The Use of Companies Big Data

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Abstract

Big data, which is also known as business intelligence, significantly influences accounting operations following the accessibility of new data types. It can avail textual, video, and image information, which may be very useful in enhancing financial reporting, managerial accounting, and financial bookkeeping. For managerial accounting, data analysis can assist in creating productive managerial control systems, as well as improving the budgeting process. It also enhances the value and applicability of bookkeeping information in financial accounting, which improves the process of decision-making among stakeholders. Business intelligence can also be instrumental in reporting by aiding in the development of accounting standards that guarantee the generation of productive information in the accounting profession. Big data is expected to influence the CPA profession. For instance, since internal audits are instrumental in observing the progress of the firm’s functions, gig data is expected to ease this mandatory process by using preprogrammed systems to analyze and evaluate data. Continuous audit and data analytics are important in refining the effectiveness of data analysis and using the information derived to make productive decisions. Currently, the demand for analysts and machines necessary for refining it is greater than the supply. This situation is an indication of the surge in popularity of big data use. Although it is popular, it comes with some downsides. The modes used to collect data may sometimes include invasion of privacy, which brings about ethical, legal, and economic questions. However, the intrusion into employee privacy, as well as micromanagement, maybe a demotivating factor to the CPA workforce. It leads to poor results. Therefore, as this paper concludes, in the use of big data, companies that will hire CPAs will have to apply ethical awareness frameworks and best practices to maximize its use.

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Introduction

Historically, financial accounting records contain data that are combined and used to come up with financial statements for both internal and external users. With the use of database systems, it has become possible to accumulate and assess both financial and non-financial data. Currently, big data offers an exceptional standard of the likely comparative analysis to the provision of assorted, capacious datasets, and complicated data breakdown. The rise in big data will affect accounting considerably. The proof of this claim will be in the accumulation and recording of data, as well as the way management will utilize the data to achieve organizational goals. Accounting records can be defined as reports that contain fiscal transactions or actions reported in monetary terms. They have become more digitalized, as opposed to the historical physical records. Data streams have influenced this development because all automatic sensor devices continuously produce data. Corporations have collected more data in the past three years, as compared to the previous two thousand years. Organizations utilize this data to boost economic performance and goal achievement. As the paper claims, since big data has become a principal asset for organizations, its accumulation or evaluation in the CPA field is becoming crucial in claiming competitive advantage.

How Big Data will Affect Managerial Accounting

Managerial accounting refers to the use of information that has been derived from accounting records to aid in managerial duties. Certified Public Accountants (CPAs) are tasked with the creation of systems, which ensure that employees and management operate in line with organizational goals. Such mechanisms that control behavior are referred to as Management Control Systems (MCSs). They are separate from decision and information support systems. Krahel and Titera assert that incentives that are tied to the behaviors of CPAs will change the actions of managers. [1] Therefore, one consideration to make when creating such incentives is to use a system that aligns them with the organizational goals. One such system is the Balanced Scorecard (BSC), which categorizes financial and otherwise incentives, which influence behavior towards the company’s objectives. Therefore, according to Anandarajan, Anandarajan, and Srinivasan, management performance is construed from comparing its results to the incentives and objectives. [2]

Big data in the CPA profession will influence the financial system by tracking the behaviors that are repetitively associated with specific goal results, thus leading to the creation of performance measures that match findings. Big data can pick out behaviors that have an impact on goal outcomes within various departments. With such information, it can help in the introduction of performance measures that are more likely to lead to the achievement of organizational goals. The analysis derived from big data will expedite the innovation of measures for CPAs to include in MCSs. With the use of metadata such as the amount of time spent on the internet, corporations will manage to follow up on the efficiency. In different companies, the same behavior may produce converse indications. According to Murphy and Tysiac, while greater communication on the telephone in a manufacturing company may mean lower productivity, the same conduct may mean higher productivity in a sales company. [3]

To obtain data, the company can monitor employee behavior in the office, computers, emails, and telephone calls, as well as their utilization of company resources. Hence, CPAs will have to turn MCS into comprehensive MCS. The downside to this move is that it will pose ethical, legal, and even economic challenges. It will also hinder the motivation and creativity of a CPA. The objective of tracking objectives about performance is to boost productivity. As a result, research by Brands and Holtzblatt has identified measures that are motivational to CPA as future employees. [4] Big data is at the center of picking out the positive from the negative measures. The ‘Association Rule Learning’ is a method of discovering connections in very wide datasets such as the link between the good performance of management and an array of variables. An example includes relating many telephone conversations to high morale from the workers. As Henry and Hicks confirm, the more data is available, the more beneficial knowledge can be discovered. [5]

According to Anandarajan et al., the process of budgeting is a representation of one of the managerial control areas. [6] Following the challenges facing traditional budgeting such as hindering competitiveness and curtailing creativity, beyond-budgeting techniques are being implemented. They use alternative information sources for evaluation of performance, goal communication, operation planning, as well as strategy formation. Hence, big data will aid in improving the beyond-budgeting accounting approach. CPAs will have to change data depositories into actionable information for big data to be helpful in management accounting. Additional resources such as data scientists will be needed for the management to reap the benefits of business intelligence in the CPA profession.

The impact of Data Analytics on the Internal and External Audit Profession

Internal auditors who form part of CPA professionals play a crucial role in monitoring the progress of several functions in a firm to ascertain the efficacy of risk management, acquiescence, as well as management activities. Conducting an assessment without computable analytics is strenuous. Hence, the need for documented metrics remains vital for the internal audit profession. Business intelligence gives internal auditors crucial insight into their organization. Most internal auditors are facing the challenge of understanding the notion of big data and the technique, which they can adapt to ensure that a company’s internal controls are operating efficiently. Big data will help internal auditors to apply simple and intricate analytical elements to evaluate data retrieved through a programmed technique. [1]

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Commentators such as Brands and Holtzblatt assert that data analytics is an economical method for retrieving audit evidence. [2] For instance, where a standard audit may cost about $4 to collect particular information, the company will use $0.01 after deploying business intelligence. Furthermore, companies that opt to construct in-house business intelligence instruments will be assured of accessing some functionality that would have been more difficult under standard audit, thanks to the positive impact of big data on the CPA profession. Nonetheless, most CPA professionals are exploiting the full potential of big data. Statistical findings by Brands and Holtzblatt reveal that only a few internal auditors are utilizing some of the most vital components of business intelligence such as the regression analysis. [3]

Despite the necessity of data analytics, firms will face two major challenges as they attempt to incorporate business intelligence into auditing processes. Foremost, companies will find it difficult to access the relevant information for their assessment. Secondly, most firms will be oblivious of how data analytics can be utilized to enhance auditing activities. Although various methods can be used to overcome the obstacles, experts insist that using audit analytics tools and standardized audit data techniques will be of great significance. The tools will avail the proper information that auditors will use to gain a deep understanding of the organization’s functionality as Anandarajan et al. confirm. [4]

While business intelligence has been applauded by most companies that apply data analytics to disclose irregularities and threats to improve internal operations, the concept has not been advanced in external auditing. Big data has the capability of revolutionizing external auditing as evidenced in internal auditing. Data analytics will enable external auditors to measure complete sets of information, instead of using samples. Moreover, CPAs will stand a chance to discover the abnormalities and drifts by equating a company’s audit evidence with those of the industry, thus helping in developing the best risk management strategies. Audit evidence can be retrieved through a complete evaluation of firms’ ledger systems. It is projected that fusing conventional auditing methods and big data will assist external auditors to have a deeper insight into accounting operations. However, as Murphy and Tysiac assert, to achieve this goal, auditors and other CPAs will need to monitor technological developments and transform them to their advancement. [5]

Ethical Implications of Big Data for Decision-making

Although business intelligence promises to be of crucial value to CPAs and institutions, various ethical issues have evolved concerning data analytics. Legislations and policies govern the information that organizations should use to guarantee confidentiality. However, the evolution and application of data analytics conflicts with policies of privacy, thus altering the balance when it comes to the information that auditors and other CPAs can retrieve and the ‘no-go zones. It poses the risk of data collectors intruding on people’s privacy. Consequently, according to Murphy and Tysiac, CPAs will need to be careful when utilizing big data technology to ensure that their actions remain ethical. [6]

Since organizations are a composition of individuals, what is ethical will be based on the discretion of the clients and stakeholders. For instance, stakeholders will have to evaluate the context of the data, including the original purpose of the information and the purpose it is being retrieved to serve. The approval and choice of the targeted party will also have to be considered since the organizations will determine if there will be the need to ensure that the affected parties understand why, how the data will be used, and if they have an option to object. Moreover, firms will need to consider whether using particular information will be reasonable and substantiated. It is only appropriate to use authoritative information. It is also important to consider whether the user information will be fair and/or if the data collectors will be ready to take accountability for any probable errors. Organizations will have to develop an ethical awareness policy to ensure that the application of business intelligence adheres to necessary morals.

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Conclusion

Data analysis will be of tremendous value to CPAs. Hence, they will need to adopt certain practices that will ensure that they utilize their full potential. Foremost, CPAs will need to strive to be conversant with their requirements, for instance, by isolating the sectors in the organization that is being assessed and/or processes that will require a CPA to use data analysis. Secondly, CPAs will need to familiarize themselves with their technology environment such that they can identify the operating systems and programs that they will require. According to Warren, Moffitt, and Byrnes, CPAs will need to cooperate with the Information Technology (IT) department to have more reliable audit evidence. [7] They will need to develop a data analytics technique that will guide them during the auditing process. The presence of best practices when using big data will guarantee CPAs the most appropriate information through data analysis.

Reference List

Anandarajan, M., Anandarajan, A. & Srinivasan, C. (2012). Business Intelligence Techniques: A Perspective from Accounting and Finance. Philadelphia, PA: Springer Science & Business Media.

Brands, K., & Holtzblatt, M. (2015). Business Analytics: Transforming the Role of Management Accountants. Management Accounting Quarterly, 16(3), 1-12.

Henry, B., & Hicks, M. (2015). A Survey of Perspectives on the Future of the Accounting Profession. CPA Journal, 85(8), 6-12.

Krahel, J., & Titera, W. (2015). Consequences of Big Data and Formalization on Accounting and Auditing Standards. Accounting Horizons, 29(2), 409-422.

Murphy, M., & Tysiac, K. (2015). Data analytics helps auditors gain deep insight. Web.

Warren, J., Moffitt, K., & Byrnes, P. (2015). How Big Data Will Change Accounting. Accounting Horizons, 29(2), 397-407.

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