Good post from Jim Jockle over at Numerix – main theme is around having an “analytics” strategy in place in addition to (and probably as part of) a “Big Data” strategy. Fits strongly around Xenomorph’s ideas on having both data management and analytics management in place (a few posts on this in the past, try this one from a few years back) – analytics generate the most valuable data of all, yet the data generated by analytics and the input data that supports analytics is largely ignored as being too business focussed for many data management vendors to deal with, and too low level for many of the risk management system vendors to deal with. Into this gap in functionality falls the risk manager (supported by many spreadsheets!), who has to spend too much time organizing and validating data, and too little time on risk management itself.
Within risk management, I think it comes down to having the appropriate technical layers in place of data management, analytics/pricing management and risk model management. Ok it is a greatly simplified representation of the architecture needed (apologies to any techies reading this), but the majority of financial institutions do not have these distinct layers in place, with each of these layers providing easy “business user” access to allow risk managers to get to the “detail” of the data when regulators, auditors and clients demand it. Regulators are finally waking up to the data issue (see Basel on data aggregation for instance) but more work is needed to pull analytics into the technical architecture/strategy conversation, and not just confine regulatory discussions of pricing analytics to model risk.