The Impact of Big Data on Accounting and Auditing

The Impact of Big Data on Accounting and Auditing

Dimitris Balios (National and Kapodistrian University of Athens, Greece)
Copyright: © 2021 |Pages: 14
DOI: 10.4018/IJCFA.2021010101
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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.
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2. The Meaning Of “Big Data” – Big Data Analytics

It seems that there is no standard definition on “Big data” as researchers adopt different views depending on the situation they are interested in. Mashey (1999) seems to be the first commonly used the term. Big data is (a) a combination of a massive amount of various types of information and various types of analytical tools (Russom, 2011) or (b) the datasets that are beyond the ability of classic database software tools to accumulate, manage and analyse (Manyika et al., 2011). Additionally, big data is “big” and significant because the available datasets are very voluminous. Big data can generate knowledge and added value because of their conversion in useful information that improves decision-making (Markus & Topi, 2015).

Various Vs characterise big data. Laney (2001) introduces the 3Vs of big data as he describes the data management in three dimensions. Laney’s three Vs are Volume, Velocity and Variety. Veracity becomes the 4th V (IBM, 2014). Nowadays, researchers suggest that big data are characterized by many Vs and one C. The Vs are Volume, Velocity, Variety, Veracity, Validity, Variability, Visualization/Visibility, Virtual and Value. The C has to do with Complexity.

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