Due to the relatively high average age of the rail infrastructure in Germany and the thus often historic plans, as-built documentation has a very high priority at Deutsche Bahn AG. The inventory and updating of existing plans represent an enormous challenge for the operator, DB Netz AG. More than 4.6 million inventory plans must be continuously checked to ensure that they are up to date, correct, adjusted and supplemented as necessary. The most fragile structures are railroad bridges. These are the focus of this paper. For now, all information of bridges such as planning documents, statics, status reports of bridge examination, etc. are collected in decentral locations of the owner or operator. The existing information is available in a wide variety of formats, e.g. pdf files, plans on paper, scanned paper plans, digitally created plans, SAPdata and photos. We tackled this problem of non-uniform and decentralized data management within the mdfBIM project. Within the scope of this project, a process model was developed that describes the merging of the various data sources in the planning process and attempts to identify the primary data source in each case. The validation and adaptation of this model was carried out continuously after it had been set up based on a railway bridge in Hannover, Germany. We used machine learning algorithms to enable an automated object classification for the most common objects to derive the highest possible degree of automation. Another important step towards automation was the consolidation of the numerous data sources. This existing, inhomogeneous data was homogenized in a defined process. During this homogenization, the data sets - ranging from existing as-built plans, photo documentation, maintenance and conversion reports, SAP extracts, construction books and construction plans to the newly recorded laser point cloud - was evaluated.