The Netherlands faces a significant challenge in maintaining its estimated 85,000 bridges, most of which were constructed between the 1950s and 1970s. Addressing this "Replacement and Repair Challenge" requires prioritizing bridges at high risk of deterioration to optimize maintenance efforts. Internationally, predictive maintenance studies have successfully utilized Bridge Deterioration Models (BDMs) that leverage inspection and supplementary data to train machine learning models for predicting bridge deterioration. This study investigates whether Dutch bridge inspection data, collected under the NEN 2767 standard, could similarly support BDMs for predictive maintenance. The aim is to evaluate the suitability of NEN 2767 data for this purpose and identify necessary modifications to enhance its predictive capabilities. Data from five provinces and seven municipalities were analyzed using the "4 Vs" framework: Volume, Variety, Velocity, and Veracity. Results indicate that, due to data inconsistencies, limited feature diversity, and insufficient volume, the Netherlands is not yet prepared to apply machine learning to NEN 2767 data. To explore these challenges further, semi-structured interviews were conducted with five government agencies and four inspectors. Findings suggest that while there is confidence in the NEN 2767 standard, significant variation exists in data collection and storage methods. Furthermore, maintenance decisions rely on additional information that is not consistently recorded in government databases alongside NEN 2767 data. A literature review on BDMs identified 25 critical features that could improve the predictive accuracy of NEN 2767 data for Dutch bridge deterioration. Based on these insights, four key recommendations are proposed. Firstly, preliminary recommendations were presented to stakeholders, after which some adaptations were maded to improve their quality. These for recommendations are as follows: (1) Extend the CUR 117 standard to enhance data collection, storage, and sharing protocols; (2) Develop a standardized Inspection Procedure, which would involve certification through the CUR 117 commitee; (3) Incorporate the 25 additional features identified as relevant for predictive maintenance; and (4) Utilize the Schouw, an annual inspection process, as a means to capture more maintenance data on bridges. These steps would collectively strengthen the predictive capabilities of NEN 2767 for proactive bridge maintenance in the Netherlands.