[flyinglizard]

Where does all this data go? It's processed and then what? Sent to decision makers? Used to run automated processes?

I'm genuinely curious and would appreciate anyone who could show a real life example of this kind of pipeline where data is accumulated, then processed, then turned into revenue at the other end.

I've implemented systems that do this but my experience is that accumulating data is (too) easy, processing it in a meaningful way is slightly more challenging but ultimately driving positive business processes according to this data, which require a lot of friction with employees (training, procedures, maintenance, support) is the most difficult part.

[lysecret]

Same experience. I think the most interesting and most public example of such a pipeline is Google/ building a search index. This is also where a lot of the methods originally came from. Nowadays a lot of this will be used to build recommendation systems / feature pipelines for ML.

[frankbinette]

These are a bit too advanced examples. Think of simple descriptive statistics which is still so important yet not sexy as ML/DL/AI. ML is great, but the main usage behind these data technologies is still simple business intelligence.

Every business in every market need to understand what is going on with their processes. How many sales did I do yesterday, last week, last month, compared to last year, in which stores, what is the average basket amount, customers buy what with what, what size t-shirt do I sell the most, etc.

[frankbinette]

Seems like you kind of answered your own question... this data is used for business intelligence purposes.