What is Audit Data Analytics?
Summary
TLDRThis 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
🔍 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.
Mindmap
Keywords
💡Data Analytics
💡Data Analysis
💡Audit Data Analytics
💡AICPA
💡Granular Detail
💡Anomalies
💡Data Modeling
💡Visualization
💡Ghost Vendors
💡Join Feature
💡Automation
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
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