Data quality is the degree to which information fits its purpose. It can be difficult for organizations to agree on data quality criteria because each team may use data towards different purposes. The following are commonly used criteria to define data quality.
RelevanceData that is useful to support processes, procedures and decision making.
Timeliness How 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.
Traceability Data that can be traced to its source. If someone changed your prices, you should be able to figure out who.
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