Data Analytics in Accounting: An Integrated Approach develops an integrated data analysis and critical thinking skill set needed to be successful in the rapidly changing accounting profession. Following a pattern-based approach to profiling, cleaning, and transforming data, the book helps explore data from a variety of perspectives for analytical purposes and key data relationships. The text guides students to develop the professional skills they need to plan, perform, and communicate data analyses effectively and efficiently in the real world.
This international edition introduces a new feature “Data Analytics and Decision Making” at the end of the book, which offers students the opportunity to see how they can use data analytics to help solve realistic business problems. In addition, topical changes have been made in select chapters and brief exercises along with multiple-choice questions have been revised in all the chapters.Table of Contents
1 Data and Analytics in the Accounting Profession
1.1 How Are Data and Analytics Transforming the Accounting Profession?
1.2 What Are the Stages of the Data Analysis Process?
1.3 What Is a Data Analytics Mindset?
1.4 How Is a Data Analytics Mindset Applied?
2 Foundational Data Analysis Skills
2.1 How Does Understanding Data Storage Help Answer Questions?
2.2 How Do Spreadsheet Functions Analyze Large Amounts of Data?
2.3 How Do We Organize Data Sets for Analysis?
2.4 Which Descriptive Measures Help Us Understand Data?
2.5 How Is Visualization Used in Data Analysis?
3 Motivations and Objectives for Data Analysis
3.1 How Does Motivation Inform Objective-Based Data Analysis Questions?
3.2 What Are Descriptive Objectives?
3.3 What Are Diagnostic Objectives?
3.4 What Are Predictive Objectives?
3.5 What Are Prescriptive Objectives?
3.6 What Are Data Analytics Motivations and Objectives in Professional Practice?
4 Planning Data and Analysis Strategies
4.1 How Do Accountants Design Data Analysis Projects?
4.2 What Should We Consider When Selecting Data for Analysis?
4.3 What Should We Consider When Selecting an Analysis?
4.4 How Do Data and Analysis Strategies Differ Across Practice Areas?
5 Analysis: Data Preparation
5.1 What Is Data Profiling?
5.2 What Does It Mean to Extract-Transform-Load (ETL) Data?
5.3 Which Patterns Extract Data?
5.4 Which Patterns Transform Columns?
5.5 Which Patterns Transform Tables?
5.6 Which Patterns Transform Models?
5.7 Which Patterns Apply to Data Loading?
6 Analysis: Information Modeling
6.1 What Is Information Modeling?
6.2 Which Patterns Implement Information Modeling Algorithms?
6.3 Which Patterns Help Develop and Implement Accounting Information Models?
7 Analysis: Data Exploration
7.1 What Is Data Exploration?
7.2 How Are Data Relationships Visualized for Exploration?
7.3 How Are Data Explored by Integrating Data Relationships?
8 Interpreting Data Analysis Results
8.1 How Do We Draw Conclusions from Data Analysis?
8.2 What Is the Relationship Between Critical Thinking and Data Analysis Interpretation?
8.3 How Do We Know the Analysis Makes Sense?
8.4 How Are Validity and Reliability Determined in Descriptive and Diagnostic Analyses?
8.5 How Are Validity and Reliability Assessed in Predictive and Prescriptive Analyses?
9 Communicating Data Analysis Results
9.1 How Do We Tell a Data Story?
9.2 What Are the Steps for Creating Effective Data Visualizations?
9.3 What Are the Characteristics of Effective Visualizations?
9.4 What Makes Data Visualizations Misleading?
9.5 How Are Data Used in Live Presentations?
10 Recent Data and Analyses Developments in Accounting
10.1 Which Data Trends Are Impacting Accounting Practice?
10.2 Which Recent Analyses Developments Are Accountants Adopting?
10.3 How Are Data and Analyses Developments Impacting the Professional Practice Areas?
