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

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

Adem Çabuk (Uludağ University, Turkey) and Alp Aytaç (Uludağ University, Turkey)
Copyright: © 2019 |Pages: 16
DOI: 10.4018/978-1-5225-7356-2.ch007

Abstract

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.
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Main Focus Of The Chapter

The Aim of the Study

The main of this chapter is to shed light on how traditional auditing transformed into the continuous auditing and where big data stands in this transformation based on related literature.

Methodology

In this chapter, the authors used an archival method. In Chiu, Liu, & Vasarhelyi (2014) study, they shed light on extant continuous auditing research and reveals its longitudinal development by reviewing, summarizing, and cross-comparing research characteristics of 118 relevant continuous auditing studies. The three main research methods identified in the CA literature are: analytical, archival, and experimental/behavioral. They define archival study as studies utilize sources from secondary records. In Brown, Wong, & Baldwin (2007) study they divided the research streams into five categories: demand factors, theory, and guidance, enabling technologies, applications, and impacts. The authors use theory and guidance from secondary sources to shed light on the transformation of auditing, how big data accelerate this transformation and the relationship between big data and continuous auditing.

Key Terms in this Chapter

Datafication: A modern technological trend turning many aspects of our life into computerized data and transforming this information into new forms of value.

Structured Data: Data that fit into a defined schema, such as relational data.

Auditing: Accumulation and evaluation of evidence about information to determine and report on the degree of correspondence between the information and established criteria.

Archival Study: Studies utilize sources from secondary records.

Big Data: The generation of data has started to outpace the processing capabilities of the typical technology tools.

Unstructured Data: Information that either does not have a pre-defined data model or is not organized in a pre-defined manner.

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