Data valuesDAMA: 25

Credibility

The degree to which data values are regarded as true and believable by data consumers.

Created: 10/30/2025
Business Impact
Positive Impacts
  • •Increased trust and confidence in data among users and decision-makers.
  • •Higher adoption rates for data-driven insights, BI tools, and analytical reports.
  • •Stronger foundation for making critical business decisions based on believable information.
  • •Enhanced reputation of data sources and data providers within the organization.
Negative Impacts (if poor quality)
  • •Users ignoring or distrusting data and reports, leading to decisions based on gut feel or intuition.
  • •Wasted investment in BI and analytics systems if users do not believe the data.
  • •Reluctance to act on data-driven recommendations.
  • •Poor data culture and skepticism towards data initiatives.
Technical Description

Grade

Examples
Good quality vs poor quality indicators
Good Quality Examples

Logistics: The vessel ETA provided by the terminal operating system is trusted because it's consistently updated with reliable AIS data and historical performance.

News Media: Financial data published by reputable government statistical agencies is generally considered highly credible.

Market Research: A clinical trial result published in a peer-reviewed journal with transparent methodology and data is seen as credible.

Poor Quality Examples

Logistics: Users distrust the sales forecast report for shipping volumes because past forecasts have been consistently and wildly inaccurate.

News Media: A news article citing data from an unverified source is met with skepticism by readers.

Market Research: A market share report based on a very small or biased sample size is not considered credible by industry analysts.

Local Network
Quick Stats
Dependent KPIs0
Improvement Standards0
CategoryData values
API Access
GET /api/dq-dimensions/23d2e9ef-1ec0-40a5-a13c-1fb46e90e9f6