From the technology standpoint, where it comes from and how it's used doesn't really make a difference - if it can be processed on a single very beefy machine when done properly, then the appropriate/efficient way to work with this data is by avoiding big data techniques.
If it cannot, then you pay the price of all the complexity and overheads of big data processing techniques so that you can get your processing done.
It's correlated with data size, bot not so strictly - you can get, for example, NLP processing problems where you need a painful pipeline split over a huge cluster for a single gb of input data, and you can have problems where the best way to process a petabyte dataset is just to stick a single powerful machine to get the performance benefits of locality and low latency, and avoid managing splits/failed nodes/whatever.
So, in the first problem you would need to use Big Data techniques and the second problem you don't, it's not related to big data and the recommendations on how best to do that won't help people who need to do big data processing.
If it cannot, then you pay the price of all the complexity and overheads of big data processing techniques so that you can get your processing done.
It's correlated with data size, bot not so strictly - you can get, for example, NLP processing problems where you need a painful pipeline split over a huge cluster for a single gb of input data, and you can have problems where the best way to process a petabyte dataset is just to stick a single powerful machine to get the performance benefits of locality and low latency, and avoid managing splits/failed nodes/whatever.
So, in the first problem you would need to use Big Data techniques and the second problem you don't, it's not related to big data and the recommendations on how best to do that won't help people who need to do big data processing.