This stems from a strong master-apprentice tradition that is passed down through the generations from professors to their students. This tradition creates a solid ethical barrier against distorting data or drawing conclusions beyond what the observational data can truly support. The sanctity of the observational scientific method builds credibility, and this is a desirable quality for all data warehouses. There must be a strong element of trust in the data in a data warehouse for it to have business value. The one thing I have not seen in data warehousing, which plays a key role in the other observational sciences, is the concept of observational error and error propagation.
All scientific observations are subject to errors.
Kepler was the first to recognize this problem in about 1600 when he was trying to work out a very accurate plot of the orbit of Mars. This is a hard concept for people who deal with commercial applications. Furthermore, it might be a good idea to add an astronomer, epidemiologist, or exploration geophysicist to your data warehousing team to bring in fresh analytical ideas. Many of the observational sciences have benefited from the cross fertilization of ideas. For example, the observational science of modern geology was invented by James Hutton in 1788 when he published his book, Theory of the Earth.