Posted by: John Erickson | July 12, 2010

Data Quality is in its Fitness to the Beholder


  1. Subjective, hence my reference to “context halo”.

    1. Accessibility (Representation and Access Protocols)

    2. Accuracy

    3. Timeliness – Methinks Change Sensitivity and Freshness

    4. Completeness — Methinks under Accuracy

    5. Consistency with other sources
    Relevance — Methinks under Navigability (follow-your-nose and SDQ aspects of Linked Data meme)

    6. Comprehensiveness — Methinks Navigability again (Inference Context delivers Navigability subject to Inference Rules)

    7. Providing a proper level of detail — Part Provenance and Part Navigability

    8. Easy to “read” — Covered by Representation formats and Access Protocols

    9. Easy to “interpret” — Covered by Representation formats.

    Based on the above, this is why I used the “cube of sugar” analogy re. the QoS factors of Data subject to the “Context Halo” or “Context Lenses” of the perceiver:

    1. Accuracy

    2. Access (Platform Agnostic Data Access Protocol)

    3. Data Representation Format Dexterity

    4. Navigability (Inference Context embellishments which also affect Relevance and Reach)

    5. Change Sensitivity (which handles Timeliness and Freshness)

    6. Provenance.

    You can make Coffee, Tea, and lots of other things with a Cube or Cubes of Sugar, but the essence of the cube ultimately affects the preferred supplier.

    What applies to Sugar also applies to “Data as a Service” oriented business models 🙂

  2. That’s why, I proposed the Info Service Ontology (, which has a hook for different Info Service Quality Ontology specifications. This should enable the user to use specific Information Service ( quality ratings/descriptions, which are provide by specific Information Service quality rating agencies. These agencies can be chosen manually or automatically by using a proper user profile.

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