Machine learning perspectives on Mexico's digital transformation

Abstract: Wide reaching and continuously evolving value propositions are the gears of network orchestrator's business models (NOBMs). Digital transformation enters when reliable data and meaningful information became digital enabler (DE) fuel of NOBMs. Moreover, Machine Learning (ML) capabilities can work as a catalyzer to increase knowledge rate acquisition for business processes or economical activities. This paper sets up DE and ML example binds for 4 different industries, proposing that high-quality data obtained from a rich context augments the profitability of the model. Finally, we conclude that the highly variable context from México provides an ideal environment in which ML augments harmonization between DE and NOBMs.