The eventual aim of Big Data Science and Analytics is to improve key performance indicators for an organization, which include improving cash-flow, timeliness, quality, customer experience, and parameters related to compliance and risk. In this technical session, we discuss as to how to build end-to-end solutions in Big Data Science. In this regard, we take an example of improving the overall efficiency for a typical paper mill (all the way from “taking in paper pulp” to “providing finished paper” to the end-clients), and we discuss step by step as to how such a solution may be built.
Founder Scry Analytics, a company that codifies different kinds of workflows in various industries
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