation of a digital traffic safety management system

Dependability ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 45-52
Author(s):  
A. М. Zamyshliaev
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Control Systems ◽  
Traffic Safety ◽  
Management System ◽  
Safety Management ◽  
Digital Transformation ◽  
Safety Management System ◽  
Real Time Traffic ◽  
The Creation

Aim.The digital transformation of the traffic safety management system in JSC RZD involves top-level integration with the operating processes of all business units in terms of integral assessment of the risk of possible events and achievement of specified indicators. The result will be the merger of the traffic safety management system with the processes of all levels of the company’s management enabled by an integrated intelligent system for managing processes and services whose functionality includes real-time traffic safety management.Methods. The paper uses system analysis of existing approaches and methods of processing of large quantities of structured and unstructered data.Results. The paper examines the development stages of train traffic safety management, as well as automated information and control systems that enable traffic safety management. General trends in the creation of systems for collection and processing of information are analyzed. The applicability of such technologies as Big Data, Data Mining, Data Science as part of advanced control systems is shown. The paper examines the performance of the above technologies by analyzing the effect of various factors on the average daily performance of a locomotive, where, at the first level, such factors as average daily run of a locomotive, average trainload are taken into consideration; at the second level, the focus is on the service speed, locomotive turnover at station, etc.; at the sixth level, the focus is on the type of locomotive, its technical state, etc. It is shown that statistical methods of factor analysis and link analysis combined with such other methods of Data Mining as methods of simulation and prediction, the average daily performance of a locomotive can be planned proactively. The author proposes a procedure of migration towards a digital traffic safety management system that would be based on models of interaction of safety and dependability factors of all railway facilities at all railway levels of hierarchy, as well as in association with other factors that have no direct relation to dependability, yet affect the safety of the transportation process.Conclusions. The primary benefit of migration towards Big Data consists in the development of a dynamic model of traffic safety, the elimination of human factor in control systems. Most importantly, it enables the creation within the Russian Railways company (JSC RZD) of an integrated intelligent process and service management system that enables real-time traffic safety management. An extensive process of development and deployment within the company of the URRAN Single Corporate Platform (SCP) enabled executive decision support as regards risk-based functional dependability and safety of transportation facilities. Thus, the URRAN SCP sets the stage for the digital transformation of the traffic safety management system in JSC RZD.

Call for Special Issue Papers: Big Data-Driven Futuristic Fabric System in Societal Digital Transformation

Big Data ◽  
2021 ◽  
Author(s):  
Dr. Chinmay Chakraborty ◽  
Prof. Muhammad Khurram Khan ◽  
Prof. Ishfaq Ahmad
Keyword(s):  
Big Data ◽  
Digital Transformation ◽  
Data Driven ◽  
Special Issue ◽  
Fabric System

Call for Special Issue Papers: Big Data-Driven Futuristic Fabric System in Societal Digital Transformation

Big Data ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 151-152
Author(s):  
Chinmay Chakraborty ◽  
Muhammad Khurram Khan ◽  
Ishfaq Ahmad
Keyword(s):  
Big Data ◽  
Digital Transformation ◽  
Data Driven ◽  
Special Issue ◽  
Fabric System

Security and Privacy Challenges in Big Data

2020 ◽  
pp. 97-124
Author(s):  
Dharmpal Singh ◽  
Ira Nath ◽  
Pawan Kumar Singh
Keyword(s):  
Big Data ◽  
Data Protection ◽  
High Performance ◽  
Digital Transformation ◽  
Security And Privacy ◽  
Privacy And Security ◽  
Data Safety ◽  
Amount Of Information ◽  
Enormous Amount ◽  
Software Skills

Big data refers to enormous amount of information which may be in planned and unplanned form. The huge capacity of data creates impracticable situation to handle with conventional database and traditional software skills. Thousands of servers are needed for its processing purpose. Big data gathers and examines huge capacity of data from various resources to determine exceptional novel awareness and recognizing the technical and commercial circumstances. However, big data discloses the endeavor to several data safety threats. Various challenges are there to maintain the privacy and security in big data. Protection of confidential and susceptible data from attackers is a vital issue. Therefore, the goal of this chapter is to discuss how to maintain security in big data to keep your organization robust, operational, flexible, and high performance, preserving its digital transformation and obtaining the complete benefit of big data, which is safe and secure.

Key Strategic Drivers for Business Digital Transformation

2020 ◽  
pp. 137-153
Author(s):  
Ronnie Figueiredo ◽  
Raquel Soares ◽  
João José de Matos Ferreira
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Prior Experience ◽  
Transformation Process ◽  
Digital Transformation ◽  
Boolean Logic ◽  
Social Changes ◽  
Logical Operators ◽  
Business Transformation ◽  
Essential Properties

The concept of digital transformation (DT) has reached a heightened demand level on the executives' agenda, especially when it is related to elements that influence the development and activities of contemporary firms being prevalent in discussions about industrial and social changes. This theme leads the authors to reflect on the key strategic drivers used by firms to carry out the qualitative process (prior experience) of digital business transformation. It reinforces the purpose of understanding the key strategic drivers used by firms in recent years to guide the digital business transformation process. The systematic literature review was performed in the “WoS database,” according to Boolean logic, aiming to capture the essential properties of the logical operators and sets of statements about the theme presented to compose the study. The results indicate a higher frequency for the big data and digitization drivers, followed by the innovation driver within the analyzed period.

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