scholarly journals Auditors’ Perceptions of and Competencies in Big Data and Data Analytics: An Empirical Investigation

2019 ◽  
Vol 1 (1) ◽  
pp. 092-113
Author(s):  
Abdullateef Omitogun Abdullateef Omitogun ◽  
Khalid Al-Adeem Abdullateef Omitogun
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Data Analytics ◽  
Empirical Investigation ◽  
Technical Skills ◽  
Risk Assessments ◽  
Technology Skills ◽  
Good Information ◽  
Audit Risk ◽  
Related Data

<p>This study presents evidence on practicing auditors&rsquo; perceptions of and competencies in applying big data and data analytics to audit engagements. An electronic questionnaire distributed to accountants shows that auditors have good information technology skills and are well-acquainted with big data and data analytics. However, they lack relevant technical skills and are unfamiliar with related data analysis tools, excluding Excel. The results reveal 64.71% of accountants have not attended any training on big data and data analytics, while 31.37% plan to enhance related knowledge. Auditors need to obtain training on substantive audit risk assessments using big data and data analytics.</p> <p>&nbsp;</p>

Big Data and Big Data Analytics for Improved Healthcare Service and Management

2020 ◽  
Vol 8 (1) ◽  
pp. 13-51
Author(s):  
Pijush Kanti Dutta Pramanik ◽  
Saurabh Pal ◽  
Moutan Mukhopadhyay
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Services ◽  
Healthcare Sector ◽  
Future Market ◽  
Data Generation ◽  
Related Data ◽  
Healthcare Data ◽  
Different Types

Like other fields, the healthcare sector has also been greatly impacted by big data. A huge volume of healthcare data and other related data are being continually generated from diverse sources. Tapping and analysing these data, suitably, would open up new avenues and opportunities for healthcare services. In view of that, this paper aims to present a systematic overview of big data and big data analytics, applicable to modern-day healthcare. Acknowledging the massive upsurge in healthcare data generation, various ‘V's, specific to healthcare big data, are identified. Different types of data analytics, applicable to healthcare, are discussed. Along with presenting the technological backbone of healthcare big data and analytics, the advantages and challenges of healthcare big data are meticulously explained. A brief report on the present and future market of healthcare big data and analytics is also presented. Besides, several applications and use cases are discussed with sufficient details.

scholarly journals Data Analytics and Mining in Healthcare with Emphasis on Causal Relationship Mining

2019 ◽  
Vol 8 (4) ◽  
pp. 195-204
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Causal Relationship ◽  
Data Analytics ◽  
Statistical Tests ◽  
Analytical Techniques ◽  
Data Sources ◽  
Healthcare Personnel ◽  
Related Data ◽  
Healthcare Data

High volumes and varieties of data is piling every day from healthcare and related fields. This big data sources if managed and analysed properly will provide vital knowledge. Data mining and data analytics have been playing an important role in extracting useful information from healthcare and related data sources. The knowledge extracted from these data sources guiding patients and healthcare personnel towards improved health conditions. Analytical techniques from statistics, functionalities from data mining and machine learning already proved their capability with significant contributions to healthcare industry. The dominant functionality of data mining is classification which has been in use in mining healthcare data. Though classification is a good learning technique it may not provide a causation model which will be a reliable model for better decision making particularly in the medical field. The present models for causality have limitations in terms of scalability and reliability. The present study is targeted to study causal models for causal relationship mining. This study tried to conclude with some proposals for causal relationship discovery which are efficient, reliable and scalable. The proposed model is going to make use of some qualities of decision trees along with statistical tests and analytics. It is proposed to build the learning models on healthcare big data sources.

scholarly journals SIDeKa: The Role of Information Technology for Knowledge Creation

2017 ◽  
Vol 2 (6) ◽  
pp. 570
Author(s):  
Cungki Kusdarjito
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Data Collection ◽  
Knowledge Creation ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Unstructured Data ◽  
Village Level ◽  
The Village

The advancement of big data analytics is paving the way for knowledge creation based on very huge and unstructured data. Currently, information is scattered and growth tremendously, containing many information but difficult to be interpreted. Consequently, traditional approaches are no longer suitable for unstructured data but very rich in information. This situation is different from the role of previous information technology in which information is based on structured data, stored in the local storage, and in more advanced form, information can be retrieved through internet. Meanwhile, in Indonesia data are collected by many institutions with different measurement standard. The nature of the data collection is top-down, carried out by survey which is expensive yet unreliable and stored exclusively by respective institution. SIDeKa (Sistem Informasi Desa dan Kawasan/Village and Regional Information System), which are connected nationally, is proposed as a system of data collection in the village level and prepared by local people. Using SIDeKa, data reliability and readiness can be improved at the local level. The goals of the SIDeKa is not only local people have information in their hand such as poverty level, production, commodity price, the area of cultivated land, and the outbreak of diseases in their village, but also they have information from the neighboring villages or event at the national level. For government, data reliability will improve the policy effectiveness. This paper discusses the implementation and role of SIDeKa for knowledge creation in the village level, especially for the agricultural activities which has been initiated in 2015.Keywords: big data analytics; SIDeKa;  unstructured data.

scholarly journals AKUNTAN DALAM INDUSTRI 4.0: STUDI KASUS KANTOR JASA AKUNTAN (KJA) DI WILAYAH KEPULAUAN RIAU

2020 ◽  
Vol 11 (2) ◽  
pp. 100-109
Author(s):  
Alfonsa Dian Sumarna
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Data Analysis ◽  
Internet Of Things ◽  
Data Analytics ◽  
Technology Development ◽  
Leadership Skills ◽  
Computer Software ◽  
Data Entry ◽  
Information Technology Development

Abstract Using robotics and data analytics (big data) can over take clerical job (data entry, bookkeeping, compliance work). Accounting profession underestimate to technologies. Competence such as data analysis, information technology development, and leadership skills must be adapted to face 4.0. Our research found that Kantor Jasa Akuntan in Kepulauan Riau Province using 80% accounting professional labor (accounting bachelor). This research confirmed about IoT (Internet of Things) that 60% of KJA use 70-100% of total hours of work using computer (software) and internet compare with manual working. KJA need accounting professional who able to work with software such as accounting software, statistic, Ms Office, Zahir, and SAP. This research also found the main softskill needed is critical thingking ability. Acording to the survey, software are not affecting accounting employment yet. Keywords: Industry 4.0; Accounting Professional; Software; Internet of Things Abstrak Penggunaan robotics dan data analytics (big data) dapat mengambil alih pekerjaan dasar yang dilakukan oleh akuntan (mencatat transaksi, mengolah transaksi, dan memilah transaksi). Profesi akuntan merasa dirugikan terkait dampak teknologi terhadap pekerjaan akuntan. Kompetensi yang penting bagi profesi akuntan dalam menghadapi 4.0 misalnya data analysis, information technology development, dan leadership skills harus dapat dikembangkan. Penelitian ini menunjukkan bahwa Kantor Jasa Akuntan di Wilayah Provinsi Kepulauan Riau masih tetap mempertahankan menggunakan tenaga profesional akuntan sebesar 80% merupakan Sarjana Akuntansi. Selain itu penelitian ini juga mengkonfirmasi penggunaan IoT (Internet of Things) yaitu sebesar 60% KJA menggunakan 70-100% total waktu menyelesaikan pekerjaan menggunakan komputer (software) dan internet dibandingkan dengan pengerjaan manual. KJA membutuhkan akuntan profesional yang menguasai software akuntansi, statistika, MsOffice, Zahir dan SAP. Selain menguasai software dalam menghadapi 4.0, penelitian ini menunjukkan bahwa softskill utama yang diperlukan adalah memiliki kemampuan berpikir kritis dan analitis. Kata Kunci: Industri 4.0; Akuntan Profesional; software; Internet of Things

Data Science and Big Data Practice Using Apache Spark and Python

2021 ◽  
pp. 67-95
Author(s):  
Li Chen ◽  
Lala Aicha Coulibaly
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Computer Science ◽  
Data Analytics ◽  
Data Science ◽  
Principal Component ◽  
Real Data ◽  
Apache Spark ◽  
Data Sets ◽  
Information Technology Students

Data science and big data analytics are still at the center of computer science and information technology. Students and researchers not in computer science often found difficulties in real data analytics using programming languages such as Python and Scala, especially when they attempt to use Apache-Spark in cloud computing environments-Spark Scala and PySpark. At the same time, students in information technology could find it difficult to deal with the mathematical background of data science algorithms. To overcome these difficulties, this chapter will provide a practical guideline to different users in this area. The authors cover the main algorithms for data science and machine learning including principal component analysis (PCA), support vector machine (SVM), k-means, k-nearest neighbors (kNN), regression, neural networks, and decision trees. A brief description of these algorithms will be explained, and the related code will be selected to fit simple data sets and real data sets. Some visualization methods including 2D and 3D displays will be also presented in this chapter.

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