QUANDL SPECIFIC: -Quandl has a pretty decent blog that I would check out, you never know what new large corporate policy enacted might get rid of it: https://blog.quandl.com/
GENERAL NOTES:
-More and more asset managers are using it and there is some worry that everyone is making the same conclusions off the same data set, and thus no money to be made. Though most practitioners say this is a none-issue, there is more and more alt. data sets out there to chose from, cleaning the data is tricky and testing the veracity of the data provided and knowing how to combine it with others sets is a key competitive advantage that not every asset manager is good at.
-The ROI is something that is top of mind but not always easily attributable throughout the year, e.g. one large insight very late in the financial year can bring +100x returns on what was paid for a data provider's software.
-Hugely successful funds like Renaissance's Medallion has likely been doing this for a long long time, coupled with top PhDs looking for a lot of statistical correlation with traditional data as well.
-More and more data sets that are being created and thrown into a self-learning financial model (aka AI) have a lot of people excited, and certainly there are a lot of small funds being created, though seems to be mostly by young people or not-so-great hedge fund managers. Getting large investors to lay down significant capital has a huge trust component to it, aka want to bet only on succesful grey-haired largely-male dominated folks -A lot of alternative data can be found directly from the Bloomberg terminal e.g. MAPS <Go> function. However my understanding is that it's not that deep, quality is an issue, and everyone has access to it (no real competitive advantage).