In today's business environment, increasing demands are placed on companies to remain competitive. The efficiency and effectiveness of operations, including asset management, must be continuously improved to meet these demands. The increase in digitalization, automation and asset intensity makes data-based decisions in maintenance necessary to ensure asset reliability and availability. In addition, there are fluctuating demands for orders and regulatory requirements. The identification and assessment of asset risks are becoming increasingly important to derive appropriate actions for changing conditions A holistic criticality assessment enables companies to analyze assets in a data-driven manner, taking into account the company's success factors. This enables companies to react flexibly to changing input factors, to create a basis for decision-making based on data and to dynamically adapt the maintenance strategy for assets. In this context, a model for criticality assessment is presented, which is based on the complexity level as well as the data quality and availability of the company and enables an identification of assets with the highest criticality. The paper consists of a theoretical part, where the scientific basis is explained, and a practical case study. In the case study, the results of the applied steps of the model at voestalpine Wire Rod Austria GmbH are described in detail, focusing on the criteria selection and definition as well as the evaluation of data quality. In the last section of the case study, the measures derived from the findings of the performed criticality assessment to improve data quality are described. These measures should enable the company to perform a criticality assessment appropriate to the level of complexity.