You need to create an effective data visualization. What’s the best way to get started?
Data visualization is a powerful tool to communicate complex and relevant information in an engaging and accessible way. Whether you want to present your findings, persuade your audience, or explore your data, you need to create an effective data visualization that suits your purpose and context. But how do you get started? Here are some tips to help you plan, design, and refine your data visualization.
-
Sarah Ali, Ph.D.Applied Economist | LinkedIn Top Voice
-
Sandra AsagadeData Analyst | Power BI Developer | Data Enthusiast & Storyteller | Transforming Numbers into Insights | YouTube…
-
Monsuru SodeeqEconomic Data Scientist | Researcher, Author & Keynote Speaker on Economic Trends, Business Strategy, and Personal…
Before you choose any software, chart type, or data source, you need to define your goal for creating a data visualization. What is the main message or insight you want to convey? Who is your intended audience and what are their needs and expectations? How will you use or share your data visualization and in what format? Answering these questions will help you narrow down your options and focus on the most relevant and impactful aspects of your data.
-
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.
Depending on your goal, audience, and format, you may need different types of software to create your data visualization. There are many options available, from basic spreadsheet tools to specialized economic modeling software. Some factors to consider when choosing your software are: the complexity and size of your data, the level of interactivity and customization you need, the compatibility and integration with other platforms or devices, and the cost and learning curve of the software. You may also want to check out some examples and reviews of different software to see what works best for your situation.
-
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.
Once you have your software and data ready, you need to select the most appropriate chart type for your data visualization. There are many types of charts, such as bar charts, line charts, pie charts, scatter plots, maps, and more. Each chart type has its own advantages and disadvantages, depending on the type and number of variables you want to show, the relationships and patterns you want to highlight, and the style and tone you want to set. You may also want to combine or layer different chart types to create more complex or interactive data visualizations.
-
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.
After you have selected your chart type, you need to design your layout for your data visualization. This involves deciding how to arrange and organize your elements, such as titles, labels, legends, axes, scales, colors, fonts, shapes, and icons. You want to make sure that your layout is clear, consistent, and coherent, and that it follows some basic principles of visual hierarchy, alignment, contrast, and balance. You also want to avoid clutter, distortion, or ambiguity that may confuse or mislead your audience.
-
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 final step in creating an effective data visualization is to refine your details. This means checking and editing your data, text, and graphics to ensure accuracy, clarity, and relevance. You may want to use some techniques such as filtering, sorting, grouping, highlighting, or annotating to emphasize or explain certain aspects of your data. You may also want to test and evaluate your data visualization with different scenarios, perspectives, or feedback to identify and fix any errors or gaps. You may also want to add some flair or personality to your data visualization to make it more appealing or memorable.
-
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.
Rate this article
More relevant reading
-
Data AnalyticsYou’re working on a data analytics project. How can you make sure your visualizations are intuitive?
-
Business OperationsYour business operations need better data visualization. What tools can help you succeed?
-
StrategyYou need to create a data-driven story. What are the best data visualization software options?
-
Data VisualizationHere's how you can enhance your data visualization skills through your portfolio.