In this paper we propose various complexity measures of a dataset in terms of Formal Concept Analysis (FCA). On the one hand, we follow the lines of the research of the "closure structure" and the "closure index" based on minimum generators of intents (closed itemsets). On the other hand, we would like to capture statistical properties of a dataset, not just extremal characteristics, such as the size of a passkey. In the following we introduce an alternative approach where we try to measure the complexity of a dataset in terms of five main elements that can be computed in a concept lattice, namely intents (closed sets of attributes), pseudo-intents, proper premises, keys (minimal generators), and passkeys (minimum generators). We study the distribution of these different elements in various datasets, both real and synthetic. We also investigate the relations that these five elements have with one another, and the relations with implications and association rules.