Background

This textbook reflects the challenging position for many accountants who simultaneously must have expertise in how businesses function, how transactions are recorded and reported, and how to use information to make decisions. These challenges have increased as the volume and complexity of business data have grown.

A prime example of the challenging position is the inclusion of data analytics in the CPA exam (Evolving CPA article). In order to compete with other skilled employees and to provide valuable information that can be used for monitoring companies and making decisions, an accountant must be part bookkeeper, part business process specialist, and now part data scientist (Future of accounting article). Most accounting training does not equip accountants to become data scientists. Data analytics seems to have become a term separate from data science that recognizes a degree of data skills that do not rise to the level of a data specialist. This separation may be useful because data science or related terms like statistics, econometrics, or computer science are vast fields that cannot be summarized in a single class or a few classes. Despite the services provided by specialists, accountants are often expected to work with or perform on their own many tasks that require data analysis and interpretation skills.

PURPOSE

The purpose of this textbook is to provide a survey of data tasks that are relevant for accountants. These tasks include the use of accounting data by individuals internal and external to a company in varying roles. The overall purpose might be broken down into the following subcategories. First, this textbook introduces general data analysis concepts and critical thinking. Second, this book demonstrates applications for accountants. Third, this book practices practical data analysis skills. Throughout the book additional resources are provided should the accountant want to venture into more technical or different data science tools and methods.

SOFTWARE

The textbook uses R and KNIME as statistical software for the data analysis. There are several reasons for this. First, data analysis projects can include tools from simple sorting and aggregating to complex machine learning. General purpose tools can be applied to a wide variety of applications and customized tasks. Second, open source software with large online communities are adapted for many tasks and can be helpful for learning and troubleshooting. Third, R and KNIME represent different access methods for similar tools. R is a coding based general purpose statistical software with a large online community. KNIME is a visually based general purpose software that may be more accessible for those without a coding background or interest.

STRUCTURE

The book begins with foundational information about the purposes of data analytics and applications in accounting. After introducing the foundations, the following chapters introduce the application of data tools to accounting data. Each of these chapters follows a similar format. The chapter first introduces a data tool. Second, the chapter demonstrates an application with an accounting function (financial accounting, managerial accounting, taxation, or auditing). Third, the chapter uses R and KNIME to apply the data tool to accounting data. The appendix provides short introductions to R and KNIME for those without backgrounds in these software.

Content and Revisions

This textbook carries a CC BY-NC-SA copyright. You many use the material in any non-commercial way. You may adapt the content as best suits your needs. If you have additions or revisions you would like to see, request, or contribute to here, please let me know.