Phenomenal data mining finds relations between the data and the phenomena that give rise to data rather than just relations among the data..Science and common sense both tell us that the facts about the world are not directly observable but can be inferred from observations about the effects of actions. What people infer about the world is not just relations among observations but relations among entities that are much more stable than observations. For example, 3-dimensional objects are more stable than the image on a person's retina, the information directly obtained from feeling an object or on an image scanned into a computer..This talk concerns what can be inferred by programs about phenomena from data and what facts are relevant to doing this. In order to infer phenomena from data, facts about their relations must be supplied. Sometimes these facts can be implicit in the programs that look for the phenomena, but more generality is achieved if the facts are represented as sentences of logic in a knowledge base used by the programs..Creating knowledge bases containing both common sense knowledge and knowledge of the domain of the data will be a lot of work. This is unavoidable..The result of phenomenal data-mining can include an extended database with additional fields on existing relations and new relations. Thus the relations describing supermarket baskets can be extended with a customer field, and new relations about customers and their properties can be introduced..