Companies gain a massive competitive edge when data is efficiently processed, analyzed, and presented. However, doing so requires every business user to create a clear, captivating data story.
Data storytelling includes choosing the right type of data visualizations and crafting a compelling narrative to make your intended message resonate with the stakeholders. This delivery method ensures that your data has a tangible impact on the business.
Using the right data visualizations, tools, and a series of insider tips, you too can become a pro at data storytelling. But before jumping into that, here’s some context about data visualizations.
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Data visualizations are graphic representations of data that help people understand patterns and trends, identify relationships between variables, and spot outliers. This is true even for complex datasets. Visual elements such as charts, graphs, and maps are used to present information in an accessible and understandable way.
With multiple types of data visualizations to choose from, it is best to familiarize yourself with the nuances of each type. This will help you understand which visualization best suits your dataset so you can boost engagement when you are telling your data story. Let’s dig in.
A line chart connects distinct data points through straight lines. Its best use case is to illuminate trends, patterns, and variable changes.
When to use line charts?
This type of chart helps measure how different groups relate to each other. This type of chart is also effective for demonstrating progression, making them suitable for scenarios like project timelines, production cycles, or population growth.
Best practices for line charts:
Ensure that the data you're representing has a logical order
Add context through annotations and labels
If the dataset is large, use transparency or spacing to improve visibility
A bar chart visually represents data using rectangular bars or columns. Here, the length of each bar corresponds proportionally to its value. You can present these bars horizontally or vertically. A horizontal bar chart is best to use when the text on the x-axis of a vertical bar chart is lengthy, meaning it would have to be presented diagonally—or even worse, cut off—to fit within the visualization.
When to use bar charts?
Bar charts are excellent for comparing the values of different categories or groups. Apart from that, these types of charts are also helpful in showing the distribution of data across different categories.
Best practices for bar charts:
Clearly label each bar and axis with concise labels
Limit the number of bars and categories to avoid cognitive overload
Purposely use colors to highlight key points and convey meaning
Scatter plots are types of visualization that show a collection of data points ‘scattered’ around the graph. The data points can be evenly or unevenly distributed.
When to use scatter plots?
Scatter plots are ideal for exploring relationships and patterns between two continuous variables. They can help you identify trends, correlations, or potential clusters in the data.
Best practices for scatter plots:
Highlight outliers if present in the graph to showcase data distribution
Add a trendline to highlight the relationship between variables
Consider using different colors or marker sizes for overlapping points
A common but limited type of visualization is the pie chart. It is a circular, statistical graphic that divides data into slices. Each slice represents a percentage or proportion of the whole.
When to use pie charts?
This classic chart is effective when you want to illustrate the proportion of each category in the dataset. However, remember not to use these types of charts for large datasets, as too many slices can create confusion. The chart is suitable when you have limited categories, ideally less than six or seven.
Best practices for pie chart:
Keep the number of slices limited to maintain clarity
Clearly label each slice with clear text.
Ensure consistency so viewers associate colors with specific categories
Column charts are the simplest, most versatile type of visualization used in data analytics. The horizontal chart displays your data in bars proportional to the values they represent.
When to use column charts?
More often than not, column charts effectively compare data across different categories. They are also helpful in displaying rankings and order in a dataset, allowing viewers to identify trends quickly.
Best practices for column charts:
Minimize distracting visual elements, such as 3D effects or excessive gridlines
Focus on presenting key data points for a better understanding
Use contrasting colors to highlight specific columns.
Maintain consistent scaling on the axis to ensure accurate interpretation.
Treemaps are hierarchical charts that allow you to visualize data as nested rectangles. These rectangles or branches convey the structure and distribution of data, making treemaps useful for visualizing categorical and hierarchical relationships.
When to use treemap charts?
Apart from visualizing hierarchical data, this type of visualization helps to illustrate part-to-whole relationships within a dataset, demonstrating how each category contributes to the overall composition.
Best practices for treemap charts:
Ensure the size of the rectangle is proportional to the size of the category
Clearly label each rectangle with concise labels
Use a single color with varying shades to show changes in data
Heatmap charts are a type of map data visualization that uses a system of color coding to represent value. Each cell in the matrix is assigned a color based on the value it holds.
When to use heatmap charts?
A heatmap is commonly used to establish relationships between two variables across a grid. In the example above, the intensity of the colors in the map clearly demonstrates the variables, making it easy to identify patterns and trends.
Best practices for heatmap charts:
Choose an intuitive color palette that effectively conveys the magnitude of values
Use visual cues to highlight significant values in the heatmap
Utilize the design principle of white space to prevent overcrowding
A Pareto chart combines a bar chart and a line graph. The rectangular bars correspond to individual values in descending order, while the line graph displays the cumulative percentage total. This type of chart follows the famous Pareto principle that emphasizes that 20 percent of causes result in 80 percent of problems.
When to use Pareto charts?
A Pareto chart effectively showcases the key contributing factors to a particular outcome. Another use case of a Pareto chart is when you want to highlight problems based on their impact.
Best practices for Pareto charts:
Arrange all the categories in descending order based on their frequency, impact, or contribution
Use colors purposefully to enhance clarity
Add context with clear, concise labels for each category
Geo charts are a type of visualization that represent data on a map. They show spatial information, such as the distribution of values across different regions, countries, or states.
When to use geo charts?
If you want to analyze geographic information in your data, you can use these types of charts to discover hidden patterns and trends. Each region, such as a country, state, or district, is shaded or colored based on the magnitude of the variable presented.
Best practices for geo charts:
Select an appropriate map projection that matches your region
Use color shading to highlight particular regions
Label the projections to represent data points on the map
A waterfall chart is like a visual story that helps you see how different things add up to a final result. It explains how an initial value is affected by a series of intermediate positive and negative values. The waterfall chart receives its name due to its shape as it shows cascading effects.
When to use waterfall chart?
This type of visualization helps communicate the sequential impact of various factors on a total value. The chart helps visualize the changes in the data and understand the flow of values.
Best practices for waterfall chart:
Add labels and use color coding to distinguish between positive and negative changes
Maintain consistent scaling on the axis
Start with the initial value and then progress by adding contributing factors
Humans barely have an attention span of 8 seconds. This means using bad data visualizations can take the viewer’s attention away from the main message.
To effectively communicate your insights and influence data-driven decisions, here are some tips you can follow.
1. Understand the data
Data visualizations make your insights actionable and understandable. However, if the takeaway is unclear or the dataset is not well-understood, it’s impossible to create an impact. That’s why it's crucial to analyze the dataset and identify the intended insights. This will help you break down complex information into easily understandable visuals.
Given this reality, here are some questions you need to ask yourself:
What are the problems or findings you are trying to showcase?
What is the dataset size?
Are you trying to compare values, show trends, or illustrate proportions?
Which are the key metrics or aggregations you want to visualize?
How evenly is the data distributed?
2. Know your audience
Developing data visualizations is more science than art. Often, users underestimate the stakeholder’s ability to comprehend complex information and create confusing visualizations, making it difficult for stakeholders to connect with insights. Failing to consider your audience’s familiarity with data and their preferences can create frustration and inaction, no matter how well-visualized your charts are.
To avoid this, here are some questions you should consider:
What do they expect to see?
What is the technical background of your audience?
How well-versed is your audience with data analysis?
What are their job titles and responsibilities?
Are they comfortable with interactive visualizations?
3. Embrace simplicity
Too many visual elements can be distracting. Because stakeholders have limited time and attention span, it’s essential to use simple charts that help you make the point straight away. Excessive labels, jarring colors, and overly complex charts are misleading.
To ensure the graphics are created in a way that makes the main takeaway crystal clear, here are some questions you should consider:
Is the chosen visualization the simplest and most effective for the data and message?
Are the axes and labels clear and easily understandable?
Is the data presented in a logical and intuitive order?
Is the visual clutter minimized?
4. Keep things interactive
Interactivity encourages users to explore data and uncover hidden insights. With modern data visualization tools, users can apply filters, drill down into details, and ask questions using conversational analytics. These interactive activities empower stakeholders to gain a hands-on understanding of their data.
Here are some questions you should consider to incorporate interactive elements and provide more context to their visualizations:
Who is the target audience for the interactive chart?
How can interactivity enhance the understanding of the data?
Does the interactivity support the narrative or story you want to tell?
Make interactive visualizations with ThoughtSpot
As your business becomes more data-driven, you need a powerful data visualization tool that helps you tell impactful stories with data. With an intuitive BI platform like ThoughtSpot’s AI-Powered Analytics, you can ask questions in natural language and get relevant, interactive visualizations.
Explore how easy it is to build dashboards, create interactive visualizations, and find hidden insights. Register for your ThoughtSpot live demo today!
