Notes from Chapter 1 of Visualizing Data by Ben Fry
“We don’t view books as long abstract sequences of words, yet when it comes to information, we’re often so taken with the enormity of the information and the low-level abstractions used to store it that the narrative is lost.”
Identify a Question
First and foremost, you must identify a question. With technological advances in creating and storing data… “it becomes easier to disassociate the data from the original reason for collecting it.” This leads to the misguided question: “How can we possibly understand so much data?” Instead, we should ask: ”Why was the data collected, what’s interesting about, and what stories can it tell?
Just because it can be measured doesn’t mean it should.
“It’s easy to collect data, and some people become preoccupied with simply accumulating more complex data or data in mass quantities. But more data is not implicitly better, and often serves to confuse the situation. Just because it can be measured doesn’t mean it should. Perhaps making things simple is worth bragging about, but making complex messes is not. Find the smallest amount of data that can still convey something meaningful about the contents of the data set.”
7 Stages to creating data visualizations:
- Acquire – Obtain the data.
- Parse – Structure the data into categories.
- Filter – Remove extraneous data.
- Mine – Discern patterns.
- Represent – Create a visual model.
- Refine – Clarify the representation and make it more engaging.
- Interact – Add methods for user browsing.