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Table 7 Breaking down telecom big data contribution to profit between top and bottom 2%% of telecom companies.
First, big data contribution to total telecom profit is minor, but in line with its relative size of investment in total telecom spent, and generates a productivity impact aligned with other research (Tambe [28], or Bughin [9]). 2.
In order to get a sense of how those factors explain the distribution of big data contribution to profit, Table 7 computes the difference between moving from the bottom 25%% to top 25%% of telecom companies in terms of profit impact.
While we witness some differences in the drivers of profit uplift by use case (for example, network use case uplift is relatively more driven by amount of investment in big data than for other cases) the underlying message is that investments in appropriate big data investments and organizational fit are material factors to big data contribution to profit.
Results are presented in Table 6; the results are then used to illustrate the difference in big data contribution to total telecom profit, between the bottom quartile telecom companies and the top quartile, 25%%, of telecom companies, for each big data use case (Table 7).
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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