Bridge maintenance decision making largely depends on the ability to predict future bridge deterioration. The increasing availability of large amounts of multi-source bridge data offers opportunities to bridge data analytics for improved bridge deterioration prediction for supporting enhanced maintenance decision making. Such data include National Bridge Inventory (NBI) and National Bridge Elements (NBE) data, bridge inspection data from bridge inspection reports, bridge health condition data from various sensors, and traffic and weather data. However, bridge data have characteristics that add to the challenges of bridge data analytics: (1) the types of data features are mixed (continuous and discrete); and (2) some of the data features are redundant and/or irrelevant. These characteristics could negatively affect the performance of bridge data analytics. Various feature discretization (FD) and feature selection (FS) methods have been proposed in the literature to deal with mixed-type and redundant/irrelevant features, respectively. However, their performance levels vary across domains and applications. There is, thus, a need to evaluate the impact of using FD and FS methods on the performance of data-driven bridge deterioration prediction. To address this need, this paper focuses on evaluating the impact of FD and FS methods on predicting deterioration using NBI data. The preliminary experimental results show that when predicting the condition ratings using NBI data, compared to the baseline: (1) the ModifiedChi2, compared to the other FD methods, achieved the highest average accuracy improvement of 37.6%; and (2) the combination of the ChiMerge FD method and the Lasso FS method, compared to the other combinations, achieved the highest average accuracy improvement of 37.7%. This shows the promise of using the ModifiedChi2 FD method alone for supporting bridge data analytics.