Charts are powerful tools to communicate data insights, but they can also be misleading or confusing if not designed well. When dealing with complex or large data sets, you need to apply some chart design principles and best practices to ensure clarity, accuracy, and relevance. Here are some tips to help you handle complex or large data sets in charts.
The first step is to choose the most appropriate chart type for your data and your message. Different chart types have different strengths and limitations, and some are better suited for certain data structures, scales, or comparisons. For example, line charts are good for showing trends over time, bar charts are good for comparing categories, and scatter plots are good for showing relationships between variables. You should avoid using chart types that distort the data, such as pie charts, 3D charts, or donut charts.
The second step is to simplify and filter the data to focus on the most relevant and important information. You can use various techniques to reduce the complexity or size of the data, such as aggregating, grouping, sorting, ranking, or sampling. You should also remove any unnecessary or redundant data, such as outliers, noise, or duplicates. By simplifying and filtering the data, you can make the chart easier to read and understand, and avoid cluttering or overwhelming the audience.
Infographics and charts are some of the most effective ways to communicate data. However, many people associate "simplify" as meaning "dumb it down". But humans are incredibly good at detecting patterns and finding the info they want or need. I highly recommend studying the works and books of the master Edward Tufte. Albert Cairo and Nick Felton are other greats to follow.
The third step is to use visual cues and annotations to enhance the chart and highlight the key points. Visual cues are elements that help the audience perceive the data, such as colors, shapes, sizes, or symbols. You should use visual cues consistently and sparingly, and avoid using too many or conflicting colors or shapes. Annotations are elements that help the audience interpret the data, such as titles, labels, legends, axes, scales, or notes. You should use annotations clearly and concisely, and avoid using too much or confusing text.
The fourth step is to test and refine the chart to ensure that it meets your goals and expectations. You can test the chart by asking yourself or others some questions, such as: Does the chart convey the intended message? Does the chart show the data accurately and honestly? Does the chart make sense and look appealing? Does the chart answer the audience's questions or needs? Based on the feedback, you can refine the chart by making adjustments or improvements, such as changing the chart type, simplifying or filtering the data, using visual cues or annotations, or fixing errors or inconsistencies.
In my experience, it is critical to evaluate a draft infographic for clarity and relevance before distributing the visual in final form. It is important to build trust with end users by being transparent about the source of data and what the data includes or excludes. Testing a chart to assess whether it conveys "the intended message" is problematic if end users are expecting objective information and the creator of the chart in question is bending data to fit a predetermined message. An impartial and industry savvy data analyst typically has an a priori sense of what relationship exists between (among) variables, but does not try to force an outcome.
The fifth step is to use interactive or dynamic features to add more functionality and flexibility to the chart. Interactive features are elements that allow the audience to manipulate or explore the data, such as filters, sliders, buttons, tooltips, or drill-downs. Dynamic features are elements that allow the chart to update or change based on the data, such as animations, transitions, or real-time updates. You can use interactive or dynamic features to make the chart more engaging and responsive, and to accommodate different levels of detail or complexity.
The sixth and final step is to follow ethical and accessibility standards to ensure that the chart is fair and inclusive. Ethical standards are principles that guide you to respect the data and the audience, such as avoiding misleading or manipulative practices, disclosing the data sources and methods, or acknowledging the limitations and uncertainties. Accessibility standards are guidelines that help you make the chart accessible and usable for people with different abilities or preferences, such as using contrast, color-blindness, or screen-reader friendly colors, fonts, or formats.