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
Powered by GitBook
On this page

Was this helpful?

  1. Advanced Topic: Implementing DQ indicators
  2. Completeness

User-Defined

Telmai provides an easy way for users to specify completeness requirements, either at the value, record, or attribute levels.

For example, the user might specify:

Each ID field should be populated - value level

Either City or Zip-Code should be provided – record level

At least 80% of E-mails should be populated – attribute level

In addition, telmai allows for completeness to be specified across multi-attributes.

The forms of such specifications can be broken down as follows:

Full Completeness No missing part of an Address, such as City, State, Zip_code, ...

Partial Completeness Either City or Zip_Code of an Address is filled At least 2 (or half) of Employee_Credentials are filled

PreviousBuilt-inNextCorrectness

Last updated 3 years ago

Was this helpful?