To truly understand the implications of Big Data analytics, one has to reach back into the annals of computing history, specifically business intelligence (BI) and scientific computing. The ideology behind Big Data can most likely be tracked back to the days before the age of computers, when unstructured data were the norm (paper records) and analytics was in its infancy. Perhaps the first Big Data challenge came in the form of the 1880 U.S. census, when the information concerning approximately 50 million people had to be gathered, classified, and reported on.
With the 1880 census, just counting people was not enough information for the U.S. government to work with—particular elements, such as age, sex, occupation, education level, and even the “number of insane people in household,” had to be accounted for. That information had intrinsic value to the process, but only if it could be tallied, tabulated, analyzed, and presented. New methods of relating the data to other data collected came into being, such as associating occupations with geographic areas, birth rates with education levels, and countries of origin with skill sets.
The 1880 census truly yielded a mountain of data to deal with, yet only severely limited technology was available to do any of the analytics. The problem of Big Data could not be solved for the 1880 census, so it took over seven years to manually tabulate and report on the data.
With the 1890 census, things began to change, ...