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Presentation transcript:

Billion Gigabytes 1 Exabyte

Origin

Name? Age? Where from? Strengths? Weaknesses? Oddities? Expertise? Bias? Politics? Groups? Relatives?

Integrity

Data is categorized N - Nominal (labels) Apples; Oranges; Bananas; O - Ordinal (ordered) Small; Medium; Large;... Q - Interval (location of zero arbitrary) Jan 19, 2010; Oct 23, 2011,... Q - Ratio (zero fixed)

Variable Structured Unstructured Uncertain Margins of error Needs Context Sample

It is sampled (Census) manipulated or biased (Politics) biased (Advocacy) error prone (human mistakes)

So We Evaluate, filter, clean, question and attribute our data SEE: Harvard Business Review

Any Tool Will Do Tableau, JavaScript, D3, Google API, Many Others

Too much stuff

Data-ink Ratio DATA INK Total Ink

Too fancy – Edward Tufte Example