Generally factors are reduced to return streams, the background for this is Arbitrage Pricing Theory. The fundamental data is used to compute the factor returns by construction long-short portfolios over a ranking of assets. This means that you can actually get daily returns if you'd like.
If you're doing some other kind of model in which you rely on the fundamental values in their raw format, then the important thing to consider is the predictive horizon of each factor. Some factors are predictive of price in the next minute, some in the next day, and some in the next year. You need to use statistics to figure out which time horizon each factor is predictive over, and then construct an overall model/algorithm that uses the predictive timelines effectively. That might mean having several portfolios, each updated at different frequencies.
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