I completely agree - there's only so many ways to slice the data. The caveat is - the type of data matters quite a bit for the data architecture. There's anothe

I completely agree - there's only so many ways to slice the data. The caveat is - the type of data matters quite a bit for the data architecture. There's another thread that mentions sensor data as a source of complexity since the data has a theoretical delay between events (i.e. period) of 0 - something few systems are built to handle, even if you sample approximations at some fixed frequency. Algorithmic trading is a similar domain that still has a huge bar for entry - a sign that _this isn't easy_. The fidelity of the data is of course important, but I would claim it's not a blocker. Yes, you need to trust the data you collect. That's table stakes - if you can't collect data correctly at all, even without worrying about the past, you're in for a world of hurt. It's P0. That said, a lot of people assume you also need to do this historically - and that's not the case - at least for ML. Reinforcement learning has been making great strides in recent years. If you're in this situation - you have a flow where you want to use a model without having any past data to train with - use something like VW's contextual bandits [1]. You don't need historical data to build your model, just real-time decision point & reward signals. Once deployed, the model converges over time to the optimal model using real-time feedback. All that said - baby steps are important. If you're in this situation, start by getting fidelity and then expand scope slowly without sacrifice to fidelity. It's a lot easier to backfill than to "fix" data - get that right and it get's easier from there. You'll need fixups regardless - mistakes happen and requirements change - but you have to start with something you trust, at least in the moment it's deployed. [1] https://vowpalwabbit.org/tutorials/contextual_bandits.html https://vowpalwabbit.org/tutorials/contextual_bandits.html