Now that you have learnt about the definition of data visualization and the key principles that distinguish good from bad visualization design (trustworthy, accessible, elegant), undertake some reflective evaluations on any visualization you come across. It might be your own work, it might be work you receive from others or it could be just work from around the web. There are hundreds being shared on a daily basis online via social media, blogs and news media, you won’t struggle to find any! (If you do, check out the reading materials alongside each chapter and you’ll find many examples). Give yourself time to fully immerse yourself and experience each one (whether small and instant or long and involving) and consider the following prompts:
- What one word describes how you instinctively feel about the work within the first 5-10 seconds? Is it positive or negative? – what are the good/bad thing about your dataset
- Very subjective but do you like the visualization (might be the subject or visual form)? What score on a scale of 0 to 10 would you give it (10 is best)? Consider what factors influenced your ratings?
Rate it based on how much its easy or hard to learn
And how much you think it can be utilized in your profession?
3. Do you feel the project successfully – and sufficiently – facilitates understanding (does it help you learn something about the subject matter or, at least, confirm/reinforce what you already knew)? What score on a scale of 0 to 10 would you give it (10 is best)? Consider what factors influenced your ratings?
4. Consider the project’s effectiveness or otherwise in demonstrating the principles of trustworthy, accessible and elegant design: where does it succeed and where does it fail?
What are the things that you think u did well in the project and what are the things that you think you need more time to learn?
5. Whilst you may not know much about the project’s hidden context, what would you do differently? How would you help to get these pair of ratings higher towards the maximum of 10?