Business Analytics, Past and Future

I have been a long time practitioner of business analytics, the use of quantitative methods to improve business processes. Starting at the Defense Department, where we built military simulation models, and ending up at P&G.  My first project there, when I arrived in 1977, was to improve warehouse efficiency using IBM's mathematical programming system: MPSX.  That followed with using the same mathematical optimization approaches for scheduling, supply chain siting and executive decision making.

So I was happy to see in the July/August Issue of Analytics Magazine an article by Arnold Greenland on the history and current state of business analytics at IBM.    This allows me to reflect on the growth and impact of analytical methods at IBM and elsewhere.  Since then the original MPSX package has disappeared, and has been replaced by the acquired Ilog/Cplex, which goes far beyond the original package, adding nonlinear methods as well.

Shortly after being introduced to MPSX, in 1980, we addressed the analysis of unstructured data, typically consumer comments in unstructured text, using recently developed methods called  'Content Analysis'.  These permitted the semantic analysis of multiple human languages.  An early attempt to look at and understand unstructured 'Big Data'.

During all of this time we also used a number of statistical methods throughout the enterprise to explore and improve systems.  SPSS and SAS were in frequent use.  A package called Clementine allowed us to use advanced logical methods, like artificial neural nets, to implement what were essentially statistical methods, to store and implement specific decision rules.   These methods could then be inserted in both software and hardware processes.  Clementine also permitted the structural exploration of the decision process.  Ultimately Clementine was acquired by SPSS, and were eventually absorbed into Modeler.

During the 1990's, a heady time for artificial intelligence, we implemented expert systems using a now defunct language called M1.  Which were successful for a number of complex industrial management processes.  Some of the same capabilities can be seen in JRules.  That work has been extended into recent AI explorations like that of IBM's Watson.

What is further interesting now is that many of these methods are now available to the small and medium sized business.  We live in a time where the tools are available,  just go out and use them.