Data standardization is the process of transforming data into a consistent format so it can be compared, combined, and analyzed reliably. It goes beyond fixing individual errors — it's about applying systematic rules across an entire dataset.
Why Standardization Matters
Without standardization, combining data from multiple sources is unreliable. If one dataset uses "ON" for Ontario and another uses "Ontario" and a third uses "Ont.", you can't join them without first standardizing the province field.
Reference Data
Reference data is a set of standard values used to validate and standardize other data. Province codes, country codes, industry classification codes, and currency codes are all reference data. Mapping your data to standard reference values is a key standardization technique.
Code Lists and Lookups
Create lookup tables that map non-standard values to standard ones. For example, a lookup table might map "Ont," "Ontario," "ON," and "on" all to the standard value "ON." Apply this lookup to clean the province field across your dataset.
Statistics Canada publishes standard classification systems used across Canadian government data: the North American Industry Classification System (NAICS) for industries, the National Occupational Classification (NOC) for occupations, and the Standard Geographical Classification (SGC) for geographic areas.
Units and Scales
Numeric data needs consistent units. If some records store distances in kilometres and others in miles, calculations will be wrong. Standardize to a single unit and document it in your metadata.
Text Normalization
For text fields, standardization typically includes: converting to consistent case (usually title case for names, uppercase for codes), removing extra whitespace, expanding abbreviations, and removing special characters.
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