How do you improve your visualization process?

由人工智能和领英社区提供技术支持

Data visualization is a powerful way to communicate insights, trends, and patterns from complex data sets. But how do you create effective and engaging visuals that tell a clear and compelling story? In this article, you will learn some tips and techniques to improve your visualization process and enhance your data analytics skills.

此文章中的业界达人

由社区从 4 条内容中精选。了解更多

1 Know your audience

Before you start designing your visuals, you need to understand who your audience is and what they want to learn from your data. Different audiences may have different levels of familiarity, interest, and expectations with your data and your message. You should tailor your visuals to suit your audience's needs, goals, and preferences. For example, if your audience is technical, you may use more detailed and complex charts and graphs, but if your audience is non-technical, you may use simpler and more intuitive visuals that highlight the key takeaways.

添加您的观点

Vishnu VU

AI-Driven Digital Marketer | B2B & B2C Strategies | SEO & SEM Specialist | Data Analytics Expert
举报内容
Know your audience and the data Create visuals what our audience is looking for like KPI, ROI, Conversion patterns. Show what audience need to see and give them the right drill down metrics

已翻译

赞

2 Choose the right type of visual

Not all types of visuals are suitable for all types of data. You should choose the type of visual that best matches your data and your message. For example, if you want to show the distribution of a variable, you may use a histogram or a box plot, but if you want to show the relationship between two variables, you may use a scatter plot or a line chart. You should also consider the amount and the format of your data, and avoid using visuals that are too cluttered, confusing, or misleading. You can use tools like Tableau, Power BI, or R to create different types of visuals and compare their effectiveness.

添加您的观点

3 Use color, shape, and size wisely

Color, shape, and size are important elements of data visualization that can help you emphasize, contrast, and group your data. However, you should use them sparingly and strategically, and avoid overloading your visuals with too many colors, shapes, or sizes. You should also follow some basic principles of color theory, such as using complementary colors for contrast, using warm colors for attention, and using cool colors for background. You should also use color-blind friendly palettes and consistent color schemes across your visuals. You can use tools like ColorBrewer, Coolors, or Adobe Color to generate and test color palettes.

添加您的观点

4 Add context and clarity

Your visuals should not only show your data, but also explain your data. You should add context and clarity to your visuals by using labels, titles, captions, legends, annotations, and other elements that help your audience understand your data and your message. You should also use clear and concise language, avoid jargon and acronyms, and use appropriate units and scales. You should also check your visuals for accuracy, consistency, and completeness, and avoid any errors or gaps that may undermine your credibility.

添加您的观点

加载更多内容

5 Get feedback and iterate

The final step of your visualization process is to get feedback and iterate on your visuals. You should seek feedback from different sources, such as your peers, your clients, your stakeholders, or your users, and ask them to evaluate your visuals based on their goals, expectations, and impressions. You should also test your visuals for usability, readability, and accessibility, and see how they perform on different devices, platforms, and settings. You should then use the feedback and the testing results to refine and improve your visuals, and repeat the process until you achieve your desired outcome.

添加您的观点

William Elliott

Dialer Manager and IT Services.
举报内容
In my experience feedback from your users should be the paramount source used for modifications to your visualizations and presentation. If the people who will be utilizing your data on a daily basis don’t understand what the visualization is trying to convey then adjustments need to be made in order to ensure your reporting is seen as necessary. Additionally when designing large scale or multiple visualization dashboards take a note of the resolution you’re creating it in and adjust to whatever your workplace standard is if necessary. Changes in user resolution can cut off key data entirely and create unnecessary confusion or feedback if the program or presentation does not automatically adjust for it.

已翻译

赞

6 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?

添加您的观点

Jay Hawkinson

Sr Director Data and Analytics at Lamb Weston | Manufacturing Companies AI and Data Expert | Chief Data and Analytics Officer | Digital Value & Transformation | Innovation & Strategy | MBA | PMP
举报内容
Consider Governance should be applied not only to the data, but to the analytics and the visualizations. Senior Executives will equally at risk for making the wrong decisions if the data is of low quality, or the visualizations are misleading. Combining business process, data, analytics and visualization in a single relatable governance model (with different views of data and information) will help drive trustworthiness across the information landscape.

已翻译

赞

Data Analytics

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

How do you improve your visualization process?

1

2

3

4

5

6

1 Know your audience

2 Choose the right type of visual

3 Use color, shape, and size wisely

4 Add context and clarity

5 Get feedback and iterate

6 Here’s what else to consider

Data Analytics

给文章评分

感谢您的反馈

更多Data Analytics相关文章

更多相关阅读内容