Preface
This book exists because the accounting profession has changed faster than most accounting curricula have adapted. Enterprise resource planning systems capture every transaction, journal entry, payment, and production order in relational databases that hold millions of records. Auditors increasingly analyze entire populations rather than small samples. Management accountants are asked to explain variances, forecast performance, and identify operational drivers using data that lives in systems they were never trained to access. Financial reporting teams must reconcile, validate, and analyze data that flows through dozens of interconnected tables before it reaches the financial statements.
The profession’s competency frameworks now reflect this change. The American Institute of Certified Public Accountants (AICPA) and the National Association of State Boards of Accountancy (NASBA) have built accounting data analytics and technology into the core of the model curriculum that prepares aspiring CPAs for the CPA Exam (AICPA & NASBA, 2021). The Institute of Management Accountants (IMA) expanded its competency framework for management accountants in 2025, with domains titled Data Analytics and Insights and Emerging Technologies in Accounting (IMA, 2025). The International Federation of Accountants (IFAC), which now issues the International Education Standards, lists information and communications technologies among the areas of technical competence that aspiring professional accountants should develop by the end of their initial professional development, in a revision effective from July 1, 2026 (IFAC, 2025). These skills are no longer supplementary. They are foundational to the practice of accounting, and accounting education needs textbooks that teach them.
This textbook teaches accounting students to extract, prepare, analyze, and visualize data with three tools widely used in professional practice. Microsoft Excel prepares, analyzes, and models data; SQL queries relational databases directly and tests whole populations without relying on pre-built reports; and Microsoft Power BI communicates findings in interactive reports that diverse audiences can explore. Together the three tools cover the analytics workflow from data access to the communication of results, and curriculum research calls for integrating technology and information systems competencies of this kind throughout the accounting curriculum (Losi et al., 2022; Sledgianowski et al., 2017). Whichever tool a chapter teaches, it works with the data of one company, introduced in About the Dataset below.
Why This Book
Four features distinguish this textbook from other analytics resources for accounting courses.
The first is its focus on accounting. General-purpose analytics textbooks teach techniques with data from marketing, healthcare, sports, or other domains, which does little to show accounting students how analytics connects to the work they will do. Every example, exercise, and case in this book uses accounting data and addresses an accounting question: students analyze revenue trends, prepare trial balances, test for duplicate payments, calculate cost variances, perform Benford’s Law analysis, and build financial reporting dashboards. The techniques are the ones general analytics courses teach, but the context is always accounting, which helps students transfer what they learn to professional practice.
The second is its three-perspective exercise structure. Every chapter of Parts I to IV closes with applied exercises in three sections. Financial Accounting exercises address reporting, disclosure, and compliance questions. Managerial Accounting exercises address costing, budgeting, performance measurement, and decision support. Auditing exercises address assurance, control testing, anomaly detection, and risk assessment. The same technique often serves all three. An aging analysis, for example, estimates the allowance for credit losses in financial reporting, assesses collection efficiency in managerial accounting, and in auditing evaluates management’s estimates and selects balances for confirmation. Students work through all three perspectives, whichever area of accounting interests them most, because analytical techniques transfer across roles.
The third is its integrated tool progression. Many textbooks teach Excel, SQL, and visualization tools in isolation. This book teaches them as complementary stages of one workflow: each tool is taught in depth in its own part, and the final part brings the three together in capstone cases that prescribe no order of tools. Students reach for SQL when a step needs a whole population or a join across tables, for Excel when it needs a schedule, a model, or a judgment documented cell by cell, and for Power BI when a reader needs to explore the result. The progression mirrors how analytics projects work in practice, where no single tool handles every stage.
The fourth is its design as an open educational resource. The book and its dataset are freely available, and so are DB Browser for SQLite and Power BI Desktop. Desktop Excel needs a license, which your institution may provide, as What You Need explains. The Open Access section on the book’s landing page describes the licensing terms.
About the Dataset
The book is built around one integrated dataset, the Charles River Accounting Dataset. Rather than switching among databases of different sizes and complexity, students learn one company deeply over the whole book. Every chapter works in the same data environment, so a customer order analyzed in an Excel chapter appears in the general ledger queried in a SQL chapter and surfaces in a Power BI report built later. This continuity reinforces learning and mirrors professional practice, where accountants work within one company’s data for long periods.
The Company
Charles River is a fictional mid-size company in the greater Boston area that designs and sells home furnishings through wholesale and direct-to-business channels. Its catalog of furniture, lighting, textiles, and decorative accessories is organized into product families that students can visualize and compare. The company manufactures selected product lines in-house from raw materials and packaging, buys other finished goods from outside suppliers, and warehouses inventory across multiple locations. It also runs an interior design services practice that bills customers by the hour, a second revenue model within the same business, and it keeps a full chart of accounts with cost center tracking across its departments.
This hybrid operating model is deliberate. A company that both buys and makes products, sells both goods and services, and employs both manufacturing labor and salaried professionals produces the range of accounting transactions that students need to encounter. Students can compare purchased with manufactured products, trace how customer demand drives purchasing and production, analyze how labor supports both operations and services, and see how all of these activities flow into the general ledger.
The Business Cycles
Charles River’s operations are organized into business cycles, which link familiar accounting events, such as a sale or a payroll payment, to the documents and records that capture them. No course in accounting information systems is assumed. The chapters introduce the cycles and their data as they need them. The Order-to-Cash cycle traces customer demand from sales orders through shipments, invoicing, and cash receipts. The Design Services cycle, a branch of the revenue cycle, covers hourly consulting engagements that are staffed, approved, and billed monthly. The Procure-to-Pay cycle covers replenishment from requisition through purchasing, goods receipt, and supplier payment. The Manufacturing cycle turns selected products into finished goods through planning, work orders, material issue, labor, and production completion. The Payroll cycle turns employee time records into payroll registers, payments, and remittances. The Manual Journals and Close cycle handles accruals, reclassifications, and period-end adjustments. Every cycle flows into the general ledger, where the company’s entire financial activity is recorded.
From Business Events to the General Ledger
Every ledger entry carries a reference to its source, so students can follow a transaction from the business event that created it to its posting: a sales invoice becomes revenue, a shipment becomes cost of goods sold, a goods receipt becomes inventory, and a payroll run becomes wage expense. Most references lead to a document in the dataset, some lead to a summary, such as a payroll run’s totals by cost center, and a few lead to no record at all, which the chapters treat as exceptions to investigate. This source-to-ledger traceability is one of the dataset’s most valuable features for teaching, and the chapters return to it repeatedly, from assessing data quality to testing journal entries in audit analytics.
Formats and Releases
The dataset comes in three formats that contain identical data, so students work with the same information whichever tool a chapter uses. The Excel workbook supports the tutorials of Part I, the Excel chapters, and the Power BI chapters, which load its Tables directly. The SQLite database supports the SQL chapters and the population-level work of the capstone cases, and it opens in DB Browser for SQLite. The CSV package suits any other tool and is a faster way to load the largest tables into Power BI. Each edition of the book is verified against one release of the dataset, and the Downloads page names that release and links to its files, so that results match the chapters. The Charles River Accounting Dataset website documents the tables, fields, and relationships, and every change to the dataset is posted there.
The dataset’s records are synthetic. They were generated to behave like a real company’s data, and alongside unusual transactions that are legitimate they include exceptions planted for students to find. An exception is a reason to investigate, not by itself proof of an error or a fraud. The fiscal years end on December 31, but some supplier payments for invoices of the final year are recorded early in the following year, so always use the period or the as-of date that a task gives. Keep the downloaded files as released and work in a copy, as the tutorials do. When you find an exception, record the original value, the evidence you checked, and any adjustment you propose, and leave the record unchanged.
How the Book Is Organized
The chapters are organized into five parts that follow a deliberate progression, and each part brings in more of the dataset.
Part I builds the conceptual foundation: the role of analytics in accounting, data and its quality, and the way Charles River’s data is organized. Its tutorials already work in the Excel workbook, exploring the tables, testing data quality, and tracing a sale from the order to the ledger.
Part II teaches Microsoft Excel: structured tables, data preparation with Power Query, PivotTables, descriptive analytics, regression and forecasting, budgeting, what-if analysis, and audit testing. It begins with a focused set of sales and general ledger tables and later draws on the planning and purchasing tables. Excel is strongest for ad hoc analysis, financial modeling, and workpaper preparation.
Part III teaches SQL, the Structured Query Language, the standard language for retrieving data from relational databases. Its chapters use SQLite, a free, lightweight database that needs no server and runs on any operating system, and they expand into the purchasing, manufacturing, and payroll data, where joins across tables become essential. SQL is strongest with large datasets, with data that spans many related tables, and with analytical procedures that are saved and rerun.
Part IV teaches data visualization with Microsoft Power BI. Its chapters connect Power BI to the same Charles River data, build data models that span the business cycles, write DAX measures, and design interactive reports that managers, auditors, and other readers can explore on their own.
Part V brings the three tools together in three independent capstone cases, in financial reporting, managerial accounting, and auditing. Each requires students to move across the full dataset and to choose the tool that each step of the problem calls for.
The appendices hold reference material and topics that not every course needs. A quick reference gathers the Excel, SQL, and DAX functions the book teaches, with the chapter that explains each one. The appendix on publishing, securing, and sharing Power BI reports covers the Power BI service, where sharing a report requires a paid license that not every institution provides. The appendix on Power BI in the browser shows readers without a Windows computer how to build the Power BI chapters’ model and report in the Power BI service instead of Power BI Desktop. A map of the exercises and cases to the competency areas of the AICPA and NASBA model curriculum, the IMA, IFAC, and AACSB International supports instructors who need to align their course with accreditation requirements.
How Each Chapter Works
The book follows a consistent structure grounded in research on how technical skills are learned. Studying worked examples before solving similar problems supports the early stages of learning a skill, including in mathematics (Atkinson et al., 2000; Barbieri et al., 2023), and Borthick and Jones (2000) make the case that collaborative discovery learning online, in which students solve realistic problems together, is more effective than lecture-based instruction in a master’s course in information systems assurance. Every chapter in Parts I to IV applies these principles in the same sequence, which becomes familiar after the first few chapters.
Each chapter opens with four to six learning objectives. They span cognitive levels from foundational understanding through application and analysis, so that both undergraduate and graduate students find appropriate challenges. An opening scenario then places the reader in a professional role at Charles River, describes a concrete task, and shows why the material matters before the chapter teaches how to do it.
The body presents concepts in narrative form, supported by figures, tables, and diagrams. One to three guided tutorials are embedded where each technique is introduced, so that a concept is practiced as soon as it is read. Each tutorial has numbered steps, expected results, and a checkpoint for verifying the work, and later tutorials build on the skills, and often the files, of earlier ones. For Parts II to IV and the appendix on publishing, the supplementary material on the Downloads page includes the start file of every tutorial that continues earlier work, so that such a tutorial can be begun, or checked, without the one before it. The Power BI start files open only in Power BI Desktop, so a reader who follows those chapters in a browser builds the model and carries it from chapter to chapter. Three kinds of callout boxes appear throughout the narrative. “In Practice” notes describe how a technique is used in professional settings, “Watch out” notes warn about common errors and pitfalls, and “Connecting the Dots” notes link the topic to other chapters and tools.
Each chapter closes with a summary, a list of key terms with definitions, ten to fifteen multiple choice questions spanning recall, application, and judgment, and the applied exercises in the three accounting perspectives. A comprehensive case at the end of each of the first four parts integrates the material from all the chapters in that part, and from Part II onward the cases use the tools the part teaches.
The chapters of Part V are capstone cases, and they adapt this structure. Each keeps the learning objectives, the opening scenario, the callouts, and the further reading, replaces the tutorials, questions, and exercises with one extended problem that students plan and solve with everything the book has taught, and adds suggested milestones and a submission checklist.
In-text citations in APA format support claims about professional practice, technique effectiveness, and adoption trends. Each chapter closes with a Further Reading section of five to eight annotated references, drawn from peer-reviewed journals, professional standards, and practitioner publications, followed by a References list of every source the chapter cites. The references ground the techniques in the professional context where they are applied. For example, the AICPA’s Guide to Audit Data Analytics (AICPA, 2026), which several chapters cite, shows how auditors can use analytics in risk assessment, in substantive procedures, and in forming an overall conclusion on the financial statements. The Further Reading sections also open pathways into the research literature that informs and extends what the book teaches.
What You Need
The dataset files are on the Downloads page. The tutorials need a desktop version of Microsoft Excel that Microsoft still supports, and Excel for Microsoft 365 is the recommended one. The companion files are built and checked in it, and on a Mac it is the only version with the Power Query Editor, which the Excel chapters use to import data. The tutorials’ newer functions, such as XLOOKUP and MINIFS, are also in Excel 2024 and Excel 2021, but Microsoft ended support for Excel 2021 on October 13, 2026, and some solution files use functions, such as HSTACK and VSTACK, that need Microsoft 365 or Excel 2024. Use the desktop application rather than Excel for the web, because the Charles River workbook is large and the browser version limits both the size of the workbooks it opens and the features it offers.
The SQL chapters use DB Browser for SQLite, a free program for Windows, macOS, and Linux.
The Power BI chapters use Power BI Desktop, which is free but runs only on Windows. A Mac user can work in a Windows environment, such as a campus computer lab or a virtual machine, or, where the institution allows it, build an equivalent model and report in the Power BI service in a browser, as the appendix on Power BI in the browser explains. A free Power BI account covers your own work in the service, but sharing reports with others needs a paid license, so publishing and sharing are optional and covered in an appendix.
To the Student
This book will change how you work with accounting data. Your accounting courses so far taught you to understand financial statements, apply standards, calculate ratios, and interpret results, and those skills remain essential. What this book adds is the ability to work directly with the data that produces them. Instead of receiving a finished trial balance, you will query the database, extract the general ledger entries, and build the trial balance yourself. Instead of reading a variance report, you will calculate the variances from production data and present them in an interactive report. Instead of reviewing a sample of transactions that someone else selected, you will test the whole population where that is the right test, select the items that need follow-up, and explain what the results do and do not establish.
You need no prior experience with analytics or programming, and no course in accounting information systems or auditing, only the accounting you learned in introductory financial and managerial accounting. If you have never written a SQL query, built a PivotTable, or opened Power BI, you are exactly the reader this book was written for: every technique is introduced from the ground up, and the complexity increases gradually from chapter to chapter. If you already know some of the tools, you will move through the early chapters faster and can spend your effort on the accounting applications and the more advanced techniques.
Three habits will help you most. First, do the tutorials yourself rather than reading through them. Open the data, follow the steps, and verify your results at each checkpoint. When you make an error, diagnosing and correcting it teaches you more than getting it right the first time. The solutions are public, so write your own answer before you open one, and when the two differ, work out why. Second, read the concepts before the steps. The concepts explain why a technique works and when it is appropriate, and the tutorials show you how to carry it out. If you can run a Benford’s Law analysis but cannot say what its results mean or when the test applies, you have a technical skill but not an analytical one, and this book aims to develop both. Third, follow the connections: the “Connecting the Dots” callouts, the same technique seen from three perspectives, and the cases that close each part. These connections are where the deepest learning happens, because they move you from knowing how to use a tool to understanding how to solve a problem.
To the Instructor
The book serves undergraduate and graduate four-credit courses in accounting analytics or accounting information systems, and one text serves both audiences: you set the depth and rigor of discussion and analysis to match the course level. An undergraduate course can focus on the guided tutorials and a selection of the applied exercises. A graduate course can emphasize the analytical judgment that the applied exercises and comprehensive cases require, assign readings from the Further Reading sections, and discuss professional standards and research findings at greater length.
The only prerequisites are the introductory financial and managerial accounting courses. Because the capstone cases of Part V are independent of one another, any one of them can serve as the course’s final project. The solutions to the exercises, the comprehensive cases, and the capstone cases are public, like the rest of the book. They are in the supplementary material on the Downloads page, with each chapter’s slides.
References
American Institute of Certified Public Accountants. (2026). Guide to audit data analytics. https://www.aicpa-cima.com/cpe-learning/publication/guide-to-audit-data-analytics
American Institute of Certified Public Accountants & National Association of State Boards of Accountancy. (2021). CPA Evolution model curriculum. https://www.thiswaytocpa.com/collectedmedia/files/cpa-evolution-model-curriculum-update.pdf
Atkinson, R. K., Derry, S. J., Renkl, A., & Wortham, D. (2000). Learning from examples: Instructional principles from the worked examples research. Review of Educational Research, 70(2), 181–214. https://doi.org/10.3102/00346543070002181
Barbieri, C. A., Miller-Cotto, D., Clerjuste, S. N., & Chawla, K. (2023). A meta-analysis of the worked examples effect on mathematics performance. Educational Psychology Review, 35(1), Article 11. https://doi.org/10.1007/s10648-023-09745-1
Borthick, A. F., & Jones, D. R. (2000). The motivation for collaborative discovery learning online and its application in an information systems assurance course. Issues in Accounting Education, 15(2), 181–210. https://doi.org/10.2308/iace.2000.15.2.181
Institute of Management Accountants. (2025). IMA expanded management accounting competency framework: Advancing competencies for the 21st-century management accountant. https://mc-69e30ef4-758e-4371-ac6f-2657-cdn-endpoint.azureedge.net/-/media/IMA/Files/Home/Career-Resources/Competency-Framework/Competency-Framework-White-Paper.ashx
International Federation of Accountants. (2025). International Education Standard (IES) 2: Initial professional development—Technical competence (2026). https://education.ifac.org/part/ies-2
Losi, H. J., Isaacson, E. V., & Boyle, D. M. (2022). Integrating data analytics into the accounting curriculum: Faculty perceptions and insights. Issues in Accounting Education, 37(4), 1–23. https://doi.org/10.2308/issues-2021-086
Sledgianowski, D., Gomaa, M., & Tan, C. (2017). Toward integration of Big Data, technology and information systems competencies into the accounting curriculum. Journal of Accounting Education, 38, 81–93. https://doi.org/10.1016/j.jaccedu.2016.12.008