What is Audit Data Analytics?

Summary

This video script demystifies data analytics in the context of auditing, explaining it as the science and art of extracting useful information from detailed data to support audit planning and performance. It distinguishes data analytics from simple data analysis, emphasizing the use of software to analyze 100% of data for anomaly detection and exception testing. The script illustrates the concept with the example of identifying ghost vendors, highlighting the comprehensive approach of data analytics in enhancing audit efficiency and accuracy.

Takeaways

• 📊 Data Analytics is a buzzword in the audit community but can be ambiguous to define.
• 🔍 Data Analysis is defined as a process of inspecting, cleansing, transforming, and modeling data to discover useful information and support decision-making.
• 📈 Basic data analysis can be done using tools like Excel to analyze, summarize, and visualize data to identify relationships and outliers.
• 🔬 Audit Data Analytics is the science and art of discovering patterns, anomalies, and useful information in data related to the audit subject matter.
• 🎨 The AICPA defines audit data analytics as involving both analysis, modeling, and visualization for audit planning and performance.
• 📝 The term 'data' in audit data analytics refers to detailed, granular transaction-level information.
• 🧐 Audit data analytics aims to analyze and test 100% of the data, moving beyond traditional random sampling methods.
• 🛠️ Data analytic tools enable auditors to extract information from the entire data set to identify anomalies and perform specific analyses.
• 👻 An example of audit data analytics is identifying potential ghost vendors by joining and matching vendor payment files to the vendor master file.
• 🔑 The join feature in data analytic tools allows auditors to perform specific analyses to identify exceptions within the data population.
• 🔍 To summarize, audit data analytics is about discovering insights within data that assist auditors throughout the audit process, using tools that can analyze the entire data population.

Q & A

What is the primary purpose of data analysis in the context of auditing?

The primary purpose of data analysis in auditing is to inspect, cleanse, transform, and model data to discover useful information, inform conclusions, and support decision-making.

What basic tools can be used for data analysis similar to the way Excel is used?

Basic tools for data analysis include spreadsheet software like Excel, which can be used to analyze rows and columns of data, summarize information, and create pivot tables or graphs to identify relationships and draw conclusions.

How is audit data analytics defined according to the AICPA?

Audit data analytics is defined by the AICPA as the science and art of discovering and analyzing patterns, identifying anomalies, and extracting other useful information in data related to the subject matter of an audit through analysis, modeling, and visualization for planning or performing the audit.

What does the term 'data' refer to in the context of audit data analytics?

In the context of audit data analytics, 'data' refers to highly granular, detailed information at the lowest level, such as the rows that are the origin of a transaction.

Why is it beneficial to use data analytics software to analyze 100% of the data instead of relying on random sampling?

Analyzing 100% of the data with data analytics software allows auditors to test all data points, identify anomalies, test for exceptions, and gain a better understanding of the process and business, which is more comprehensive than relying on random sampling.

What is an example of a data analytic task that an auditor might perform?

An example of a data analytic task is determining if there are any potential ghost vendors by joining the vendor payments file to the vendor master file, matching by vendor number, and identifying records with no secondary match to the vendor master.

What feature of data analytic tools allows auditors to perform specific data analytics on the population?

The 'join' feature of data analytic tools allows auditors to combine different datasets and perform specific data analytics to identify exceptions and analyze the data comprehensively.

What is the significance of the term 'science and art' in the definition of audit data analytics?

The term 'science and art' signifies that audit data analytics involves both systematic, methodical processes (science) and creative, interpretive skills (art) to effectively analyze and derive insights from data.

How can data analytics tools help auditors in identifying anomalies within the data?

Data analytics tools can analyze 100% of the data population, using features like pattern recognition, anomaly detection, and visualization to help auditors identify and understand exceptions and outliers.

Where can one find more information on data analytics as it relates to auditing?

For more information on data analytics in auditing, one can visit the website 'automation.com' or contact '[email protected]' as mentioned in the script.