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Discover Ludwig'disparate data' is a commonly used expression in written English.
You can use it to describe multiple pieces of data that are dissimilar, coming from different sources, and/or that are not compatible. For example, you could say "This study analyzed disparate data from different countries to draw conclusions about global trade."
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Combining such disparate data creates statistical challenges.
Accounting is sea of disparate data threatening to drown us.
Working with such disparate data sources presents challenges, however.
Our analysis draws upon disparate data sources to compile projections.
In maintenance, the problem of disparate data sources is important.
Disparate data has been generated and submitted during regulatory review.
WebFountain is a set of processes for organizing and analyzing huge sets of disparate data.
Identify and consolidate those disparate data sets and create systems for ongoing collection.
As the authors explain, "systems thinking enables Serial Innovators to integrate disparate data and information.
Taking disparate data sources and giving them a unified view is tricky: do the data sets match?
Yet adjustments and interpolations are always necessary when disparate data sets are combined.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com