Ambitious analytics projects have a tangible risk of failure. Uncertainty breeds anxiety. There are known techniques to lower the uncertainty, guarantee failure and shift the blame onto others. We outline a few proven methods of analytics sabotage and their application. In honor of Steven Potter call this activity “statsmanship” which we define as pursing the goal of making your analytics group cry.

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Fast Portfolio re-Balancing as a Fractional Linear Program is an example of the kind of work we have done encoding client problems (in this case optimal portfolio selection) as optimization problems (so we can use purchased software to solve them). Its a bit mathy- but we are excited we got permission to share this. Continue reading →

We have been living in the age of “big data” for some time now. This is an age where incredible things can be accomplished through the effective application of statistics and machine learning at large scale (for example see: “The Unreasonable Effectiveness of Data” Alon Halevy, Peter Norvig, Fernando Pereira, IEEE Intelligent Systems (2009)). But I have gotten to thinking about the period before this. The period before we had easy access to so much data, before most computation was aggregation and before we accepted numerical analysis style convergence as “efficient.” A small problem I needed to solve (as part of a bigger project) reminded me what theoretical computer scientists did then: we worried about provable worst case efficiency.

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## The Applied Theorist's Point of View