登录查看更多内容

How can you overcome common challenges in data visualization teams?

由人工智能和领英社区提供技术支持

Data visualization is a powerful skill that can help you communicate insights, tell stories, and persuade audiences. But working in a data visualization team can also pose some challenges, such as aligning goals, managing expectations, collaborating effectively, and ensuring quality and consistency. How can you overcome these common hurdles and create impactful data visualizations as a team? Here are some tips and best practices to help you.

此文章中的业界达人

由社区从 8 条内容中精选。了解更多

Alvin Iskandar

Business Intelligence | Data Modeler
Tobi Oladimeji

Community Leadership & Building | CEO @ Data Fellows | Making Data Accessible for Small Businesses | Events & Marketing…

1 Define your objectives and audience

Before you start designing your data visualization, you need to have a clear idea of what you want to achieve and who you want to reach. What is the main message or question you want to answer with your data? Who are the users or stakeholders of your data visualization? What are their needs, interests, and preferences? How will they access and interact with your data visualization? By defining your objectives and audience, you can ensure that your data visualization team is aligned on the purpose and scope of the project, and that you can tailor your design and content to suit your audience.

添加您的观点

Alvin Iskandar

Business Intelligence | Data Modeler
举报内容
Define the Scope: Clearly define the scope of the data visualization project, including the specific data sets, metrics, and insights you want to present. Conduct Audience Research: Understand the needs, interests, and preferences of the target audience by conducting user surveys, interviews, or feedback sessions. Set Objectives and Metrics: Establish measurable objectives for the data visualization. Determine the key performance indicators (KPIs) that will help evaluate its effectiveness in achieving the stated objectives. Choose the Right Visualization Types: Based on the data and the audience's preferences, select appropriate visualization types (e.g., bar charts, line graphs, maps) that effectively communicate the information.

已翻译

赞
Geetanjali Srivastava

Sr. Product Success Analyst | Speaker | Servicenow, Tableau, Power BI | Helping Companies Leverage Big Data To Make decision Making Easy
举报内容
Facing challenges while presenting your dashboards is a common issue, the best way to handle it is by reading your audience. Being attentive to their reactions, this will help you in focusing on the areas where the audience is more interested

已翻译

赞

2 Establish roles and responsibilities

Data visualization projects often involve multiple roles and tasks, such as data collection, analysis, cleaning, transformation, design, development, testing, and deployment. To avoid confusion, duplication, or gaps in your data visualization team, you need to assign roles and responsibilities to each team member, and communicate them clearly. You can use tools like RACI matrix or Kanban board to define who is responsible, accountable, consulted, or informed for each task, and to track the progress and status of the project. You can also use tools like GitHub or Google Drive to share and version your code and data files, and to collaborate on edits and feedback.

添加您的观点

Alvin Iskandar

Business Intelligence | Data Modeler
举报内容
Identify Key Roles Collaborative Tools Regular Communication Set Deadlines and Milestones Document the Process Review and Learn from Previous Projects:

已翻译

赞

3 Choose the right tools and formats

Data visualization can be done with various tools and formats, depending on the type, complexity, and interactivity of your data and design. For example, you can use Excel, Tableau, Power BI, or R to create charts and dashboards; you can use D3.js, Plotly, or Vega to create web-based interactive data visualizations; you can use Illustrator, Photoshop, or Sketch to create static or animated graphics; or you can use HTML, CSS, or JavaScript to create custom web pages or applications. You need to choose the right tools and formats for your data visualization project, based on your objectives, audience, data, and skills. You also need to ensure that your data visualization team is familiar and comfortable with the tools and formats you choose, and that you can integrate them smoothly and efficiently.

添加您的观点

Alvin Iskandar

Business Intelligence | Data Modeler
举报内容
Define Objectives and Audience Assess Data and Complexity Evaluate Team Skills and Familiarity Consider Interactivity and Customization Opt for Simplicity and Quick Deployment Prioritize Flexibility and Customizability Think about Integration and Scalability Explore Open Source Solutions

已翻译

赞
Tobi Oladimeji

Community Leadership & Building | CEO @ Data Fellows | Making Data Accessible for Small Businesses | Events & Marketing | Youth Speaker
举报内容
Collaboration with colleagues is a cornerstone of my approach to data visualization. For this purpose, I find Power BI Server to be invaluable. Its capabilities facilitate seamless collaboration - enabling us to add comments, identify errors, collectively work on visuals, and promptly update them. What's particularly noteworthy is the real-time synchronization of edits within the same workspace, courtesy of a well-structured refresh schedule. Considering the audience, the platform also offers a user-friendly means of sharing our work. This holistic approach ensures effective teamwork, enhances accuracy, and fosters efficient communication throughout the project lifecycle

已翻译

赞

4 Follow data visualization principles and standards

Data visualization is not only about aesthetics, but also about accuracy, clarity, and ethics. To create high-quality and trustworthy data visualizations, you need to follow some data visualization principles and standards. These include using appropriate data types, scales, formats, colors, shapes, sizes, and labels for your data and design. Additionally, you should use appropriate charts and layouts for your data and design while avoiding misleading or cluttered data visualizations. Furthermore, it's essential to cite your data sources and provide metadata and documentation for your data and design. Lastly, you should test your data visualization for usability, accessibility, and functionality.

添加您的观点

Alvin Iskandar

Business Intelligence | Data Modeler
举报内容
Define objectives and audience. Use appropriate data types, formats, scales, colors, shapes, and sizes. Choose suitable charts and layouts. Avoid misleading or cluttered visualizations. Provide clear labels and titles. Cite data sources and offer documentation. Test for usability, accessibility, and functionality.

已翻译

赞
Tobi Oladimeji

Community Leadership & Building | CEO @ Data Fellows | Making Data Accessible for Small Businesses | Events & Marketing | Youth Speaker
举报内容
Absolutely, data visualization principles hold immense importance. In my workflow, I'm deeply committed to synchronizing visuals with the company's core theme. To achieve this, I leverage a color picker to extract the company's palette from the logo or website. Employing tools like PowerPoint and Figma, I enrich the dashboard's aesthetics. I focus on maintaining uniform card dimensions, font sizes, and types. Furthermore, I meticulously harmonize background colors with the established theme. These diligent measures culminate in a visually engaging and harmonious data visualization that radiates professionalism and elegance.

已翻译

赞

5 Seek feedback and iterate

Data visualization is an iterative process that requires constant feedback and improvement. You need to seek feedback from your data visualization team, as well as from your users or stakeholders, to evaluate and enhance your data visualization. You can use methods like peer review, user testing, surveys, or analytics to collect and analyze feedback, and to identify and address any issues or gaps in your data visualization. You can also use feedback to generate new ideas, insights, or questions for your data visualization, and to refine your objectives and audience. By seeking feedback and iterating, you can ensure that your data visualization team delivers data visualizations that meet the needs and expectations of your users or stakeholders, and that achieve your goals.

添加您的观点

Alvin Iskandar

Business Intelligence | Data Modeler
举报内容
Conduct peer review. Perform user testing. Collect feedback through surveys. Analyze analytics data. Generate new ideas and refine objectives. Iterate and improve. Repeat the feedback cycle.

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

Data Visualization

+ 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

查看全部

How can you overcome common challenges in data visualization teams?

1

2

3

4

5

6

1 Define your objectives and audience

2 Establish roles and responsibilities

3 Choose the right tools and formats

4 Follow data visualization principles and standards

5 Seek feedback and iterate

6 Here’s what else to consider

Data Visualization

给文章评分

感谢您的反馈

更多Data Visualization相关文章

更多相关阅读内容

How can you overcome common challenges in data visualization teams?

1

2

3

4

5

6

1 Define your objectives and audience

2 Establish roles and responsibilities

3 Choose the right tools and formats

4 Follow data visualization principles and standards

5 Seek feedback and iterate

6 Here’s what else to consider

Data Visualization

给文章评分

感谢您的反馈

查看其他技能