Data visualization is a powerful way to communicate complex and meaningful information, but it also requires a deep understanding of the data and the domain. How do you work with domain experts to create data visualizations that are accurate, relevant, and engaging? Here are some tips and best practices to help you collaborate effectively and create impactful data stories.
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 insight you want to convey? Who are the potential users or viewers of your visualization? What are their needs, expectations, and preferences? How will they access and interact with your visualization? These questions will help you define the purpose and audience of your data visualization, and guide your choices of data sources, formats, and styles.
In addition, define how frequently will they use the visuals. If the visuals are in interactive and regular reports, we may spend some time on creating an engaging story and interactive user experience. Otherwise, if this visualization is going to be used in ad hoc reporting, delivery on time is the key.
Analysis, sample exploration and collection of appropriate information Analysis is an important element of the type of data that experts should pay attention to in terms of gender, race, religion and culture, determining age groups, active areas of the target sample, the most important industrial, commercial and educational interests, and the qualifications obtained by the target audience, as well as setting goals and determining the services to be worked on.
Domain experts are people who have extensive knowledge and experience in a specific field or topic, such as health, education, or finance. They can help you understand the context, meaning, and implications of the data you are working with, and provide feedback and suggestions on how to present it effectively. Consult with domain experts throughout the data visualization process, from data collection and analysis to design and evaluation. Ask them to explain the key concepts, terms, and metrics in their domain, and how they relate to your data visualization. Ask them to review your data visualization for accuracy, clarity, and relevance, and to point out any errors, gaps, or biases.
Different domains may have different conventions, standards, and expectations for data visualization. For example, some domains may prefer certain types of charts, colors, or symbols over others, or have specific rules or guidelines for displaying data. Some domains may also have specialized data visualization techniques that are designed to address their specific challenges or goals, such as geospatial maps, network graphs, or heatmaps. Use appropriate data visualization techniques that match the domain and the data you are working with, and that support your purpose and audience. Avoid using techniques that are unfamiliar, misleading, or confusing for your domain experts or users.
To make your data visualization more relevant and engaging for your domain and audience, you can incorporate domain-specific elements that add context, detail, or narrative to your data. These elements can include labels, annotations, captions, titles, legends, icons, images, logos, or stories that relate to the domain or the data. For example, if you are creating a data visualization for a health domain, you can use icons or images that represent different health conditions, or add captions or stories that illustrate the impact or outcomes of the data. These elements can help you connect with your domain experts and users, and convey your data visualization more effectively.
The final step in creating a data visualization is to test and refine it with your domain experts and users. Testing and refining your data visualization can help you identify and fix any issues or problems that may affect its usability, functionality, or quality. You can use various methods to test and refine your data visualization, such as user testing, usability testing, feedback sessions, surveys, or analytics. These methods can help you collect data and feedback on how your domain experts and users interact with, understand, and respond to your data visualization, and how you can improve it to meet their needs and expectations.
One of the key important factor before working on the visualisation is data cleansing, and transforming the data properly, and handling NULL values.