Abstract
The last two decades have seen various transformative technologies that have dominated businesses. They include artificial intelligence, robotic processing, and block-chain. All points to the increased use of data in the business industry. Although big data and data analytics in accounting is still in its early development stages, it is expected that by 2025, its market will grow to over 50 percent to hit the $100 billion mark. The continued growth of data analytics and big data presents unique opportunities in the accounting field for auditors and accountants to sharpen their skills. Business communities also have the chance to improve on their efficacy, efficiency, and effectiveness in meeting market demands.
Outline
Introduction
Big data reveals associations, trends, and patterns. Big data entails structured and unstructured data
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The most critical aspect of big data is how organizations utilize it in their day to day accounting operations.
It provides insights into strategic volume trends and better decisions (Richardson & Shan, 2019)
Data analytics is used by organizations to reveal valuable information on finances, better management, and identification of ways to improve efficiency.
Importance of Big Data
Detecting fraudulent activities
Recalculating risks
Generating coupons according to consumer’s buying tendencies
Determining the main causes of defects, issues, and failures (Trom & Cronje, 2019).
Big Data At Work
Business must consider how big data works and flows along its various sources, locations, users, and owners.
Taking charge of big data is thus important for both semi-structured, unstructured, structured, and traditional data
Decisions need to be data driven
Analysis of data is critical
Store, manage, and access data (Dai, Vasarhelyi & Medinets, 2019)
Identify the primary sources of big data
Social Media
Public data (EU Open Data Portal, CIA World Fact Book, and US govt. data.gov) (Trom & Cronje, 2019)
Others sources (cloud data, data lakes, customers, and suppliers sources)
Establish a big data approach
Impact of Big Data on Accounting
On Financial Accounting
Big data is gradually being integrated into accounting framework such as video/audio into traditional data
Fair Value such as merging of big data into value of liabilities and assets
On Managerial Accounting
Professional managers are gradually changing how they create value from data that involve insightful decision making, information security, use of benchmark metrics, and the use of all-inclusive control and monitoring systems (Rezaee & Wang, 2017).
On Auditing
Big data is gradually changing how auditors manage their clients by the use of new technologies such as database-to database systems
On accounting Standards
Accounting standards use more of sensitive and protection data (Richardson, Chang & Smith, 2017)
Data Analytics
Data Analytics is the scientific process in which data is analyzed, cleansed, inspected, and transformed for the purpose of making accounting and business informed decisions (Huerta & Jensen, 2017). It also refers to quantitative and qualitative strategies and procedures applied in enhancing business gain and productivity
Analysis methods in data analytics vary depending on organizational needs;
Model fitting and building
Statistical Analysis
Patterns and Trend Visualization
To understand big data, it is essential to identify and know the main types of data analytics
Descriptive Analytics; entails classification and categorization of information. It involves reporting and analyzing money flow within a firm such as sales tax, inventory counts, expenses, and revenue (Gärtner & Hiebl, 2017). Accuracy, verification, and compilation are critical for accurate accounting.
Diagnosis Analytics; diagnosis is useful in monitoring any modifications in the data (Salijeni, Samsonova-Taddei & Turley, 2019). Calculating historical analysis is critical for establishing reasonable financial forecasts.
Predictive Analytics; Data is to make future predictions. Accountants identify patterns and build forecasts.
Prescriptive Analytics; Critical financial decisions and tangible actions arise from which future recommendations are made.
References
Dai, J., Vasarhelyi, M. A., & MEDINETS, A. (2019). Rutgers Studies in Accounting Analytics: Audit Analytics in the Financial Industry.
Gärtner, B., & Hiebl, M. R. (2017). Issues with Big Data. In The Routledge Companion to Accounting Information Systems (pp. 161-172). Routledge.
Huerta, E., & Jensen, S. (2017). An accounting information systems perspective on data analytics and Big Data. Journal of Information Systems, 31(3), 101-114.
Rezaee, Z., & Wang, J. (2019). Relevance of big data to forensic accounting practice and education. Managerial Auditing Journal.
Richardson, V. Chang, C. J & Smith, R. E. (2017). Accounting Information Systems. McGraw-Hill Higher Education. 2nd Ed.
Richardson, V. J., & Shan, Y. (2019). Data Analytics in the Accounting Curriculum. Advances in Accounting Education: Teaching and Curriculum Innovations (Advances in Accounting Education, Vol. 23), Emerald Publishing Limited, 67-79.
Salijeni, G., Samsonova-Taddei, A., & Turley, S. (2019). Big Data and changes in audit technology: contemplating a research agenda. Accounting and Business Research, 49(1), 95-119.
Trom, L., & Cronje, J. (2019, March). Analysis of data governance implications on big data. In Future of Information and Communication Conference (pp. 645-654). Springer, Cham.
