Article review of “Big Data Analytics: Opportunity or Threat”

Article review of “Big Data Analytics: Opportunity or Threat”

 

How to present this article in Harvard reference format?

Richins, G., Stapleton, A., Stratopoulos, T.C. & Wong, C., 2017. Big Data Analytics: Opportunity or Threat for the Accounting Profession? Journal of Information Systems, 31(3), pp.63–79. https://doi.org/10.2308/isys-51805

 

What are the key research issues the article wants to address?

The article "Big Data Analytics: Opportunity or Threat for the Accounting Profession?" addresses several key research issues centered around the impact of Big Data analytics on the accounting profession:

1.                                  The Threat of Automation to Accounting Jobs: The article explores the prediction by Frey and Osborne (2013) that accounting jobs face significant risk of automation and possible extinction due to advances in artificial intelligence, machine learning, and Big Data analytics.

2.                                  Accountants' Ability to Create Value in a Big Data World: Contrary to the threat of automation, the article investigates how accountants can still add value by leveraging their expertise in problem-driven analysis of structured data and extending their roles into problem-driven analysis of unstructured data as well as supporting data scientists in exploratory Big Data analytics.

3.                                  Conceptual Framework for Roles of Accountants in Big Data Analytics: The authors propose a framework categorizing data types (structured vs. unstructured) and analysis approaches (problem-driven vs. exploratory) to clarify where accountants have advantages and how they can complement data scientists.

4.                                  Implications for Accounting Education and Practice: The article discusses what skills future accountants need to acquire to remain valuable in the evolving Big Data landscape, emphasizing the necessary adjustments in curricula, professional training, standards, and organizational roles.

5.                                  Future Research Directions and Limitations: The paper highlights limitations of its forward-looking recommendations and encourages future empirical studies on the role of Big Data analytics in various accounting functions such as tax and internal auditing, as well as considerations around cybersecurity.

6.    The Strategic Role of Accountants in Business Using Big Data: Investigates how accountants' deep understanding of business fundamentals and strategy equips them to interpret Big Data effectively, thereby turning Big Data analytics into a complement rather than a threat.

In sum, the article aims to address how Big Data analytics can simultaneously pose challenges and opportunities for the accounting profession and details how accountants can adapt and thrive by leveraging their unique skills.

Describe two main theories employed in this article.

The article employs two main theoretical perspectives to analyze the impact of Big Data analytics on the accounting profession:

1.              Task-Based Approach to Automation Risk: The article references Frey and Osborne’s (2013) task-based theory of job automation risk, which assesses the likelihood of occupations being computerized based on the nature of tasks performed—routinized, formulaic tasks are more prone to automation while tasks requiring complex problem-solving, creativity, and interpersonal skills are less susceptible. This theory underpins the discussion regarding which accounting tasks (especially entry-level and routine) are vulnerable to automation and which roles can be sustained or augmented through Big Data analytics.

2.              Structured/Unstructured Data and Problem-Driven/Exploratory Analysis Framework: The authors develop a conceptual framework classifying data into two types—structured and unstructured—and analyses into two approaches—problem-driven and exploratory. This framework helps position accountants’ core strengths as excelling in problem-driven analysis of structured data, while also being well-suited to expand into problem-driven analysis of unstructured data and supporting data scientists in exploratory data analysis for Big Data. It theorizes accountants’ evolving roles in leveraging their business knowledge and familiarity with structured data to complement Big Data analytics rather than being replaced by it.

Together, these two theoretical perspectives guide the authors’ argument that, despite the automation threat, accountants’ strategic business knowledge and adaptability position them to add value in a Big Data environment.

Highlight 2 main primary findings reported in this article. 

Two main primary findings reported in the article are:

1.              Accountants Are Well-Positioned to Lead Problem-Driven Analysis of Unstructured Data: The article finds that accountants already excel in problem-driven analysis of structured data and, because of their domain-specific business knowledge and familiarity with structured datasets, they are well positioned to extend this expertise to the problem-driven analysis of unstructured data in a Big Data context. This positions accountants to create significant value by incorporating new types of data (e.g., social media sentiment, textual data) into their analyses, facilitating deeper business insights and decision-making.

2.              Big Data Analytics Presents Both Threats and Opportunities for the Accounting Profession: While many routine and entry-level accounting tasks are susceptible to automation through Big Data analytics, the article finds that automation is unlikely to eliminate the profession entirely. Instead, task structures within accounting will change, with accountants leveraging their expertise to complement Big Data analytics processes, particularly in areas requiring judgment, subjectivity, and strategic thinking. Public accounting firms can also capitalize on new consultancy and assurance services related to Big Data, especially in auditing unstructured data.

These findings highlight a complementary role for accountants alongside Big Data technologies, emphasizing the need for education and training adjustments to prepare accountants for these evolving roles.

 

Describe 3 main claims of the article in terms of Toulmin's model of argument.

Using Toulmin’s model of argument, which consists of Claim, Grounds (evidence), Warrant (link between grounds and claim), Backing (support for warrant), Qualifier (degree of certainty), and Rebuttal (counter-arguments), three main claims from the article can be described as follows:

1.    Claim 1: Accountants will not become obsolete due to Big Data analytics; rather, they will create new value by extending their expertise to analyze unstructured data.

·        Grounds: Accountants already have strong competencies in problem-driven analysis of structured data and possess deep knowledge of business fundamentals.

·        Warrant: Familiarity with structured data and business knowledge enables accountants to transition effectively to problem-driven analysis of unstructured data, a growing need with Big Data.

·        Backing: Examples such as Best Buy’s integration of social media sentiment with sales data demonstrate the value of accountants analyzing unstructured data to support decision making. Also, specialists in accounting already handle large structured datasets, supporting this capability expansion.

·        Qualifier: This transition is likely over the next ten years, requiring education and training adjustments.

·        Rebuttal: Accountants may not dominate exploratory Big Data analysis due to its technical complexity, which favors data scientists.

2.    Claim 2: Big Data analytics will automate many routine accounting tasks but will not eliminate the accounting profession because many accounting tasks require judgment and strategic thinking.

·        Grounds: Studies predict high automation likelihood for routine tasks, yet auditing involves subjective decisions and professional judgment that are less automatable.

·        Warrant: Routine tasks are mechanizable, but tasks involving interpretation, judgment, and strategic thinking require human intervention.

·        Backing: Frey and Osborne (2013) note the difficulty of automating tasks with significant subjectivity; auditors play roles in ensuring data quality and interpreting complex evidence.

·        Qualifier: While many tasks will be automated, the profession as a whole is resilient to technological unemployment.

·        Rebuttal: Increased competition from technology-based firms could disrupt traditional audit markets if accounting firms do not evolve.

3.    Claim 3: To remain relevant in a Big Data world, accounting education, standards, and professional development must evolve to integrate Big Data skills and knowledge.

·        Grounds: Accountants need expanded competencies in data analytics and unstructured data analysis to fulfill emerging roles.

·        Warrant: Without appropriate education and standards, accountants may fail to adapt, diminishing their value in data-driven business environments.

·        Backing: Professional bodies like the AICPA acknowledge the demand for accountants skilled in Big Data; ongoing education is necessary to extend these skills broadly across the profession.

·        Qualifier: Education and standards development are necessary and urgent to prepare future generations and retool existing professionals.

·        Rebuttal: The evolving nature of Big Data and auditing standards presents uncertainties about the exact role of accountants going forward.

These claims, supported by evidence and reasoning in the article, form a coherent argument that Big Data analytics represents both a challenge and an opportunity for the accounting profession.

  

Describe 2 main research limitations of the study.

Two main research limitations of the study identified by the authors are:

1.              Lack of Empirical Validation: The paper's arguments and recommendations regarding the impact of Big Data analytics on the accounting profession are forward-looking and conceptual rather than empirically tested. The authors acknowledge that they are unable to subject their recommendations to rigorous empirical scrutiny at this stage, representing a limitation of their work. They encourage future research to empirically examine the validity of their propositions.

2.              Scope of Analysis Limited to Certain Accounting Areas: The study focuses primarily on management accounting, financial accounting, and auditing in the context of Big Data analytics. Other important areas within the accounting profession, such as tax and internal auditing, are not analyzed in detail. The authors note this limited scope as a limitation and suggest that expanding research to include these and other areas, including implications for cybersecurity, would be valuable for future research.