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Episode 73

It seems like predictive analytics gets all the attention these days but generally speaking, it requires either a Data Scientist or a machine learning algorithm operating on lots of event data, in order to predict the all-important dimension of time, at least to any degree of useful certainty. Enter prognostic analytics. In a closed system of uniform conditions, prognostic analytics can make better predictions about the “when”.
Listen to this podcast (or read the transcript) with Moritz von Plate about how the characteristics of this older-school stats make it very well suited for predictive maintenance in Industrial IoT ...


Big data analytics is generally answering questions about the past, whereas streaming analytics answers questions about the present. But what if you could bring the two together and answer questions about the present based on what happened in the past?
Watch this video (or read this transcript) to see Eric Tran-Le discuss a state of the art analytics ...




Episode 66

It takes a lot, and I mean a lot, to get me to download yet another app on my phone, especially one that’s going to try to sell me something… but, I’m open to Retail-IoT tech. I’m not much of a physical shopper, probably because I find the whole shopping experience so dreary, but this so called offline-online convergence within retail has piqued my interest.
Listen to this podcast (or read the transcript) where I speak with Oleg Puzanov about proximity marketing and where it’s headed with IoT ...


The key to value creation in the Internet of Things is the model. The model is used by both the app and analytics. It quantifies the value proposition, so the better the model, the higher the value. Developing these models in traditional markets is time consuming enough but given the volume, velocity and variety of IoT data, the load on the IoT data scientist can be overwhelming. Enter machine learning or ML for short. Machine learning can augment the skills of the data scientist by helping to select the algorithms or weighted ensemble of algorithms that provide the underlying structure for the model.
Watch this video (or read the transcript) video to see Rob Patterson discuss how machine learning is being used to help create and maintain Internet of Things models ...




Episode 49

In this episode Bruce recounts recent meetings with clients and discussions with IoT design houses to discuss the current state of the art in data analytics in IoT deployments.
Listen to this analysis episode (or read the transcript) with Bruce Sinclair for the reasons why data analytics isn’t usually considered by clients and why that’s OK ...


In the data science of IoT there’s no one size fits all data model. Each situation needs to be analysed separately by your data scientist. Then the output too, needs to be custom tailored to your customer - internal or external. This output, often in the form of a dashboard, is critical in aiding the identification of value with your data. Therefore, iterate on its design as often as you do on the design of the IoT product.
Watch this video (or read the transcript) to see Christian Mastrodonato discuss standing up an Internet of Things Pilot from a Data Scientist’s perspective ...




Episode 27

Descriptive analytics is nothing new, however IoT is applying evolutionary forces to make it adapt to unstructured sensor data and evolve into a mechanism of discovery rather than report generation. Tools that blend traditional business intelligence, analytical modeling and visualization now help data scientists discover the story behind the data which can lead to valuable insights for the enterprise.
Listen to this podcast (or read the transcript) with Dave Rubal about how to apply descriptive analytics to your Internet of Things ...