Many real world problems that result in data are decidedly medium: small enough to fit in excel, large enough to be too big to comfortable handle in excel.
I thought the biggest leap in NN and deep learning in recent history was the realization that we need a ton of data to get maximal effectiveness from them; it now sounds counterproductive to forget this and cry they don’t work well with 10,000 rows.
It is an important lesson to be communicated.
I'd like to present a conjecture: everyone thinks their data is big until they have worked on much larger dataset. ("We have 10k samples, it is quite big!" -> "We have 1m data records, is quite big!" -> "Our process outputs that much per day")