Data

Data (the plural form of datum) is essentially information that is not yet in context, or without any applied meaning. For example, if you were told that a particular elephant in a zoo weighed 5,800 pounds, you could comprehend how heavy that is, but it would be hard to act on it. You might not know the gender of the animal and may not know the average weight of the species, so you could not categorize the animal as large or small, and could not, for example, take actions to improve its health.

For that reason, the data must be applied to a situation to be useful. When data has meaning attached to it, it turns into information.

Data may be categorized in many ways:

• Quantitative Data: Numerical
  • Continuous or Variable Data: Infinitely divisible
  • Discrete Data: Countable
• Qualitative Data: Descriptive
  • Attribute Data: Data that describes a characteristic (i.e. plant, animal, or mineral)
    • Ordinal Data: Attribute data that can be ordered. (high, medium, or low satisfaction)
    • Binary Data: Either/or data (i.e. Pass/Fail)
    • Nominal Data: Unordered categories of data (red, yellow, blue)
  • Open Data: Free response answers, such as the comments section of a reply card

Note that you may hear these types broken down in different ways. Some people consider discrete data and attribute data to mean the same thing, which can be confusing. Like all continuous improvement terminology, the exact definition you choose is less important than making sure that the entire organization is using the terms consistently.