Telmai Academy
  • Data Quality and Observability Academy
  • Basics of Data Observability
  • Data Quality Indicators
    • Introduction - Indicators of Data Quality
    • Selecting Data Quality Indicators
    • Completeness
    • Uniqueness
    • Freshness
    • Validity
    • Accuracy
    • Consistency
    • Data Lineage
  • Advanced Topic: Implementing DQ indicators
    • Completeness
      • Built-in
      • User-Defined
  • Correctness
    • Categorical (Nominal or Ordinal)
    • Numerical (Discrete or Continuous)
    • Structured
    • Semi-Structured
    • Unstructured
    • Uncommon Types
    • Designated Values
  • Profiling data
    • Basics of profiling
    • Interactive Profiling
  • Monitoring data quality
  • Monitoring definitions
    • SLO
    • SLI
    • Policies
    • Setting up policies and alerting
  • Monitoring Sources
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  1. Correctness

Categorical (Nominal or Ordinal)

Categorical data represent types of data which may be divided into groups. This data being of categorical nature often requires maintaining accurate lists of accepted values.

Examples: Gender, country, counties, cities, etc

When designing data quality for such data you can provide a reference set and validate the data against that set.

Telmai can offer:

Out of the box lists for most common types of categorical data

Automated identification of potential accepted values based on frequency analysis

Easy way to compile custom list of accepted values

PreviousCorrectnessNextNumerical (Discrete or Continuous)

Last updated 3 years ago

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