Published on

January 3, 2024


Financial Data Visualization: How To Do It Right

Financial data visualization is a powerful tool that can streamline decision-making. Learn how to create effective dashboards in a few clicks.
Julia Dunlea
VP of Marketing

Unlocking the power of financial data visualization can transform the way businesses analyze and communicate complex information. Through effective visualizations, companies can streamline decision-making processes, enhance collaboration among stakeholders, and ultimately drive better business outcomes. Are you ready to harness the potential of this game-changing tool?

Key Takeaways

  • Financial data visualization is essential for providing stakeholders with accessible insights into complex financial data;
  • Visualizing financial data is often the first step to forecasting revenue;
  • Common types of financial visualizations include bar charts, line graphs, scatter plots and heat maps to effectively communicate information;
  • Akkio offers a comprehensive solution for creating effective financial data visualizations that enable users to access actionable insights and drive performance.

The Importance of Financial Data Visualization

Source Dataset: Financial Data S&P500 companies (kaggle)

Financial data visualization is instrumental in breaking down complex financial data into understandable information for stakeholders. Through visually appealing and easy-to-understand charts and graphs, financial data visualization makes it easier to comprehend intricate information, thus enabling better decision-making and improved communication among stakeholders.

Various performance metrics, such as financial reports and financial statements, can provide insights into:

  • liquidity
  • budgets
  • expenses
  • cash flow

Financial data can be easily visualized and tracked using data visualization tools, which not only helps to visualize financial data but also aids in identifying trends and patterns. By leveraging data analytics, businesses can make informed decisions and drive growth.

Employing the right data visualization software, businesses can effectively implement data-driven strategies and capitalize on the strength of effective charts and graphs. Financial data visualizations, when created following best practices and using suitable tools, can become a vital tool in an organization’s decision-making process.

Common Types of Financial Data Visualizations

A chart showing different types of data visualizations used to analyze financial data
Source Dataset: Financial Data S&P 500 companies (kaggle)

There are several common types of data used in financial data visualizations:

  • Bar charts
  • Line graphs
  • Scatter plots
  • Heat maps

Each type fulfills a specific purpose, hence, the choice of the right visualization for the data at hand is paramount. Choosing the right visualization allows businesses to effectively communicate complex financial information, ensuring user understanding.

For instance, a working capital graph can provide an overview of a company’s financial health by displaying current liabilities, current assets, and total working capital. The Gross Profit Margin chart is another example which shows the overall revenue. It’s derived by subtracting the cost of goods sold from total sales revenue. These visualizations transform raw data into meaningful insights, enabling users to make accurate predictions and informed decisions.

Grasping the range of data visualizations and their applications in finance is crucial in choosing the perfect data visualization tool. This ensures that the tool not only meets the needs of the finance team but also empowers users to analyze data effectively and efficiently.

Key Components of Effective Financial Data Visualization

Clarity, simplicity, and interactivity are the three key components to focus on for creating impactful financial data visualizations. These elements ensure that users can easily understand and interact with the data presented, leading to more efficient decision-making and understanding of financial insights.

We’ll further explore the significance of these components.


Clarity is crucial in financial data visualization, as it ensures that complex financial information is presented in a comprehensible and straightforward manner. Employing straightforward and intuitive visual components, organizing the data effectively, and providing context can significantly enhance clarity in financial data visualizations.

A lack of clarity can create problems, such as misinterpretation or confusion, whereas clear visualizations generate a more conducive atmosphere for decision-making. Clear financial data visualizations enable users to quickly interpret and grasp the information, leading to more efficient decision-making and understanding of financial insights.


Simplicity in financial data visualization makes it easier to understand, as it helps users comprehend the information presented rapidly. It entails focusing on the most pertinent information while avoiding the inclusion of superfluous details. This enables the audience to effectively comprehend the insights and trends in the data without getting overwhelmed by complexity.

Applying simplicity allows users to identify key insights and trends more swiftly, thus leading to improved decision-making and understanding of financial information. On the other hand, a lack of simplicity can lead to misunderstanding and misinterpretation of the data, with users potentially failing to recognize relevant insights and trends.


Interactivity in financial data visualization allows users to:

This makes interactivity an essential component of effective real-time financial data visualization.

Interactive features, such as tooltips, filters, and drill-down capabilities, can significantly enhance the user experience and offer more detailed insights. Moreover, interactivity allows users to rapidly explore data, recognize patterns, contrast scenarios, and evaluate the data directly, which can be particularly advantageous when attempting to discover relationships between various data sets or when attempting to distinguish outliers.

Choosing the Right Data Visualization Tool

A robot, generated by AI, looking for the best financial data visualization tool
Generated with Midjourney

Choosing the correct data visualization tool is a vital step towards effectively visualizing financial data. Factors such as ease of use, compatibility, and customization should be considered when choosing a tool that meets the needs of the finance team and ensures seamless integration with existing systems.

These factors will be explored in more detail in the subsequent subsections.

Ease of Use

When selecting a data visualization tool, ease of use is a key consideration as it aids in the quick creation and understanding of visualizations. A user-friendly interface and functionality make it easier for users to generate visualizations without needing substantial technical knowledge or coding abilities, ensuring that the tool is intuitive and straightforward to navigate.

Tools such as:

  • Akkio (w/ AI superpowers)
  • Microsoft Power BI
  • Tableau
  • Qlik Sense
  • Google Charts

These tools are widely used for creating charts tables graphs, making data visualization more accessible and efficient.


For data visualization tools, compatibility is crucial to ensure users can access and analyze data from a variety of sources without encountering compatibility issues or data loss. This includes the ability of the tool to operate effectively with different data sources, file formats, and operating systems, ensuring seamless integration and data analysis.

To ensure compatibility, it is advised to test the tool with various data sources, file formats, and operating systems to guarantee that the tool can import, process, and visualize data without any compatibility issues or data loss. Furthermore, it is essential to ensure that the visualizations created with the tool can be viewed and interacted with across multiple devices and platforms.


Data visualization tools’ customization options empower users to create visualizations that cater to their unique needs and preferences. This includes the ability to modify and personalize various aspects of the visualizations, such as the appearance, layout, colors, fonts, and labels of the visual elements.

Customization can also offer interactive features, such as tooltips, filters, and drill-down capabilities, to improve the user experience and provide more in-depth insights. Moreover, customization may involve integrating external data sources, establishing custom calculations or formulas, and establishing specific data mappings or transformations to tailor the visualization to specified requirements or business needs.

Best Practices for Creating Financial Data Visualizations

Best Practices for Financial Data Visualizations
Best Practice Description
Simplicity Keep the visualizations simple. Avoid clutter and unnecessary elements that don't add value to the interpretation of the data.
Consistency Maintain consistent colors, fonts, and styles throughout your visualizations to avoid confusion.
Use of Scale Always use an appropriate scale for your data. For instance, if you're comparing values in the billions with those in the thousands, a logarithmic scale might be appropriate.
Data Accuracy Ensure that the data being visualized is accurate and up-to-date. Misleading visualizations can result in poor business decisions.
Annotation Use annotations to highlight key points or outliers in the data. This can guide the viewer's attention to critical insights.
Legibility Ensure that all text, including labels, titles, and legends, is easily readable. Avoid using overly decorative fonts.
Color Consideration Use color purposefully. Reserve bright or distinct colors for highlighting specific data points or trends. Ensure that the visualization is still clear in grayscale and is accessible to those with color vision deficiencies.
Interactivity If possible, add interactivity to your visualizations, especially for large datasets. This can help users drill down into specific data points or time periods.
Time-Series Analysis For financial data that spans over time, use line or area charts to show trends. Make sure the time axis is clearly labeled.
Comparisons When comparing data across different entities (e.g., companies or products), bar or column charts can be effective.
Percentages For data that represents parts of a whole (like market share), pie charts or donut charts can be useful. However, use them sparingly and only when the number of segments is limited.
Avoid 3D Effects 3D effects can distort data representation and make it harder to interpret. Stick to 2D visualizations for clarity.
Tool Tips In digital visualizations, use tooltips to provide additional information when a user hovers over a data point.
Data Source Always mention the source of your data, especially if it's from an external or third-party source.
Continuous Learning Financial data visualization is both an art and a science. Continuously learn from feedback and adjust your visualizations accordingly.

Adhering to best practices focusing on clarity, simplicity, and interactivity is crucial to creating effective financial data visualizations. Responsible formatting, avoiding 3D charts, and using colors sparingly and consistently are some examples of best practices that can enhance the visual appeal and comprehensibility of the visualizations.

Taking into account the audience and objectives is a crucial aspect when creating effective financial data visualizations. Understanding what matters to the audience, their level of knowledge, and what they expect to gain from the visuals, finance teams can create visualizations that resonate with the viewers and effectively communicate the desired information.

Mastering storytelling with data visualization is an important aspect to consider. By crafting a compelling narrative that highlights key insights and trends, finance teams can better convey the significance of the data and drive informed decision-making processes within the organization.

Real-World Examples of Financial Data Visualization

Share of Apple's income vs the total profit of the S&P 500 companies

There are many real-world examples that demonstrate the power and effectiveness of financial data visualization. For instance, the graph above showscases the share of Apple's profit versus all the other companies in the S&P 500, using the same dataset as the other graphs in this article: the S&P 500 from Kaggle.

Another example could be a visualization of consumer spending habits at quick-service restaurants in Delaware. By analyzing this data, companies can understand where their customers are spending their money, the demographics of their customers, and the busiest months for business. Payment managers, like Square and Visa, can use this data to determine which restaurant chains they should target for wider adoption. This helps them to tailor their payment solutions to meet the individual needs of those restaurants.

These real-world financial data visualization examples showcase how data visualization can transform complex data into actionable insights, driving better decision-making and business growth. By leveraging the right visualization techniques and tools, businesses can unleash the full potential of their financial data and make informed decisions.

Overcoming Challenges in Financial Data Visualization

Financial data visualization faces several challenges, including:

  • Lack of data visualization literacy: a user might not understand how to interpret a complex scatter plot.
  • Oversimplification of data: a bar chart that only shows total sales over time, without breaking it down into different product categories.
  • Poor data quality: data with many missing or incorrect entries can lead to misleading visualizations.
  • Regulatory requirements: certain data might be confidential and not allowed to be visualized due to privacy laws.
  • Data integrity and quality: inconsistent data entry practices can lead to incorrect visualizations.
  • Human error in data inputs and algorithms: a user might accidentally input incorrect data, leading to inaccurate visualizations.
  • Over-reliance on visualization: a team might overlook important data because it wasn't included in the visualization.
  • Storytelling with data visualization: a poorly designed visualization might not effectively communicate the key insights in the data.

Implementing strategies addressing data quality, integration, and user adoption is crucial to overcome these challenges.

Ensuring data accuracy, completeness, and timeliness is essential for overcoming data quality and integration challenges. Organizations should:

  • Implement data integration across systems
  • Store data in a secure environment to maintain the integrity
  • Provide training and support to users to guarantee that they understand how to utilize the data visualization tools effectively, ensuring successful user adoption.

By addressing these challenges and following best practices, businesses can ensure the successful implementation and usage of financial data visualization tools and techniques. This, in turn, leads to more effective decision-making and a deeper understanding of financial insights and trends.

Financial Data Visualization with Akkio

create graphs in plain english in Akkio with Chat Explore
Chat Explore is a GPT-4 powered chart maker

Akkio provides a powerful solution for all types of data visualization. The platform requires no coding and comes equipped with three artificial intelligence-powered tools for data analysis.

Chat Explore generates graphs and enables users to interact with their data conversationally. Chats can be shared and there are over 20+ supported chart types.

Chat Data Prep handles all data manipulation needs across any data table. This includes clustering, automatic data labeling, and formatting requirements.

Generative Reports allow users to auto-generate a multitude of reports in a dashboard, depending on their specific objective. These features make Akkio an invaluable tool for any business looking to make data-driven decisions.

Once your data is ready, you can use our no-code machine learning models to make prediction, forecast revenue, and even detect anomalies. You can ship your models into your data warehouse, CRM tools like HubSpot and Salesforce, or as an HTML app. Take a look at Akkio's integrations here and try it for free - no credit card required!


Financial data visualization is a powerful tool that can streamline decision-making, improve communication among stakeholders, and ultimately drive business growth.

By focusing on the key components of clarity, simplicity, and interactivity, and selecting the right data visualization tool, businesses can transform raw financial data into clear and actionable insights. As the world becomes increasingly data-driven, mastering financial data visualization is an essential skill for navigating the complexities of today’s business environment and charting a course towards success.

Akkio can help organizations of all sizes produce effective data visualizations with a few clicks. Try it out for free (no cc required) today!

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