Data Quality Dimensions: Building Trust in Data
Good quality data that business trusts can be defined as ‘the Right Data relevant to given use case, being available to the Right Consumer (person or system) at the Right Time’. There are different Data quality dimensions defined by various thought leaders, regulators (e.g. APRA CPG-235) and specialists (e.g. DAMA). Leveraging from these, RegCentric has defined 9 Data Quality dimensions covering the 3 focus areas.
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Accuracy: Data is error free and its content aligns to what it represents
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Completeness: Data is not missing any components; its breadth and depth meet the needs of intended purpose
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Consistency: Attributes of a given data element is in alignment (e.g., value) and contextis maintained as is regardless of where it is stored/published
Right Data
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Confidentiality: Restriction on data access for authorised consumption factoring in Data privacy and Ethics
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Authenticity & non-repudiation: Condition of data is genuine, and all events related to given data is traceable
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Accountability: Clear ownership & accountability for Data, with Ability to attribute responsibility to for action.
Right Consumer
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Timeliness: Data is current and up-to-date for intended purpose
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Availability : Data is accessibility and usable at the time it is required
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Fitness for use: Degree to which data is relevant, appropriate and meets specification for intended purpose
Right Time