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The biggest problem with the methods of machine learning used today inbusiness analytics is that they do not generalize well and often fail whenapplied to new data. One of the possible approaches to this problem is toenrich these methods (which are almost exclusively based on statisticalalgorithms) with some intrinsically deterministic add-ons borrowed fromtheoretical physics. The idea proposed in this note is to divide the set of KeyPerformance Indicators (KPIs) characterizing an individual business into thefollowing two distinct groups: 1) highly volatile KPIs mostly determined byexternal factors and thus poorly controllable by a business, and 2) relativelystable KPIs identified and controlled by a business itself. It looks like,whereas the dynamics of the first group can, as before, be studied usingstatistical methods, for studying and optimizing the dynamics of the secondgroup it is better to use deterministic principles similar to the Principle ofLeast Action of classical mechanics. Such approach opens a whole bunch of newinteresting opportunities in business analytics, with numerous practicalapplications including diverse aspects of operational and strategic planning,change management, ROI optimization, etc. Uncovering and utilizing dynamicallaws of the controllable KPIs would also allow one to use dynamical invariantsof business as the most natural sets of risk and performance indicators, andfacilitate business growth by using effects of parametric resonance withnatural business cycles.

Business Dynamics in KPI Space. Some thoughts on how business analytics can benefit from using principles of classical physics