How is data wrangling different than a data cube processing?

I know that a data cube is a transforming multi-dimensional data which keeps changing with time, whereas data wrangling definition says it's transforming the data making it more valuable.

Isn't a data cube more meaningful and valuable piece of denormalized data ? I haven't been able to find any example to clear the symmetry, they both sound same to me.. Please help!

Solution

I found this article which claims to bring a perspective to this question -

I did not find true example but after reading and speaking to data analysts following line calls it a closure for me -

Data Wrangling is applied by functional experts on data in question to clean it off of it's veracity

On the other hand

Data Cube Processing is when a data analyst does a projection on structured data to output a report with some KPIs (Key Performance Indicators)

One is a 'cleanup' whereas another is a 'projection'

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