Merci pour cet article qui élève le ’débat’ ambiant !

et devrait permettre de prendre du recul par rapport à une addiction aux chiffres :

"If you lack some of the data you need, then the results will be inaccurate because the data doesn’t accurately represent what you’re trying to measure. You may be able to get the data from an external source but bear in mind that third-party data may also suffer from quality problems. A current example is COVID-19 data, which is recorded and reported differently by different sources.

...

It’s been said that if one wants better answers, one should ask better questions. Better questions come from data scientists working together with domain experts to frame the problem. Other considerations include assumptions, available resources, constraints, goals, potential risks, potential benefits, success metrics, and the form of the question. "

cf https://www.informationweek.com/big-data/why-data-science-isnt-an-exact-science/a/d-id/1338438