Data Quality refers to how well data suits its intended purpose. It is typically evaluated using criteria such as accuracy, completeness, consistency, timeliness, validity, uniqueness, and integrity.
Information Quality concerns whether that data has been made meaningful, credible, relevant, usable, accessible, and understandable in the context in which a person must interpret it and act.
Information Quality therefore includes:
- Intrinsic integrity — accuracy, credibility, and consistency.
- Context of use — relevance, currency, importance, and usability.
- User access — ease of access, ease of understanding, and concise, consistent representation.
High-quality data does not guarantee high-quality information. For instance, data may be technically correct, yet differences in definitions, missing context, or unsuitable presentation can undermine its usefulness for specific decisions.
Therefore, Data and Information Quality depend on more than just correction and cleansing. They also require clear ownership, shared definitions, authoritative sources, provenance, business context, and awareness of potential misinterpretations or misuses.
The integrity of a decision is only as good as the integrity of the data it is based on.
Within governance, Data and Information Quality help ensure decisions remain connected to organisational reality. The aim is not perfect data, but information that is sufficiently trustworthy, meaningful, and fit for the decision, action, or learning it must support.
Related Source Notes
- Information Quality is Not Binary — It Is Interpreted
- Common Data Models and Organisational Meaning
- From Data → Wisdom: Fragility and Risk
Related Subject Areas
- SA Governance
- SA – Enterprise Architecture — Foundation of Shared Knowledge
- SA Organisational Awareness