Last updated on Feb 14, 2024
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Know your data
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Know your audience
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Choose the right visual elements
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Choose the right visual forms
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Use interactivity and animation
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Test and iterate
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Here’s what else to consider
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Data visualization is the art and science of presenting data in a visual form that makes it easy to understand, explore, and communicate. However, not all data is simple or straightforward. Sometimes, you need to deal with complex, multidimensional, or dynamic data that requires more than a basic chart or graph. How can you design effective data visualization for complex data? Here are some tips and best practices to help you.
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- Wabiner Douglas Ferrinho LinkedIn Top Voice | Marketing | Consumer Behavior | E-commerce | Leadership | Public Speaking | Entrepreneurship |…
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1 Know your data
Before you start designing, you need to know your data well. What are the main variables, dimensions, and relationships in your data? What are the data types, formats, and sources? How reliable, accurate, and complete is your data? Knowing your data will help you choose the right visualization methods, tools, and techniques for your project.
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- Wabiner Douglas Ferrinho LinkedIn Top Voice | Marketing | Consumer Behavior | E-commerce | Leadership | Public Speaking | Entrepreneurship | Digital Business | Growth Hacking | Product Head | Innovation | Ux Design | Sales | Consultant
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Before designing, deeply understand your data: variables, dimensions, relationships, types, formats, and sources. Evaluate data reliability and accuracy to select appropriate visualization methods, tools, and techniques.
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To design complex data visualizations effectively, simplify the information and use clear labels. Break down the data into digestible chunks, prioritize key insights, and employ intuitive color coding. Prioritize user interactions like zoom and filter options to enhance exploration. Testing with users ensures the visualization remains understandable and user-friendly.
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- Ayan Hafeez Senior UI/UX designer and Webflow developer. I help startups and companies with meaningful designs. Additionally, I share my expertise by teaching about these topics.
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The best way to design complicated data visuals is to keep things simple and clear.Use the right type of visual like charts or graphs. Use colors and sizes smartly to show patterns.Label things clearly and give context. Add interactive stuff so users can dig deeper.Keep testing and improving to make sure it's easy for users to understand.Simple, clear, and focused on users' needs for clear and easy-to-understand visuals ;)
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2 Know your audience
Another key factor to consider is your audience. Who are they, and what are their goals, needs, and expectations? How familiar are they with the data and the domain? How will they access, interact, and use the data visualization? Knowing your audience will help you tailor your design to their level of expertise, interest, and context.
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- Trish-Antoni Baker - SFPC Senior UX/UI Designer | Designing Experiences that bring brands to life
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It's very important to tailor your visualizations to the needs and preferences of your audience is essential for ensuring their effectiveness. Consider factors such as their level of expertise with the subject matter, their expectations for the visualization, and the context in which they will be viewing it. This understanding will help guide your design decisions and ensure that your visualizations resonate with your audience.
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- Sam Lester Making Complex Products Simple – Co-Founder at Inktrap
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Tailoring your design to the audience's level of expertise and requirements is crucial.If your audience is general, simplify and focus on clear, easily digestible visuals. For a more specialised audience, you can dive into finer details and technical aspects.Most importantly, know the story you want to tell or the question you're answering. Who's viewing the visualisation and why?
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3 Choose the right visual elements
When creating a data visualization, the visual elements you choose are integral to conveying the meaning and message of your data. You must select the right shapes, colors, sizes, positions, and texts for your data and audience. For example, shapes can represent data points, colors can encode values or categories, sizes can indicate magnitude or importance, positions can show distribution or correlation, and texts can provide additional information or context. It is essential to use visual elements that are appropriate, consistent, and clear. Too many or too few visual elements can make a data visualization confusing or dull.
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- Trish-Antoni Baker - SFPC Senior UX/UI Designer | Designing Experiences that bring brands to life
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By selecting the appropriate visual elements, such as charts, graphs, and maps, we can effectively convey our data insights. It's important to consider the type of data you are working with and the message you want to communicate and choose visual elements that support and enhance that message. Don't forget to to pay attention to factors such as clarity, simplicity, and visual hierarchy to ensure that your visualizations are easy to understand and interpret.
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4 Choose the right visual forms
When creating a data visualization, you must select the right visual forms to organize the elements into a coherent and meaningful whole. Common visual forms include charts, which graphically represent quantitative or qualitative data; maps, which show spatial or location-based information; tables, which present numerical or textual data; and diagrams, which illustrate logical or conceptual aspects. It's important to choose visual forms that are suitable, effective, and engaging for your data and audience. Additionally, you should avoid using too many or too few visual forms as this can make your data visualization overwhelming or incomplete.
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Designing effective data visualizations is all about balance.Choose the right visual form—charts for trends, maps for spatial info, tables for details, or diagrams for processes.Simplicity is key; too much overwhelms, too little oversimplifies.Aim for clarity and insight, tailoring your design to what your audience needs to understand.The goal is to make complex data accessible and engaging.Focus on adding value, keeping designs simple yet meaningful.This approach not only informs but also enlightens.
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Hay muchos gráficos para elegir pero no todos valen para todo. Por ejemplo, un gráfico de tarta no sirve para una progresión de datos en el tiempo. Ni es muy efectiva cuando hay muchos datos involucrados.En ese último caso, los gráficos de barra verticales suelen ser la mejor opción, pero si queremos mostrar en pantalla todos los valores, habrá que asegurarse de que caben. De no ser así, habría que considerar una disposición horizontal.Y a la hora de mostrar una progresión en el tiempo, nada como un gráfico de líneas. Pero precaución si se van a mostrar varias líneas en el mismo gráfico: deberían ser pocas y bien diferenciadas.
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5 Use interactivity and animation
Using interactivity and animation can be a great way to enhance your data visualization for complex data. With these tools, you can show more details or perspectives of the data on demand, such as tooltips and filters, as well as highlight or emphasize the important parts of the data. Moreover, you can explore hidden patterns or insights of the data, such as drill-downs and comparisons. Additionally, interactivity and animation can help you communicate or narrate the story or message of the data. However, it is important to use interactivity and animation that are intuitive, responsive, and informative for your data and your audience. Too much or too little interactivity and animation can make your data visualization distracting or dull.
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Añadir interactividad a los gráficos nos permite, entre otras cosas:- mostrar datos adicionales que de primeras no podrían aparecer en pantalla (principalmente por problemas de espacio)- hacer zoom o "entrar" en un determinado dato para ver un gráfico detallado de este- seleccionar datos para resaltarlos o compararlos- editar datos, en caso de que se permita, para producir una simulación, por ejemplo.
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6 Test and iterate
The final tip for designing effective data visualization for complex data is to test and iterate. This testing process can validate the accuracy, reliability, and completeness of your data, as well as evaluate the usability, accessibility, and aesthetics of your data visualization. Additionally, it allows you to gather feedback from your audience and refine the performance, functionality, and compatibility of your data visualization. It is essential to test and iterate your data visualization with both your data and your audience at different stages of the design process, such as prototyping, development, deployment, etc.
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Practice makes perfect. Every data visualization differs from the rest, but certain common patterns usually work fine. Testing will help you fine-tune the final details for an optimal experience.
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7 Here’s what else to consider
This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?
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