Data and Analytics in Accounting: An Integrated Approach, 1st Edition
By Ann Dzuranin, Guido Geerts, and Margarita Lenk
Data and Analytics in Accounting, 1st Edition helps students develop a data analytics and critical thinking mindset needed to be successful in the rapidly changing accounting profession. Through the MOSAIC framework and SPARKS critical thinking model, students practice critical thinking in every step of the data analytics process by learning how to articulate and evaluate questions, analyses, results, and communication of results. This course integrates both the professional standards and AACSB standards as represented in the CPA Evolution Model Curriculum and updated CPA exam structure, as well as presents a modular organization that addresses topics from Financial, Managerial, Audit, Tax, and Accounting Information Systems.
By providing a start-to-finish sequence of data analytics steps with integrated real-world assessment and case-based learning, utilizing leading software in industry with robust data sets, students learn how to effectively use data analysis techniques and tools to solve real world problems.
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Develop a Data Analytics Mindset
The MOSAIC Framework (Motivation, Objective, Strategy, Analysis, Interpret, Communicate) focuses on the three stages of the data analysis process – Plan, Analyze, and Report and provides students with the tools they need for each step. This framework is integrated throughout the course to help students develop a data analytics mindset with exposure to a start-to-finish data analysis process to prepare them for the reality of the projects they will encounter in the workforce.
Meaningfully integrate critical thinking in every step of the data analysis process through the SPARKS Framework (Stakeholders, Purpose, Alternatives, Risks, Knowledge, Self-Reflection), which addresses the six essential elements of critical thinking. By consistently applying the SPARKS critical thinking framework to data analysis topics, students refine their ability to think critically as they develop their data analysis skills.
Applying Critical Thinking feature boxes are integrated within each chapter and illustrate how a disciplined, reasoned approach to data analysis is the foundation of a data analytics mindset.
The patterns-based approach presents a unique way for students to learn data analytics, which they can leverage across various data analytics projects. Each pattern represents a real-world scenario commonly encountered during data analysis and presents best practices and techniques on how to address each scenario. Patterns are introduced, explained, and summarized at key points and provide the foundational knowledge needed to be successful with data analyses.
Build In-Demand Digital Skills
Help students develop foundational data analytics software skills with Data Analysis Technology Bootcamps, available for Excel, Tableau, Power BI, Alteryx, and SQL. Each bootcamp includes Overview Videos, How-To Videos, and Solution Walkthrough Videos to ensure students have the support they need to learn how to use the software and perform specific tasks, as well as assignable application exercises and data sets to help students demonstrate their understanding of the tool in an accounting context.
How-To Walkthroughs are step-by-step guides for recreating chapter illustrations or examples using a technology tool covered in the course – Power BI, Alteryx, Tableau, or Excel with alternative technology options presented when available. There are also How-To Walkthrough Videos as part of the Data Analysis Technology Bootcamps or available as a separate video resource within the WileyPLUS course.
Apply It Features offer hands-on, integrated practice opportunities for students at the end of each learning objective so they can further develop and confirm their understanding before moving on to more advanced material. There are also Apply It Videos within WileyPLUS to support and accompany the Apply It Features.
Integrate Real-World Learning
Professional Application Cases are available at the end of each chapter and provide data for a company that can be used to perform analyses in each area of accounting: financial, managerial, AIS, audit, and tax accounting.
Each chapter begins with a Professional Insights box, which features an insight from an accounting professional introducing key topics covered within the chapter. These insights provide a window into how data analysis is being used in the accounting profession.
The Le Grind continuing case features a regional commercial distributor of organic, fair trade certified coffee beans in the United States. Students work through the three stages of the data analysis process as outlined in the MOASIC Framework (Plan, Analyze, and Report). Through the running case, students have the opportunity to apply what they’re learning to real-world business scenarios and by the end of the course, they will have completed a data analysis of gross margin and prepared a communication of the results.
Incorporate Cutting-Edge Topics
Introduce your students to ESG in the accounting context with the WileyPLUS ESG Module, designed to help students understand what ESG is, why it’s important, and how it’s changing the modern accounting and business world. Students work through each pillar of ESG through lessons on Environment, Social, Governance, and Reporting, along with relatable business scenarios with recognizable companies, access to real-world sample reports, and assignable quiz questions.
Additional Key Features
- Over 125 unique, robust data sets are available and include examples from small businesses, manufacturing, hotels, restaurants, services, hospitals, municipalities, state taxes, retail, financial services, and wholesale companies. There are data tags attached to chapter examples and end-of-chapter material that indicate when students can access related data in WileyPLUS.
- Professional Accounting Standards and the latest AACSB Standards are integrated within the text as represented within the CPA Evolution Model Curriculum and updated CPA exam structure.
- Chapter topics are consistently applied to accounting areas such as financial, managerial, AIS, auditing, and tax accounting. Application opportunities and end-of-chapter assessment are also tagged to help students practice applying data analysis to these respective accounting areas.
- The tool-agnostic approach builds the foundation for helping students develop the technical agility that accounting professionals working with data need.
- A variety of pedagogical resources to help students learn to communicate with data and demystify creating effective data visualizations include easy-to-understand decision trees, best practice summaries, and data story guidelines.
- Chapter Introduction Videos provide a high-level, introductory overview of the content and skills that are covered within each chapter, and why they are important.
- Combo Course: Data and Analytics in Accounting, 1st Edition has WileyPLUS combo course options available for Intermediate Accounting, 18th Edition, Auditing, 2nd Edition, Cost Accounting, 1st Edition, and Accounting Information Systems, 1st Edition. These combo courses are available for instructors that want to add more advanced data analysis applications into their Intermediate, Auditing, Accounting Information Systems, or Cost Accounting courses. The combo courses include the full course eTextbooks, homework and assessment questions, and student resources available.
- Lesson Plans feature suggested instructional designs for each chapter.
- Instructor Guide Videos offer further guidance for meaningfully implementing the various chapter resources.
Ann Dzuranin is the KPMG Endowed Professor of Accountancy at Northern Illinois University. She earned her BS in accounting from Fairleigh Dickinson University, an MBA from New York University and a Ph.D. from the University of South Florida. She is a CPA with 15 years of experience in both public and corporate accounting. Prior to pursuing her doctoral degree, she was the director of management accounting and international reporting for a Fortune 100 financial service company. She is co-author of Wiley’s Data and Analytics in Accounting: An Integrated Approach, 1st Edition coming out in early 2023.
Guido Geerts, Ph.D, is a professor and Ernst & Young faculty scholar at the Lerner College of Business, University of Delaware, where he teaches accounting information systems and big data technologies. Guido has received numerous awards for excellence in teaching, research, and service, and is the former chair of the Technology Task Force for the Pathways Commission Recommendation 4. He previously served on the American Institute of Certified Public Accountants (AICPA) Academic Executive Committee (AEC) and currently serves as a trustee of the AICPA Foundation. Guido is the author of Wiley’s Introduction to Microsoft Power BI course and co-author of Wiley’s new data analytics textbook. He is the 2022 recipient of the AAA/J. Michael and Mary Anne Cook/Deloitte Foundation Prize for outstanding undergraduate professor.
Margarita Maria Lenk, Ph.D., CMA (Emerita) was an associate professor at Colorado State University. Her areas of expertise include data analytics; machine learning; accounting information systems; controllership, enterprise risk management; governance and internal controls; and sustainability accounting. Professor Lenk taught accounting courses at the undergraduate and MACC program level. She is a national consultant on curricular reforms and a regular presenter at the American Accounting Association conferences on data analytics. Professor Lenk’s articles have been published widely and she has won over 20 teaching awards, including a AAA/Michael J. and Marianne Cook/Deloitte Prize for outstanding undergraduate professor. She is co-author of Wiley’s new accounting data analytics textbook.
Chapter 1: Data and Analytics in the Accounting Profession
Chapter 2: Foundational Data Analysis Skills
Chapter 3: Motivations and Objectives for Data Analysis
Chapter 4: Planning Data and Analysis Strategies
Chapter 5: Analysis: Data Preparation
Chapter 6: Analysis: Information Modeling
Chapter 7: Analysis: Data Exploration
Chapter 8: Interpreting Data Analysis Results
Chapter 9: Communicating Data Analysis Results
Chapter 10: Recent Data and Analyses Developments in Accounting