Auditing Section Research Summaries Space

A Database of Auditing Research - Building Bridges with Practice

This is a public Custom Hive  public

research summary

    Infer, Predict, and Assure: Accounting Opportunities in Data...
    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 
    Infer, Predict, and Assure: Accounting Opportunities in Data Analytics.
    Practical Implications:

    This article is important to practitioners as well as academics because they will be using data analytics in accounting and auditing tasks and will need to specify system design characteristics needed to effectively accomplish these tasks. The authors identify several research questions for further study.


    Schneider, G. P., J. Dai, D. J. Janvrin, K. Ajayi, and R. L. Raschke. 2015. Infer, Predict, and Assure: Accounting Opportunities in Data Analytics. Accounting Horizons 29 (3): 719-742.

    AIS meta-theory, data analytics, task processes
    Purpose of the Study:

    The objective of this paper is to examine how data analytics will impact the accounting and auditing environment, identify emerging management and regulatory challenges, and outline new research opportunities. To incorporate and process both structured and unstructured data to support decisions, accountants are working with a new set of sophisticated tools known as data analytics. Data analytics is the process of using structured and unstructured data through the applications of various analytic techniques such as statistical and quantitative analysis and explanatory and predictive models to provide useful information to decision-makers. Data analytics involves complex procedures that extract useful knowledge from large data repositories. Compared with conventional approaches, data analytics offer advantages in terms of cost-effectiveness), scalability, and capability to identify new patterns in real time.

    Several challenges and risks may arise with data analytics. First, how can voluminous data stored in heterogeneous and differently organized data sources be converted into structured, hence well interpretable, format? In doing so, uncorrelated data needs to be filtered out. The challenge is to identify what data needs to be filtered out. Further, how can structured data repositories be managed, processed, and transformed in order to derive needed information for decision-making purposes? Finally, data analytics applications often are highly scalable.

    Design/Method/ Approach:

    This article is a commentary. 


    The authors expand upon the challenges and risks via adopting the organizing principles of the metatheory of AIS and apply it to data analytics. The first principle states that data analytics research should be task-focused. Their analysis concentrated on three tasks to which accountants often apply data analytics: infer, predict, and assure. The second organizing principle notes that task requirements are the start of the process that establishes the set of system design characteristics needed. They note that the lack of accepted models of data analytics and related perceptions is a significant challenge that should be considered. The third principle suggests that the impact of data analytics on task performance should be examined within the context of cognitive, technological, and organizational contingency factors. They identify several research questions related to each of these contingency factors. Finally, the fourth principle states that the outcome of data analytics is task performance. The authors discuss how evaluating the infer, predict, and assure tasks completed with data analytics may occur at either the individual or organizational level. In addition, often the outcome of data analytics contains private and/or confidential information and more research is needed to examine how organizations can address their responsibilities to maintain privacy and confidentiality.

    Auditing Procedures - Nature - Timing and Extent
    Impact of Technology on Audit Procedures Confirmation – Process and Evaluation of Responses