Approaches to automating product classification adopt mainly algorithms and tools for classification in general, such as Vector Space Model, Bayesian Classification and K-Nearest Neighbor Classification, or take classification systems as (database) schemas that have to be integrated. Much work has been carried out on applying, evaluating and improving these tools for this specific domain, product data in B2B e-commerce. The formal specification of a product classification system (PCS) is seen as input data; hence this data has to be imported and often converted into an internal representation. This paper takes a closer look at this input data and examines its structure, semantic richness and degree of standardization. The reason is that standards for product classification systems and standardized specifications address the data exchange issues of these systems - and more important, are able to increase the success of automated product classification. To do so the paper will derive and analyze major standardization trends, show their interdependencies and evaluate their impact on automated classification.