A guide to creating data visualizations for business intelligence

Choosing the right visualization: types, criteria, and common visualizations used in business intelligence

8 min readFeb 3, 2023

Introduction

Data visualization is a way of presenting information and data in a graphical and easily digestible format. It’s a powerful tool that helps people understand complex information and make better decisions. In the business world, data visualization plays a crucial role in business intelligence by turning vast amounts of data into meaningful insights.

Data visualization enables businesses to quickly identify trends, patterns, and relationships within their data. This can help with decision-making, problem-solving, and overall strategy formulation. With data visualization, businesses can see the big picture, identify areas for improvement, and make data-driven decisions with confidence.

The purpose of this guide is to provide a comprehensive overview of data visualization and how it can be used in business intelligence. We will cover everything from understanding the data to choosing the right visualization, designing the visualization, interpreting the results, and tips and tricks for creating effective data visualizations.

Whether you’re a data analyst, business owner, or just starting out with data visualization, this guide will provide you with the knowledge and tools you need to create meaningful and impactful data visualizations for your business. By the end of this guide, you will have a solid understanding of how to turn your data into valuable insights that drive business success.

Understanding the Data

Before you start creating data visualizations, it’s important to understand the data you’re working with. This starts with preparation — ensuring that your data is clean, accurate, and relevant. Poorly prepared data can lead to incorrect insights and incorrect decisions, so taking the time to properly prepare your data is critical.

Types of Data Used in Business Intelligence:

There are many types of data that can be used in business intelligence, including numerical, categorical, ordinal, and text data. Understanding the type of data you’re working with will help you choose the right visualization to effectively communicate your insights.

Numerical data is data that can be measured and expressed as a number, such as sales or profit. Categorical data is data that can be divided into categories, such as product types or customer demographics. Ordinal data is data that has a specific order, such as customer satisfaction ratings. And text data is data that consists of words, such as customer feedback or product descriptions.

Best Practices for Collecting and Cleaning Data:

Collecting accurate data is the first step in creating effective data visualizations. To ensure that your data is clean and accurate, consider these best practices:

Verify the accuracy of your data
Ensure that your data is complete and up-to-date
Remove duplicates
Correct any errors
Handle missing values appropriately

Once your data is collected, it’s important to clean it. Cleaning involves removing or correcting inaccuracies and inconsistencies in the data. This can include fixing spelling errors, removing irrelevant data, and transforming the data into a usable format.

By taking the time to properly prepare your data, you can ensure that your data visualizations are accurate and meaningful, leading to better insights and decisions.

Choosing the Right Visualization

There are many different types of data visualizations, including bar charts, line charts, pie charts, scatter plots, and heat maps, to name a few. Each type of visualization has its own strengths and weaknesses, and it’s important to choose the right visualization for the type of data you’re working with.

Criteria for Selecting the Right Visualization:

When choosing the right visualization, consider the following criteria:

The type of data you’re working with
The message you want to communicate
The audience you’re presenting the visualization to
The purpose of the visualization

Common Visualizations Used in Business Intelligence:

Some of the most common visualizations used in business intelligence include:

Bar charts: used to compare data across different categories
Line charts: used to show trends and changes over time
Pie charts: used to show the distribution of data across different categories
Scatter plots: used to show the relationship between two variables
Heat maps: used to visualize data in a two-dimensional grid

Each of these visualizations has its own strengths and weaknesses, and choosing the right visualization for your data and audience is crucial for effectively communicating your insights. By understanding the different types of data visualizations and the criteria for selecting the right visualization, you can make informed decisions and create data visualizations that are impactful and effective.

Designing the Visualization

Best Practices for Creating Effective Visualizations:

Creating an effective visualization requires more than just choosing the right type of chart. There are several best practices that you should consider when designing your visualization:

Make the visualization easy to read and understand
Highlight key information
Minimize clutter and distractions
Ensure that the visualization is scalable
Use color, font, and design elements consistently

Guidelines for Color, Font, and Overall Design:

When it comes to color, font, and overall design, there are several guidelines to follow:

Use colors that are easy to distinguish and have good contrast
Choose a font that is easy to read and consistent with your brand
Keep the overall design simple and uncluttered
Use design elements to guide the viewer’s attention and highlight key information

Balancing Simplicity and Detail:

One of the most important aspects of designing a visualization is balancing simplicity and detail. On one hand, you want to include enough detail to effectively communicate your insights. On the other hand, you want to keep the visualization simple and easy to understand. Striking this balance can be challenging, but it’s crucial for creating effective visualizations.

By following these best practices and guidelines, you can ensure that your data visualizations are impactful, effective, and easy to understand. With the right combination of visualization type, design, and message, you can turn your data into valuable insights that drive business success.

Interpreting the Results

Understanding the Meaning of the Data:

Data visualization is a powerful tool, but it’s important to understand the meaning of the data you’re visualizing. This includes understanding the data’s context, identifying any outliers or anomalies, and ensuring that you’re interpreting the data correctly.

Identifying Patterns and Trends:

One of the key benefits of data visualization is that it can help you identify patterns and trends in your data. This can include identifying seasonality, trends over time, or correlations between different variables. By identifying these patterns and trends, you can gain valuable insights into your business operations and make data-driven decisions.

Making Data-Driven Decisions:

Ultimately, the goal of data visualization is to help you make data-driven decisions. This involves using the insights gained from your visualizations to inform your decision-making process, and using data to validate or invalidate your assumptions.

By understanding the meaning of the data, identifying patterns and trends, and making data-driven decisions, you can turn your data into valuable insights that drive business success. With a comprehensive understanding of data visualization and its role in business intelligence, you can effectively communicate your insights and make informed decisions that drive success for your business.

Tips and Tricks

Common Mistakes to Avoid:

When creating data visualizations, it’s important to avoid common mistakes that can undermine the effectiveness of your visualization. Some common mistakes to avoid include:

Overcomplicating the visualization
Misrepresenting the data
Using too many colors or design elements
Ignoring the context of the data
Not considering the audience for the visualization

Enhancing the Visualization for Better Insights:

To enhance your visualization for better insights, consider the following tips:

Use annotations and labels to provide context
Highlight key information
Use interactive features to allow the viewer to explore the data
Test different visualizations to find the best one for your data and audience
Consider using animation or other visual aids to make the data easier to understand

Tools for Creating and Sharing Data Visualizations:

There are many tools available for creating and sharing data visualizations, including:

Each of these tools has its own strengths and weaknesses, and the right tool for you will depend on your specific needs and preferences. When choosing a tool, consider factors such as ease of use, cost, and the ability to share and collaborate with others.

By avoiding common mistakes, enhancing your visualization for better insights, and choosing the right tool for your needs, you can create effective data visualizations that drive business success. With a comprehensive understanding of data visualization and the tips and tricks for creating effective visualizations, you can turn your data into valuable insights that inform your decision-making and drive success for your business.

Conclusion

In this guide, we’ve covered the important aspects of creating data visualizations for business intelligence. We discussed the significance of data visualization, the importance of understanding and preparing your data, choosing the right visualization, designing the visualization for maximum effectiveness, and interpreting the results to make data-driven decisions.

Creating effective data visualizations is a valuable skill for business intelligence professionals. By following the best practices outlined in this guide, you can turn your data into valuable insights that drive business success.

Additionally, it’s important to continue learning and exploring new techniques and tools for creating effective data visualizations. This can help you stay ahead of the curve and create more effective visualizations in the future.

If you’re interested in further reading and learning about data visualization, there are many resources available, including books, online courses, and blogs. Some recommended resources include:

“Data Visualization with D3.js” by Scott Murray
“Data Visualization with Python” by Kamil Kaczmarek
“Visualization Analysis and Design” by Tamara Munzner

By taking advantage of these resources and continuing your learning journey, you can become an expert in creating data visualizations for business intelligence. With a comprehensive understanding of data visualization and the best practices for creating effective visualizations, you can turn your data into valuable insights that inform your decision-making and drive success for your business.

References

List of Sources Used in the Guide:

In this guide, we referenced several sources to provide the information and best practices outlined. Some of the sources used include:

“Data Visualization with D3.js” by Scott Murray
“Data Visualization with Python” by Kamil Kaczmarek
“Visualization Analysis and Design” by Tamara Munzner
Blogs and articles from industry experts on data visualization and business intelligence

Additional Resources for Further Learning:

If you’re interested in further learning about data visualization and business intelligence, there are many resources available. Some additional resources for further learning include:

“The Visual Display of Quantitative Information” by Edward Tufte
“Information Dashboard Design” by Stephen Few
Online courses and tutorials from platforms such as Coursera, Udemy, and LinkedIn Learning