Aug 8, 2023

Essential Charts for Data Visualization apart from the regular ones

Data visualization is an essential tool in today's data-driven world. It goes beyond mere representation and transforms complex data sets into meaningful insights. While bar charts and pie graphs are widely used, there are several lesser-known yet highly valuable charts that can take your data analysis to the next level. In this blog, we'll explore some of these hidden gems and understand their use cases and best practices.

But first what is Data Visualization?

Data visualization is the graphical representation of information and data to facilitate understanding and gain insights into patterns, trends, and relationships. By presenting data visually, it becomes easier to grasp complex concepts and identify actionable insights that might not be apparent in raw data.

The Necessity for Data Visualization

1. Simplified Communication: Visuals help convey complex information concisely and comprehensively, making it easier for decision-makers to understand critical data points.

2. Discover Hidden Patterns: Visualization uncovers hidden patterns, trends, and correlations that might not be immediately evident from rows of numbers.

3. Enhanced Decision-Making: Visual representation allows stakeholders to make data-driven decisions more confidently and accurately.

Different Types of charts

1. Dual Axis Chart

A dual axis chart combines two different scales on the same graph to compare two distinct data sets. It's ideal for showcasing relationships or disparities between two variables.

Source: atonce

Use cases

  • Comparing sales and marketing expenditures to identify potential correlations.
  • Analyzing the relationship between website traffic and conversions.

Best practices for Dual Axis Chart:

  • Ensure both data series are relevant and comparable.
  • Use consistent scales for both axes.
  • Clearly label each axis and provide a legend for easy understanding.

2. Bubble Chart:

The bubble chart visualizes three dimensions of data using bubbles, where the size of each bubble represents the value of the third variable. It's great for illustrating relationships and comparisons among multiple data points.

Image Source

Use cases:

  • Time spent online by male and female for different age group.
  • Visualizing the population, GDP, and life expectancy of different countries.
  • Comparing product features based on customer satisfaction and price.

Best practices for Dual Axis Chart:

  • Limit the number of bubbles to prevent clutter.
  • Use color and size effectively to convey information.
  • Provide tooltips to display precise data values on hover.

3. Gantt Chart:

A Gantt chart provides a timeline view of tasks, start and end dates, and dependencies in a project. It's widely used in project management to plan, track, and manage project progress effectively.

Image source

Use cases

  • Managing and tracking tasks and timelines in a software development project.
  • Planning a marketing campaign with multiple deliverables and deadlines.

Best practices for Dual Axis Chart:

  • Keep the timeline realistic and well-defined.
  • Use different colors to differentiate tasks and milestones.
  • Clearly indicate task dependencies for better project management.

4. Tree Map:

Tree Maps use nested rectangles to display hierarchical data, with the size of each rectangle representing a specific value. They're useful for illustrating proportions and hierarchies within a dataset.

Image source

Use cases

  • Representing the market share of various product categories in a retail store.
  • Analyzing the distribution of expenses across different departments in a company.

Best practices for Dual Axis Chart:

  • Organize the hierarchy logically for easy comprehension.
  • Use color gradients or patterns to represent data categories.
  • Include labels or tooltips to provide additional information about each rectangle.

5. Waterfall Chart:

A waterfall chart tracks the incremental changes in a value over a series of positive and negative contributions. It's excellent for understanding the cumulative effect of each intermediate step.

Image source: stackoverflow

Use cases

  • Illustrating the net profit breakdown for a financial quarter, considering revenue, expenses, and taxes.
  • Showing the impact of different cost factors in a manufacturing process.

Best practices for Dual Axis Chart:

  • Start with a clear baseline value.
  • Use distinctive colors for positive and negative changes.
  • Highlight the most significant changes for emphasis.

6. Funnel Chart:

Funnel charts show the progressive reduction of data as it moves through different stages of a process. They are commonly used in marketing and sales to visualize conversion rates at each stage of a customer journey.

Image source: Pluralsight

Use cases

  • Analyzing the sales pipeline to identify potential bottlenecks and conversion rates at each stage.
  • Tracking the customer journey on an e-commerce website from site visit to purchase.

Best practices for Dual Axis Chart:

  • Arrange stages in a logical sequence.
  • Use contrasting colors for better visual impact.
  • Add conversion rates or percentages to each stage.


Data visualization is a powerful tool that allows us to unlock valuable insights from complex data sets. By exploring charts like the Dual Axis Chart, Bubble Chart, Gantt Chart, Tree map, Waterfall Chart, and Funnel Chart, we can elevate our data analysis and make more informed decisions. Remember to choose the appropriate chart based on the data you want to visualize and implement best practices to ensure clarity and effectiveness. So, go ahead and explore these charts to gain deeper insights into your data and supercharge your decision-making process!

We at Alphaa AI are on a mission to tell #1billion #datastories with their unique perspective. We are the community that is creating Citizen Data Scientists, who bring in data first approach to their work, core specialisation, and the organisation.With Saurabh Moody and Preksha Kaparwan you can start your journey as a citizen data scientist.

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