125 Data Sharing and Usage | What is Data?

To frame the learning objectives for this module, it will be important to define data and related terms and concepts. Consider three categories of data (from Robin Kichin, The Big Data Revolution: Big Data, Open Data, Data Infrastructure & their consequences, 2014):

Representative data Typically involves measurement (e.g., annual precipitation, age)
Implied data Inferred to fill an absence of data (e.g., voter preferences from social media posts
Derived Data Produced from other data (e.g., predictive outcome data)

Information vs. facts

  • Don’t conflate information and facts.
  • Think of facts as building blocks.
  • Robert Losee’s definition “Information is one or more statements of facts that are received by a human and that have some form of worth to the recipient.

Bias in Data

Data does not exist independently of the ideas, instruments, practices, contexts and knowledge used to generate, process and analyze them. Data should be considered non-neutral.50

Ownership and/or Intellectual Property Protection of Data

  • Laws and regulations as they relate to data are evolving. For example, in Canada Bill C-11 has completed the first reading. This potential legislation would update Canada’s laws related to privacy and personal information (somewhat following the recent changes in the European Union).
  • Treatment under various laws and regulations may depend on the specific data and geographical use.
  • Value to industry partners typically related to implied or derived data
  • Many laws and regulations focus on representative data.
  • In addition to copyright, ownership and use rights may be established by agreement

(Source – Data Ownership. Dr. Teresa Scassa. CIGI Paper No. 187)

 


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