This is a good, more nuanced than you think white paper by Donald Clark on the use of big data, more specific in education.
There is a dose of realism in the white paper that I like, but he also acknowledges the possibilities of large data. Large data you ask?
“However, we need to start with an admission, that big data in learning is really just ‘large data’. We’re not dealing with the unimaginable amounts of relevant data that Google bring to bear when you search or translate. The datasets we’re talking about come from individual learners, courses, individual institutions and sometimes, but rarely from groups of institutions, national tests and examinations and rarer still, from international tests or large complexes of institutions where the same platform is used.”
So dealing with large data is different than with big data:
“We should also remember that data, when it is not ‘big’ enough to be used with Google-like brute force, is more likely to be used with traditional conditional branching, rule-sets and algorithms, to be effective as a feed into adaptive, personalized learning. If you don’t have a large enough data set to cope with the messiness of the data and high-probability pattern matching, then you employ additional maths to select and interpret the data.”