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    Big data as complementary audit evidence.
    research summary posted September 11, 2015 by Jennifer M Mueller-Phillips, last edited September 11, 2015, tagged 08.0 Auditing Procedures – Nature, Timing and Extent, 08.09 Impact of Technology on Audit Procedures, 09.0 Auditor Judgment, 09.03 Adequacy of Evidence 
    Big data as complementary audit evidence.
    Practical Implications:

    Incorporating Big Data into an audit poses several challenges. This article establishes how Big Data analytics satisfy requirements of audit evidence, namely that it is sufficient, reliable, and relevant. The authors bring up practical challenges (such as transferring information, privacy protection, and integration with traditional audit evidence) and provide suggestions for addressing them in incorporating Big Data into audit evidence. They also suggest that Big Data can complement tradition audit evidence at every level of audit evidence: financial statement, individual account, and audit objective.


    Yoon, K., L. Hoogduin, and L. Zhang. 2015. Big data as complementary audit evidence. Accounting Horizons 29 (2): 431-438.

    Big data, audit evidence
    Purpose of the Study:

    This paper frames Big Data in the context of audit evidence, specifically looking at the requirements for something to be considered audit evidence, to provide an argument for the usefulness of Big Data to auditors. The authors address the sufficiency, reliability, and relevance of Big Data analytics; they then outline potential challenges to using Big Data for adequate audit evidence.

    Design/Method/ Approach:

    The authors summarize existing literature on audit evidence as it applies to Big Data. They perform no original analyses, but rather discuss the characteristics of Big Data analytics as they relate to regulations and research findings.


    The authors address:

    • Sufficiency: The authors suggest thatwhen used appropriatelyBig Data analytics can meet sufficiency requirements for audit evidence. They provide the example of using an employee’s emails to identify motivation or rationalization of fraud to demonstrate Big Data supplementing traditional audit evidence where traditional methods may be deficient in sufficiently documenting audit conclusions.
    • Reliability: Big Data, being typically from a third party and massive in nature, is argued to be generally reliable for audit evidence. They note that Big Data can validate things such as shipping terms to independently verify cutoff.
    • Relevance: The relevance of Big Data is primarily driven by the timeliness of its availability. Traditional audit evidence is often gathered after-the-fact, however Big Data-based auditing can analyze current trends to provide timely information. They provide several examples, such as using management’s discussion of forecasts. Research has linked overly optimistic press releases to fraud, so using Big Data techniques on earnings forecasts may assist in assigning fraud risk.
    • Integration with Traditional Audit Evidence: The authors acknowledge that Big Data may not always easily bridge into traditional audit evidence, however they provide a discussion of weighting evidenceas you would traditional audit evidenceso that more weight is given to the more sufficient, reliable, and relevant evidence.
    • Information Transfer: Access to data provides benefits which may be leveraged based on economies of scale, however clients may restrict access to proprietary data. The authors suggest specifically contracting for use of internal data.
    • Information Privacy: A common fear of releasing information is that it may be used for a secondary purpose. The authors acknowledge this and suggest that auditors should cooperate with information providers and ensure that information is anonymized.
    Auditing Procedures - Nature - Timing and Extent, Auditor Judgment
    Adequacy of Evidence, Impact of Technology on Audit Procedures Confirmation – Process and Evaluation of Responses