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What are the 5 steps to audit data analytics (ADA)?

Universal CPA Review has included audit data analytics in the platform and has lecture videos, study guides, and multiple choice questions on the blueprint topic. The materials are located in the Audit section, specifically chapter 3 module 7. Start a 14-day trial here.

The AICPA has outlined 5 key steps to using audit data analytics (explained in more detail below). The visual below is your mental map to help you truly understand these topics. Every time you see a question or simulation on audit data analytics, you’ll have your mental map to guide you.

Audit Data Analytics and Baking a Cake???

The video below will take you through the 5 steps of audit data analytics and how to bake a cake. By the end of this ~16 min video, you will have the knowledge necessary to nail this topic on the audit exam.

AICPA 5 Steps to Audit Data Analytics

According to the AICPA, audit data analytics (ADAs) are techniques that help auditors leverage current technologies and move toward a more data-driven approach to planning or performing an audit. While audit data analytic techniques are often applied to external audits, they can be applied to internal audit engagements as well. The 5-step approach includes:

Step 1) Plan the Audit Data Analytics

The audit team should brainstorm where and when to apply audit data analytics within the audit engagement. This will consider the nature, extent, and timing (“NET”) of the audit engagement and whether consideration will be made during the risk assessment phase or when performing test of internal controls and/or substantive test of details.

Step 2) Access and prepare the data for the audit data analytics (ADA)

The audit team will subsequently identify the data and determine its original source to verify that it is in a usable format. Furthermore, the auditor will prepare the data for the analytical tools, a process known as data transformation. The data may need to be cleansed and/or normalized. Cleansed will help improve the quality of the information, while normalized eliminates duplicate data. Cleaning may be altering the date format to be more usable while normalizing is more about making similar items are treated the same even if there are slight differences in the values.

Step 3) Consider the relevance and reliability of the data used

In order to consider the overall relevance and reliability of data used within the audit data analytics, the auditor must first understand how the data was entered (e.g., system or manual entry). In addition, they must understand the data’s original source (e.g., internally or externally generated data) and whether the data is the original data or if it has been manipulated by the audit team (or anybody else) prior to the auditor receiving it. Finally, the auditor must assess the quality of the data received. 

Step 4) Perform the ADA

Once the auditor has performed the audit data analytics, they must subsequently analyze the results. If the initial results of the ADA indicate that aspects of its design or performance need to be revised, make appropriate revisions and reperform the ADA. If the auditor concludes that the ADA has been properly designed and performed, and the ADA has identified items that warrant further auditor considerations, plan and perform additional procedures on those items consistent with achieving the purpose and specific objectives of the ADA.

Step 5) Evaluate the results and come to conclusion on ADA’s overall effectiveness

Once the audit data analytics (ADA) has been performed, the auditor must assess the results and come to an appropriate conclusion.


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