Auditing Section Research Summaries Space

A Database of Auditing Research - Building Bridges with Practice

This is a public Custom Hive  public

research summary

    Toward Effective Big Data Analysis in Continuous Auditing.
    research summary posted September 21, 2015 by Jennifer M Mueller-Phillips, tagged 08.0 Auditing Procedures – Nature, Timing and Extent, 08.09 Impact of Technology on Audit Procedures 
    255 Views
    Title:
    Toward Effective Big Data Analysis in Continuous Auditing.
    Practical Implications:

    The Big Data qualities of Volume, Velocity, Variety, and Veracity contribute to the creation of the following Big Data Gaps: Data Consistency, Data Integrity, Data Identification, Data Aggregation, and Data Confidentiality. These Big Data Gaps create challenges for current CA systems. The paper outlines possible solutions to these gaps along with needed research topics with the aim of increasing the applicability of continuous auditing systems to Big Data. Big Data is a business phenomenon that is here to stay, and CA systems need to adapt to its challenges.

    Citation:

    Zhang, J., X. Yang, and D. Appelbaum. 2015. Toward Effective Big Data Analysis in Continuous Auditing. Accounting Horizons 29 (2): 469-476.

    Keywords:
    Big Data, continuous auditing, gap analysis
    Purpose of the Study:

    Big Data originates from traditional transactions systems, as well as new sources such as emails, phone calls, Internet activities, social media, news media, sensor recordings and videos, and RFID tags. Since much of this Big Data informs and affects corporate decisions that are important to both internal and external corporate stakeholders, auditors will need to expand their current scope of data analysis.

    Certain qualities, known as the four Vs, define the term Big Data: namely, massive Volume or size of the database, high Velocity of data added on a continuous basis, large Variety of types of data, and uncertain Veracity. Due to volume and velocity, the application of continuous auditing (CA) has become increasingly relevant for the automation and real-time analysis of Big Data. However, massive volume and high velocity also introduce gaps between the present state of audit analytics and the requirements of Big Data analytics in a continuous audit context. Moreover, variety and uncertain veracity present challenges beyond the capability of current CA methods. The purpose of this paper is to identify these gaps and challenges and to point out the need for updating the CA system to accommodate Big Data analysis.

    Design/Method/ Approach:

    This article is a commentary.

    Findings:

    The authors identify and discuss potential remediation for the five Big Data Gaps:

    • Data Consistency: Big Data systems supporting key business processes usually consist of a patchwork of different systems, where data may be fully or partially replicated, the informational content may be overlapped, and more derived data may be stored. This situation gives rise to the serious gap in data consistency.
    • Data Integrity: the volume and types of data are so expansive that it becomes more difficult to identify individual data as well as data sets that have been modified/ deleted/ hidden/ destroyed because of operating error, procedural error, illegal access, and/or network transmission failures. This difficulty in identifying integrity issues can create a domino effect that causes other reliable data to lose their value for audit analysis purposes, thus increasing audit risk in a Big Data, continuous audit environment.
    • Data Identification: refers to records that link two or more separately recorded pieces of information about the same individual or entity.
    • Data Aggregation: necessary for the normal operation of continuous auditing using Big Data and to meaningfully summarize and simplify the Big Data that is most likely coming from different sources.
    • Data Confidentiality: certain data, or more often the associations among data points, are sensitive and cannot be released to others.

    The authors identify the nine CA Challenges:

    • Audit on data with different formats
    • Audit on asynchronous data
    • Audit on conflicting data
    • Audit on illegally tampered data
    • Audit on incomplete data
    • Audit on data with various identifiers
    • Audit on aggregated data
    • Search encrypted data
    • Audit on encrypted data
    Category:
    Auditing Procedures - Nature - Timing and Extent
    Sub-category:
    Impact of Technology on Audit Procedures Confirmation – Process and Evaluation of Responses