The Pros and Cons of Machine-Generated Data

John Myers recently discussed what he refers to as the promise and pitfalls of machine-generated data. He writes, "The truly game changing use case for Big Data is the predicted increase in data coming from machine-to-machine (M2M) sensor and activity log information. This data is going to come from all sorts of devices – cars, trains, planes, refrigerators, air conditioners and wireless devices like smart phones, tablets and medical devices. All of this data will allow for the cost optimization of various industries such as utility grids, logistic supply chains and location-based services."

He continues, "Probably one of the reasons that machine-generated data sources get so much play with the Big Data hype/messaging is that there are a vast number of potential platforms to generate sensor and log data. Planes, trains and trucks offer a wealth of enterprise-focused business models. They offer the potential of managing logistical costs in a supply chain, minimizing the largest single cost in many transportation business models – fuel expense. Cars and wireless devices offer excellent opportunities for consumer-focused business models. These mobile platforms represent the key to geo-location analytics for marketing and other location-based offerings. Home appliances, air conditioners and home heating systems offer a wealth of opportunities for regulated utilities and government entities to optimize the cost and operation of their utility girds. From power generation to new capital investment, smart meters and their associated data offer great potential."

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