This is an interesting thread. Some comments:
Yes, i think you are right, and this is where the value is. As @bkitz mentioned, there are many tools (fast commoditizing) to pull in, aggregate and analyze data in very powerful ways, from Tableau, to PowerBI, to Google Data Studio and more. What doesn't exist in these tools is a way to "drag in" a reliable machine learning algorithms to predict glucose or stress levels given a good set of feature data for example.
The funny thing with self-tracking is that, because the data is so personal, it is much harder to convince people to share it with an an online service provider. A compelling library of proven well-being related ML algorithms will serve as bait for those reluctant to share their data. The idea mentioned by @JeremyGordon offers a good option to overcome the confidentiality issues while still benefiting from a market for QS algorithms - worth exploring that one..
The challenge with such a market is how to abstract the algorithms so they work across individuals. I have been analyzing my family's self-tracking data using PowerBI and Python for two years and any value i get from them depends heavily on my knowledge of the environment and context surrounding our day-to-day lives, which is not necessarily codified in the data i collect, for example.
@Agaricus mentions Open MHealth and I agree they seem pretty good. I am also defining my own if any one is interested.