The Impact of Big Data on Accounting and Auditing

2021 ◽  
Vol 8 (1) ◽  
pp. 1-14
Author(s):  
Dimitris Balios
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Audit Quality ◽  
Big Data Analytics ◽  
Management Decision ◽  
Management Accountants ◽  
Management Decision Making ◽  
The Impact ◽  
Information Providers

Big data and big data analytics will unavoidably change the role of accountants. This paper considers the impact of big data on accounting and auditing. Financial accountants need to move beyond the book-keeping process and become key information providers to decision-makers. That upturns accountants' consulting role and their ability to think strategically, providing critical help in management decision making. The relationship between managers and management accountants becomes closer and more effective because of big data. Management accountants can use additional analytical methods to detect processes and product excellence, combined with diminishing cost. Big data and big data analytics in auditing ensure audit quality and fraud detection. Upgraded information systems and automation in business procedures diminish the need for staff participation. Inevitably, the skills of accountants and knowledge must be associated with big data and big data analytics and modern accountants must develop an analytics mindset by being familiar with data and technologies.

scholarly journals The Impact of Big Data Analytics and Innovative Information Technologies on Audit Quality

2021 ◽  
Vol 5 (520) ◽  
pp. 302-307
Author(s):  
M. I. Yaremyk ◽  
◽  
K. Y. Yaremyk ◽  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Information Technologies ◽  
Professional Training ◽  
Audit Quality ◽  
Big Data Analytics ◽  
Data Uncertainty ◽  
Professional Judgment ◽  
Effective Use ◽  
The Impact

The article is aimed at examining and systematizing the factors influencing innovative information technologies on the auditor’s professional judgment, efficiency and quality of audit, as well as studying the impact of big data analytics on the skills and competence required by auditors to conduct an audit in a big data environment. The state of use of data analytics instruments in auditing is characterized. Based on the analysis of the views of scientists, experts and practicing auditors, the main tasks of the audit are systematized, in which the effective use of big data analytics is possible, among which the following are distinguished: assessment of continuity of the enterprise’s activities along with bankruptcy forecasting; detection of financial fraud and assessment of the effectiveness of control; use of visualization dashboards to form a professional judgment of the auditor on the scope of control, the uniformity of the the sample set and size, the threshold of materiality. The main factors that hinder the use of big data analytics in the audit are highlighted, namely: inconsistency in assessing the reliability and accuracy of data in the sets; ensuring the confidentiality and integrity of the client’s data; uncertainty about several methodological bases of the audit, in particular sampling-based testing, as well as the lack of skills and competencies among auditors regarding the use of big data analytics instruments. Prospects for further research in this direction are to identify the possibility of eliminating inconsistencies in the use of big data at the level of standards, as well as to improve educational and professional training programs for accounting and audit specialists with a focus on obtaining skills in working with big data.

scholarly journals Big Data Analytics in urology: the story so far and the road ahead

2021 ◽  
Vol 13 ◽  
pp. 175628722199813
Author(s):  
B. M. Zeeshan Hameed ◽  
Aiswarya V. L. S. Dhavileswarapu ◽  
Nithesh Naik ◽  
Hadis Karimi ◽  
Padmaraj Hegde ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Early Stage ◽  
Big Data Analytics ◽  
Positive Trend ◽  
The Road ◽  
Healthcare Data ◽  
Urological Disorders ◽  
Different Sources

Artificial intelligence (AI) has a proven record of application in the field of medicine and is used in various urological conditions such as oncology, urolithiasis, paediatric urology, urogynaecology, infertility and reconstruction. Data is the driving force of AI and the past decades have undoubtedly witnessed an upsurge in healthcare data. Urology is a specialty that has always been at the forefront of innovation and research and has rapidly embraced technologies to improve patient outcomes and experience. Advancements made in Big Data Analytics raised the expectations about the future of urology. This review aims to investigate the role of big data and its blend with AI for trends and use in urology. We explore the different sources of big data in urology and explicate their current and future applications. A positive trend has been exhibited by the advent and implementation of AI in urology with data available from several databases. The extensive use of big data for the diagnosis and treatment of urological disorders is still in its early stage and under validation. In future however, big data will no doubt play a major role in the management of urological conditions.

Big Data and Sustainable Consumption: A Review and Research Agenda

2021 ◽  
pp. 097226292110225
Author(s):  
Shobhana Chandra ◽  
Sanjeev Verma
Keyword(s):  
Big Data ◽  
Consumer Behaviour ◽  
Business Models ◽  
Sustainable Consumption ◽  
Big Data Analytics ◽  
Consumption Behaviour ◽  
Consumer Experiences ◽  
Systematic Understanding ◽  
The Impact

Big data (BD) is making advances in promoting sustainable consumption behaviour and has attracted the attention of researchers worldwide. Despite the increased focus, the findings of studies on this topic are fragmented, and future researchers need a systematic understanding of the existing literature for identification of the research scope. This study offers a systematic review of the role of BD in promoting sustainable-consumption behaviour with the help of a bibliometric analysis, followed by a thematic analysis. The findings suggest that businesses deploy BD to create sustainable consumer experiences, predict consumer buying patterns, design and alter business models and create nudges for sustainable consumption, while consumers are forcing businesses to develop green operations and supply chains to reduce the latter’s carbon footprint. The major research gaps for future researchers are in the following areas: the impact of big data analytics (BDA) on consumerism, the role of BD in the formation of sustainable habits and consumer knowledge creation for sustainable consumption and prediction of green consumer behaviour.

scholarly journals The Rising Role of Big Data Analytics and IoT in Disaster Management: Recent Advances, Taxonomy and Prospects

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 54595-54614 ◽  
Author(s):  
Syed Attique Shah ◽  
Dursun Zafer Seker ◽  
Sufian Hameed ◽  
Dirk Draheim
Keyword(s):  
Big Data ◽  
Disaster Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Recent Advances

scholarly journals Big Data Analytics for Search Engine Optimization

2020 ◽  
Vol 4 (2) ◽  
pp. 5 ◽  
Author(s):  
Ioannis C. Drivas ◽  
Damianos P. Sakas ◽  
Georgios A. Giannakopoulos ◽  
Daphne Kyriaki-Manessi
Keyword(s):  
Big Data ◽  
Cultural Heritage ◽  
Search Engine ◽  
Data Analytics ◽  
User Behavior ◽  
Big Data Analytics ◽  
Data Types ◽  
Search Engine Optimization ◽  
The Impact ◽  
The Web

In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web.

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