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IoT data offers answers to a simple question: “Are things changing or staying the same?”

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Smart cities, connected cars, logistics and industrial equipment will all be changed by IoT. With this new information come new opportunities for optimization and prediction. However, some industries are discovering the challenging differences between IoT data and other streams of analytics data.

Before using the data for optimizing business processes,
analysts must put a few things in place.

Setting up a ‘Data Historian’
A Data Historian is the place where data are indexed and made available for analytic software and routines - be it a standard database or data warehouse
Finding out what you need to predict
In order to find interesting patterns for their models, analysts can rely on the analytic strategies that are used for other data sets
A Native Distributed Analytics Architecture (NDAA)
that runs analytics on aggregates computed in a database, gateway or device/sensor, wherever it may reside

This e-book shows you how to find useful patterns in IoT data and how to use them to predict and model successfully.

The next hurdle for Internet of Things is to tailor traditional analytics techniques to IoT data. Analysts can then derive useful and actionable information from such data and figure out how to make IoT work for both industry and consumers.

The days when only statisticians and those with PhDs in mathematics could unlock the power in data to innovate are long gone. In today’s world, with easily available and accessible data, big or small – fast or at rest, analytics is becoming democratized.

Read this e-Book if:

you are interested in new forms of data, but are just starting out, or
are already data native working on complex data modelling

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Contacts us at editor@dataconomy.com