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Data veracity is the degree to which data is accurate, precise and trusted. Data is often viewed as certain and reliable. The reality of problem spaces, data sets and operational environments is that data is often uncertain, imprecise and difficult to trust. The following are illustrative examples of data veracity.
BiasesAn organization makes a decision using a calculated value that suffers from statistical bias. Data Lineage An organization gets data from hundreds of sources. It discovers that one of the sources is extremely inaccurate but lacks the data lineage information to identify where the data has been stored in various databases.BugsA software bug causes data to be calculated or transformed incorrectly.
NoiseA self-driving car needs to decide if a plastic bag being blown by the wind is a dangerous obstacle.AbnormalitiesTwo weather sensors in close proximity report dramatically different conditions.An organization's data is changed by an advanced persistent threat.SourcesA large number of negative comments about a brand show up in social media. It is unclear if they are from a robotic source or if customers are actually unhappy.
FalsificationA false rumor is spread by social media that a company is about to release negative news.A closely watched statement released by a government agency includes ambiguous language.Out of DateAn organization's customer database is out-of-date such that business processes often fail due to poor data quality.A customer enters their phone number incorrectly.
A company states that revenue will be between $200 - $400 million and an analyst is expected to submit a reasonably accurate profit estimate based on this guidance.|
| Type | | | Definition | The degree to which data is accurate, precise and trusted. | | Related Concepts | |
Data Science
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