The task of integrating various types of data has been left to individual customers and consulting firms. The newest trend among quantitative analysts is the desire to analyze transactional, or tick data, rather than daily data. For example, an analyst studying equities now wants a data set containing the price and volume for each and every trade for a particular stock over a number of years. A server-centric model is required. In financial services, the ability to price an asset or to understand the risk measures of a trade more quickly than the rest of the market results in windfall profits.
Analysis generally reduces the amount of data, sometimes down to a single figure.
The sooner this analysis takes place, the better. It is the result that is needed, not the raw data. Doing the analysis in the database server also means that the identical analysis is available to disparate client processes. Consider a simple example of calculating the variance-covariance matrix for the 100 shares over 1 year using the daily closing prices. If the variance-covariance matrix can be produced in the server, then only the result needs to be sent to the client.