Critical thinking is a must-have skill for accounting and finance professionals, encompassing abilities such as analysis, evaluation, inference, and explanation. Accountants need to be able to interpret financial data, assess risks, and make strategic decisions, all of which require strong critical-thinking capabilities. In today’s data-driven business environment, it’s crucial for accountants to not only possess these skills, but also continuously enhance them.
One effective way for students to develop critical-thinking skills is through the preparation and analysis of data visualizations. Data visualization enables students to uncover trends, patterns, and relationships in data that aren’t immediately apparent in raw metrics. With that in mind, let’s examine the role of data visualization in accounting, its connection to critical thinking, and the benefits and challenges of incorporating it into accounting education.
The Importance of Critical Thinking in Accounting
Critical thinking is when someone analyzes and evaluates information to make reasoned judgments and decisions. It’s an integral part of the data analysis process, from planning and analysis to interpretation and communication of results, say Dzuranin, Geerts, and Lenk in their 2023 text “Data and Analytics in Accounting: An Integrated Approach.”
A 2018 study by Byrd and Asunda on enhancing critical-thinking skills through data visualization explains why the integration of critical thinking is crucial for accounting and finance professionals who want to navigate complex, data-driven environments.
- Analytical kills. Critical thinking is deeply intertwined with analytical skills. Accountants must be able to read graphs, charts, and dashboards to extract meaningful insights. So they must understand the underlying data, identifying trends and patterns, and evaluating the significance of these findings. Analytical skills are crucial for making sense of complex data sets and ensuring the conclusions drawn from data are accurate and reliable.
- Decision making. Critical thinking enhances decision-making processes by enabling accountants to evaluate the relevance and reliability of data so they can make informed decisions based on accurate and relevant information. Visual data further enhances this process by presenting information in a clear and concise manner.
Data Visualization in Accounting
Traditionally, accountants present data in tabular formats, often within spreadsheets. This method, while functional, lacks the ability to convey complex relationships and trends effectively. As technology and Big Data evolve, there’s a growing opportunity and need for more sophisticated presentation methods. Early visual representations, such as basic pie charts and bar graphs, have evolved into dynamic dashboards and interactive charts that offer deeper insights and facilitate more informed decision-making.
The integration of data-visualization tools into accounting software has transformed how accountants analyze and report data. These are now standard in accounting departments, enabling professionals to create detailed and interactive visualizations. These tools help accountants not only process and present financial data but also integrate data from various sources, providing a comprehensive view of an organization’s financial health.
As the volume and complexity of data continues to grow, the demand for data-visualization skills in accounting will likely increase. Future accountants will need to be comfortable using advanced visualization techniques to interpret Big Data, identify trends, and communicate findings effectively. This growing demand underscores the importance of integrating data-visualization training into accounting education.
Teaching Data Visualization in Accounting Education
Incorporating data visualization into accounting courses can be achieved through a combination of theoretical and practical approaches. Courses should cover the principles of data visualization, the use of various tools, and the application of visualization techniques to real-world accounting problems. Integrating these components into existing curricula can help students develop both the technical and analytical skills needed for modern accounting practices.
Several tools are popular to teach data visualization in accounting education:
- Microsoft Excel: Widely used for its accessibility and versatility in creating basic to advanced charts and graphs.
- Tableau: Known for its powerful data visualization capabilities and ease of use, making it ideal for creating interactive dashboards.
- Power BI: Microsoft's business analytics tool that provides robust data visualization and reporting features integrated with other Microsoft services.
Practical assignments and projects can greatly enhance students’ learning experiences. For example, students might be tasked with analyzing a data set of a company’s financial performance, creating visualizations to highlight key trends and insights, and presenting their findings in a report. Case studies that involve real-world data visualization scenarios can also provide valuable hands-on experience.
Developing Critical Thinking Skills through Data Visualization Assignments
A common problem faculty face when adding data visualization assignments to a course is that there’s often little room for additional topics.
Instead of removing a topic, find ways to include these assignments within existing topics in the course. What follows are examples of how to layer in assignments that help students not only develop their data-visualization skills, but also their problem-solving, pattern recognition, evaluative, and communication skills. Each assignment gives example prompts aligned to each level of Bloom’s Taxonomy. This allows faculty to apply assignments from freshman to graduate levels in accounting courses. (For a more detailed example, see Appendix: Example Data Visualization Assignment for Huskie Motor Corporation)
Problem-Solving
Example assignment: Provide students with a data set of a company's financial transactions and ask them to identify discrepancies and anomalies that could indicate fraud. Have them create visualizations such as heat maps or scatter plots to highlight unusual patterns and present their findings.
- Bloom’s Taxonomy:
- Remembering: Identify relevant financial transactions from the data set.
- Understanding: Explain the significance of the discrepancies found.
- Applying: Use visualization tools to create charts that represent the data.
- Analyzing: Examine the visualizations to uncover potential fraud patterns.
- Evaluating: Assess the reliability of the visualizations and the accuracy of the findings.
- Creating: Develop a comprehensive report summarizing the findings and suggesting preventive measures.
Pattern Recognition
Example assignment: Give students a historical data set of a company's sales performance. Have them use time-series charts and trend lines to identify seasonal patterns and forecast future sales.
- Bloom’s Taxonomy:
- Remembering: Recall methods for creating time-series charts.
- Understanding: Describe the observed seasonal patterns.
- Applying: Use software to generate trend lines for the sales data.
- Analyzing: Interpret the trend lines to understand sales fluctuations.
- Evaluating: Critique the forecasting model used.
- Creating: Construct a detailed sales forecast report based on the visualized data.
Evaluative Judgments
Example assignment: Have students analyze a company's investment portfolio using various visualization techniques. Ask them to evaluate the performance of different investments and recommend which to hold, sell, or buy.
- Bloom’s Taxonomy:
- Remembering: List the investments in the portfolio.
- Understanding: Explain the metrics used to evaluate investment performance.
- Applying: Create visualizations to compare investment returns.
- Analyzing: Assess the visualizations to determine the best-performing investments.
- Evaluating: Judge the reliability and validity of the investment data.
- Creating: Propose investment strategies based on the evaluation.
Communication Skills
Example assignment: Have students prepare a presentation for a company's board of directors, explaining the company’s financial health using dashboards and interactive visualizations.
- Bloom’s Taxonomy:
- Remembering: Identify key financial metrics to include in the presentation.
- Understanding: Clarify the significance of these metrics.
- Applying: Utilize visualization tools to create a comprehensive dashboard.
- Analyzing: Break down complex financial data into understandable components.
- Evaluating: Review the presentation for clarity and effectiveness.
- Creating: Deliver an engaging and informative presentation that communicates the financial status to non-experts.
Integrating the Critical-Thinking Model
To fully harness the potential of data visualization in fostering critical thinking, educators should integrate a structured critical thinking model into their teaching. In Data Analytics in Accounting: An Integrated Approach (2023), Dzuranin, Geerts, and Lenk discuss how to apply a critical-thinking mindset across planning, analysis, and interpretation stages of data analysis to make informed decisions. The authors developed the SPARKS framework (Stakeholders, Purpose, Alternatives, Risks, Knowledge, Self-Reflection) based on Paul and Elder’s Critical Thinking Framework. SPARKS (see Figure 1) provides a comprehensive approach on how to apply critical thinking throughout the data analysis process:
- Understand stakeholders: Identify all relevant stakeholders affected by the data analysis results. Internal stakeholders might include managers and employees, while external stakeholders can include investors, creditors, customers, and regulatory bodies. Understanding the stakeholders helps to better understand the impact of the analysis and guides the choice of data and analysis methods.
- Identify the purpose: Clearly define the purpose and specific questions of the data analysis to maintain focus and relevance. This ensures the analysis stays aligned with the main objectives and avoids distractions from irrelevant findings. For instance, if the objective is to understand why a particular product’s sales have dropped, the analysis should focus on only factors affecting that product.
- Consider alternatives: Generate and evaluate various options and strategies to choose the best approach for data analysis. This involves considering multiple models and methods to ensure robustness. For example, when analyzing sales data, alternatives might include different statistical models or visualization techniques to highlight trends.
- Assess risks: Identify potential risks, assumptions, and biases in the data analysis. Ensuring data completeness and accuracy, choosing appropriate analysis methods, and recognizing biases that could affect results are crucial steps. For example, if data is incomplete or contains errors, the analysis might lead to incorrect conclusions, which could significantly impact decision making.
- Identify knowledge: Recognize knowledge gaps that are necessary to complete the analysis and acquire that knowledge from reliable sources. This involves understanding industry specifics, technical skills, and analytical methods required for the analysis. For instance, understanding market trends and economic indicators might be necessary to analyze sales performance accurately.
- Perform self-reflection: Continuously reflect on the analysis process to identify what worked, what didn’t, and how to improve future analyses. This involves critically reviewing each step in the analysis to pull out lessons learned that can be applied to future projects. For example, reflecting on a project might reveal that certain data sources were more reliable than others, informing how data is collected in the future.
Class Act: Critical Thinking + Data Visualization
Teaching data visualization in accounting education is crucial for students to develop critical-thinking skills. Being able to interpret and present data effectively not only enhances a student’s technical skills, but also fosters their analytical thinking, problem-solving, and decision-making abilities. As the demand for data visualization skills grows in the accounting profession, educators must prioritize integrating these skills into their curricula. And by doing so, they’ll better prepare the next generation of accounting and finance professionals.
Appendix: Example Data Visualization Assignment for Huskie Motor Corporation
Objective
This assignment is designed to help students develop critical-thinking skills in problem solving, pattern recognition, evaluative judgments, and communication through data visualization. Using the provided data set from Huskie Motor Corporation (HMC), students will analyze the data to gain insights and present their findings using various data-visualization tools.
Assignment Overview
Students are tasked with analyzing the provided HMC data set to identify key trends, anomalies, and insights related to sales performance, profitability, and market segmentation. They will use data visualization tools to create comprehensive reports and dashboards that convey their findings effectively to stakeholders.
Steps and Requirements
1. Problem solving
- Task: Identify discrepancies and anomalies in the data set that could indicate potential issues in sales, production, or marketing strategies.
- Example: Use a scatter plot to identify outliers in vehicle sales data by region and model.
- Bloom’s Taxonomy:
- Remember: Identify key data points and variables in the data set.
- Understand: Explain the significance of detected anomalies.
- Apply: Use visualization tools to create scatter plots or box-and-whisker charts highlighting anomalies.
- Analyze: Examine patterns in the anomalies to determine potential causes.
- Evaluate: Assess the impact of these anomalies on overall performance.
- Create: Develop a report detailing the anomalies, their potential causes, and recommended actions.
2. Pattern recognition
- Task: Identify seasonal sales patterns and trends for different vehicle models and regions.
- Example: Create time-series charts to show sales trends over multiple quarters.
- Bloom’s Taxonomy:
- Remember: Recall the steps for creating time-series charts.
- Understand: Describe observed trends in sales data.
- Apply: Generate time-series charts for different models and regions.
- Analyze: Interpret the trends and seasonal patterns in the data.
- Evaluate: Critique the effectiveness of marketing strategies during different seasons.
- Create: Produce a detailed sales forecast report based on identified patterns.
(see Figure 2)
3. Evaluative judgments
- Task: Evaluate the performance of different vehicle models and recommend strategies for improvement.
- Example: Use bar charts and heat maps to compare profitability and sales volumes of various models.
- Bloom’s Taxonomy:
- Remember: List the models and key performance metrics.
- Understand: Explain the metrics used for evaluating performance.
- Apply: Create bar charts and heat maps to visualize model performance.
- Analyze: Assess the visualizations to identify high- and low-performing models.
- Evaluate: Make informed judgments on which models to prioritize or discontinue.
- Create: Develop strategic recommendations based on the performance evaluation.
4. Communication skills
- Task: Prepare a presentation for the HMC executive team, summarizing key insights and recommendations using interactive dashboards.
- Example: Use Tableau or Power BI to create an interactive dashboard that includes key sales, profitability, and market data.
- Bloom’s Taxonomy:
- Remember: Identify key data points and metrics to include in the dashboard.
- Understand: Clarify the importance of these metrics to the executive team.
- Apply: Utilize visualization tools to build the interactive dashboard.
- Analyze: Break down complex data into understandable components for the presentation.
- Evaluate: Review the dashboard for clarity and effectiveness.
- Create: Deliver an engaging presentation that communicates the findings to non-experts.
5. Data set analysis
The data set provided in the Excel file includes various data points such as sales amounts, marketing expenses, costs, vehicle details (model, brand, year), and regional sales data. Students will need to:
- Clean and preprocess the data to ensure accuracy.
- Use statistical analysis and data visualization tools to explore the data.
- Identify key trends, patterns, and anomalies.
- Develop actionable insights and recommendations based on their analysis.
Suggested deliverables:
- Visualizations: A set of visualizations including scatter plots, time-series charts, bar charts, and heat maps.
- Reports: Written reports that detail findings from the data analysis, including problem identification, pattern recognition, and evaluative judgments.
- Dashboard: An interactive dashboard summarizing key metrics and insights.
- Presentation: A professional presentation designed for the HMC executive team, highlighting key findings and strategic recommendations.
Evaluation criteria
- Accuracy and completeness: Correct and thorough analysis of the data set.
- Clarity and effectiveness: Clear and effective communication of insights through visualizations and presentations.
- Critical thinking: Demonstration of problem solving, pattern recognition, evaluative judgment, and communication skills.
- Creativity and innovation: Use of innovative approaches to data visualization and strategic recommendations.
By completing this assignment, students will develop a deeper understanding of how to use data visualization to enhance their critical-thinking skills and make informed business decisions in the context of accounting and finance.
Source: Dzuranin, A. C., Perols, J., & Hart, D. L. (2018). Huskie Motor Corporation: Visualizing the Present and Predicting the Future. IMA Educational Case Journal, 11(2)
