08 Jul Makin’ Bacon and the Three Main Classes of IoT Analytics
n the Internet of Things there are generally three classes of analytics performed: real-time analytics done of the fly alerting you to anomalies; predictive analytics performed as a post process yielding a prediction and confidence level and descriptive analytics that reports on past, present or future data with visualizations that often result in the biggest insights. In this episode of the IoT Business Show, I discuss the differences in these types of analyses with Shepherd Shi, as well as the steps that are taken before and after.
In this episode of the IoT Business Show, I discuss the differences in these types of analyses with Shepherd Shi, as well as the steps that are taken before and after.
Shepherd is the IBM Distinguished Engineer leading the Internet of Things analytics project. He has over twenty five years of experience in high performance software development and in addition to IoT analytics, he is also involved in IT operational performance analytics and predictive analytics for inventory and warehouse management.
Depending on the business information and intelligence you need, your IoT implementation may require all three types of analytics – each with their own tech requirements. Listen to this episode to become more familiar with how each of the analytics work so the mapping of tech you your requirements is smoother and more efficient.
Here’s What We’ll Cover in this Episode
- The three main classes of IoT analytics: predictive analytics, real-time analytics and descriptive analytics.
- The output of each type of analytics.
- How predictive analytics works through statistics.
- How real-time analytics works uses rules engines.
- How predictive analytics often provides the biggest insights.
- The steps before and after the actual analysis in IoT analytics.
- Why parallelization is the key in both real-time and predictive analytics.
- Use cases for real-time and predictive analytics.
Mentioned in this Episode and Other Useful Links
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