DataDAMA: 31

Interpretability

The degree to which data are in an appropriate language and units of measure.

Created: 10/30/2025
Business Impact
Positive Impacts
  • •Clear understanding of data by users, regardless of their technical background.
  • •Reduced misinterpretation and more accurate application of data in business contexts.
  • •Improved communication and collaboration when discussing data-driven insights.
  • •Enhanced data literacy across the organization.
Negative Impacts (if poor quality)
  • •Misunderstanding of data leading to flawed conclusions, poor decisions, or incorrect actions.
  • •Wasted time as users struggle to decipher cryptic codes, ambiguous terms, or unclear units.
  • •Barriers to effective data use, particularly for non-technical business users.
  • •Low data literacy and reluctance to engage with data.
Technical Description

%

Examples
Good quality vs poor quality indicators
Good Quality Examples

Logistics: All financial reports clearly state the currency used (e.g., USD, EUR, JPY) for all monetary values.

International Trade: Product specifications are available in multiple languages, and all measurements clearly state their units (e.g., 'Length: 10 meters', 'Weight: 500 Kilograms').

Scientific Data: All data values are accompanied by metadata defining their units (e.g., 'Temperature (Celsius)', 'Pressure (Pascals)').

Poor Quality Examples

Logistics: A report shows 'Container Weight' as a numerical value but doesn't specify if it's in kilograms (KG) or pounds (LBS).

International Trade: Product descriptions are only available in one language, making them difficult for international partners to understand.

Scientific Data: A dataset contains a column labeled 'Temp' with numerical values, but no indication of whether it's Celsius, Fahrenheit, or Kelvin.

Local Network
Quick Stats
Dependent KPIs0
Improvement Standards4
CategoryData
API Access
GET /api/dq-dimensions/558d8cb4-d7dd-4b9c-8641-5a5bce33887c