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.

Outlines

00:00

🔍 Understanding Data Analytics in Auditing

This paragraph introduces the concept of data analytics in the context of auditing, noting its prevalence as a buzzword and the need for clarity. It outlines the official definition of data analysis as a process involving inspection, cleansing, transformation, and modeling of data to extract useful information and support decision-making. The paragraph also introduces the term 'audit data analytics,' emphasizing its role in discovering patterns, anomalies, and useful information related to the audit subject matter through analysis, modeling, and visualization. The American Institute of Certified Public Accountants (AICPA) provides the definition and highlights the importance of analyzing detailed, granular data for comprehensive audit planning and performance.

Keywords

💡Data Analytics

Data Analytics is a buzzword in the audit community that refers to the process of inspecting, cleansing, transforming, and modeling data to discover useful information, inform conclusions, and support decision-making. In the context of the video, it is crucial for auditors to understand this concept as it relates to analyzing detailed, granular data to identify patterns, anomalies, and extract useful information for audit planning and performance.

💡Data Analysis

Data Analysis is defined as the process involving various steps such as inspecting, cleansing, transforming, and modeling data. The goal is to uncover insights that can inform conclusions and support decision-making. In the video, it is the fundamental activity that underpins audit data analytics, with examples including the use of Excel for analyzing data and creating pivot tables or graphs to identify relationships and outliers.

💡Audit Data Analytics

Audit Data Analytics is described as both a science and an art that involves discovering and analyzing patterns, identifying anomalies, and extracting useful information from data related to the audit subject matter. The video emphasizes its importance in the planning and performance of an audit, highlighting how it can be used to analyze 100% of the data, as opposed to relying on random sampling.

💡AICPA

The American Institute of Certified Public Accountants (AICPA) is mentioned in the video as the provider of the definition for audit data analytics. The AICPA is a professional organization for CPAs in the United States and provides guidance and standards in the field of accounting and auditing, including the use of data analytics in these professions.

💡Granular Detail

Granular Detail refers to the low-level, detailed information that makes up the data set. In the video, it is the highly detailed, transaction-level data that audit data analytics aims to analyze. The script suggests that analyzing this granular data is more effective than relying on summary information or random sampling.

💡Anomalies

Anomalies are unusual patterns or deviations in data that do not conform to expected norms. The video discusses how audit data analytics can be used to identify these anomalies within the data, which can be crucial for uncovering potential issues or errors during an audit.

💡Data Modeling

Data Modeling is the process of creating a representation of data structures to better understand and analyze the data. In the context of the video, it is one of the steps in the data analysis process that can help auditors to discover useful information and support decision-making.

💡Visualization

Visualization in the video refers to the technique of graphically representing data, such as through pivot tables or graphs, to help identify relationships, draw conclusions, and discover outliers. It is a key aspect of data analytics that aids in the audit process by making complex data more understandable.

💡Ghost Vendors

Ghost Vendors is an example given in the video to illustrate a specific type of data analytic that could produce exceptions. It refers to potential fraudulent vendors that do not match records in the vendor master file, and identifying them is an application of audit data analytics to uncover anomalies in vendor payments.

💡Join Feature

The Join Feature mentioned in the video is a data analytic tool capability that allows the auditor to combine data from different sources, such as the vendor payments file and the vendor master file, to perform specific analyses. This feature is crucial for identifying exceptions and performing detailed data analytics during an audit.

💡Automation

Although not explicitly defined in the video, the term Automation is implied through the context of using data analytic tools to perform tasks that were traditionally done manually. The video suggests that automation can help auditors analyze 100% of the data more efficiently, which is a key benefit of using data analytics in the audit process.

Highlights

Data analytics is a buzzword in the audit community but can be ambiguous to define.

Data analysis involves inspecting, cleansing, transforming, and modeling data to support decision-making.

Audit data analytics is the science and art of discovering patterns and anomalies in data related to an audit.

AICPA defines audit data analytics as a technique for planning or performing an audit through analysis, modeling, and visualization.

Audit data analytics focuses on analyzing highly granular, detailed data at the transaction level.

Data analytic tools allow auditors to test 100% of the data, moving beyond random sampling.

Auditors can use data analytics to identify anomalies, test for exceptions, and better understand business processes.

Data analytics can help auditors perform tasks such as identifying potential ghost vendors by analyzing vendor payments.

The join feature in data analytic tools allows auditors to match records and identify exceptions in the data population.

Data analytics is not just a tool but a comprehensive approach to auditing that enhances the audit process.

Audit data analytics is essential for modern auditors to perform thorough and efficient audits.

The transcript emphasizes the importance of data analytics in enhancing the quality and efficiency of audits.

Data analytics provides a detailed and systematic approach to uncovering insights and anomalies in audit data.

The use of data analytics in auditing is a significant advancement from traditional methods of data analysis.

Data analytics tools offer a range of features that can be tailored to specific audit needs.

For more information on data analytics in auditing, resources are available on the website automation.com.

Contact [email protected] for further inquiries about data analytics in the auditing process.

Transcripts

what is data analytics when it comes to

auditing what's in a name data analytics

has been a big buzzword in the audit

community yet the term data analytics

can seem a little ambiguous and

therefore hard to define and understand

my goal in this short video is to cut

through the ambiguity and lay out a

clear explanation of what data analytics

is as it relates to the auditor but

before we do that let's take a step back

and look at some official definitions

we'll start first with data analysis so

data analysis is defined as a process of

inspecting cleansing transforming and

modeling data with the goal of

discovering useful information informing

conclusions and supporting decision

making so essentially data analysis can

be anything we do with data to help us

understand develop conclusions and

support decision making at the most

basic level we do this when we use tools

like Excel when analyzing row and

columns of data we may even summarize

the information and create a pivot table

or a graph which helps us identify

relationships help draw conclusions and

even discover outliers in the data now

this brings me to the next definition

and that is audit data analytics audit

data analytics is defined as the science

and art of discovering and analyzing

patterns identifying anomalies and

extracting other useful information in

data underlying or related to the

subject matter of an audit through

analysis modeling and visualization for

planning or performing the audit so here

the definition specifically mentions

extracting information from the data

related to planning and performing an

audit in addition it describes the

technique as both a science and art the

definition is provided by the AICPA and

is quoted in their guide to data

analytics now let's go back to the term

audit data analytic

and let's focus on the word data the

data refer to here is not summary

information no the data is the detail

it's the lowest common denominator

the highly granular detail row that are

the origin of a transaction ideally this

is what we want to analyze and test

using data analytic software because now

we can test 100% of the data could you

random sample you could but why go that

route when you can test everything idea

is a data analytic tool that helps the

auditor extract information from 100% of

the data in order to identify anomalies

test for exceptions and analyze the data

to better understand the process and the

business it drives the tasks executed in

idea helped the auditor perform the data

analytic for example if the auditor

wanted to determine if there are any

potential ghost vendors that would be a

type of data analytic that could produce

an exception the order would join the

vendor payments file to the vendor

Master File match it by vendor number

and choose records with no secondary

match records that don't match to the

vendor master the join feature is the

task that would allow the order to

perform the data analytic that

identifies any exceptions that exist in

the population

so to summarize audit data analytics is

the science and art of discovering

things within data the helps an auditor

perform throughout the audit data

analytic tools can look at 100% of the

population and the auditor can utilize

its features to perform specific data

analytics on the population for more

information on data analytics you can

visit our website at automation comm or

contact us at info at automation com

see you in the next video