For WizardKim-DPR1 T1

Week 1 Discussion Post Topic 1 Response 1:

Instructions:

Respond to the post below with any inputs or suggestions.

· All posts (both initial and responses) must be substantial (several paragraphs each) and each of your initial posts must be supported by 3 peer reviewed or authoritative sources, not including the textbook, cited properly in APA format. Responses should have proper support with at least 1 different source as applicable.  

Big Data analysis has gotten a lot of hype recently, and for good reason. Companies are excited to be able to access and analyze data that they’ve been collecting or want to gain insight from but have not been able to manage or analyze effectively. 

Big Data basically refers to the fact that we can now collect and analyze data in ways that were simply impossible even a few years ago. There are two things that are fueling this Big Data movement: the fact we have more data on anything and our improved ability to store and analyze any data. (Marr, 2016) 

Big Data is the capability to manage a huge volume of disparate data, at the right speed and within the right time frame, to allow real-time analysis and reaction. Big data is typically broken down by three characteristics, including volume (how much data), velocity (how fast that data is processed), and variety (the various types of data). (Kaufman, et al., 2013) 

Volume has been a matter for accounting previously – traditional accounting and financial transactions involve an impressively high volume for large and international groups. Velocity is not new either, considering the need for monitoring real-time finance trends, for example, for commodity risk control. Variety is part of the accounting tradition, due to the presence, in the majority of accounting systems, of balanced scorecard and dashboards, where there are financial and non-financial indicators coming from different sources. Finally, value and veracity have been always a matter of accounting in its quest for information reliability and significance for decision making. (Michela, et al., 2017) 

The use of big data in accounting can open up massive possibilities for growth by providing valuable insights to assist leaders in decision-making regarding finances, compliance, and risk management. 

After revealing the definition of Big Data and its impact on the accounting field, I want to analyze how Big Data Analysis can be used in the Audit area. 

Auditing is the core of the accounting industry. It helps analyze a company’s financial assets and performance. However, in this age, traditional accounting procedures are time-consuming and don’t provide valuable insights. Big data and data analytics are transforming the audit process from being sample-based to data-based, providing information about all key areas of the business. It helps leaders understand their business better by providing detailed information. Big data helps track expenditure accurately in real-time and is, thus, highly helpful with periodic auditing. Combining the power of big data, analytics, and other tools such as RPA can not only automate the auditing process but also help reduce errors usually encountered in the manual process. Thus, they provide greater accuracy and compliance than conventional methods. (Naveen, 2020) 

Ernst&Young (EY) states: “Data analytics, new technology and access to detailed industry information will all combine to help auditors better understand the business, identify risks and issues and deliver additional insights. Moreover, the ability to review and analyze entire sets of data, rather than applying sampling techniques, will help bring more confidence to the audit.” 

The conventional methods of data handling are not good enough to manage such huge and variety of data, particularly to auditing and accounting. The sheer volume and variety are forcing the firms to move towards advanced methods of analysis. The tools available to the auditors may include cluster analysis, predictive models, data layering, visualizations, and “what if” scenarios. For example, with audit data analytics, auditors may be in a sound position to predict more accurately and reliably about the going concern position of the clients or their credit worthiness, collectability of account receivables, etc. (Prem, et al., 2018) 

Big data may assist the auditors in the following areas: (Prem, et al., 2018) 

Providing audit evidence through a comprehensive analysis of organizations‟ general ledger systems  

Assisting them in risk assessment through the identification of anomalies and trends, and comparison with industry data 

  Testing complete sets of data instead of taking sample testing  

Planning proper field works for auditors 

  Fraud detection and improving other forensic accounting 

  Using predictive models to improve forecasting 

  Sound judgement on clients‟ going concern issue 

Institute of Internal Auditors (IIA) Research Foundation stated that Big Data Analytics may be useful in performing internal audit functions such as compliance, fraud, risk assessment, detection and investigation, operational performance, and internal controls. (Prem, et al., 2018) 

All in all, I think that the correct use of Big Data helps to conduct more complete and accurate analysis in all areas of accounting, thereby reducing the time to transmit information and simplifying the accounting work. However, I believe that Big Data does not replace the judgment of the accountants, but rather is a complement to them. Therefore, we cannot trust that the reports released by Big Data will solve all the problems of entities or predict the future of these entities perfectly.  

One thing is for sure, Big Data and analytics are here to stay and it’s only going to get more sophisticated. We need to embrace it, operate ethically, deliver value in exchange for the data, and apply its significant benefits for the betterment of our world. (Marr, 2015)   

References: 

Kaufman, M., Halper, F., Nugent, A., Hurwitz, J. S., Nugent, A., & Hurwitz, J. S. (2013). Big data for dummies. ProQuest Ebook Central https://ebookcentral.proquest.com 

Marr, B. (2015). Big data: Using smart big data, analytics, and metrics to make better decisions and improve performance. ProQuest Ebook Central https://ebookcentral.proquest.com 

Marr, B. (2016). Big data in practice: How 45 successful companies used big data analytics to deliver extraordinary results. ProQuest Ebook Central https://ebookcentral.proquest.com 

Michela, A., Cristiano, B., & Suresh, C. (2017). Accounting, accountability, social media, and big data: revolution or hype? Accounting, Auditing & Accountability Journal, 30(4), 762-776. http://dx.doi.org/10.1108/AAAJ-03-2017-2880 

Naveen, J. (2020, November 6). Big Data is making a big impact in accounting. https://www.bbntimes.com/technology/big-data-is-making-a-big-impact-in-accounting 

Prem, L. J., & Marthandan, G. (2018). The Hype of Big Data Analytics and Auditors. Emerging Markets Journal, 8(2), 1-4. http://dx.doi.org/10.5195/emaj.2018.153