01 / THE FOUNDATION
The principles you start with.
A guide that people and AI tools can read directly. Share this page’s link or download the source guide below.
Source by Elijah Joshua Pryor
Based on an editorial preview awaiting the author’s review. Examples are hypothetical.
01
Define quality in relation to a specific use and decision, rather than an abstract score.
02
Follow a problem back to where data is captured; give the people there useful feedback.
03
Assign an owner to each issue and use evidence to decide whether the intervention worked.
Read the approach and working outputs
Suggested approach
- Choose one data product and describe the decision, users and consequences of an error.
- Trace a representative issue to its source and agree a test, an owner and a correction route.
- Measure a baseline, test the correction and review its effect with the people using the data.
Working outputs
- A quality question tied to a decision
- An issue-to-owner feedback loop
- A measurement plan with proposed acceptance criteria