###Getting to Know Data: ####General
- Who published?
- Just like the sources we interview have motivations, so too do data providers
- Advocacy groups
- Studies
- Trade groups
- When was it relevant?
- Is the information current?
- What does the data encompass?
- How was it compiled?
- Ask about the methodology and how calculations were made
- If receiving from a database, ask for a record layout or schema
- Ask someone to walk you through the findings
- What does it tell you?
- What doesn't it tell you?
- Spot check everything.
- Check for weird or conflicting values.
- Incorrect percentages and calculations.
- Multiple spellings of people, places and things.
- How can it be benchmarked against other data sets?
###Diagnosing Data for Representation: ####Stats/Math
- How are numbers contextualized in the representation?
- How is the reference point for visual read introduced (baseline of 0 on charts? visual order of the image to suggest legibility)
- How is math done in the viz? (are they explicit about % vs. abolute values and how faithfully the #s represent their images)
- How are outliers, anomalies, awk data points explained/handled?
####Design
- How many font styles/types are used?
- How is color used? To convey data or for decor? Where could it be simplified? Do that.
####UX
- Is the representation of data an effortless read?
- How are graphs and numbers incremented, what is the baseline/reference point for charts?