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The IoT explosion will also influence the amount of useful data, or data that could be analyzed to
produce some meaningful results or predictions. By comparison, in 2013, only 22 percent of the
information in the digital universe was considered useful data, with less than 5 percent of that useful
data actually being analyzed. That leaves a massive amount of data still left unprocessed and
underutilized. Thanks to the growth of data from the IoT, it is estimated that by 2020, more than 35
percent of all data could be considered useful data. This is where you can find today’s data “goldmines”
of business opportunities and understand how this trend will continue to grow into the foreseeable
future.
One additional benefit from the proliferation of IoT devices and the data streams that will keep
growing is that data scientists will also have the unique ability to further combine, incorporate, and
refine the data streams themselves and truly optimize the IQ of the resultant business intelligence we
will derive from the data. A single stream of IoT data can be highly valuable on its own, but when
combined with other streams of relevant data, it can become exponentially more powerful.
Consider the example of forecasting and scheduling predictive maintenance activities for elevators.
Periodically sending streams of data from the elevator’s sensor devices to a monitoring application in
the cloud can be extremely useful. When this is combined with other data streams like weather
information, seismic activity, and the upcoming calendar of events for the building, you have now
dramatically raised the bar on the ability to implement predictive analytics to help forecast usage
patterns and the related preventative maintenance tasks.
The upside of the current explosion of IoT devices is that it will provide many new avenues for
interacting with customers, streamlining business cycles, and reducing operational costs. The downside
of the IoT phenomena is that it also represents many new challenges to the IT industry as organizations
look to acquire, manage, store, and protect (via encryption and access control) these new streams of
data. In many cases, businesses will also have the additional responsibility of providing additional levels
of data protection to safeguard confidential or personally identifiable information.
One of the biggest advantages of machine learning is that it has the unique ability to consider many
more variables than a human possibly could when making scientific predictions. Combine that fact with
the ever-increasing quantities of data literally doubling every 18 months, and it’s no wonder there
could not be a better time for exciting new technologies like Azure Machine Learning to help solve
critical business problems.
IoT represents a tremendous opportunity for today’s new generation of data science entrepreneurs,
budding new data scientists who know how to source, process, and model the right data sets to
produce an engine that can be used to successfully predict a desired outcome.
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