It is context dependent, but I can give a basic idea. In the example you've given, high four figures per customer per quarter, assuming you just give them a deliverable report once a month and the data is turnkey enough that many customers (30 - 40) can "subscribe" to it. For a fund that wants something more boutique, like exclusivity or a specific company, five figures becomes the floor.
The nature of the business is significant upfront work (setup of data collection and analysis model) followed by infrequent tweaks going forward. Lucrative data on a single hot company could be sold for mid - high six figures per year with very high profit margin for as long as the data is available and the analysis model is both functional and interesting (i.e. delivers an insight not already priced into the market).
I'm also curious as to the level of "polish" that you are expected to provide. Let's say you know how many widgets Acme sold this last quarter. Is that by itself enough information, or would you need to calculate projected expenses / other stuff to actually be able to deliver an earnings estimate? What if you just had projected revenue?
Thank you!
I once briefly chatted with a man who ran a company that used satellite imagery to predict crop yields. I found it amusing how rather than providing this information to farmers ("It looks like you're going to produce only 60% of what you did last year, time to cut some expenses!"), there was so much more money in providing this information to hedge funds and trading firms. Morality aside, the potential for technology like this is exciting.
It's kind of cool how the combination of big data, scraping, statistics, etc. allows this small niche of analytics to thrive. I have a decent background in most of these subjects, and this seems like a very freelance-able job, so I'd love to dabble in this area and learn how it works.