Cloud Accounting: The Transition of Accounting Information Model in the Big Data Background

2015 ◽  
Author(s):  
Jiao Feng
Keyword(s):  
Big Data ◽  
Accounting Information ◽  
Information Model

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.

scholarly journals THE IMPACT OF BIG DATA ANALYTICS ON IMPROVING FINANCIAL REPORTING QUALITY

2020 ◽  
Vol 4 (03) ◽  
Author(s):  
Nagat Mohamed Marie Younis
Keyword(s):  
Big Data ◽  
Financial Reporting ◽  
Data Analytics ◽  
Information Quality ◽  
Accounting Information ◽  
Big Data Analytics ◽  
Reporting Quality ◽  
Financial Reporting Quality ◽  
Accounting Information Quality ◽  
The Impact

Purpose – The current study aims to clarify the importance of big data analytics and its role in changing the accounting profession and the roles of accountants, in addition to testing the impact of big data analytics on improving financial reporting quality in the Saudi environment. Design/ methodology/ approach – To achieve the study's goals and validate hypotheses, relevant previous literature and research are referred. Also, a field study is conducted by distributing a questionnaire of (154) individual academics, financial analysts, accountants, and experts in the field of analyzing big data in the Kingdom of Saudi in 2019. Data are analyzed by using the program of Statistical Package for Social Science (SPSS 17.0). Findings – The study concluded that although business organizations face several challenges when analyzing data, big data analytics has a significant role in achieving high competitiveness for institutions, improving the accounting information quality, providing appropriate information that helps in rationalizing decisions within the economic unit, and providing future information affecting stakeholder's decisions. The study also has proved that there is a statistically significant effect of big data analytics on improving the quality of accounting information, as big data analytics clearly affects the characteristics of the accounting information quality, positively affecting the quality of financial reports. Originality/ Value – Originality/ Value – The analytics of big data is one of the most important topics where it positively affects the improvement of accounting information quality, which reflects on financial reporting quality. Hence, academics and institutions should pay attention to this topic and follow their new ideas. The present study is one of the first studies that deal with this topic and examine the relationship between big data analytics and the characteristics of accounting information which positively affecting financial reporting quality.

An REA Ontology-Based Model for Mapping Big Data to Accounting Information Systems Elements

2017 ◽  
Vol 31 (3) ◽  
pp. 45-61 ◽  
Author(s):  
Uday S. Murthy ◽  
Guido L. Geerts
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Information Systems ◽  
Accounting Information ◽  
Data Sources ◽  
Complex Data ◽  
Accounting Information Systems ◽  
Business Transaction ◽  
Business Transactions ◽  
Data Elements

ABSTRACT The term “Big Data” refers to massive volumes of data that grow at an increasing rate and encompass complex data types such as audio and video. While the applications of Big Data and analytic techniques for business purposes have received considerable attention, it is less clear how external sources of Big Data relate to the transaction processing-oriented world of accounting information systems. This paper uses the Resource-Event-Agent Enterprise Ontology (REA) (McCarthy 1982; International Standards Organization [ISO] 2007) to model the implications of external Big Data sources on business transactions. The five-phase REA-based specification of a business transaction as defined in ISO (2007) is used to formally define associations between specific Big Data elements and business transactions. Using Big Data technologies such as Apache Hadoop and MapReduce, a number of information extraction patterns are specified for extracting business transaction-related information from Big Data. We also present a number of analytics patterns to demonstrate how decision making in accounting can benefit from integrating specific external Big Data sources and conventional transactional data. The model and techniques presented in this paper can be used by organizations to formalize the associations between external Big Data elements in their environment and their accounting information artifacts, to build architectures that extract information from external Big Data sources for use in an accounting context, and to leverage the power of analytics for more effective decision making.

Towards Convergence in Information Systems Design

2020 ◽  
pp. 247-263
Author(s):  
Deepika Prakash
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Information Systems ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Systems Design ◽  
Information Model ◽  
Information Systems Design ◽  
Nosql Databases ◽  
Nosql Database

Three technologies—business intelligence, big data, and machine learning—developed independently and address different types of problems. Data warehouses have been used as systems for business intelligence, and NoSQL databases are used for big data. In this chapter, the authors explore the convergence of business intelligence and big data. Traditionally, a data warehouse is implemented on a ROLAP or MOLAP platform. Whereas MOLAP suffers from having propriety architecture, ROLAP suffers from the inherent disadvantages of RDBMS. In order to mitigate the drawbacks of ROLAP, the authors propose implementing a data warehouse on a NoSQL database. They choose Cassandra as their database. For this they start by identifying a generic information model that captures the requirements of the system to-be. They propose mapping rules that map the components of the information model to the Cassandra data model. They finally show a small implementation using an example.

Big Data Scalability

2016 ◽  
Vol 11 (02) ◽  
Author(s):  
Ruchi Saxena
Keyword(s):  
Big Data ◽  
Data Flow ◽  
High Volume ◽  
Information Model ◽  
Hardware Requirement ◽  
Meaningful Information ◽  
Volume Velocity ◽  
Effective Models

Big Data is described by 5 V’s, Volume, Velocity, Value, Veracity and variety by Bernard Marr. As the data of high volume at uncertain speed in different variety is travelling the architecture designed for data flow it is essential to gather the information out of the data and which can help in creating effective models later. The process of turning big data to meaningful information model is scaling. In this paper we will study scalability in details and the hardware requirement to achieve scalability. The study also includes the issues of scalability.

PRINCIPLES OF FORMATION AND THE CONCEPT OF DEVELOPMENT OF REGISTRATION AND ANALYTICAL MAINTENANCE OF MANAGEMENT IN BUDGET ORGANIZATIONS

2015 ◽  
Vol 10 (6) ◽  
pp. 350-360
Author(s):  
Проняева ◽  
Lyudmila Pronyaeva ◽  
Горынин ◽  
Vyacheslav Gorynin
Keyword(s):  
Public Sector ◽  
Management System ◽  
Accounting Information ◽  
Information Model ◽  
Accounting System ◽  
Modern Management ◽  
Accounting Principles ◽  
Public Sector Organizations ◽  
Methods And Techniques

In the context of reforms of the budget sector, improving the independence of public sector organizations, the requirements for accounting and analytical support are changing, which generate information for management purposes. To implement accounting and analytical tasks there is a need of improving their approaches, methods and techniques. The systematization of accounting principles in budget organizations is made, the basis of which became the qualitative requirements for accounting information and analytical support of modern management system. The concept of registration and analytical maintenance of management in budget organizations and its structural and information model is proposed, which determines the direction of the development of new theoretical propositions, scientific guidelines, tools of accounting system.

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