1. Data is only useful if its function is useful.
2. Data, for the sake of data, is of no function at all.
3. Context and explanation must be self-contained within the data. Data which requires explanation is like a painting which requires a description.
4. Any data in which context and explanation cannot be self-contained has little effective usefulness and will degrade over time.
5. An action will be sufficient to produce data, but data is not necessary for every action.
6. Data collection and quantification takes time. If the time consumed in data collection outweighs the time saved by collecting such data it is a redundant process.
7. Mathematics is absolute, data is not. Data requires interpretation and that is subject to human/computing error. Failing to remove bias and error from the reading of the data will make the data redundant.
8. All data processes and procedures can be improved, no matter how refined they are. What do you think? Is there anything you would add or remove on here? Is there anything specific to your industry that you would add?