In the contemporary society, the establishment of Big Data analytics is growing to evaluate and scrutinize the large volume of financial data from separate commercial units and their environment. This large amount of data is accumulated in the form of huge sets of information downloaded from various websites. The application of analytics on big data opens new doors for organizations to acquire a more prominent understanding, foresee future results and mechanize non-routine errands. The implication of Big Data analytics bares immense importance in the modern era for business industries. Furthermore, Big Data analytics provide immense opportunities for the accounting profession to help different businesses with changing their dynamics in various areas. This paper tends to unveil the significant effect of Big Data analytics on the field of accounting.
The term “Big Data” can be explicated as very large amount of datasets which are normally greater than one petabyte (1015 terabytes) and that carries certain storage and analyzing challenges (Mashey 1999). These extremely large data sets are unstructured/ raw data that is gathered from different discrete sources at a massive speed which is beyond the processing power of a customary server. The data is in large volume that it is measured in terabyte and zettabytes. Big Data can be clarified by its three characteristics. These three characteristics are volume, velocity, and variety. The application of analytics to big data provides immense positive attributes for the accounting and finance industry.
The application of Big Data analytic provides successful effects in the accounting field. Big Data analytics perform error-free and accurate calculations which cannot be performed by outdated accounting methods. The operations conducted by Big Data analytics are rapid and save abundant time. Big Data analytics cannot run on a traditional server because of its speed and consistency. The processing of the data occurs on a real-time basis. The technological innovations and its analytical functions of large voluminous data have reduced the staff cost. The implication of Big Data analytics will deliver numerous opportunities for accounting organizations to gain noteworthy perceptions, foretell future consequences, and mechanize their regular financial tasks. The technological system of Big Data analytics has minimized the staff cost that was required to analyze and organize that large amount of unstructured data in the accounting profession. Big Data analytics also provides expert decisions and calculations for the future which have massive prospect in the fields of accounting.
In the past few years, the accounting profession requires the strategic tasks of data analytics within a short time. However, the traditional accounting methods cannot cope up with time and the big volume of data. This makes it difficult for accounting staff to fulfill their tasks on their given deadline every month. Big Data analytics provides real-time access to the accounting data which helps accounting firms to meet deadlines. Furthermore, it can instantly correct the errors in reports and increase the efficiency/ accuracy level of the data analytics.
The provision of risk-free and accurate financial services is the essential component for accounting firms. The implication of Big Data analytics will identify the potential risks and will provide data analysis reports of financial data which will be error-free and accurate. The large volume of data comes with many errors and security issues. The accountants can have real-time access to their data and provide risk-free evaluations. Big Data analytics ensures to provide risk-free data analysis reports and financial services for accounting firms.
Big Data analytics provides visualization software to accounting firms. This visualization software will provide patterns, flows, irregularities, and exceptional patterns in a large amount of data. This will help the accountants to visualize these patterns of the data easily. It also helps them with better insights, more variables, and comparison with previous analytical reports. The traditional accounting methods cannot provide such visualizing insights for the accountants.
In the modern era, external and internal auditors use Big Data analytics for their auditing processes. The traditional methods of auditing on ledger-based sampling are now considered redundant and time taking. The auditors use Big Data analytics to visualize a large sets of financial data with greater comprehension. With the help of visualization software in data analytics, the auditors can identify irregular/ exceptional patterns, errors, and pressure points. This will improve future predictions and enhanced decisions for the auditors. Furthermore, the exploitation of analytical reports generated by Big Data analytics will help the auditors to make improved business choices and exhibit significant performance standards.
In conclusion, the implication of Big Data analytics has the prospective of changing the old accounting standards to new improvised accounting standards. Big Data analytics accelerates the improvisation of decision-making and insight within the accounting paradigm of the business industry. It challenges the traditional accounting standards and provides new accounting principles to cater to the evolving culture of Big Data. Big Data analytics offers modest advantages of lucrative decision making, data insights, and fraud exposure/avoidance to the accounting and financial firms.
Alles, M. G. (2015). Drivers of the use and facilitators and obstacles of the evolution of big data by the audit profession. Accounting Horizons, 29(2), 439-449.
Brands, K. (2014). Big Data and Business Intelligence for Management Accountants’, Strategic Finance, vol. 96, no. 6, pp. 64-5.
Cadambi, B. V., Mayer-Schonberger, V., & Cukier, K. (2013). Big Data: a Revolution that will Transform how We Live, Work and Think. SAMVAD, 6(2), 94-98.
Fanning, K., & Grant, R. (2013). Big data: implications for financial managers. Journal of Corporate Accounting & Finance, 24(5), 23-30.
Mashey, J. R. (1999, June). Big data and the next wave of InfraStress problems, solutions, opportunities. In 1999 USENIX Annual Technical Conference.
Sönmez, F., Perdahçı, Z. N., & Aydın, M. N. (2020). Big Data Analytics and Models. In Handbook of Research on Big Data Clustering and Machine Learning (pp. 10-33). IGI Global.