When I first arrived to the work world 30 years ago, I encountered two roadblocks to my enthusiasm for mathematical and statistical optimization techniques in business. The first was a data management problem. Collecting, integrating, organizing and manipulating data was a very thorny, sometimes intractable task, consuming almost all analytical energy. Technology to facilitate data management, both hardware and software, was just beginning to evolve from the mainframe/COBOL/network database paradigm. Machine cycles were rationed, storage was scarce and expensive, programming was low level and data was unreliable. Probably 98 percent of statistical effort revolved on building trustworthy data sets. While data quality issues persist to the present, many of the other hardware and software problems have been solved. In fact, the ascent of mini/micro/personal computers with UNIX and Windows as well as the emergence of relational databases should probably be heralded as fundamental enablers of modern business intelligence.
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