The reason that "traditional" NoSql databases will not get much up take in finance is that they are designed to solve a different problem. Most NoSql databases from the web world are designed with two central design parameters. First key lookups should be very fast. Second is that operations should be atomic at the row level and should not span records. This allows the database to be sharded very effectively since no operation should span multiple machines and in turn this allows them to build scale out architectures. These databases are built for very write heavy workloads and to be able to have fast key lookups to render webpages. While it is possible to use them to store tick data its not really what they are good at.
Financial data tends to be small relative to large web companies data. So scale out in finance is less important. Also financial data tends to partition well (at the day, symbol, or almost any other key) so replica placement tends to be more explicit.
Map reduce is the typical way to build analytical applications for these data stores either using hadoop (Cassandra) or internal map reduce (MongoDB has a java script MR API). This is typically not the ideal paradigm for analyzing time series data.
Traditional column oriented stores with integrated time series analytics tools or custom built platforms will continue to be the preferred way to store and process time series data. I don't think that there will (or should) be much convergence between the databases built for running very high scale websites and the databases built to store and analyze time series information.
All that being said we use mongodb extensively for metadata storage and as a sort of very large cache. But we don't use it to store or analyze financial data.