Determine Your Data Story

Determine the key message you want your audience to take away from the visualization. Consider whether there are any secondary messages or insights you also want to convey. 

Know Your Audience

The next step in selecting the visualization is knowing your audience. The effectiveness of your data story depends on how well the visualization communicates to the needs and expectations of the stakeholders. Top things to consider include the audience's familiarity with the subject, the level of data literacy, and the context in which they will view the visualization. For example, a technical audience might appreciate detailed charts with complex metrics, while a general audience may need simpler visuals with clear labels and minimal jargon. Understanding your audience allows you to tailor your visualizations to resonate with them, ensuring the data is both accessible and impactful. By aligning your visualization style with the audience's preferences and knowledge, you can deliver insights that are not only informative but also engaging. For a deeper dive on this topic, check out the Know Your Audience section of the toolkit. 

Selecting the appropriate chart, graph, or diagram is essential for accurately conveying your information. Below are explanations of commonly used visualization types.

A Table is a structured format for organizing and displaying data in rows and columns, making it easier to read, analyze, and compare information. Tables are ideal for presenting precise, detailed datasets where multiple variables or categories are involved. Each row typically represents a unique record or observation, while columns denote specific attributes or variables. Tables are versatile and can range from simple data listings to complex summary formats, such as contingency or pivot tables, that aggregate and group data for deeper insights. Their clear structure allows for direct data retrieval and comparison, making them a foundational tool in data analysis and reporting.

A Chart is a graphical representation of data designed to make information easier to understand and analyze. By using visual elements such as lines, bars, pie slices, points, or charts present numerical or categorical data in a way that highlights patterns, trends, comparisons, and relationships. Common types of charts include bar charts for category comparisons, line charts for trends over time, pie charts for proportions, and scatter plots for relationships between variables. Charts help translate complex datasets into intuitive visuals, enabling clearer communication and quicker decision-making. Below are explanations of commonly used charts. 

Bar chart (also called a bar graph) is a chart or graph that presents categorical data with rectangular bars with heights or lengths proportional to the values that they represent. The bars can be plotted vertically or horizontally. A vertical bar chart is sometimes called a column chart. Bar charts are great to use when comparing categories. 

Scatter plot (also called a scatter plot, scatter graph, scatter chart, scattergram, or scatter diagram) is a type of plot or mathematical diagram using Cartesian coordinates to display values for typically two variables for a set of data.  The data are displayed as a collection of points, each having the value of one variable determining the position on the horizontal axis and the value of the other variable determining the position on the vertical axis. 

Pie chart (or a circle chart) is a circular statistical graphic, which is divided into slices to illustrate numerical proportion. In a pie chart, the length of each slice (and consequently its central angle and area) is proportional to the quantity it represents. Pie charts are easy to read and ideal for representing proportions, or part-to-whole comparisons. 

Heat map (or heatmap) is a data visualization technique that shows the magnitude of a phenomenon as color in two dimensions. The variation in color may be by hue or intensity, giving obvious visual cues to the reader about how the phenomenon is clustered or varies over space. There are two fundamentally different categories of heat maps: the cluster heat map and the spatial heat map. Heat maps make it easy for viewers to quickly identify trends in the data. 

Stacked bar chart is a type of bar chart that visualizes data by stacking rectangular bars to represent the composition and comparison of multiple categories within a dataset. Each bar is divided into segments, with the length of each segment corresponding to the value of a subcategory. The total height (or length) of the bar represents the combined value of all segments for that category. Stacked bar charts are useful for displaying part-to-whole relationships and comparing the contributions of different subcategories across multiple categories on the horizontal axis.

When selecting the type of chart for your visualization, consider the narrative you wish to convey through your data. Aligning your visual representation with these intended outcomes can help you choose the most appropriate graph, chart, or table. Below are common types of charts, graphs, and diagrams, organized by the strategy in how you intend to present the data.

In data visualization, combining text, shapes, and design elements is essential for effectively communicating insights and creating visually appealing graphics. These elements work together to highlight key points, structure information, and engage the audience. Below are some best practices for incorporating these elements:

Using Text 

• Clear and concise titles provide context, while labels help the audience understand what each part of the visualization represents.

• Use annotations sparingly to explain data points or highlight specific trends.

• Provide legends to clarify categories, ensuring they are easy to read.

• Choose simple, legible fonts. Avoid decorative fonts that can distract or confuse.

Applying Design Principles

• Apply consistent color schemes to categorize or highlight data. Ensure color choices are accessible for all viewers, including those with color blindness.

• Align text, shapes, and other elements neatly to avoid visual clutter.

• Use size, boldness, or position to emphasize the most critical information.

• Avoid overloading the visualization with too many elements. Each shape, design, or text should have a clear purpose.

Always keep it simple! Color can be used to highlight information, and it can be associated with meaning. Too many colors can be overwhelming and using too much of the same color can cause the data to blend. If using multiple colors, be sure the colors are complementary. 

Your selection of a color palette in your data visualization will depend on properties of your data. The three primary types of color palettes include:

Online tools like the Data Viz Color Palette Generator and the APCA Contrast Calculator can help you select an engaging and accessible color palette for all users. Stephanie Evergreens post on making data accessible is also a valuable resource for enhancing the design of your visualization. Regardless of the color story you choose, maintaining a consistent color scheme throughout your visualizations is crucial to avoid over-complicating them.

Data visualization checklist. (Evergreen Data)

Data visualization tips for more effective and engaging design. (Tableau) 

Evergreen, S. 2024. 10 Ways to make your graphs more accessible. (Evergreen Data)

Healey, C. Perception in visualization. (North Carolina State University)

Hess, K. 2022. 80 types of charts & graphs for data visualization. (datylon) 

Ibrahim, M. 2024. The ultimate guide to data visualization for data analysts. (LinkedIn)

Miller, C.  2019.  Data visualization techniques all professionals should know.  (Harvard Business School)

Yi, M & Restori, M. 2024. How to choose the right data visualization. (Atlassian)

What do you want to show? (The Data Visualisation Catalogue Data)