Data Analytics, An Integral Part Of Modern Audit Practices

Data analytics includes the process of the analysis after collecting, of data to determine patterns as well as all types of information. Businesses profit from this data analysis and how it has been classified.

Data Mining is an analytical process that primarily involves searching through vast amounts of data to spot useful, but initially undiscovered, patterns. The data mining process typically involves three major steps—exploration, model building and validation and finally, deployment.

Data analytics is the science of examining raw data with the purpose of drawing conclusions about certain information that is drawn from the data. By gathering data, it must be captured and reviewed then it can be turned into information. There are different types of analytics such as descriptive, diagnostic, predictive, and prescriptive. Descriptive analytics will describe, what happened during the process. Diagnostic analytics describes, why did it happen. Predictive analytics describes, what will happen. Prescriptive analytics describes, how can the process happen with a different approach. By applying these different types of analytics, it will answer several questions during the auditing process. Involving analytics to a process it requires

In the New Science of Winning book, (Davenport & Harris, 2007, p.7) analytics is defined as “the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions.” [1]. To be successful in today’s competition, my current employer, DLL Financial Solutions Partner (DLL), is competing on analytics and fully aligned its core strategies to be supported by extensive statistical and computer based decisions. DLL is a global financial services company with operation in 36 countries, and its main focus is in the commercial equipment finance sector. In the following paragraph, I will explain DLL’s position in the industry and its ability to successfully compete on analytics with regards to its core business functions.

The addition of computer technology in accounting systems has not only changed the way information is accessed, stored, and controlled, but encouraged auditors to become familiar with accounting technology and software. Entities using technology for information management provided a different perspective to the role IT played in accounting. At an accelerated pace, auditors need to become educated on capturing and analyzing information technology. While training and professional performance of an auditor are addressed in the standards, the AICPA has not updated this standard since 1975 (Krahel and Titera, 2015). Auditing Standard 1010: Training and Proficiency of the Independent Auditor requires an audit "to be performed by a person or persons having adequate technical training and proficiency as an auditor" became a guideline for auditor knowledge and professional development (Public Company Accounting Oversight

Can big data analytics be used by management accountants to provide a better understanding of a business’s position and outlook?

The amount of data in our world has been rapidly increasing and analyzing these large data sets, or big data, has become crucial for businesses in increasing their success. Many businesses use big data to model their business structures, control processes, and run the business. The availability of this data leads to a more accurate analysis of the target market. More accurate analyses lead to more confident decision making and better decisions means greater operational efficiencies, cost reductions and reduced risk. There are many ways in which big data can be successfully implemented in an organization. Big data allows businesses to segment their target market, creating more precisely tailored products and services. Big data is also used to conduct controlled experiments to make better management decisions. Finally, big data can unlock value by making the captured information transparent and usable at much higher frequency (Manyika, “Big data: The next frontier for innovation, competition, and productivity”).

Analysis of data is a process of inspecting, cleaning, transforming, and modelling data with the goal of discovering useful information, suggesting conclusions, and supporting decision making. Data analysis has multiple approaches, encompassing inverse techniques under a variety of names, in different business.

Modern data processing systems pose new, risk-laden challenges to the traditional audit process. Whereas it was once possible to conduct a financial statement audit by assessing and monitoring the controls over paper-based transaction and accounting systems, businesses have increasingly turned to electronic transaction and accounting systems. SAS 94 offers guidance on collecting sufficient, competent evidence in an electronic processing environment. It pays particular attention to identifying circumstances when the system of control over electronic processing must be

No matter if you are looking at data or big data, it is no value without analysis. So if managers really want to use big data to create

The third assignment is based on the following topics: Computer-assisted audit tools and techniques, Data structures and CAATT’s for Data extraction, and auditing the revenue cycle. This paper will provide solutions to the problems specified in this third assignment. The solutions can be referenced back to the provided problems from the text Information Technology Auditing & Assurance on pages 325, 384 & 458 respectively.

Big data is a popular term used to describe the exponential growth and availability of data, both structured and unstructured. And big data may be as important to business – and society – as the Internet has become. Why? More data may lead to more accurate analyses. More accurate analyses may lead to more confident decision making. And better decisions can mean greater operational efficiencies.

Big Data has gained massive importance in IT and Business today. A report recently published state that use of big data by a retailer could increase its operating margin by more than 60 percent and it also states that US health care sector could make more than $300 billion profit with the use of big data. There are many other sectors that could profit largely by proper analysis and usage of big data.

This software was developed by Grant Bodie who has over 30 years of development and management experience in data access and audit analytics. Grant is a pioneer in the audit analytics field. As founder and software architect of ACL Services Ltd until 2003, he defined audit analytics for most users. Grant is also a Chartered Accountant and prior to ACL spent 12 years in the Deloitte organization, specializing in audit analytics. His passion for customer-driven product development has enabled Arbutus to continue to define the state of the art in audit analytics capabilities. Arbutus ' proven and powerful analytics support a broad range of auditors and other professionals.

Concern about Big Data has been heightened in recent years. The report intents to first discourses the definition of Big Data, relationship between business analytics and Big Data, and several commercial softwares of Big Data. Then the report will illustrate a case study on a global e-commerce company called Alibaba (China) Co, Ltd with company background information, challenges when facing and applying an accounting information system of Big Data and Benefits that Big Data bring to the company. It should be also noted that the report heavily emphases the impact of Big Data particularly through an accounting perspective. As a consequence, the report will come into a conclusion on implications of Big Data to business organizations.