Data uncertainty is the degree to which data is inaccurate, imprecise, untrusted and unknown. The following are illustrative examples.
SourcesSources that are difficult to trust. For example, a data provider that is known for its low quality data.
Uncertainty as to where data came from or how it was calculated.
NoiseNoise such as inaccurate posts in social media or information posted by bots.
AbnormalitiesAbnormalities such as two trusted sources that report different values for the same thing.
Inherent Uncertainty Calculated values such as probabilities that are inherently uncertain. Generally speaking, complex systems can't be predicted with certainty.
PrecisionA measurement of 1.2 inches for an engineering calculation that requires greater precision such as 1.2000103 inches.
AmbiguityUnclear data such as natural language filled with vague statements.
This is the complete list of articles we have written about data quality.
If you enjoyed this page, please consider bookmarking Simplicable.
© 2010-2023 Simplicable. All Rights Reserved. Reproduction of materials found on this site, in any form, without explicit permission is prohibited.
View credits & copyrights or citation information for this page.