We discuss relationships of implications between attributes in object-attribute data tables to various important notions in computer science and artificial intelligence: functional dependencies, horn theories, emergent patterns, disjunctive version spaces, etc. The intractability of computing implication bases seems to be the main challenge for the use of implications in analyzing large data collections. Alternatives to generation of implication bases such as lazy-learning classification, target-driven generation of classifiers, and sampling are considered.