scholarly journals The Role of Big Data in Industry 4.0 in Mining Industry in Serbia

2020 ◽  
Vol 2 (1) ◽  
pp. 166-173
Author(s):  
Eva Tylečková ◽  
Darja Noskievičová
Keyword(s):  
Big Data ◽  
Information Systems ◽  
Industry 4.0 ◽  
New Technologies ◽  
Rapid Development ◽  
Mining Industry ◽  
Poor Quality ◽  
Quality Data ◽  
Quality Of Data

AbstractThe current age characterized by unstoppable progress and rapid development of new technologies and methods such as the Internet of Things, machine learning and artificial intelligence, brings new requirements for enterprise information systems. Information systems ought to be a consistent set of elements that provide a basis for information that could be used in context to obtain knowledge. To generate valid knowledge, information must be based on objective and actual data. Furthermore, due to Industry 4.0 trends such as digitalization and online process monitoring, the amount of data produced is constantly increasing – in this context the term Big Data is used. The aim of this article is to point out the role of Big Data within Industry 4.0. Nevertheless, Big Data could be used in a much wider range of business areas, not just in industry. The term Big Data encompasses issues related to the exponentially growing volume of produced data, their variety and velocity of their origin. These characteristics of Big Data are also associated with possible processing problems. The article also focuses on the issue of ensuring and monitoring the quality of data. Reliable information cannot be inferred from poor quality data and the knowledge gained from such information is inaccurate. The expected results do not appear in such a case and the ultimate consequence may be a loss of confidence in the information system used. On the contrary, it could be assumed that the acquisition, storage and use of Big Data in the future will become a key factor to maintaining competitiveness, business growth and further innovations. Thus, the organizations that will systematically use Big Data in their decision-making process and planning strategies will have a competitive advantage.

Emerging Industrial Revolution: Symbiosis of Industry 4.0 and Circular Economy: The Role of Universities

2020 ◽  
Vol 25 (3) ◽  
pp. 505-525 ◽  
Author(s):  
Seeram Ramakrishna ◽  
Alfred Ngowi ◽  
Henk De Jager ◽  
Bankole O. Awuzie
Keyword(s):  
Economic Growth ◽  
Circular Economy ◽  
Industry 4.0 ◽  
Business Models ◽  
Industrial Revolution ◽  
New Technologies ◽  
Production Systems ◽  
Fourth Industrial Revolution ◽  
New Jobs

Growing consumerism and population worldwide raises concerns about society’s sustainability aspirations. This has led to calls for concerted efforts to shift from the linear economy to a circular economy (CE), which are gaining momentum globally. CE approaches lead to a zero-waste scenario of economic growth and sustainable development. These approaches are based on semi-scientific and empirical concepts with technologies enabling 3Rs (reduce, reuse, recycle) and 6Rs (reuse, recycle, redesign, remanufacture, reduce, recover). Studies estimate that the transition to a CE would save the world in excess of a trillion dollars annually while creating new jobs, business opportunities and economic growth. The emerging industrial revolution will enhance the symbiotic pursuit of new technologies and CE to transform extant production systems and business models for sustainability. This article examines the trends, availability and readiness of fourth industrial revolution (4IR or industry 4.0) technologies (for example, Internet of Things [IoT], artificial intelligence [AI] and nanotechnology) to support and promote CE transitions within the higher education institutional context. Furthermore, it elucidates the role of universities as living laboratories for experimenting the utility of industry 4.0 technologies in driving the shift towards CE futures. The article concludes that universities should play a pivotal role in engendering CE transitions.

An Accounting Information Systems Perspective on Data Analytics and Big Data

2017 ◽  
Vol 31 (3) ◽  
pp. 101-114 ◽  
Author(s):  
Esperanza Huerta ◽  
Scott Jensen
Keyword(s):  
Big Data ◽  
Information Systems ◽  
Data Analytics ◽  
Accounting Information ◽  
Security Requirements ◽  
Future Research ◽  
Accounting Information Systems ◽  
Systems Perspective ◽  
Analytic Skills

ABSTRACT Forty-six academics and practitioners participated in the second Journal of Information Systems Conference to discuss data analytics and Big Data from an accounting information systems perspective. The panels discussed the evolving role of technology in accounting, privacy within the domain of Big Data, and people and Big Data. Throughout all three panels, several topics emerged that impact all areas of accounting—developing enhanced analytical and data handling skills; evaluating privacy, security requirements, and risks; thinking creatively; and assessing the threat of automation to the accounting profession. Other topics were specific to a segment of the profession, such as the growing demand for privacy compliance audits and the curriculum adjustments necessary to develop data analytic skills. This commentary synthesizes and expands the discussions of the conference panels and suggests potential areas for future research.

Big Data Myth

2019 ◽  
pp. 223-237
Author(s):  
Agata Mardosz-Grabowska
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analysis ◽  
Qualitative Data ◽  
New Technologies ◽  
Marketing Management ◽  
Individual Experience ◽  
Decision Making Process

Organizations are expected to act rationally; however, mythical thinking is often present among their members. It refers also to myths related to technology. New inventions and technologies are often mythologized in organizations. People do not understand how new technologies work and usually overestimate their possibilities. Also, myths are useful in dealing with ambivalent feelings, such as fears and hopes. The text focuses on the so-called “big data myth” and its impact on the decision-making process in modern marketing management. Mythical thinking related to big data in organizations has been observed both by scholars and practitioners. The aim of the chapter is to discuss the foundation of the myth, its components, and its impact on the decision-making process. Among others, a presence of a “big data myth” may be manifested by over-reliance on data, neglecting biases in the process of data analysis, and undermining the role of other factors, including intuition and individual experience of marketing professionals or qualitative data.

Why privacy is not enough privacy in the context of “ubiquitous computing” and “big data”

2014 ◽  
Vol 12 (2) ◽  
pp. 93-106 ◽  
Author(s):  
Tobias Matzner
Keyword(s):  
Big Data ◽  
Ubiquitous Computing ◽  
New Technologies ◽  
Personal Data ◽  
Quality Of Data ◽  
Content Type ◽  
Privacy Threats ◽  
New Concepts ◽  
New Perspective

Purpose – Ubiquitous computing and “big data” have been widely recognized as requiring new concepts of privacy and new mechanisms to protect it. While improved concepts of privacy have been suggested, the paper aims to argue that people acting in full conformity to those privacy norms still can infringe the privacy of others in the context of ubiquitous computing and “big data”. Design/methodology/approach – New threats to privacy are described. Helen Nissenbaum's concept of “privacy as contextual integrity” is reviewed concerning its capability to grasp these problems. The argument is based on the assumption that the technologies work, persons are fully informed and capable of deciding according to advanced privacy considerations. Findings – Big data and ubiquitous computing enable privacy threats for persons whose data are only indirectly involved and even for persons about whom no data have been collected and processed. Those new problems are intrinsic to the functionality of these new technologies and need to be addressed on a social and political level. Furthermore, a concept of data minimization in terms of the quality of the data is proposed. Originality/value – The use of personal data as a threat to the privacy of others is established. This new perspective is used to reassess and recontextualize Helen Nissenbaum's concept of privacy. Data minimization in terms of quality of data is proposed as a new concept.

scholarly journals Creating Informative Data Warehouses: Exploring Data and Information Quality through Data Mining

10.28945/2584 ◽  
2002 ◽  
Author(s):  
Herna L. Viktor ◽  
Wayne Motha
Keyword(s):  
Data Mining ◽  
Information Quality ◽  
Data Warehousing ◽  
Poor Quality ◽  
Quality Data ◽  
Data Warehouses ◽  
Quality Of Data ◽  
Poor Quality Data ◽  
Daunting Task

Increasingly, large organizations are engaging in data warehousing projects in order to achieve a competitive advantage through the exploration of the information as contained therein. It is therefore paramount to ensure that the data warehouse includes high quality data. However, practitioners agree that the improvement of the quality of data in an organization is a daunting task. This is especially evident in data warehousing projects, which are often initiated “after the fact”. The slightest suspicion of poor quality data often hinders managers from reaching decisions, when they waste hours in discussions to determine what portion of the data should be trusted. Augmenting data warehousing with data mining methods offers a mechanism to explore these vast repositories, enabling decision makers to assess the quality of their data and to unlock a wealth of new knowledge. These methods can be effectively used with inconsistent, noisy and incomplete data that are commonplace in data warehouses.

scholarly journals Data Cleaning in Cloud Platform

2020 ◽  
Vol 9 (1) ◽  
pp. 2535-2539
Keyword(s):  
Data Cleaning ◽  
Data Association ◽  
Poor Quality ◽  
Quality Data ◽  
Quality Of Data ◽  
Useful Knowledge ◽  
Big Data Applications ◽  
Poor Quality Data ◽  
Free Data ◽  
The Given

: Data is very valuable and it is generated in large volumes. The Use of high-quality data for making quality decisions has become a huge task which helps people to make better decisions, analysis, predictions. We are surrounded by data with errors, Data cleaning is a delayed, complicated task and considered costly. Data polishing is important since it is necessary to remove errors from the data before transferring to the data warehouse since poor quality data is eliminated to get the desired results. The Error-free data will produce precise and accurate results when queried. Hence consistent and proper data is required for the decision making. The characteristics of data polishing is data repairing and data association. Identifying the homogeneous object and linking it to the most associated object is defined as Association. The process of making the database reliable by repairing and finding the faults is defined as repairing. In the case of big data applications, we do not use all the existing data, we use only subsets of appropriate data. Association is the process of converting extensive amounts of raw data to subsets of appropriate data that are useful. Once we get the appropriate data, the available data is analyzed and it leads to knowledge [14]. Multiple approaches are used to associate the given data and to achieve meaningful and useful knowledge to fix or repair [12]. Maintaining polished quality of data is referred to as data polishing. Usually the objectives of data polishing are not properly defined. This paper will discuss the goals of data cleaning and different approaches for data cleaning platforms

scholarly journals Improvement of the Production Process in the Industry 4.0 Context

2018 ◽  
Vol 1 (1) ◽  
pp. 55-62
Author(s):  
Sandra Grabowska
Keyword(s):  
Heat Treatment ◽  
Industry 4.0 ◽  
Industrial Revolution ◽  
Treatment Process ◽  
New Technologies ◽  
Rapid Development ◽  
Heat Treatment Process ◽  
Metallurgical Enterprise ◽  
Ishikawa Diagram ◽  
Intensity Of Competition

Abstract Dynamically changing conditions of business activity, rapid development of new technologies, increasing intensity of competition, progressing globalisation pose for entrepreneurs new, much more difficult principles than before, especially due to the increase of intensity and complexity of the environment. It is reflected in the necessity of continuous improvement of processes and their quick reorganisation. The aim of the article is to present research conducted in metallurgical enterprise. In the article individual stages of heat treatment process, taking into consideration complications, errors and quality defects of the product arising at the stage of manufacturing the product were described. In order to minimize resulting defects, quality improvement system was implemented, using, among others Ishikawa diagram. In view of the fact that the world stands on the threshold of next industrial revolution, directions of improvement of heat treatment process in the context of Industry 4.0 were indicated.

scholarly journals NEW PARADIGM IN ACCOUNTING INFORMATION SYSTEMS – THE ROLE OF THE LATEST INFORMATION TECHNOLOGY TRENDS

2020 ◽  
Author(s):  
Zoran Minovski ◽  
Bojan Malchev ◽  
Todor Tocev
Keyword(s):  
Information Systems ◽  
Information Technologies ◽  
New Technologies ◽  
Relevant Literature ◽  
Accounting Information ◽  
New Paradigm ◽  
Accounting Information Systems ◽  
Blockchain Technology ◽  
The Impact

The purpose of this paper is to identify the impact and benefits of the latest information technologies on Accounting Information Systems (AIS). Taking into account the numerous papers related to new technologies and their application in the accounting profession within Industry 4.0, and conducted survey about perception of practitioners in Republic of North Macedonia, this paper summarizes the characteristics and key benefits of some of the new technologies for the functioning of AIS in the digital age. First of all, the evolution of AIS is elaborated, based on theoretical and empirical analysis of the accounting process from the appearance of the first AIS up to nowadays’ services and techniques available for supporting the accounting function. The first technology to be elaborated is Big Data and its potential to change the business landscape, especially in the field of automating operation processes, customer engagements, and predictive decision-making process. Secondly, the Blockchain Technology as an example of Distributed Ledger Technology (DLT), which adoption brings new possibilities in eliminating or redefining the role of entities external to the company. Cloud Computing i.e. Cloud Accounting is the third technology which is elaborated in this paper through the services it offers on the cloud, especially the way AIS process, store and backup the sensitive and confidential data. Last but not least, Artificial Intelligence (AI), a technology that could change the professional services, the need, and opportunities that are provided for a solution to the current accounting issues. In summary, taking into account the relevant literature and the perception of the respondents-practitioners, increased use of these technologies is necessary because their application reduces costs; increases transparency and confidence in information; flexibility, i.e. no time and space restrictions on their use, etc., which is especially useful in the current state of Pandemic, caused by the virus COVID-19.

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