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Audit data analytics
Audit data analytics (ADAs) is a technique that can be used to enhance relevancy and value of the
financial statement audit and in continuing to improve audit quality. Large data sets can now be analyzed
for audit relevancy by way of technology advancements and the proliferation of mainstream analytical
software solutions. This data can be internally and externally sourced, and leveraged by internal and
external auditors to produce audit evidence. Benefits include the following:

 Enhanced audit quality. Data analysis techniques and methods allow audit teams to analyze client
data earlier in the audit process, which can help teams individualize their audit approach and provide
a better audit with more relevant audit evidence. These methods can also help auditors find risk
areas through identifying anomalies, trends, correlations, and fluctuations. Also, performing
transaction tests on entire populations, instead of strictly samples, allows auditors to consider wider
sets of audit relevant data, thus producing higher-quality audit evidence.
 Increased audit effectiveness. Auditors can use data analytics to examine large volumes of
information quickly, and in turn have a better understanding of the entity and its systems. Data
analytics also allows auditors to perform testing at shorter intervals more often, not just at the end of
the year. Continuous testing and monitoring of data results in improved identification of risks, more
accurate control assessments, and more timely and relevant audit reporting.
 Improved client service. Auditors must always first comply with professional ethics and independence
requirements when engaging with clients. As long as these requirements are properly considered,
data analytics can provide value beyond the traditional audit of historical financial statements. Audit
data analytics can offer deeper insight into risk and control assessment than a more traditional audit
of historical financial statements. These insights can improve the client experience, allowing them to
gain a better understanding of their own information through different analysis. It is important to
note, that the use of data analytics should be applied only after the professional ethics and
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independence regulations and requirements are met.

Integrating analytics into the audit process
Analytics can be integrated into the following phases of the audit process:

 Audit planning. To help identify and assess risks and develop scope. What should we be auditing and
is it audit-worthy?
 Analytical procedures. To engage in innovative new ways.
 Substantive procedures. To detect misstatements due to fraud or error.
 Control testing. To test the effectiveness of a control used by a client.
 Audit reporting. To graphically visualize and communicate audit findings in a business friendly way.

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See www.ifac.org/global-knowledge-gateway/audit-assurance/discussion/audit-data-analytics-opportunities-
and-tips, accessed August 10, 2019. For more information, please refer to Analytical Procedures Audit Guide.
Durham, NC: AICPA, 2017.
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See footnote 20.

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