Rail infrastructure managers must work with increasing sustainably and efficiency as they are faced with increasing cost pressure. Against this background track engineers face a growing difficulty in legitimizing essential measures owing to strict budget restrictions. This situation requires an objective tool enabling a proper condition monitoring as well as component-specific, preventive maintenance planning. The present research deals with such an evaluation of railway track condition using innovative track data analyses. Applying functional knowledge - both IT and railway skills – allows for extracting smart data out of big data for railway asset management. Due to a bottom-up approach this methodology enables both the establishing of net-wide maintenance and renewal demands and an in-depth assessment of specific track sections. The planning of specific renewal and maintenance measures for track sections and also strategic asset management will thus both be possible on a net-wide scale.