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    Commentaries on Big Data's Importance for Accounting and...
    research summary posted September 21, 2015 by Jennifer M Mueller-Phillips, tagged 01.0 Standard Setting, 01.02 Changes in Audit Standards 
    Commentaries on Big Data's Importance for Accounting and Auditing.
    Practical Implications:

    This set of commentaries presents a strong challenge to the accounting and auditing profession. Perhaps the greatest risk is the slow pace of adjustment of auditors and accountants to the new realities of Big Data/business analytics, which most accounting firms today would agree is absolutely essential to managing and maintaining competitive advantage. Academics, as educators, certainly must revamp their accounting and auditing curricula to provide the necessary skills for Big Data in the accounting and auditing profession. Researchers, too, have a responsibility to analyze Big Data datasets and to generate results that would better understand the contribution of Big Data for operations and decision making, and how the benefits of Big Data might flow to firm managers, company stakeholders, and the public at large.


    Griffin, P. A., and A. M. Wright. 2015. Commentaries on Big Data's Importance for Accounting and Auditing. Accounting Horizons 29 (2): 377-379.

    management control, accounting standards, Big Data
    Purpose of the Study:

    The commentaries in this forum on Big Data confront one of the profession’s most pressing challenges. How should the accounting profession respond to Big Data? Big Data and business analytics now permeate almost all aspects of major companies’ decision making and business strategies. A large U.S. company, for example, might process a billion data elements every day to understand its competitive environment. Moreover, by its very nature, Big Data cannot avoid running head-on into the traditional systems of accounting and auditing that have served the profession so well in the past.

    This issue presents eight commentaries by expert academics and business professionals, who have studied and thought much about the core issues and challenges. Collectively, these commentaries not only define and frame the important issues for accounting and auditing but, also, they identify many feasible, yet difficult, pathways forward, wherein Big Data and traditional accounting and auditing might meld to better serve firms, stakeholders, and the public. These commentaries also identify controversies within the field that imply hard choices. At minimum, the expectation is that these commentaries will stimulate a healthy discussion on the topic among students, academics, and professionals. In this way, they will also serve a key aim of this journalto bridge the gap between theory and practice.

    Design/Method/ Approach:

    This article is a commentary.

    • Vasarhelyi, Kogan, and Tuttle (2015) argue strongly that Big Data matters fundamentally because it changes radically what the profession means by information.
    • Warren, Moffat, and Byrnes (2015) continue this theme but focus mostly on behaviors internal to the firm. Big Data will affect the design and operation of management control systems, they contend, by helping identify behaviors correlated with specific goal outcomes, which would prompt formulation of new performance measures.
    • Krahel and Titera (2015), on the other hand, offer a more sobering hypothesis, contending that current accounting and auditing standards, which emphasize presentation, aggregation, and sampling, are headed in the wrong direction. They state, Aggregation and arbitrary allocations made on static, paper-based financial statements are artifacts of a bygone era of high transmission costs and slow data collection speeds.”
    • Several commentaries discuss how Big Data will change auditing.
    • Brown-Liburd, Issa, and Lombardi (2015) also raise challenges for the auditing profession, but these relate more to its behavioral repercussions; in particular, how Big Data might affect the quality of auditors’ judgments.
    • Zhang, Yang, and Appelbaum (2015) develop their views around the four Vs of Big Datavolume, velocity, variety, and veracity.
    Standard Setting
    Changes in Audit Standards