Popping Big Data Fallacies On the Edge

Organizations today are drowning in data. There’s no argument about that. But there continues to be vigorous debate on the best way to deal with that data. While some advocate creating big data lakes to store data that will subsequently be used for training machine learning models, there’s a growing chorus of voices calling for a simpler and more real-time approach.

You can count Simon Crosby, CTO of, among proponents for a lighter-weight and less expensive approach to data collection and analysis, at least for a certain class of real-world machine learning problems at the edge. During a recent conversation with Datanami, Crosby threw cold water on the notion that uploading data to the cloud for storage and machine learning was the best way to get value out of the morasses of data created on edge devices.

Author: Alex Woodie


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