We often hear that in practice, not enough data of sufficient quality, consistency, recency, etc. is available for feeding into mathematical optimization models. Example: my university wanted to plan/optimize their weekly timetable using an integer program, but they did not know the number of rooms (let alone capacities, availabilities, equipment, location, etc.), they did not know the preferences of professors, nor which courses they actually taught (the system listed them as "responsible" for a course which did not imply that they were actually teaching that course!); they didn't know the number of students to expect in a course. I could contribute a lot of such stories.
Now, many companies (truthfully) claim that they collect data. E.g., sensor data from production, temperatures, filling rates, number of faulty products per hour, web clicks, customer orders, energy prices, etc., etc. I can't really grasp what makes we reject such data as "suitable" for optimization, and I am looking for a definition of what "different kind of data" needs to be collected in order to feed a typical mathematical program for e.g., timetabling, production planning, facility layout, or designing tariff zones. I thought for a while that the notion I am looking for is "actionable", but this doesn't capture it. Ideally, I would like to contrast this "optimization data" to data that is typically fed into machine learning algorithms (which extrapolate, cluster, predict, find trends, anomalies, patterns, etc.).
How would you call the number of students in a course, the availabilities of teachers, the capacities of rooms, the data that a course belongs to a certain curriculum?