Understanding Big Data (BD)

BD refers to the huge volume of data that may be in any form. BD can be understood as the data that are so large in volume that traditional data processing system can’t handle its processing, and thus require new technology (Raban & Gordon, 2020). It is the collection of information that the organizations can mine for the purpose of business analytics, product design and development, and other activities through the use of machine learning, predictive modeling, and other advanced data analytics application (Phillips, 2021). Primarily, Big Data is in the raw form, and without any processing it is useless; however, if processed properly, results from the analysis of the Big Data may prove to be valuable. The sources of BD might be various digital channels such as mobile phones, internet, social media, e-commerce, search engines, etc.

In the recent times, BD has proven to be a very useful data source for making various business-related decisions, and thus organizations have now started to decipher the raw data into some meaningful information using the latest tools available in the industry. Sectors like retail, banking, investment analysis, fraud detection, operational analysis, customer centric applications, etc. have witnessed huge improvement due to the application of information mined from BD (Nadikattu, 2020).

The 5 Vs Characterizing Big Data

BD is characterized by 5 Vs, as described below:

• 1. Volume: It refers to the quantity of data. In the year 2016 only, the estimated volume of data generated by the global mobile traffic was 6.2 billion gigabytes (Geeksforgeeks, 2022). We can’t even imagine how this data will be gathered, analyzed, and converted into some meaningful information that will be useful for the stakeholders.
• 2. Velocity: It is the speed at which the data is accumulating. For example, Google processes about 8.5 billion searches in a day. Tools for analyzing BD must be robust and able to analyze the data that is pouring in at a lightening velocity.
• 3. Variety: It refers to the nature of data i.e., structured, semi-structured, or unstructured. Variety of BD is a challenge while performing any analysis.
• 4. Veracity: It refers to inconsistencies and uncertainty in data.
• 5. Value: BD has no value unless it is converted into meaningful information that could be used by the organizations.

“Big Data impacts the business world and society at large, only when such data is analyzed and resulting information is put to use in some context.”

Benefits of Big Data in the Accounting Industry

BD is not helpful unless and until it is analyzed, and some meaningful interpretation is derived from it. We must use data analytics and visualization tools in order to gain insights from the BD. Big Data impacts the business world and society at large, only when such data is analyzed and resulting information is put to use in some context. It is playing a pivotal role in data analytics, artificial intelligence, machine learning, management, and governance (Sun & Huo, 2021). It is becoming an important instrument for the decision makers subject to proper analysis and extraction of relevant information. Not only internal information like supply, production, financial data, etc. are important for making decisions in the organization; but the realm of big data like customers preferences, market trends, supply chain analysis, social media posts, etc., have also become imperative while making an informed and effective decision in the organizations. Earlier, business decisions were taken based on historical data and some human insight derived from such historical data. However, a prudent decision is now based on the historical data, output gathered from the analysis of BD, and the insights or trends such analysis shows for the forthcoming future.

BD has the possibility to impact every aspect of accounting, auditing, tax, and advisory service; the following aspects of BD in the accounting services are mostly characterized as value addition work of the accountants (Boomer, 2018):

• Accountants can show why competitors are doing better than the client by analyzing BD instead of presenting the ratios only.
• BD can help the accountants see the bigger picture by predicting customer behavior, identifying red flags, and anticipating economic trends.
• Sampling will be an old concept, and various tools may be used to test the control against the entire population under study and analyzing the red flags thereon.

Core Pillars of Big Data Utilization in Accounting

Domain Area	Impact & Functional Application
Audit Sampling	Shift from selective sample testing to comprehensive population-level auditing and real-time automated exception detection.
Revenue Generation	Unlocking high-value business advisory frontiers, predictive consumer intelligence, and client growth consulting.
Decisions and Strategizing	Transitioning from retrospective historical ratios to forward-looking trend forecasting across operations and market behavior.
Risk Assessment	Detecting unidentified risks, hidden systemic vulnerabilities, and emerging transactional red flags via data pattern learning.

The fusion of massive volumes of data with the latest technology like Blockchain, Machine Learning, and Artificial Intelligence can lead to the development of strong and automated accounting processes (aeologic, 2020).

Further, audit has now slowly moved from sample-based technique to data driven population level audit – restructuring the ways of doing audit as well. Accountants are now expected to provide financial advisory service, offering valuable insights that would be helpful in making business decisions rather than performing repetitive accounting tasks only. It has become difficult to identify risks and implement controls to mitigate those risks due to unidentified risks which could be revealed using BD analysis. BD also helps visualize the bigger picture of a given scenario which results in better decision making. BD Analytics could enable continuous audit by identifying exceptions automatically based on the system’s ability to learn the data patterns in BD.

Accountants now have access to a vast pool of data. Using the right technique and tools, accountants will be able to forecast the financial performance, sales, demand, and other business-related parameters with higher accuracy than ever before by using both financial and non-financial information available.

Further, tax authorities and regulators are taking huge advantage of BD analysis in identifying tax frauds, and other types of financial frauds. They may use the data from social media, intergovernmental bodies, e-commerce activities, etc. in order to identify the fraudulent activities, and take necessary action.

Risks and Challenges Associated with Big Data

Since we’ve only been discussing the positive aspects of the BD and the fruits of BD analytics, it is also important to discuss some of the issues related to the privacy, storage, deliberate misuse and abuse of BD. Issues of privacy of data has led to the formulation and implementation of General Data Protection and Regulation (GDPR) in Europe, CCPA in US, Digital Data Protection Bill in India and similar laws across the world. Further, amassed with BD, various organizations providing services to the general public might be able to protect the data from outside attacks. However, there is no guarantee that they might not use it for unethical purposes or to manipulate the customer’s interest and behavior – it might alter how someone thinks and controls their behavior as well. The case of Cambridge Analytica in influencing the US presidential elections is a classic example of how organizations having access to advanced data analytical resources which can manipulate the outcome based on their preference.

Similarly, one of the major risks associated with BD is that the data is not available in the traditional form which can be easily analyzed using various spreadsheet tools that most of the accountants are acquainted with. Now, the data may be in a structured, semi-structured, or unstructured form. Hence, if accountants do not get acquainted with the latest data analytics tools, there lies the risks of providing inappropriate opinion or advice, or soon they might be struggling to retain the clients or acquire more clients or satisfy the needs of the clients – thereby impairing trust of the stakeholders on the accountants. Due to the size and volume of data, one of the pertinent challenges is the storage and sorting of the colossal amounts of data that’s being generated every moment of time (Herath & Woods, 2021). Further, even if the data is stored, another challenge is related to the tools of analyzing such huge volume of data. Either the tools are not readily available or are expensive and require specialized knowledge and skillset to perform such analysis.

“Data analysis has become an integral part of audit and advisory assignments due to their impact on auditing techniques and outcomes.”

Tools and Skills for Big Data Analysis

There are some generalized tools and skills that are essential for the analysis of BD, and they are described in brief in the following points:

1. Data Visualization Tools: Tools such as Tableau, Power BI, and QlikView help accountants visualize complex data, making it easier to identify trends and patterns. These tools are imperative for presenting data-driven insights to the stakeholders and for making informed decisions.
2. Data Analytics Software: Software like Excel, Alteryx, and KNIME are used for data cleaning, transformation, and analysis. Mastering these tools allows accountants to process and analyze large datasets efficiently.
3. Programming Languages: Languages like Python, R, and SQL are essential for data manipulation and analysis. Python and R have extensive libraries and packages for data analysis, while SQL is vital for querying databases. Acquiring these programming skills enables accountants to automate tasks, extract insights from data, and create custom analytics solutions.
4. Machine Learning and Artificial Intelligence: Understanding the basics of machine learning (ML) and artificial intelligence (AI) techniques, such as regression, clustering, and classification, is crucial for accountants to leverage Big Data effectively. Familiarity with ML/AI tools like TensorFlow, scikit-learn, and H2O can help accountants create predictive models, identify anomalies, and detect fraud.
5. Cloud Computing and Big Data Platforms: Platforms like Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure offer Big Data processing and storage capabilities. Accountants should be familiar with these platforms to handle and analyze large datasets, as well as ensure data security and privacy.
6. Data Management and Governance: Understanding data management principles and best practices, such as data quality, data lineage, and data governance, is essential for accountants to maintain the integrity of data and ensure compliance with regulations.
7. Soft Skills: Communication, critical thinking, and problem-solving skills are crucial for accountants working with Big Data, and its multitudes of tools don’t offset the human intelligence that accountants possess. The ability to convey complex insights to non-technical stakeholders and to collaborate effectively within interdisciplinary teams is critical to success in this field.

Conclusion and Recommendation

Accountants, without having adequate knowledge and skill set to perform data analysis, might lose businesses in the near future. Data analysis has become an integral part of audit and advisory assignments due to their impact on auditing techniques and outcomes. Accountants need strong cutting-edge data analytics skills in order to stay in the business in today’s rapidly changing landscape. BD helps accountants to generate insights that could be valuable to the clients. Instead of generally serving the clients in a traditional way, accountants can now become a partner and advisor to the business of the clients.

Finally, it is imperative for the accountants to learn and hone the skill sets required to analyze and interpret BD. It is the role of the accountants to ensure public trust, uphold integrity, and act as a watchdog. BD is restructuring the ways accountants serve their clients. Hence, it is of paramount importance to welcome BD and be ready to analyze and interpret it by embracing the tools necessary for such analysis.

References

• aeologic. (2020, February 6). aeologic. Retrieved from aeologic: https://www.aeologic.com/blog/role-and-impact-of-big-data-in-the-accounting-industry/
• As Big Data Applications Expand, Accounting Pivots To Keep Up. (2022, September 2). Retrieved from Wiley Efficient Learning: https://www.efficientlearning.com/blog/big-data-accounting/
• Boomer, J. (2018, September 10). The Value of Big Data in an Accounting Firm. Retrieved from CPA Practice Advisor: https://www.cpapracticeadvisor.com/2018/09/10/the-value-of-big-data-in-an-accounting-firm/31045/
• Computer Hope. (2021, June 11). Computer Hope. Retrieved from Computer Hope: https://www.computerhope.com/jargon/d/data.htm
• Duarte, F. (2023, April 3). Amount of Data Created Daily (2023). Retrieved from Exploding Topics: https://explodingtopics.com/blog/data-generated-per-day
• Elgendy, N., & Elragal, A. (2014). Big Data Analytics: A Literature Review Paper. Springer International Publishing, 214-227.
• Geeksforgeeks. (2022, June 28). Retrieved from Geeksforgeeks: https://www.geeksforgeeks.org/5-vs-of-big-data/?ref=lbp
• Herath, S. K., & Woods, D. (2021). Impacts of big data on accounting. The Business and Management Review 12(2), 195-203.
• Nadikattu, R. R. (2020). RESEARCH ON DATA SCIENCE, DATA ANALYTICS AND BIG DATA. International Journal of Engineering, Science and Mathematics, 99-106.
• Narayanan, K. (2018, July 17). Innovation. Retrieved from Forbes: https://www.forbes.com/sites/forbestechcouncil/2018/07/17/the-evolution-of-data/?sh=1b3cdbf9c95f
• Phillips, A. (2021, April 1). A history and timeline of big data. Retrieved from Techtarget: https://www.techtarget.com/whatis/feature/A-history-and-timeline-of-big-data
• Raban, D. R., & Gordon, A. (2020). The evolution of data science and big data research: A bibliometric analysis. Scientometrics, 1563-1581.
• Sun, Z., & Huo, Y. (2021). The Spectrum of Big Data Analytics. Journal of Computer Information Systems, 152-162.
• Vaughan, J. (2019, July). searchdatamanagement. Retrieved from TechTarget: https://www.techtarget.com/searchdatamanagement/definition/data

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Author may be reached at: rijal255@gmail.com and eboard@icai.in