1. A method for personalizing software programs in a computer system, comprising: determining default values for personalization data for a plurality of end-users at start-up of a program in the computer system, the default values being personalized individually with regard to a specific end-user such that the program started-up by the end-user runs in a fashion that is adapted to the individual needs or preferences of the end-user as determined by the personalization data; providing two levels of personalization, in each of which personalization data for the end-users can be stored, the two levels of personalization including a first, administrative personalization level containing administrative personalization data for the end-user which can be set by a system administrator, but not by the end-user, and a second, individual personalization level containing additional, individual personalization data, which can be set by the end-user, wherein the individual personalization data of the individual personalization level overrides, modifies, or limits the administrative personalization data of the administrative personalization level; displaying, to the end-user, information related to the personalization data stored in the levels for the end-user, at the start-up of the program, the displayed information corresponding to an input mask used to capture the personalization data; arranging, in the computer system, a hierarchical personalization data filling system comprising personalization nodes and an ordering node arranged in a tree structure, the personalization nodes being assigned at least one personalization characteristic, wherein the ordering node is used for implementing an ordering function and is not associated with a personalization characteristic, and wherein when the program reads out the personalization data on personalization characteristics of a lower-ranking personalization node, the program is automatically provided also with the personalization data on personalization characteristics of personalization nodes ranking higher in the direct analytical path; and accessing the program remotely by using a different computer system such that the parameterized program is automatically and remotely adapted to the preferences of the end-user based on the personalization data.