How do you align your visualization goals and audience with the secondary data you use?

Align visualization goals and audience with secondary data by selecting visualizations that effectively communicate key insights to the target audience. Consider audience preferences, knowledge level, and objectives when choosing visualization types and formats. Tailor visualizations to highlight relevant trends, patterns, and comparisons in the data. Use clear labeling, colors, and design elements to enhance understanding and engagement. By aligning visualization strategies with audience needs, the impact and effectiveness of conveying insights can be maximized.

Knowing what you want to show and who you're showing it to makes a huge difference. This approach not only ensures that the visualization is impactful but also that it effectively communicates the intended insights and prompts the desired reactions from the audience.

Proper citation is key to giving credit and allowing others to verify and trust your sources. By clearly documenting your sources, you provide a foundation for others to assess the quality and reliability of the data, enhancing the overall impact and trustworthiness of your visualization.

One should also check for the authority of the research article or paper, as there are many researchers who just copy other research in their paper. Therefore, going through the acidic credentials, experience, expertise, affiliation with University or Collegea as year of publication also lays a huge role. No one can retrieve any relevant insights from a 10 years old article, especially in today's economy.

Selecting the right format for presenting secondary data is essential for effective visualization. To further enrich your visualization, consider integrating narrative elements that provide context and make the data more relatable. This approach not only improves the clarity of your message but also transforms raw data into a compelling and memorable story, ensuring that it resonates effectively with your audience.

While adding colors, you must make sure that it is only used to add attractiveness and not a purpose, it is due to tha fact that the Bar graph, Line chart or any other representation could be viewed by a partially or fully color blind Researcher or individual, making the visualization unreadable and sometimes frustrating to understand. Hence they might not be able to add valuable suggestions to your research.

Meanwhile, make sure not to add adding too much information in on visualization, that could lead to information overload, unreadability, and confusion. And try to include not more than 3 data types in one representation.