Machine Learning Increases The Demand For Formal Theoretical Models of Entire Spaces

In presence of development of the internet into what now is a huge source of valuable information for firms, but with necessity of harvesting, sifting, and aggregating of the data for arrival at information that has value for managerial decision making, big data and machine learning algorithms have become buzz words within business communities.

Harvesting, sifting, and aggregation of data for arrival at valuable information has required development of new algorithms, platforms, and softwares, this because data harvested from the internet comes in many variety of forms. Comments by customers, which are non-numeric in character, and information on purchases, which by definition are numeric, are a case in point.

In order for comments penned by customers to be aggregable with data on purchases, comments have to be harvested. Clearly, algorithms that do a good job of harvesting comments must of necessity differ from those that do a good job of harvesting numbers, yet at end of the day must have capacity for conversion of ‘comments data’ into numeric data that are aggregable with purchase data that already are in numeric form. Given there typically will not exist any causal relations between say, a customer’s comments and purchases, big data is not expected to generate anything more…