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What are the most important data visualization design principles for business analysis?

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Data visualization is a powerful tool for business analysis, especially in transportation management. It can help you communicate complex data, identify patterns and trends, and support decision making. But how can you design effective and engaging data visualizations that convey your message clearly and accurately? Here are some of the most important data visualization design principles that you should follow.

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Benita Lee

Helping multinationals navigate the ever changing international landscape of Trade Compliance & Customs.

1 Know your audience

Before you create any data visualization, you need to understand who your audience is, what they already know, and what they want to learn. Different audiences may have different levels of data literacy, expectations, and preferences. You should tailor your data visualization to suit your audience's needs and goals, and avoid confusing or overwhelming them with too much or irrelevant information. For example, if you are presenting to senior managers, you may want to focus on the key insights and recommendations, while if you are presenting to analysts, you may want to provide more details and context.

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Assefa Bekele

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The most important data visualization design principles for business analysis include: Simplicity: Keep the visualizations simple and clutter-free. Clarity: Ensure that the visualizations are easy to understand and interpret. Relevance: Only include data that is relevant to the analysis and decision-making process. Consistency: Maintain consistency in the design elements such as colors, fonts, and formatting across different visualizations. Highlight key insights: Emphasize the most important findings or trends. Interactivity: Incorporate interactive features like tooltips, filters, or drill-down capabilities Accessibility: Ensure that the visualizations are accessible to all users

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2 Choose the right type

There are many types of data visualizations, such as charts, graphs, maps, tables, and dashboards. Each type has its own strengths and weaknesses, and can convey different kinds of data and relationships. You should choose the type that best matches your data and your purpose, and avoid using inappropriate or misleading types. For example, if you want to show the distribution of transportation costs across regions, you may use a map or a bar chart, but not a pie chart or a line chart. Similarly, if you want to show the change in customer satisfaction over time, you may use a line chart or a scatter plot, but not a table or a map.

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3 Simplify and clarify

One of the main goals of data visualization is to make data easier to understand and interpret. Therefore, you should simplify and clarify your data visualization as much as possible, and remove any unnecessary or distracting elements. You should use clear and consistent labels, titles, legends, and colors, and avoid clutter, noise, and ambiguity. You should also use appropriate scales, axes, and formats, and avoid misleading or distorted representations. For example, if you are using a bar chart, you should start the y-axis at zero, and use equal intervals and widths. If you are using a map, you should use a projection that preserves the shape and size of the regions.

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4 Highlight and compare

Another goal of data visualization is to highlight and compare the most important or interesting aspects of your data. Therefore, you should use visual cues, such as color, size, shape, position, and contrast, to draw attention to the key points and differences. You should also use reference lines, annotations, and captions, to provide context and explanation. You should also use interactive features, such as filters, sliders, and tooltips, to allow your audience to explore and customize your data visualization. For example, if you are using a dashboard, you should use color to indicate the performance of different transportation modes, and use filters to let your audience select different time periods and regions.

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5 Tell a story

Finally, data visualization is not just about presenting data, but also about telling a story. Therefore, you should use data visualization to convey a clear and compelling narrative, that engages your audience and persuades them to take action. You should use a logical and coherent structure, that guides your audience from the problem to the solution, and from the general to the specific. You should also use emotion, humor, and creativity, to make your data visualization memorable and appealing. For example, if you are using a slideshow, you should use a catchy title, a hook, a climax, and a call to action, to capture your audience's interest and motivate them to improve their transportation management.

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Benita Lee

Helping multinationals navigate the ever changing international landscape of Trade Compliance & Customs.
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Data storytelling is an art & science. The Observatory of Economic Complexity (oec) is a site that does this well. Check it out & play #tradle with me. It's like wordle but based on export data.

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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?

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Gary Buchs

Truck Business Coaching , Overdriveonline.com Extra Blog contributor , creative solutions at Truck Business Coaching
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If information isn’t in real time and available quickly how valuable is it when you need to make those daily decisions. You can not run a business with the mindset that you have a blank check. Not long ago I read where some asked readers how often they reconciled their bank accounts? It was less than 10% of the responders , I was shocked can you not reconcile your account and expect to have control of your finances? There were all kinds of excuses I don’t buy it if you don’t know it if it isn’t written down her real is it? Where are the greatest things? I discovered for independent contractors on businesses when I set up a system that fed them their average P&L weekly. Why would that be valuable? Ask yourself?

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What are the most important data visualization design principles for business analysis?

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2

3

4

5

6

1 Know your audience

2 Choose the right type

3 Simplify and clarify

4 Highlight and compare

5 Tell a story

6 Here’s what else to consider

Transportation Management

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What are the most important data visualization design principles for business analysis?

1

2

3

4

5

6

1 Know your audience

2 Choose the right type

3 Simplify and clarify

4 Highlight and compare

5 Tell a story

6 Here’s what else to consider

Transportation Management

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