My Notes on Noah Iliinsky’s presentation – The Steps to Beautiful Visualizations

It surprised me that there was a talk such as Noah’s embedded in the program since it takes considerations back to a conceptual phase purely, and I personally really liked it. He basically went into detail with the premise “What’s obvious isn’t always that obvious bu it should be” and “People tend to complicate things that are normally very simple”.

My notes on his talk:

  • Concepts
    • Visualization for analysis vs presentation
    • Analysis – You don’t have enough data yet, no story to tell
    • Presentation – You have the story to tell
    • A data visualization vs infographics – Infographics can’t be automatically generated and don’t have all the numeric data, but they can have rich content.
    • Education vs persuasion – educational information is just presented to inform and doesn’t look to push people towards a certain point of view.
    • Complexity is the number of information axes represented
  • Qualitative relationships are hard because of the fewer conventions than with quantitative relationships.
  • Make good choices – Intentional over arbitrary choices
    • Understand your goals and their needs
    • Choose what to include, where and how
  • If you can’t concisely articulate your goal, you’re doing it wrong!! (“We’re going to be the market leader” isn’t concise!)
  • Different goals require different methods.
  • Understand your customers, ‘cause your success is defined by their success
  • Consider the meaning of the elements in your design. Does your audience already associate them with something else?
  • What to include – four types of content
    • Data
    • Redundant coding
    • Decoration
    • Noise
  • Where to put it
    • People ascribe meaning to location
    • Relative and absolute placement matters
  • Use a format that fits the data
  • Patterns are really important for exposing meaning. Be careful when using them because, even if they are not intended, they will be interpreted.
  • Things that are the same should look the same and things that are different should look different! It’s simple! So why not?
  • Pick appropriate encodings – Consistent, highly differentiable encodings