A3-M9-AUDIT DATA ANALYTICS

Define audit data analytics (ADAs).

ADAs are data analytic techniques that enable auditors to analyze and review both financial and nonfinancial data to discover patterns, relationships, and anomalies during an audit.

List some benefits of ADAs.

• better understanding of clients and their operations

• advanced assessment of risk

• expanded audit coverage through testing of entire populations

• increased efficiency of applied procedures

• enhanced fraud detection

• insights gained from evaluating metadata and relationships among data

• improved communication through data visualizations and other reports

What are the steps an auditor should use when applying an ADA?

1. plan the ADA

2. access and obtain the data

3. review and analyze the relevance of reliability of the sourced data

4. perform the ADA using the selected tools and techniques

5. evaluate outcomes to ensure the objective was achieved

Describe the three components of the ETL process.

Extract, transform, and load.

• data extraction involves the identification and obtaining of source data

• transforming data entails taking unstructured data, cleaning it, and validating it to ensure it is accurate and ready for analysis

• loading the data into a software program for analysis or into a data storage location is the final step of the ETL process

What are the four broad categories of data analytics that can be applied as ADAs?

1. descriptive

2. diagnostic

3. predictive

4. prescriptive

Define descriptive data analytics.

Descriptive analytics explain what happened or what is happening now. Descriptive analytical techniques help to gain a high-level understanding of the location of central tendency, spread, shape, and other descriptive values of the data being analyzed.

Define diagnostic data analytics.

Diagnostic analytics work to uncover correlations, patterns, and relationships among data to explain outcomes. Diagnostic analytics are utilized when an organization wants to understand the underlying cause of results; essentially, why something happened with the data.

Define predictive data analytics.

Predictive analytics use historical data to make predictions, estimates, and assertions about future events.

Define prescriptive data analytics.

Prescriptive analytics build on predictive analytics and shift the focus from addressing what will happen to how to make something happen. Examples include what-if analysis and decision support and automation.

How can ADAs be applied to TODs?

For TODs, ADAs can perform sequence checks, test entire populations, compare transactions against external data, and evaluate source data to identify missing data.

How can ADAs be applied to APs?

• compare current year data to preceding year data

• compare industry trends to those at the audited entity

• develop expectations for transactions or balance amounts

• perform drill-down analyses of differences found between expected and actual amounts

How does a relational database work?

A relational database allows data to be stored in different tables, and the tables can be linked through relationships using key values.

What different methods can be used to obtain ADAs data?

ADA data may be obtained using a variety of techniques and methods, including:

• utilizing built-in reporting provided by information systems

• custom queries of information systems

• data mining

• data-pulls

• walk-throughs and interviews of clients

• research and external sites

How are data visualizations used in ADAs?

Data visualizations are used to turn complex content into easy-to-ready graphs or charts to provide the auditor with insights to make decisions.

List some techniques that can be used to interpret ADAs reults.

ADA techniques to interpret results include regression analysis, variance analysis, period-over-period analysis, classification, and trend analysis.