Which of the following best describes what audit data analytics are
In response, the American Institute of CPAs (AICPA) developed a new publication entitled, Guide to Audit Data Analytics. This guide demonstrates how audit data analytics (ADAs) can be applied to the current financial statement audit to gather audit evidence. This guide complements and expands upon the AICPA’s Analytical Procedures guide and will benefit firms of all sizes. From those who may not be familiar with ADAs to those who may use them on a day-to-day basis, this guide has something for every expertise. Our intent is to introduce auditors who may not be familiar with ADAs to basic concepts and illustrative examples of how they might be used in practice. We also offer guidance to those who may be more familiar with how they can be used within the current auditing standards. Show
Audit data analyticsThis guide defines ADAs 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 the purpose of planning or performing the audit.” Simply put, ADAs are techniques that can be used to perform a number of audit procedures (i.e., risk assessment, tests of details, substantive analytical procedures and in forming an overall conclusion) to gather audit evidence. The benefits of using ADAs can include:
Guide overviewGuide to Audit Data Analytics will help auditors decide whether — and, if so, how — to use ADAs throughout the financial statement audit. The following chapters are included in the guide:
Each chapter includes a separate appendix with various illustrative examples detailing how ADAs can be applied to each phase of the audit. The following additional appendix also is included:
ConclusionWe believe that Guide to Audit Data Analytics is a critical first step to understanding what ADAs are and how they can be applied to the audit under the current auditing standards. It is crucial for the profession to move forward and stay abreast of these new techniques. AcknowledgmentsThe AICPA’s Guide to Audit Data Analytics was developed by its Assurance Services Executive Committee (ASEC) and Auditing Standards Board’s (ASB) Audit Data Analytics Guide Working Group. Data analytics is the science of analyzing raw data to make conclusions about that information. Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption. Key Takeaways
Understanding Data AnalyticsData analytics is a broad term that encompasses many diverse types of data analysis. Any type of information can be subjected to data analytics techniques to get insight that can be used to improve things. Data analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of information. This information can then be used to optimize processes to increase the overall efficiency of a business or system. For example, manufacturing companies often record the runtime, downtime, and work queue for various machines and then analyze the data to better plan the workloads so the machines operate closer to peak capacity. Data analytics can do much more than point out bottlenecks in production. Gaming companies use data analytics to set reward schedules for players that keep the majority of players active in the game. Content companies use many of the same data analytics to keep you clicking, watching, or re-organizing content to get another view or another click. Data analytics is important because it helps businesses optimize their performances. Implementing it into the business model means companies can help reduce costs by identifying more efficient ways of doing business and by storing large amounts of data. A company can also use data analytics to make better business decisions and help analyze customer trends and satisfaction, which can lead to new—and better—products and services. SQLSome of the early days of modern data analytics are due to SQL. Created in 1979, this computing language allows relational databases to be queried and resulting data sets to be more easily analyzed. SQL is still widely used today. Data Analysis StepsThe process involved in data analysis involves several different steps:
Types of Data AnalyticsData analytics is broken down into four basic types.
Data analytics underpins many quality control systems in the financial world, including the ever-popular Six Sigma program. If you aren’t properly measuring something—whether it's your weight or the number of defects per million in a production line—it is nearly impossible to optimize it. Some of the sectors that have adopted the use of data analytics include the travel and hospitality industry, where turnarounds can be quick. This industry can collect customer data and figure out where the problems, if any, lie and how to fix them. Healthcare combines the use of high volumes of structured and unstructured data and uses data analytics to make quick decisions. Similarly, the retail industry uses copious amounts of data to meet the ever-changing demands of shoppers. The information retailers collect and analyze can help them identify trends, recommend products, and increase profits. As of December 2021, the average total for a data analyst in the United States was just over $93,000. Data Analytics TechniquesThere are several different analytical methods and techniques data analysts can use to process data and extract information. Some of the most popular methods are listed below.
Data Analytics ToolsIn addition to a broad range of mathematical and statistical approaches to crunching numbers, data analytics has rapidly evolved in technological capabilities. Today, data analysts have a broad range of software tools to help acquire data, store information, process data, and report findings. Data analytics has always had loose ties to spreadsheets and Microsoft Excel. Now, data analysts also often interact with raw programming languages to transform and manipulate databases. Open-source languages such as Python are often utilized. More specific tools for data analytics like R can be used for statistical analysis or graphical modeling. Data analysts also have help when reporting or communicating findings. Both Tableau and Power BI are data visualization and analysis tools to compile information, perform data analytics, and distribute results via dashboards and reports. Other tools are also emerging to assist data analysts. SAS is an analytics platform that can assist with data mining, while Apache Spark is an open-source platform useful for processing large sets of data. Data analysts now have a broad range of technological capabilities to further enhance the value they deliver to their company. Why Is Data Analytics Important?Data analytics is important because it helps businesses optimize their performances. Implementing it into the business model means companies can help reduce costs by identifying more efficient ways of doing business. A company can also use data analytics to make better business decisions and help analyze customer trends and satisfaction, which can lead to new—and better—products and services. What Are the 4 Types of Data Analytics?Data analytics is broken down into four basic types. Descriptive analytics describes what has happened over a given period. Diagnostic analytics focuses more on why something happened. Predictive analytics moves to what is likely going to happen in the near term. Finally, prescriptive analytics suggests a course of action. Who Is Using Data Analytics?Data analytics has been adopted by several sectors, such as the travel and hospitality industry, where turnarounds can be quick. This industry can collect customer data and figure out where the problems, if any, lie and how to fix them. Healthcare is another sector that combines the use of high volumes of structured and unstructured data and data analytics can help in making quick decisions. Similarly, the retail industry uses copious amounts of data to meet the ever-changing demands of shoppers. The Bottom LineIn a world increasingly becoming reliant on information and gathering statistics, data analytics helps individuals and organizations make sure of their data. Using a variety of tools and techniques, a set of raw numbers can be transformed into informative, educational insights that drive decision-making and thoughtful management. What is audit data analytics?Audit analytics, or audit data analytics, means the intelligence generated from reviewing audit-related information, often through the use of technology. Like other types of data analytics, audit analytics typically involve analyzing large sets of numbers (but could involve text) to find actionable audit insights.
Which definition best describes the true power of data analytics?Data analytics is best described as the science of reducing extremely large datasets down to more manageable databases that can then be processed using traditional tools.
What is meant by data analytics?Data analytics (DA) is the process of examining data sets in order to find trends and draw conclusions about the information they contain. Increasingly, data analytics is done with the aid of specialized systems and software.
How can the use of data analytics help in the audit of inventory?Given the massive size of some inventories, data analysis is being used to conduct inventory audits. It is being used to identify potential or obsolete inventory, slow-moving inventory, reconciling the inventory count with the general ledger, and performing sampling, valuations, and testing on inventory data.
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