Article review of “How Big Data Will Change Accounting”
How to present
this article in Harvard reference format?
Warren,
J.D., Moffitt, K.C. & Byrnes, P., 2015. How Big Data Will Change
Accounting. Accounting Horizons, 29(2),
pp.397–407. https://doi.org/10.2308/acch-51069
What are the key
research issues the article wants to address?
The key research issues addressed in the article
"How Big Data Will Change Accounting" include:
1.
Impact of Big Data on Accounting
Practice: The article explores how Big Data, comprising vast and
diverse datasets including video, audio, and textual information, will
transform traditional accounting practices both in managerial and financial
accounting.
2.
Enhancement of Managerial
Accounting: It investigates the potential contribution of Big Data
to the development and evolution of effective management control systems (MCSs)
and budgeting processes. The article discusses how Big Data can improve the
alignment of organizational goals with management and employee behaviors
through enhanced measurement and control systems like the Balanced Scorecard.
3.
Improvement in Financial
Accounting and Reporting: The research highlights how Big
Data can enhance the quality, relevance, and transparency of financial accounting
information, thereby improving stakeholder decision-making. It also addresses
the role of Big Data in advancing fair value accounting and facilitating
convergence of accounting standards such as U.S. GAAP and IFRS.
4.
Incorporation of New Data Types:
The article focuses on the challenges and opportunities presented by
incorporating unstructured data (approximately 90% of Big Data) such as video,
images, audio, and text into accounting records, and how these can supplement
traditional financial information to provide more comprehensive evidence and
improve audit efficiency.
5.
Limitations and Risks of Big
Data: It also considers the potential limitations and risks
related to Big Data usage in accounting, such as issues of data quantity,
quality, accessibility, and the legal implications associated with e-discovery
and potential litigation outcomes.
Overall, the article aims to understand how Big Data
technologies will reshape accounting processes, enhance existing systems, and
present new challenges and opportunities for researchers and practitioners in
the accounting field.
Describe two main
theories employed in this article.
The article employs the following two main theoretical
frameworks:
1.
Management Control Systems (MCS)
Theory: The article draws extensively on the theory of
management control systems, which are tools used by organizations to align
managerial and employee behaviors with organizational goals. MCS theory
emphasizes creating performance measures and incentives that encourage
behaviors supporting company objectives. For example, the Balanced Scorecard
(BSC) is discussed as a widely used MCS that includes financial and
nonfinancial measures across four areas: financial, customer, internal business
process, and learning and growth. Big Data is theorized to enhance MCSs by
discovering new behavioral metrics correlated with outcomes that can be
incorporated into these systems for better control and performance monitoring.
2.
Financial Reporting and Fair
Value Accounting Theory: The article also invokes
theories around financial accounting and reporting, especially related to the
preparation and dissemination of financial statements in accordance with GAAP
and International Financial Reporting Standards (IFRS). It discusses how Big
Data can augment the valuation and existence assertions underlying financial
reporting by providing additional objective and nontraditional data sources
(e.g., video, audio, textual data) to enhance transparency and decision-making.
Furthermore, the article references the fair value accounting approach,
emphasizing how Big Data can support dynamic and market-based asset valuations,
potentially influencing the convergence and evolution of accounting standards.
These two theoretical lenses—management control systems
theory and financial reporting/fair value accounting theory—frame the article’s
exploration of how Big Data analytics can transform accounting practices both
internally (management-oriented) and externally (financial reporting and
auditing).
Highlight 2 main primary findings reported in
this article.
Two main primary findings reported in the article are:
1.
Big Data Enhances Managerial
Accounting through Improved Management Control Systems (MCSs):
The article finds that Big Data can play a significant role in enhancing
management control systems by discovering new behaviors correlated with
organizational goals, which can then be incorporated as performance metrics.
For instance, Big Data sources such as web usage, internal emails, vocal cues
from customer service calls, and employee computer use can be analyzed to
provide richer, more precise behavioral insights. This allows companies to
develop more comprehensive monitoring and control systems (CMCSs), improving
productivity and alignment with company goals.
2.
Big Data Improves Financial
Accounting and Reporting Transparency and Accuracy:
The article reports that Big Data can augment financial accounting by
supplementing traditional financial information with multimedia data (video,
audio, textual), which enhances transparency, valuation, and audit processes.
For example, ERP systems complemented by video clips on fixed assets provide more
comprehensive and reliable evidence supporting asset existence and valuation
assertions. Big Data also supports the use of fair value accounting by
providing dynamic, market-based data that can reduce reliance on arbitrary
depreciation schedules, potentially accelerating convergence of accounting
standards and improving stakeholder decision-making.
These findings demonstrate how Big Data has the potential
to significantly transform both internal managerial processes and external
financial reporting practices, providing more timely, accurate, and relevant
accounting information.
Describe 3 main claims of the article in
terms of Toulmin's model of argument.
Using Toulmin's
model of argument, which involves Claim, Grounds (evidence), and Warrant
(underlying assumption connecting grounds to claim), the article's three main
claims can be described as follows:
1.
Claim 1: Big Data
will enhance managerial accounting by improving management control systems and
budgeting processes.
- Grounds
(Evidence):
- Big Data provides new types
of data (video, audio, textual) that can be mined to identify behaviors
correlated with goal attainment (e.g., web use, emails, vocal cues).
- Management can incorporate
such metrics into balanced scorecards and beyond budgeting techniques,
improving alignment of behaviors with organizational objectives.
- Warrant
(Assumption):
- More detailed and varied
data about employee and process behaviors leads to better-informed control
systems and improved incentives, which in turn improve organizational
performance.
2.
Claim 2: Big Data
will improve the quality and relevance of financial accounting information,
enhancing transparency and stakeholder decision-making.
- Grounds
(Evidence):
- Video, audio, and textual
data can complement traditional financial records (e.g., ERP systems) to
provide more evidence for assertions like existence and valuation of
assets.
- These data improve audit
efficiency by providing richer documentation and enable better estimation
and detection of irregularities.
- Warrant
(Assumption):
- Incorporating diverse,
real-time, and objective data sources enhances the accuracy and
credibility of financial information, making it more useful for external
stakeholders.
3.
Claim 3: The
integration of Big Data will influence the evolution of accounting standards
and financial reporting practices.
- Grounds
(Evidence):
- Big Data technologies
facilitate continuous, real-time measurement and valuation, supporting the
shift toward fair value accounting and potential convergence between U.S.
GAAP and IFRS.
- Reporting can incorporate
new data types, helping accounting standards evolve to keep up with the
dynamic global economy.
- Warrant
(Assumption):
- Advances in data analytics
and accessibility will drive standard setters to adapt accounting
principles to reflect more comprehensive and timely information.
These claims,
supported by specific evidence and assumptions, capture the article's core
arguments about how Big Data is poised to transform both managerial and
financial accounting domains.
Describe 2 main
research limitations of the study.
The article
identifies two main research limitations related to the application of Big Data
in accounting:
1.
Data Quality,
Quantity, and Accessibility Limitations:
- Many
organizations face challenges such as insufficient volume of data
(quantity), questionable reliability or irrelevance of data sources
(quality), and lack of expertise or resources to extract meaningful
information (accessibility).
- These
limitations restrict the usefulness of Big Data techniques and may prevent
some companies from effectively leveraging Big Data in their accounting
processes, potentially diminishing the quality and confidence in
accounting records.
2.
Risks Related to
Complexity and Legal Liability:
- The complexity of Big Data
analysis, particularly in areas like litigation and damage valuation, may
lead to difficulties in understanding and interpreting results by judges,
juries, or other stakeholders, creating risks of confusion or
misapplication.
- Furthermore, the expanded
use of extensive data archives in legal settings could increase corporate
liability, as defendants might face more robust evidence derived from Big
Data sources, raising concerns about responsible handling and the
potential adverse consequences.
These limitations
underscore the challenges that must be addressed before Big Data can be fully
and reliably integrated into accounting practices.
