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Usually, you would not care about both of them simultaneously. Depending on the goal of your analysis (say, description vs. prediction), you would only care about one. For description, multicollinearity is just a fact to be mentioned, just one of the characteristics of the data. For prediction, omitted variable bias is largely irrelevant as you are not interested in model's coefficients per se, only in predictions.