Data visualization is a powerful skill that can help you communicate insights, tell stories, and persuade audiences. But how do you know if the data visualization advice you find online is trustworthy and reliable? In this article, you will learn some tips and criteria to evaluate the quality and credibility of data visualization advice from different sources.
The first thing to consider when you encounter data visualization advice is the source and context of the information. Who is the author or creator of the advice? What are their credentials, experience, and reputation in the field of data visualization? What is their purpose and motivation for sharing the advice? What is the intended audience and medium of the advice? These questions can help you assess the expertise, authority, and bias of the source, as well as the relevance and applicability of the advice for your own situation and goals.
Here are some elements to consider: 1. Credentials and experience: Does the person have an established background in the field? 2. Reputation: Are they respected and acknowledged by their peers? 3. Evidence of their work: Have they published insightful articles, blog posts, or books? Do they have a portfolio? 4. Consistency: Is their advice consistent with best practices in the field? 5. Intention: Are they genuinely interested in sharing knowledge and advancing the field, or are they motivated by other factors? It's crucial to remember that even highly credible sources may offer advice that only applies to their unique situation. Ultimately, learning to discern and adapt advice based on your individual needs is an essential skill.
Considering the purpose and motivation of the advice's creator is crucial. Data visualizations can be crafted to persuade, inform, or provoke specific responses from the audience. If the motivation is to promote a particular agenda or viewpoint, the advice may be biased and selectively present the data to serve that purpose. Understanding the creator's intent helps recognize potential biases and evaluate the advice's objectivity.
The second thing to consider is the evidence and reasoning behind the data visualization advice. How does the author or creator support their claims and recommendations? What data, examples, references, or arguments do they use to justify their choices and opinions? How do they address possible counterarguments, limitations, or exceptions? These questions can help you evaluate the validity, accuracy, and completeness of the evidence and reasoning, as well as the logic, coherence, and consistency of the advice.
In addition to considering the evidence and reasoning behind data visualization advice, it is crucial to recognize the potential limitations and uncertainties when dealing with complex data. Data visualization is often employed to simplify intricate datasets, making them more accessible and understandable. However, this simplification process may lead to the omission of specific details or nuances that could impact the overall interpretation.
The third thing to consider is the style and clarity of the data visualization advice. How does the author or creator present and organize their information? How do they use language, visuals, and formatting to convey their message and engage their audience? How do they balance simplicity and complexity, creativity and convention, emotion and rationality? These questions can help you assess the readability, usability, and aesthetics of the style and clarity, as well as the effectiveness, impact, and appeal of the advice.
In addition to considering the style and clarity of data visualization advice, it is essential to recognize that aesthetics and presentation can significantly influence how information is perceived and understood. Data visualizations are not just objective depictions of data, they are also tools of communication that can evoke emotions, shape narratives, and influence decision-making. The style and design choices made by the creator can impact the visual appeal and engagement of the advice. Well-designed data visualizations can enhance comprehension and retention of information, making complex data more accessible and memorable. However, it is crucial to strike a balance between aesthetics and accuracy.
The fourth thing to consider is the feedback and reviews of the data visualization advice. How do other people react and respond to the advice? What are the ratings, comments, likes, shares, or citations of the advice? How do the feedback and reviews compare and contrast with your own impressions and opinions? These questions can help you gauge the popularity, reputation, and influence of the feedback and reviews, as well as the diversity, quality, and credibility of the perspectives and experiences of the advice.
Comparing and contrasting the feedback with one's impressions is an opportunity for self-reflection and critical analysis. Identifying areas where one's biases or preferences may align with or differ from the opinions expressed in the feedback is possible. Understanding these biases can help make more informed decisions about the relevance and applicability of the advice to one's unique context. Data visualization advice can be applicable in various domains and industries, and what works well in one context may be less effective in another. Taking the context of the feedback into account allows for a more nuanced assessment of the advice's suitability for specific use cases.
The fifth thing to consider is the alternatives and options to the data visualization advice. How does the advice fit into the broader landscape of data visualization knowledge and practice? What are the other sources, methods, tools, or approaches that you can explore or compare with the advice? How do the alternatives and options differ or agree with the advice in terms of strengths, weaknesses, advantages, disadvantages, or trade-offs? These questions can help you broaden your horizon, expand your choices, and deepen your understanding of the alternatives and options, as well as the opportunities, challenges, and implications of the advice.
In the process of exploring alternatives, one may even come across novel or cutting-edge visualization techniques that challenge traditional practices. This exposure to innovation can inspire creativity and encourage experimentation, ultimately leading to more effective and engaging data visualizations. Additionally, considering alternatives fosters a more critical approach to data visualization advice. Cross-referencing and validating information from multiple sources can mitigate the risks of relying on potentially biased or misleading. Embracing diverse perspectives empowers data practitioners to create more impactful & effective visualizations while staying adaptable and open to the dynamic nature of data visualization as a discipline.
The sixth thing to consider is your own judgment of the data visualization advice. How do you feel and think about the advice? How do you apply and test the advice in your own data visualization projects? How do you reflect and learn from the results and feedback of your data visualization projects? These questions can help you develop your own judgment, confidence, and skills in data visualization, as well as your own voice, style, and vision in data visualization.
There are many blogs and posts about how data visualizations may portray a biased view. Once you have seen those it becomes easier to identify such instances. 'How to Lie with Statistics' is one example.