Difference between revisions of "Data Scaling"
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Data Scaling will typically be required when the [[Measurement]] process generating the data sets is using a temporal, spatial or other scope boundary that is not aligned with the usage requirements (e.g., different time interval). The scaling adjustment, while a mathematically simple operation may imply [[Model Assumptions]] that may or may not be true. | Data Scaling will typically be required when the [[Measurement]] process generating the data sets is using a temporal, spatial or other scope boundary that is not aligned with the usage requirements (e.g., different time interval). The scaling adjustment, while a mathematically simple operation may imply [[Model Assumptions]] that may or may not be true. | ||
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== See Also == | == See Also == | ||
* [[Data Cleansing]] | * [[Data Cleansing]] | ||
Revision as of 11:41, 5 January 2022
Definition
Data Scaling is a loose term that refers to data transformation activities that aim to improve the informational content of the data by adjusting an existing data set so that it conforms with a set of requirements.
Data Scaling will typically be required when the Measurement process generating the data sets is using a temporal, spatial or other scope boundary that is not aligned with the usage requirements (e.g., different time interval). The scaling adjustment, while a mathematically simple operation may imply Model Assumptions that may or may not be true.
