The Transformation of Auditing From Traditional to Continuous Auditing in the Era of Big Data

2019 ◽  
pp. 137-152
Author(s):  
Adem Çabuk ◽  
Alp Aytaç
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Present Situation ◽  
Decision Makers ◽  
Reliable Information ◽  
Continuous Auditing ◽  
Digital Devices ◽  
Volume Velocity ◽  
Future Direction ◽  
Shed Light

Massive usage of internet and digital devices make it easier accessing the desired information. In the past, auditing was a periodic, reactive approach, but this must change. Today, volume, velocity, variety, veracity, and value of the information, which are the main criteria of big data, are crucial. Decision makers demand timely, true, and reliable information. This need has affected every sector including auditing. For this reason, the continuous auditing system comes to debate in the big data era. The main aim of this chapter is to shed light on how traditional auditing transformed into the continuous auditing and where big data stands in this transformation. It is concluded that even though many obstacles arise, continuous auditing systems and harvesting big data benefits are crucial to gain a competitive advantage. Also, using big data analytics and continuous auditing system together, management and shareholders gain detailed information about the company's present situation and future direction.

Big Data Analytics With Service-Oriented Architecture

2017 ◽  
pp. 216-234 ◽  
Author(s):  
Triparna Mukherjee ◽  
Asoke Nath
Keyword(s):  
Big Data ◽  
Service Oriented Architecture ◽  
Big Data Analytics ◽  
Dominant Mode ◽  
Volume Velocity ◽  
It Systems ◽  
Trading Partners ◽  
Integration Of Services ◽  
Service Oriented ◽  
Data Explosion

This chapter focuses on Big Data and its relation with Service-Oriented Architecture. We start with the introduction to Big Data Trends in recent times, how data explosion is not only faced by web and retail networks but also the enterprises. The notorious “V's” – Variety, volume, velocity and value can cause a lot of trouble. We emphasize on the fact that Big Data is much more than just size, the problem that we face today is neither the amount of data that is created nor its consumption, but the analysis of all those data. In our next step, we describe what service-oriented architecture is and how SOA can efficiently handle the increasingly massive amount of transactions. Next, we focus on the main purpose of SOA here is to meaningfully interoperate, trade, and reuse data between IT systems and trading partners. Using this Big Data scenario, we investigate the integration of Services with new capabilities of Enterprise Architectures and Management. This has had varying success but it remains the dominant mode for data integration as data can be managed with higher flexibility.

A Conceptual Model for Describing the Integration of Decision Aspect into Big Data

2022 ◽  
pp. 294-318
Author(s):  
Fatma Chiheb ◽  
Fatima Boumahdi ◽  
Hafida Bouarfa
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Competitive Advantage ◽  
Conceptual Model ◽  
Big Data Analytics ◽  
Decision Makers ◽  
Modern Business ◽  
Definition Of ◽  
A Company ◽  
Support Decision Making

Big Data is an important topic for discussion and research. It has gained this importance due to the meaningful value that could be extracted from these data. The application of Big Data in the modern business allows enterprises to take faster and smarter decisions, achieving a real competitive advantage. However, a lot of Big Data projects provide disappointing results that don't address the decision-makers' needs due to many reasons. The main reason for this failure can be summarized in neglecting the study of the decision-making aspect of these projects. In light of this challenge, this study proposes the integration of decision aspect into Big Data as a solution. Therefore, this article presents three main contributions: 1) Clarify the definition of Big Data; 2) Presents BD-Da model, a conceptual model describes the levels that should be considered to develop a Big Data project aiming to solve a problem that calls a decision; 3) Describes a particular, logical, requirements-like approach that explains how a company develops a Big Data analytics project to support decision-making.

A Conceptual Model for Describing the Integration of Decision Aspect into Big Data

2019 ◽  
Vol 10 (4) ◽  
pp. 1-23
Author(s):  
Fatma Chiheb ◽  
Fatima Boumahdi ◽  
Hafida Bouarfa
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Conceptual Model ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Decision Makers ◽  
Modern Business ◽  
Definition Of ◽  
A Company ◽  
Support Decision Making

Big Data is an important topic for discussion and research. It has gained this importance due to the meaningful value that could be extracted from these data. The application of Big Data in the modern business allows enterprises to take faster and smarter decisions, achieving a real competitive advantage. However, a lot of Big Data projects provide disappointing results that don't address the decision-makers' needs due to many reasons. The main reason for this failure can be summarized in neglecting the study of the decision-making aspect of these projects. In light of this challenge, this study proposes the integration of decision aspect into Big Data as a solution. Therefore, this article presents three main contributions: 1) Clarify the definition of Big Data; 2) Presents BD-Da model, a conceptual model describes the levels that should be considered to develop a Big Data project aiming to solve a problem that calls a decision; 3) Describes a particular, logical, requirements-like approach that explains how a company develops a Big Data analytics project to support decision-making.

scholarly journals Big data analytics and international market selection: An exploratory study

2020 ◽  
Vol 10 (2) ◽  
Author(s):  
Jonathan Calof ◽  
Wilma Viviers
Keyword(s):  
Big Data ◽  
Decision Maker ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Organizational Factors ◽  
International Market ◽  
Decision Makers ◽  
Market Selection ◽  
Selection Decisions ◽  
The Impact

A great deal of information is available on international trade flows and potentialmarkets. Yet many exporters do not know how to identify, with adequate precision, thosemarkets that hold the greatest potential. Even if they have access to relevant information, thesheer volume of information often makes the analytical process complex, time-consuming andcostly. An additional challenge is that many exporters lack an appropriate decision-makingmethodology, which would enable them to adopt a systematic approach to choosing foreignmarkets. In this regard, big-data analytics can play a valuable role. This paper reports on thefirst two phases of a study aimed at exploring the impact of big-data analytics on internationalmarket selection decisions. The specific big-data analytics system used in the study was theTRADE-DSM (Decision Support Model) which, by screening large quantities of marketinformation obtained from a range of sources identifies optimal product‒market combinationsfor a country, industry sector or company. Interviews conducted with TRADE-DSM users aswell as decision-makers found that big-data analytics (using the TRADE-DSM model) didimpact international market-decision. A case study reported on in this paper noted thatTRADE-DSM was a very important information source used for making the company’sinternational market selection decision. Other interviewees reported that TRADE-DSMidentified countries (that were eventually selected) that the decision-makers had not previouslyconsidered. The degree of acceptance of the TRADE-DSM results appeared to be influenced byTRADE-DSM user factors (for example their relationship with the decision-maker andknowledge of the organization), decision-maker factors (for example their experience andknowledge making international market selection decisions) and organizational factors (forexample senior managements’ commitment to big data and analytics). Drawing on the insightsgained in the study, we developed a multi-phase, big-data analytics model for internationalmarket selection.

Emerging Trends and Techniques in Cloud-Based Data Analytics

2021 ◽  
pp. 37-47
Author(s):  
Shaila S. G. ◽  
Monish L. ◽  
Lavanya S. ◽  
Sowmya H. D. ◽  
Divya K.
Keyword(s):  
Social Media ◽  
Cloud Computing ◽  
Big Data ◽  
Data Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Decision Makers ◽  
Huge Data ◽  
Emerging Trends ◽  
Media Applications

The new trending technologies such as big data and cloud computing are in line with social media applications due to their fast growth and usage. The big data characteristic makes data management challenging. The term big data refers to an immense collection of both organised and unorganised data from various sources, and nowadays, cloud computing supports in storing and processing such a huge data. Analytics are done on huge data that helps decision makers to take decisions. However, merging two conflicting design principles brings a challenge, but it has its own advantage in business and various fields. Big data analytics in the cloud places rigorous demands on networks, storage, and servers. The chapter discusses the importance of cloud platform for big data, importance of analytics in cloud and gives detail insight about the trends and techniques adopted for cloud analytics.

A systematic literature review with bibliometric analysis of big data analytics adoption from period 2014 to 2018

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zeeshan Inamdar ◽  
Rakesh Raut ◽  
Vaibhav S. Narwane ◽  
Bhaskar Gardas ◽  
Balkrishna Narkhede ◽  
...  
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Literature Review ◽  
Bibliometric Analysis ◽  
Systematic Literature Review ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Sustainable Supply Chain ◽  
Content Type ◽  
Future Direction

PurposeThe volume of data being generated by various sectors in recent years has increased exponentially. Consequently, professionals struggle to process essential data in the current competitive world. The purpose of the study is to explore and provide insights into the Big Data Analytics (BDA) studies in different sectors.Design/methodology/approachThis study performs a systematic literature review (SLR) with bibliometric analysis of BDA adoption (BDAA) in the supply chain and its applications in various sectors from 2014 to 2018. This paper focuses on BDAA studies have been carried out across different countries and sectors. Also, the paper explores different tools and techniques used in BDAA studies.FindingsThe benefits of adopting BDA, coupled with a lack of adequate research in the field, have motivated this study. This literature review categorizes paper into seven main areas and found that most of the studies were carried out in manufacturing and service.Practical implicationsThis research insight and observations can provide practitioners and academia with guidance on implementing BDA in different sustainable supply chain sectors. The article indicates a few remarkable gaps in the future direction and trends regarding the integration of BDA and sustainable supply chain development.Originality/valueThe study derives a new categorization of BDA, which investigates how data is generated, organized, captured, interpreted and evaluated to give valuable insights to manage the sustainable supply chain.

scholarly journals Study of Big Data in Healthcare: Analysis, Management, Techniques and Applications

2021 ◽  
pp. 61-66
Author(s):  
Ragini Munnaprasad Gupta
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Data Sets ◽  
Healthcare Industry ◽  
Medical Field ◽  
The Past ◽  
Management Techniques ◽  
Future Direction ◽  
Different Sources ◽  
The Internet Of Things

Clinical, suppliers-providers of healthcare, policymakers, and patients are encounter exciting opportunities in big data sets. Big data capability that emerged in the past decades. Due to the rapid growth of communication in the healthcare industry big data play an important role. I have explained how healthcare uses big data analytics due to its great potential that. In the healthcare industry, different sources of big data include clinic records, medical records of patients, consequences of medical assessments, and devices that are a piece of the internet of things. Firstly, I am beginning with the core concept of big data and healthcare. Secondly, discuss the Process of Big data Analysis and Management. Thirdly talk about the techniques of big data that uses in the medical field. Finally, the Application of the healthcare industry and future direction are discussed.

scholarly journals Business Intelligence Model to analyze Social Media through Big Data analytics

2021 ◽  
Vol 119 ◽  
pp. 07006
Author(s):  
Kawtar Mouyassir ◽  
Mohamed Hanine ◽  
Hassan Ouahmane
Keyword(s):  
Social Media ◽  
Big Data ◽  
Business Intelligence ◽  
Qualitative Data ◽  
Big Data Analytics ◽  
Decision Makers ◽  
Social Business ◽  
Social Media Networks ◽  
Big Data Technologies ◽  
Voice Of Customer

Business Intelligence (BI) is a collection of tools, technologies, and practices that include the entire process of collecting, processing, and analyzing qualitative information, to help entrepreneurs better understand their business and marketplace. Every day, social networks expand at a faster rate and pace, which sees them as a source of Big Data. Therefore, BI is developed in the same way on VoC (Voice of Customer) expressed in social media as qualitative data for company decision-makers, who desire to have a clear perception of customers’ behaviour. In this article, we present a comparative study between traditional BI and social BI, then examine an approach to social business intelligence. Next, we are going to demonstrate the power of Big Data that can be integrated into BI so that we can finally describe in detail how Big Data technologies, like Apache Flume, help to collect unstructured data from various sources such as social media networks and store it in Hadoop storage.

Big data analytics capability as a major antecedent of firm innovation performance

2021 ◽  
pp. 146575032110508
Author(s):  
Arshad Muhammad ◽  
Chen Kun Yu ◽  
Aneela Qadir ◽  
Waqar Ahmed ◽  
Zahid Yousuf ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Quantitative Research ◽  
Positive Impact ◽  
Big Data Analytics ◽  
Innovation Performance ◽  
National Database ◽  
Firm Innovation ◽  
Future Direction ◽  
Practical Implications

Purpose: This study aimed to investigate the big data analytics capabilities (BDAC) model using resource-based theory (RBT) and dimensions of big data analytics (management, technological, and talent) that influenced the firm innovation performance. Design/methodology/approach: The research uses quantitative research design where 548 respondents were selected for the survey from Pakistan electronic media regulatory authority (PEMRA), national database and registration authority (NADRA), and cellular companies. Only 394 useable responses were received from the respondents. Findings: The findings revealed that BDAC has a statistically positive impact on firm innovation performance. All of the proposed hypotheses were approved in this study. Research limitations/implications: The study gives future direction to the researchers and practitioners to implement this model in other industries. Practical implications: The research makes important theoretical and methodological contributions to the business and society's nexus in developing country firms that are under economic pressure. Originality/value: The paper is new in the context of the developing firm's innovation.

Managerial Controversies in Artificial Intelligence and Big Data Analytics

2022 ◽  
pp. 1745-1764
Author(s):  
Kenneth David Strang ◽  
Zhaohao Sun
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Action Research ◽  
Literature Review ◽  
Systems Thinking ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Business Services ◽  
Volume Velocity ◽  
Technology Process

This chapter discusses several fundamental and managerial controversies associated with artificial intelligence and big data analytics which will be of interest to quantitative professionals and practitioners in the fields of computing, e-commerce, e-business services, and e-government. The authors utilized the systems thinking technique within an action research framework. They used this approach because their ideology was pragmatic, the problem at hand, was complex and institutional (healthcare discipline), and they needed to understand the problems from both a practitioner and a nonhuman technology process viewpoint. They used the literature review along with practitioner interviews collected at a big data conference. Although they found many problems, they considered these to be already encompassed into the big data five V's (volume, velocity, variety, value, veracity). Interestingly, they uncovered three new insights about the hidden healthcare artificial intelligence and big data analytics risks; then they proposed solutions for each of these problems.

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