Critical to data quality:
Data that can be accessed as required.
The data accurately reflects reality.
An audit trail records the source of data and all access and changes.
The data includes the required elements and attributes.
The data includes context information such as source.
Data that is true.
Free of unauthorized alteration and data corruption.
There is sufficient data that describes the data.
Evidence that data such as transactions, documents and communications are authentic.
Recorded at an appropriate level of detail and granularity.
The data is not meaningless or low value.
Data that can be trusted.
Single source of truth
Data can have copies but it needs to be clear which copy is the master.
The data complies to standards such as templates and standard formats.
The data is well architected and designed.
Up-to-date information that has arrived fast enough.
There is a record of the data source and changes to data.
The data has been validated against defined rules, controls and standards.
RelevanceData that is useful to support processes, procedures and decision making.
TimelinessHow quickly data is created, updated and deleted.
PrecisionThe exactness of data. For example, a company that has annual revenue of $3,451,001,323 as opposed to a 3 billion dollar company.
CorrectnessData that is free of errors, omissions and inaccuracies.
CompletenessData that is compete relative to your business purpose. For example, an order for an economy car may need configuration details such as color, wheel size and electronics package. An order for a luxury car may require additional details such as engine type, seat and interior package.
CredibilityData that stems from reputable sources such as verified company press releases as opposed to social media rumors.
TraceabilityData that can be traced to its source. If someone changed your prices, you should be able to figure out who.
|Overview: Data Quality|
Information that fits its purpose.