In opportunistic mobile networks, existing schemes rely on the assumption that data can be entirely transmitted at each contact. However, in an opportunistic mobile network, the transmission probability exponentially decreases as the data size increases. That is, the contact duration in each contact might be insufficient to deliver large data. Therefore, it is reasonable to partition original data into small data chunks and each chunk is forwarded through an opportunistic path. The objective of this paper is to find an optimal data partition strategy where the data delivery ratio is maximized under a given deadline. There is a trade-off in data partitioning. Each small chunk in a path has a higher delivery probability than the original data, and consequently, a shorter delivery latency under the persistent transmission model with re-transmission. However, the destination needs to receive all chunks in multiple paths (a path is a sequence of contacts) to retrieve the data. A delay in any path will lead to a longer delivery latency. We formulate the data partitioning problem and propose solutions in blind flooding. In the blind flooding scenario, we find the optimal data partitioning size. Network coding technique is used to the proposed method to further improve the performance. Extensive experiments on realistic traces show that our scheme achieves a much better performance than those without partitioning.