Vous devez créer une visualisation de données efficace. Quelle est la meilleure façon de commencer ?

Before any visualization task, ensure to define your goals. Goals are what you aim to achieve from your visualization (simply put the message you want to pass to your audience). Before defining goals, it's important to firstly understand the data you're working with. It's only when you understand your data, you'd be able to define goals. Each visual has their unique purpose. You might have a goal in mind, but the visual you make use of does not pass your message well. Trust me, I've been there. So, it's important to also understand the different use cases of the visuals you'd be making use of.

To get started with creating an effective data visualization: 1. Define your goals and audience. 2. Choose the right type of visualization for your data. 3. Select a suitable tool for creating the visualization. 4. Clean and prepare your data. 5. Design the visualization with clear and concise elements. 6. Test and refine the visualization as needed. Following these steps will help you create a clear and impactful data visualization.

Most important is the availability data. If you do not have enough, reliable data - how can you make a good visualization? Another question is how to get the data, from where at which costs?

To create an effective data visualization, it is important to follow a few key steps. First, one should define objective and determine what we want to communicate with our data visualization. This will help us choose the right type of visualization and ensure that our message is clear and concise. Next, we should choose the right type of visualization based on the type of data we have and the message we want to convey. Another Important aspect of creating an effective data visualization is to tell a story. Data visualization should tell a story that our audience can follow. Use clear headings, subheadings, and annotations to guide audience through the data and help them understand the key takeaways.

Choosing the right software for data visualization in economics involves considering factors like data complexity, interactivity, and ease of use. Power BI excels in detailed analytics and integration with Microsoft products, ideal for intricate datasets. Google Looker Studio (former Data Studio), known for its user-friendliness, works well with Google's ecosystem, making it a good choice for straightforward projects. For those comfortable with coding, Python and R (with Shiny) offer high customization and deep analysis capabilities, though they have a steeper learning curve. The decision should be based on your project's requirements, your technical skills, and the desired level of detail and integration.

Bar charts are great for comparing discrete categories, while line charts excel in showing trends over time. Pie charts can effectively display proportions within a whole, but can become cluttered with too many categories. Scatter plots are ideal for illustrating relationships between two variables, and maps are useful for geographical data. The choice depends on the type and number of variables displayed, the relationships and patterns you wish to emphasize, and the overall style and tone of your presentation. Sometimes, combining chart types, like overlaying a line chart on a bar chart, can provide deeper insights or enhance interactivity. Consider your data's story and how best to convey it visually to make the most impactful choice.

Drawing from my extensive experience crafting analytical products for Investment Advisors and Portfolio Managers, a foundational principle for delivering impactful solutions is to structure your data visualization as a compelling narrative. Guide your audience through a journey marked by a clear beginning, middle, and end, seamlessly weaving a story that unfolds logically and sustains their interest. Ensure the accuracy and reliability of your data. Transparency builds trust, and users are more likely to engage with visualizations that are built on solid data foundations.

In designing your data visualization layout, adopt a "less is more" approach. Focus on arranging elements like titles, labels, legends, axes, and scales for clarity. Employ visual hierarchy to highlight key information and use alignment for a clean layout. Select colors, fonts, and shapes that aid understanding without causing distraction. A minimal, purposeful design is crucial to avoid clutter and confusion. Overly complex layouts can overwhelm or mislead your audience. Aim for simplicity and effectiveness in conveying your data, ensuring that your audience easily grasps the intended message without being bogged down by unnecessary details.

The story will influence the visual of choice. If I want to show a trend over time, I will use a line graph to show the increase or decrease of the data series. For example, the U.S. labor force participation rate is best shown on a line graph as over time one can see it has declined. If I want to further dig into the reasons why Americans have left the workforce, I might create a tree map which visually shows the different reasons for not working as reported by the U.S. Census Bureau Household. Sometimes, it's also useful to plot similar data series on one graph and leverage the power of a dual axis. Plotting job openings on one axis and layoffs & discharges and quits on another axis might be useful. It's an iterative process.

"Storytelling with Data Visualization" is a great guide for enhancing data visualization skills. The book underscores storytelling's importance in making data comprehensible and engaging. It provides practical advice on selecting chart types, crafting layouts, and strategically using color and text. More than just focusing on technicalities, the book teaches how to communicate data to audiences and illustrates how to turn statistics into compelling narratives, making data points persuasive. Readers learn to create not just informative, but also impactful visualizations. Applying its principles, one can transform complex data into clear, influential stories, boosting their ability to convey intricate information engagingly and memorably.

To create effective data visualizations in finance and economics, start by understanding your audience and defining your purpose. Choose the right chart type and use meaningful labels and titles to enhance clarity. Avoid clutter and excessive visual elements, and strategically use color to highlight important information. Finally, test and refine your visualization based on feedback from your target audience. Following these steps will help ensure that your data visualizations effectively communicate your message without confusion or distortion.