Predictive Analytics

predictive analytics

Predictive Analytics – The Crucial Linchpin for IoT Analytics

Predictive Analytics take on a new meaning in a connected world.  Let’s look at how Internet of Things (IoT) data and real-time analytics have taken predictive analytics to an entirely new level where tremendous revenue opportunities and cost savings exist.

According to Gartner’s IT Glossary, predictive analytics describes an approach to data mining that has an emphasis on predicting what will happen in the future using rapid analysis to easily generate relevant business insights.

In a connected world where there is plenty of device data available due to the Internet of Things (IoT), predictive analytics brings a host of new challenges and opportunities for organizations that seek to generate business value quickly.  The high volume of real-time data offers rich potential for new business insights that could be leveraged to raise revenue, cut costs and risks, or increase operating efficiency.

As organizations capture IoT device data from disparate sources in real-time and compare it with historical experience, they need to be able to put it all in context to assess if any decisions or actions need or should be taken on the new information and data.

Doing this requires understanding in real-time how this new data compares with historical experience for the application or use case.  Making predictions based on historical data is a challenging discipline itself – doing so in real-time is even more challenging.

Nevertheless, that is precisely what is required with IoT Analytics.  It is simply not enough to make predictions on historical data and act on them when convenient.  IoT by its very nature is based on constant data that streams in continuously, and if it is not put in context and acted upon quickly the value disappears.

Leveraging predictive analytics in the context of IoT requires several critical steps:

  • Leveraging historical data via machine learning and other techniques to create predictive models of what could happen in the future.
  • Analyzing current events in real-time to refine the predictions based on real-world events
  • Updating the predictive models in real-time to reflect the real-time activity

Operations managers and others can then use those updated predictions to take quick action.  As you can see, using predictive modeling in real-time with IoT is significantly more demanding than traditional predictive modeling which only leverages historical data.

This type of IoT-centric predictive analytics also sets the stage for prescriptive analytics where specific actions will be recommended.

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