scholarly journals Big Data’s Disruptive Effect on Job Profiles: Management Accountants’ Case Study

2021 ◽  
Vol 14 (8) ◽  
pp. 376
Author(s):  
Adriana Tiron-Tudor ◽  
Delia Deliu
Keyword(s):  
Big Data ◽  
Data Sources ◽  
Disruptive Effect ◽  
Positive Effects ◽  
External Data ◽  
Management Accountants ◽  
Flexible Framework ◽  
Potential Risks ◽  
The Impact ◽  
Job Profile

The abundance of new innovative data sources creates opportunities and challenges for all professions and professionals working with information. One of these professionals is the management accountant (MA). Although their tasks have expanded over time and especially recently, MAs have not fully employed all the available internal and external data sources to describe, diagnose, visualize, predict and prescribe possible solutions that enable smart decisions with positive effects on businesses. Thus, the paper investigates the impact of Big Data, including Data Analytics, on MA’s job profile. Through a review of the most recent academic and professional publications, the paper contributes to the debate surrounding the redefinition of the role of MAs in organizations in a novel informational perspective of Abbott’s theory. The results could serve as a research agenda and incentive for further studies, as well as provide MAs with a guide on the topic of the enlargement of their role(s), respectively, the augmentation of their tasks and responsibilities regarding the analysis of Big Data. Furthermore, the research may provide both a rich and flexible framework to help practitioners in their analysis of potential risks, opportunities and challenges when handling Big Data, and a lens for professional accounting associations and bodies by helping them to prioritize the holding and seizing of jurisdictions as an imperative part of safety and security.

scholarly journals DATA SOURCES FOR BUSINESS INTELLIGENCE

2021 ◽  
Author(s):  
Naveen Kunnathuvalappil Hariharan
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Personal Data ◽  
Data Sources ◽  
Enterprise Information System ◽  
Enterprise Information ◽  
External Data ◽  
Operational Systems ◽  
Internal Sources ◽  
Key Questions

As organizations' desire for data grows, so does their search for data sources that are both usable and reliable.Businesses can obtain and collect big data in a variety of locations, both inside and outside their own walls.This study aims to investigate the various data sources for business intelligence. For business intelligence,there are three types of data: internal data, external data, and personal data. Internal data is mostly kept indatabases, which serve as the backbone of an enterprise information system and are known as transactionalsystems or operational systems. This information, however, is not always sufficient. If the company wants toanswer market and industry questions or better understand future customers, the analytics team may need to look beyond the company's own data sources. Organizations must have access to a variety of data sources in order to answer the key questions that guide their initiatives. Internal sources, external public sources, andcollaboration with a big data expert could all be beneficial. Companies who are able to extract relevant datafrom their mountain of data acquire new perspectives on their business, allowing them to become morecompetitive

scholarly journals Influence of Information Quality via Implemented German RCD Standard in Research Information Systems

Data ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 30
Author(s):  
Otmane Azeroual ◽  
Joachim Schöpfel ◽  
Dragan Ivanovic
Keyword(s):  
Information Systems ◽  
Data Quality ◽  
Information Quality ◽  
Research Performance ◽  
Data Sources ◽  
Steady Increase ◽  
Research Information ◽  
External Data ◽  
Core Dataset ◽  
The Impact

With the steady increase in the number of data sources to be stored and processed by higher education and research institutions, it has become necessary to develop Research Information Systems, which will store this research information in the long term and make it accessible for further use, such as reporting and evaluation processes, institutional decision making and the presentation of research performance. In order to retain control while integrating research information from heterogeneous internal and external data sources and disparate interfaces into RIS and to maximize the benefits of the research information, ensuring data quality in RIS is critical. To facilitate a common understanding of the research information collected and to harmonize data collection processes, various standardization initiatives have emerged in recent decades. These standards support the use of research information in RIS and enable compatibility and interoperability between different information systems. This paper examines the process of securing data quality in RIS and the impact of research information standards on data quality in RIS. We focus on the recently developed German Research Core Dataset standard as a case of application.

Interactive visualization of big data in the field of accounting

2019 ◽  
Vol 20 (4) ◽  
pp. 497-525 ◽  
Author(s):  
Lisa Maria Perkhofer ◽  
Peter Hofer ◽  
Conny Walchshofer ◽  
Thomas Plank ◽  
Hans-Christian Jetter
Keyword(s):  
Big Data ◽  
Visual Analytics ◽  
Interactive Visualization ◽  
Ease Of Use ◽  
Data Sources ◽  
Sample Population ◽  
Perceived Ease Of Use ◽  
Content Type ◽  
Technological Support ◽  
The Impact

Purpose Big Data introduces high amounts and new forms of structured, unstructured and semi-structured data into the field of accounting and this requires alternative data management and reporting methods. Generating insights from these new data sources highlight the need for different and interactive forms of visualization in the field of visual analytics. Nonetheless, a considerable gap between the recommendations in research and the current usage in practice is evident. In order to understand and overcome this gap, a detailed analysis of the status quo as well as the identification of potential barriers for adoption is vital. The paper aims to discuss this issue. Design/methodology/approach A survey with 145 business accountants from Austrian companies from a wide array of business sectors and all hierarchy levels has been conducted. The survey is targeted toward the purpose of this study: identifying barriers, clustered as human-related and technological-related, as well as investigating current practice with respect to interactive visualization use for Big Data. Findings The lack of knowledge and experience regarding new visualization types and interaction techniques and the sole focus on Microsoft Excel as a visualization tool can be identified as the main barriers, while the use of multiple data sources and the gradual implementation of further software tools determine the first drivers of adoption. Research limitations/implications Due to the data collection with a standardized survey, there was no possibility of dealing with participants individually, which could lead to a misinterpretation of the given answers. Further, the sample population is Austrian, which might cause issues in terms of generalizing results to other geographical or cultural heritages. Practical implications The study shows that those knowledgeable and familiar with interactive Big Data visualizations indicate high perceived ease of use. It is, therefore, necessary to offer sufficient training as well as user-centered visualizations and technological support to further increase usage within the accounting profession. Originality/value A lot of research has been dedicated to the introduction of novel forms of interactive visualizations. However, little focus has been laid on the impact of these new tools for Big Data from a practitioner’s perspective and their needs.

scholarly journals Using mobile big data to support emergency preparedness and address economically vulnerable communities during the COVID-19 pandemic in Nigeria

Data & Policy ◽  
2021 ◽  
Vol 3 ◽  
Author(s):  
Joanne Gilbert ◽  
Olubayo Adekanmbi ◽  
Charlie Harrison
Keyword(s):  
Big Data ◽  
Resource Planning ◽  
Data Sources ◽  
Data Driven ◽  
Second Phase ◽  
Case Scenario ◽  
Worst Case ◽  
Mobile Big Data ◽  
The Government ◽  
The Impact

Abstract With the declaration of the coronavirus disease 2019 (COVID-19) pandemic in Nigeria in 2020, the Nigeria Governors’ Forum (NGF) instigated a collaboration with MTN Nigeria to develop data-driven insights, using mobile big data (MBD) and other data sources, to shape the planning and response to the pandemic. First, a model was developed to predict the worst-case scenario for infections in each state. This was used to support state-level health committees to make local resource planning decisions. Next, as containment interventions resulted in subsistence/daily paid workers losing their income and ability to buy essential food supplies, NGF and MTN agreed a second phase of activity, to develop insights to understand the population clusters at greatest socioeconomic risk from the impact of the pandemic. This insight was used to promote available financial relief to the economically vulnerable population clusters in Lagos state via the HelpNow crowdfunding initiative. This article discusses how anonymized and aggregated mobile network data (MBD), combined with other data sources, were used to create valuable insights and inform the government, and private business, response to the pandemic in Nigeria. Finally, we discuss lessons learnt. Firstly, how a collaboration with, and support from, the regulator enabled MTN to deliver critical insights at a national scale. Secondly, how the Nigeria Data Protection Regulation and the GSMA COVID-19 Privacy Guidelines provided an initial framework to open the discussion and define the approach. Thirdly, why stakeholder management is critical to the understanding, and application, of insights. Fourthly, how existing relationships ease new project collaborations. Finally, how MTN is developing future preparedness by creating a team that is focused on developing data-driven insights for social good.

Geographic Information System and Big Spatial Data

2020 ◽  
Vol 16 (4) ◽  
pp. 101-145
Author(s):  
Raja Sher Afgun Usmani ◽  
Ibrahim Abaker Targio Hashem ◽  
Thulasyammal Ramiah Pillai ◽  
Anum Saeed ◽  
Akibu Mahmoud Abdullahi
Keyword(s):  
Information System ◽  
Big Data ◽  
Geographic Information System ◽  
Spatial Data ◽  
Spatial Information ◽  
Geographic Information ◽  
Data Sources ◽  
Spatial Datasets ◽  
Big Data Technologies ◽  
The Impact

Geographic information system (GIS) is designed to generate maps, manage spatial datasets, perform sophisticated “what if” spatial analyses, visualize multiple spatial datasets simultaneously, and solve location-based queries. The impact of big data is in every industry, including the GIS. The location-based big data also known as big spatial data has significant implications as it forces the industry to contemplate how to acquire and leverage spatial information. In this study, a comprehensive taxonomy is created to provide a better understanding of the uses of GIS and big spatial data. The taxonomy is made up of big data technologies, GIS data sources, tools, analytics, and applications. The authors look into the importance of big spatial data and its implications, review the data sources, and GIS analytics, applications, and GIS tools. Furthermore, in order to guide researchers interested in GIS, the challenges that require substantial research efforts are taken into account. Lastly, open issues in GIS that require further observation are summarized.

scholarly journals Capturing the personal through the lens of the professional: The use of external data sources in autoethnography

2019 ◽  
Vol 12 (1) ◽  
pp. 205979911982557 ◽  
Author(s):  
Catherine Lee
Keyword(s):  
Teacher Identity ◽  
Critical Incident ◽  
Data Sources ◽  
Professional Identities ◽  
Discursive Practices ◽  
Behaviour Therapy ◽  
External Data ◽  
Cognitive Behaviour ◽  
The Impact ◽  
Insight Into

This article shows how external data sources can be utilised in autoethnographic research. Beginning with an account of a critical incident that examines the incompatibility of private and professional identities, I show how, through the collection of data sources, I capture the impact of homophobic and heteronormative discursive practices on health, wellbeing and identity. In the critical incident, I explore how I prospered as a teacher at a British village school for almost 10 years by censoring my sexuality and carefully managing the intersection between my private and professional identities. However, when a malicious and homophobic neighbour and parent of children at the school exposed my sexuality to the Headteacher, I learned the extent to which the rural school community privileged and protected the heteronormative discourse. A poststructuralist theoretical framework underpins this article. My experience of being a subject is understood as the outcome of discursive practices. Sexual identity, teacher identity and autoethnographer identity are understood to be fluid, and constantly produced and reproduced in response to social, cultural and political influences. The article describes how email correspondence, medical records and notes from a course of cognitive behaviour therapy were deployed to augment my personal recollection and give a depth and richness to the narrative. As the critical incident became a police matter, examination takes place of how I sought to obtain and utilise data from the police national computer in the research. Attempts to collect data from the police and Crown Prosecution Service were problematic and provided an unexpected development in the research and offered additional insight into the nature of the British rural community and its police force.

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 Big(ger) data as better data in open distance learning

2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Paul Prinsloo ◽  
Elizabeth Archer ◽  
Glen Barnes ◽  
Yuraisha Chetty ◽  
Dion Van Zyl
Keyword(s):  
Big Data ◽  
Data Sources ◽  
Data Driven ◽  
Descriptive Case Study ◽  
Student Data ◽  
Current State ◽  
The University ◽  
The Impact ◽  
Dominant Paradigm

<p>In the context of the hype, promise and perils of Big Data and the currently dominant paradigm of data-driven decision-making, it is important to critically engage with the potential of Big Data for higher education. We do not question the potential of Big Data, but we do raise a number of issues, and present a number of theses to be seriously considered in realising this potential.</p><p>The University of South Africa (Unisa) is one of the mega ODL institutions in the world with more than 360,000 students and a range of courses and programmes. Unisa already has access to a staggering amount of student data, hosted in disparate sources, and governed by different processes. As the university moves to mainstreaming online learning, the amount of and need for analyses of data are increasing, raising important questions regarding our assumptions, understanding, data sources, systems and processes.</p><p>This article presents a descriptive case study of the current state of student data at Unisa, as well as explores the impact of existing data sources and analytic approaches. From the analysis it is clear that in order for big(ger) data to be better data, a number of issues need to be addressed. The article concludes by presenting a number of theses that should form the basis for the imperative to optimise the harvesting, analysis and use of student data.</p>

The Impact of Big Data Research on Practice, Policy, and Cancer Care

2019 ◽  
pp. e167-e175 ◽  
Author(s):  
David A. Chambers ◽  
Eitan Amir ◽  
Ramy R. Saleh ◽  
Danielle Rodin ◽  
Nancy L. Keating ◽  
...  
Keyword(s):  
Big Data ◽  
Policy Research ◽  
Clinical Trial Data ◽  
Policy Decision ◽  
Data Sources ◽  
Complex Data ◽  
Multiple Sources ◽  
Data Types ◽  
Biomedical Knowledge ◽  
The Impact

The concept of “big data” research—the aggregation and analysis of biologic, clinical, administrative, and other data sources to drive new advances in biomedical knowledge—has been embraced by the cancer research enterprise. Although much of the conversation has concentrated on the amalgamation of basic biologic data (e.g., genomics, metabolomics, tumor tissue), new opportunities to extend potential contributions of big data to clinical practice and policy abound. This article examines these opportunities through discussion of three major data sources: aggregated clinical trial data, administrative data (including insurance claims data), and data from electronic health records. We will discuss the benefits of data use to answer key oncology practice and policy research questions, along with limitations inherent in these complex data sources. Finally, the article will discuss overarching themes across data types and offer next steps for the research, practice, and policy communities. The use of multiple sources of big data has the promise of improving knowledge and providing more accurate data for clinicians and policy decision makers. In the future, optimization of machine learning may allow for current limitations of big data analyses to be attenuated, thereby resulting in improved patient care and outcomes.

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